Contents

• What makes a legacy app?
• Nine signs your app is screaming for an upgrade 
• The nuts and bolts of the legacy application modernization process: 7 steps to follow 
  • 1. Run an assessment to define the problem areas
  • 2. Identify the relevant modernization approach and technique
  • 3. Update your Vision & Scope and create a roadmap
  • 4. Set up app modernization team
  • 5. Execute modernization 
  • 6. Invest in end-user training
  • 7. Support and optimize
• It’s time to act

When your software does not meet challenges of time, it might be wise to consider legacy application modernization. But here’s the kicker: these initiatives are only successful in 26% of cases. No wonder many business owners cling to the idea of “why fix what’s not completely broken”, delaying necessary upgrades until it’s almost too late.   

Bad news: this mindset won’t cut it anymore. Why? Well, what should we start with? Sluggish performance and glaring security vulnerabilities are just the beginning. Legacy systems drain resources with high maintenance costs and struggle to scale with your growing business.

Fifteen or twenty years ago, you could have used software for up to eight years stress-free. Today, the lifespan of tools and frameworks has halved, forcing companies to go through the cycles of modernizing their apps every four years. 

— Dzmitry Shchyhlinski, Lead Solution Architect, *instinctools

Good news: we know how to get software back on the right track before it goes south. In this article, *instinctools’ very own head of backend development and lead solution architect will walk you through all of the stages of the modernization journey and legacy system modernization approaches, providing a slew of real-life examples along the way. 

What makes a legacy app?

Our vision of a legacy app isn’t limited to obsolete technology. At *instinctools, we stick with Gartner’s definition, considering any solution outdated if it relies on software, technology, methodology, or process that has become ineffective while remaining critical to daily operations. 

Nine signs your app is screaming for an upgrade 

You know what they say: if it looks like a duck, swims like a duck, and quacks like a duck, it’s probably a duck. The same goes for legacy software: if it looks old, runs slowly, and requires a lot of effort to make any improvements, it’s probably outdated. Anyway, to help you make a well-informed choice we’ve curated a list of some blatant indicators, signaling your app needs a makeover. 

• Skyrocketing infrastructure costs due to an outdated technology stack
• Obsolete UX/UI failing to meet user expectations
• Limited scalability that hinders your growth potential
• Low flexibility, making integration with modern apps impossible
• Poor performance as your app’s daily norm
• Numerous security vulnerabilities and compliance risks
• Inability to find staff with the right skills to support the app
• Code bloat, making maintenance pricey and challenging 

Spotting the apps calling for legacy modernization isn’t a hard nut to crack. Modernization itself, though, might be — given how deeply your software is embedded in your company’s daily operations. 

Legacy app modernization has many faces

Legacy application modernization comes in various forms: 

• Encapsulating provides a gain with no pain when you reuse the existing app’s valuable features with minimal changes by making them accessible for other applications through an API.   
• Rehosting implies keeping the old code and features as they are while redeploying the app component to other infrastructure (on-premise; private, public, or hybrid cloud).
• Replatforming covers elevating the software performance and scalability and reducing operational costs by migrating to a new platform, while preserving the previous code structure and core functionality.
• Refactoring includes optimizing the existing code in line with up-to-date coding standards to eliminate technical debt.
• Rearchitecting entails substantial changes to the application architecture and code by replacing a monolith with microservices or, sometimes, the other way around when the microservices were written at different times by different developers and are easier to maintain if consolidated in a single monolith.
• Rebuilding involves completely rewriting or redesigning software components while keeping the app’s overall scope and specifications. 
• Replacing existing systems with new ones is a last-ditch step required when the end users’ needs have completely changed and, to meet them, none of the current app’s components and layers can be reused. 

As you can see, there’s no need to burn your boats, when you can sail smoothly into massive improvements with time-saving, budget-friendly modernization strategies. If identifying the right one feels overwhelming, legacy application modernization services from a trusted software development company provide expert guidance. 

Risk it for the biscuit: measured rewards of legacy modernization 

The prospect of IT legacy modernization can look intimidating, but the rewards are well worth the effort. Here’s how transforming your software can benefit you in multiple ways: 

• Maintenance cost. Aligning your app with current code practices, architecture, design, etc., allows you to trim down the cost of software upkeep by at least 30%*.
• Quality. Reducing the amount of flawed code in production to less than 1%* sounds valuable, isn’t it? 
• Speed. Releasing new features up to x50* faster can put you ahead of the pack.
• Regeneration. Minimizing the downtime with the average mean time to recover (MTTR) of just 2–10 minutes* raises the app’s reliability.
• Integration. Zero drawbacks when integrating your app with other solutions contribute to the hitch-free functioning of your software ecosystem.

*Based on our client’s legacy modernization projects 

The nuts and bolts of the legacy application modernization process: 7 steps to follow 

Despite the complexities and peculiarities of software modernization initiatives, there are guaranteed strategies you can follow to tackle legacy modernization challenges head-on.

1. Run an assessment to define the problem areas

The measure-twice-cut-once principle is also valid for legacy modernization.

A clear understanding of your business drivers and an in-depth investigation of software problem areas enable you to set the right priorities for app modernization and strategize future growth.

Revamping a legacy app should start with a thorough assessment that covers all the bases: 

• Project documentation. Outdated software often lacks solid documentation. Catalog all the data on the app to identify any missing mandatory documents.
• Technology analysis. Evaluate your current tech stack, and choose the most suitable programming languages, tools, frameworks, integrations, based on the available tech expertise and trends, shaping the landscape in your industry.
• Architecture audit. Examine the systems’ architecture design, consistency, and quality against industry standards to get an idea of what can be done to achieve seamless scalability, high integration capabilities, and glitch-free maintenance.
• Code review. Is your source code up to snuff? Compare it against the present-day coding best practices to spot and weed out any spaghetti code.
• Data quality estimation. Inspect the app’s data layer for consistency, accuracy, and completeness to ensure no junk data sneaks into your updated software database. 
• UI/UX evaluation. How user-friendly is your app? Analyze overall usability, ease of navigation, element layout, and visual design to identify the areas that need a facelift.
• Performance testing. Put your app through its paces. Check the app’s stability, scalability, speed, and responsiveness under various workloads to uncover performance bottlenecks.

Prioritizing the assessment stage upfront is your secret weapon against future headaches. If your project vision is blurry or stakeholders aren’t on the same page, consider a discovery workshop. This helps synchronize your modernization strategy with your business context and user demands. 

If you don’t have the capacity or specific experts on board to tackle modernization without affecting your ongoing projects, find a tech partner who can steer the ship and bring you to the next destination of your legacy system modernization journey.

2. Identify the relevant modernization approach and technique

After assessing the existing legacy system, you can chart the right course and choose which of the legacy system modernization approaches works best for you. 

In general, there are two main options for a legacy system transformation —  the big-bang (revolutionary) method and the band-aid (evolutionary) one. Truth be told, business owners tend to choose risk-prone gradual improvements over cliff-jump actions.  

There’s only one situation when you have to put everything on the line and follow the big-bang approach. When major security issues or compliance violations arise, you don’t have the luxury of taking it slow. It’s either go big or go home. You have to act now to avoid losing customers and facing massive fines. 

— Dzmitry Shchyhlinski, Head of Backend Development and Solution Architect, *instinctools

We’ve put together a comparison chart to help you weigh the pros and cons of each modernization technique we’ve mentioned earlier, in terms of cost, time, complexity, and risk.

An iterative approach lets you mix and match two or more techniques to achieve the highest value for your business. For example, one of our clients, a provider of SaaS personalization engines, started the modernization process with code refactoring of their flagship software and then decided to proceed with the redesign. That way, they were able to move toward simplified product maintenance and design consistency across all of their current solutions. 

3. Update your Vision & Scope and create a roadmap

The reason why 74% of modernization initiatives fail is mismatched project visions among stakeholders. Consistent, well-organized documentation keeps everyone aligned and on track. 

First of all, make sure the app’s vision and scope reflects the latest end-user needs, compliance, and security requirements. 

Second, you should put a premium on creating a project roadmap with the goals and objectives of your legacy system modernization, key project deliverables, business risks, success criteria, a timeline with milestones, and a team lineup. 

4. Set up app modernization team

Planning is vital, but you’ll need the doers to pull off your legacy app modernization. 

An all-embracing roadmap, outlining mandatory roles within the team and the number of FTEs, enables you to allocate your resources wisely when arranging a team for a legacy system modernization. 

However, it’s not carved in stone, and your initial goals may evolve, requiring team decomposition or expansion.

Take, for instance, a SaaS LMS provider that partnered with *instinctools. Initially, they sought our solution architects to rearchitect their current educational platform. However, amid our cooperation, they decided to strengthen their internal team with our senior frontend developers, aiming for a complete system redesign.  

5. Execute modernization 

At this point, actions you take will hinge on the specifics of your modernization project. For example, redesigning involves prototyping, wireframing, and usability testing, while rehosting requires data and storage migration and database rehosting. 

The common best practice for any modernization technique is moving in small iterations so that you can spot and address any risks in the bud and avoid divergence of vision among team members and stakeholders. 

6. Invest in end-user training

Present-day tools and frameworks, modern platforms, revamped architecture, and top-notch design mean nothing without user support. While consumer demands often drive updates to customer-facing apps, internal technology changes tend to neglect employee feedback. 

How to prevent staff resistance to your modernization efforts? We suggest initiating user training before rolling out the renovated app to familiarize your employees with the new system. For significant software changes or complete replacements, consider implementing a digital adoption platform (DAP) for contextual in-app guidance. 

7. Support and optimize

Ensuring your renewed system runs flawlessly requires continuous proactive monitoring. Regular tech, UX/UI, and performance audits, code reviews, and data quality and security checks before, during, and after modernizing legacy systems increase the odds of their smooth operation. 

It’s time to act

Staying stuck with dust-covered software infrastructure, design, and legacy code means losing out to more agile and innovative competitors. But there’s a way forward through modernizing legacy systems in a well-thought-out, calibrated manner. 

And that’s where we come in. Instinctools is your tech partner for every step of the modernization process. We focus on maximizing what works and fixing the trouble spots, ensuring your app is sturdy and cost-effective.

The post Legacy Application Modernization: Inch-By-Inch Guide On How To Make It Right appeared first on *instinctools.

Contents

• A pulse check on AI and privacy in 2024
• The dark side of AI: how can it jeopardize your organization’s data security?
  • Lack of control over what happens to the input data or who has access to it
  • Unclear data residency 
  • Reuse of your data for training the vendor’s model 
  • Dubious quality of data sources used to fine-tune the model
  • Personally Identifiable Information (PII) violations
  • Security in AI supply chains
• Front-page data breaches involving AI: lessons learned?
• Navigating legal waters: AI regulatory rules of thumb
  • Reducing data usage to the essential minimum
  • Providing understandable explanations of how AI systems function and make decisions
  • Incorporating human review mechanisms to oversee AI decisions
  • Identifying and understanding different risk levels associated with AI systems
  • Paying special attention to profiling workloads
  • Ensuring AI systems operate reliably and do not pose risks to users or the environment
• 6 practices to wipe out AI data privacy concerns 
  • 1. Establish AI vulnerability management strategy
  • 2. Take a hard stance on AI security governance
  • 3. Build in a threat detection program
  • 4. Secure the infrastructure behind AI
  • 5. Keep your AI data safe and secure
  • 6. Emphasize security during AI software development lifecycle 
• Balancing innovation and privacy

Today, artificial intelligence is billed as a superpower that brings about unprecedented technological advancements in virtually every industry — and rightly so. But with this incredible progress comes a growing concern: is AI infringing on our privacy? AI privacy concerns have been the subject of many debates and news headlines lately, with one clear takeaway: protecting consumer privacy in AI solutions must be a top business priority. 

Is your privacy governance ready for AI? Let’s find out.

A pulse check on AI and privacy in 2024

The heady growth of generative AI tools has revived concerns about the security of AI technology. The data chills it triggers have been long plaguing AI adopters — except they’re now exacerbated by unique gen AI capabilities. 

Inaccuracy, cybersecurity problems, intellectual property infringement, and lack of explainability are some of the most common generative AI privacy concerns that refrain 50% of organizations from scaling gen AI responsibly.

The worldwide community is echoing a security-focused approach of AI leading players, with a sweeping set of new regulations and acts advocating for more responsible AI development. These global efforts are initiated by actors ranging from the European Commission to the Organization for Economic Co-operation and Development to consortia like the Global Partnership on AI.

First time in history we might be prioritizing security over innovativeness, as we should. Microsoft has finally sorted the wheat from the chaff and started to pay deserved attention to security:

“If you’re faced with the tradeoff between security and another priority, your answer is clear: Do security,” Microsoft CEO Satya Nadella said in a memo issued to his employees last month.

“In some cases, this will mean prioritizing security above other things we do, such as releasing new features or providing ongoing support for legacy systems.“

But although supra-national and region-specific regulations on responsible AI and data protection are underway, it’s still incumbent on individual companies to establish a set of risk-related best practices — especially, if they’re looking to derive value from AI-driven innovation. 

The dark side of AI: how can it jeopardize your organization’s data security?

No matter what type of AI solutions you are integrating into your business, prebuilt AI applications or self-built ones, the adoption of AI systems demands a heightened level of vigilance. When left unattended, AI-related privacy risks can metastasize, potentially causing a range of dire consequences, including regulatory fines, algorithmic bias, and other pitfalls.

Lack of control over what happens to the input data or who has access to it

Once an organization’s data enters the gen AI intelligence stream, it becomes extremely difficult to pinpoint how it is used and secured due to unclear ownership and access rights. Along with black box issues, reliance on third-party AI vendors places companies at the mercy of external data security practices that may not always live up to the company’s standards, potentially exposing business data to vulnerabilities.

Unclear data residency 

An overwhelming majority of generative AI applications offer little oversight of data storage and processing destinations, which may be an inconvenient circumstance if your organization has strict requirements around data residency. Your company’s legal or regulatory obligations might conflict with relevant data privacy laws in your jurisdiction, potentially putting you at risk of hefty fines.

So unless you indicate a specific preference or turn to regionally hosted models, your AI solution places your data in the red zone. 

Reuse of your data for training the vendor’s model 

When a company signs up for a vendor-owned AI system, they unknowingly consent to a hidden curriculum. Most third-party models collect data and reuse it to train vendor’s foundational models, not just your specific use case. This may raise significant privacy concerns associated with sensitive data. Data reuse also works in reverse, introducing biases into your model’s output. 

Dubious quality of data sources used to fine-tune the model

‘Garbage in, garbage out’ — this adage holds true even for the most advanced AI models. Poor quality of the source data used for fine-tuning can trigger inaccurate outputs. In most cases, there’s little a company can do to head off this pitfall since organizations have limited control over the origin and quality of data used by vendors during fine-tuning.

Personally Identifiable Information (PII) violations

You might think that data anonymization techniques place PII under wraps. In reality, even anonymized and scrubbed of all identifiers data can be effectively re-identified by AI based on users’ behavioral patterns. Not to mention, that some smart models struggle to anonymize information properly, leading to privacy violations and serious repercussions for organizations.

Also, the General Data Protection Regulation, California Consumer Privacy Act, and other bodies set a very high bar, unreachable for most AI models, when it comes to the effectiveness of anonymization.

Security in AI supply chains

Any AI infrastructure is a complex puzzle consisting of hardware, data sources, and the model itself. Ensuring all-around data privacy demands the vendor or the company to introduce safeguards and privacy protections into all components as any breach in the AI supply chain can have a far-flung effect on the entire ecosystem, including poisoned training data, biases, or derailed AI applications.

Front-page data breaches involving AI: lessons learned?

If there’s one thing we can learn from the tech news is that even high-profile companies fail to effectively protect user data. And with AI technologies, this mission becomes even more formidable due to the expanded vulnerability surface.

Microsoft’s massive data exposure incident that took place in 2023 is one of the many stark reminders, highlighting AI and data privacy concerns. In this incident, Microsoft’s AI research team accidentally exposed 38 terabytes of data from employee workstations. As a result, a wide range of highly sensitive information slipped through the cracks, including personal computer backups, passwords, secret keys, and other data. Consequently, the attacker gained complete control over the system, including the ability to delete and manipulate existing files at will.

Foundational model owners aren’t immune to data bridges either. Recently, OpenAI faced scrutiny after its ChatGPT model made payment-related and other personal information of 1.2% of users visible to some users. This incident underscored concerns from industry experts who have previously criticized OpenAI’s insufficient data security practices.

Undoubtedly, every A-list company that has been exposed to any kind of data breach was quick to take crucial post-breach responses  — by patching, adjusting cloud configurations, or taking their applications offline. But considering the ever-high cost of data breaches, no measure is more effective than proactive prevention. 

Tech players like Accenture, AWS, and IBM take prevention to a whole new level by shoring up capabilities and processes for responsible AI development and use. While specific points in their blueprints may differ, a common thread runs through their strategies — an unwavering commitment to compliance, data privacy, and cybersecurity.

Navigating legal waters: AI regulatory rules of thumb

Not all AI products are inherently flawed, some popular solutions like Simplifai, Glean, and Hippocratic AI demonstrate that success can be achieved while meeting privacy regulations and paying due diligence to privacy protection. But we get it: the regulatory landscape is changing fast, making AI development an uncharted territory for first-time technology adopters. 

The good news is there are key principles that can drift your development efforts in the right direction and save you a lot of headaches down the road.

Reducing data usage to the essential minimum

First and foremost, you can get a lion’s share of AI data privacy concerns out of the way by keeping the amount of training and operating data to the necessary minimum from the get-go. 

There’s a lot you can do to achieve minimal data usage in AI applications:

• Give your data a good scrub — clean and filter the data to get rid of duplicated input, structural errors, and noisy information before training the model. 
• Double down on the most relevant variables — leverage feature engineering and distill the most useful patterns from the data.
• Piggyback pre-trained models with larger datasets — turn to transfer learning to train your data on a smaller, specialized dataset.
• Artificially create new data points — use data augmentation techniques to increase the training dataset without collecting additional input.

Providing understandable explanations of how AI systems function and make decisions

A bad reputation associated with the lack of data privacy demonstrated by AI can be partly attributed to the black-box nature of the latter. Safeguarding data becomes a tall order when there’s little explainability in the decisions of machine learning algorithms and deep learning techniques. That’s why building a transparent and explainable system with clear underlying mechanisms and decision-making processes is crucial for an organization to build trust and confidence in emerging technologies.

Making your AI systems explainable boils down to the main three features, including prediction accuracy, traceability, and decision understanding. 

Incorporating human review mechanisms to oversee AI decisions

Different regulations, the GDPR and EU AI Act in particular, set out certain obligations for human intervention or human oversight as a means of preventing decision-making based solely on machine intelligence. To meet the requirement, organizations should employ robust review practices to avoid perpetual biases.

There are four main ways to put a rein on the outputs of the smart solution. The most common one is a human-in-the-loop system that is often used in high-risk applications. In this case, a human reviewer is directly involved in the decision-making process alongside AI algorithms. Organizations can also apply post-hoc reviews and exception-handling rules, to promote more accurate output and make sure the system doesn’t disclose any personal data.

Identifying and understanding different risk levels associated with AI systems

Just as the old saying goes ‘forewarned is forearmed’, knowing the risks and possible doomsday scenarios beforehand allows companies to devise effective mitigation strategies. 

While there is no one-size-fits-all for conducting a risk assessment for artificial intelligence tools, most frameworks require companies to assign a category of risk to the system and draw up a risk mitigation strategy based on the risk profile.

Ongoing monitoring and system refinement are other non-negotiables of a holistic risk assessment framework that can give you a heads-up about any emerging risks.

Paying special attention to profiling workloads

Some AI applications like facial recognition software or customer services chatbots scan personal user data to create audience profiles based on user’s behavior, preferences, and other criteria. As this exercise involves processing large amounts of personal information, US companies must make sure their profiling is conducted in line with the GDPR, CCPA, PDP Bill, or any other relevant regulation.

In reality, though, users might not be in the know about their data being used for profiling purposes, which is a hard no for ethical and responsible AI use. Also, profiling datasets may become an easy target for hackers, especially if the system has rickety security controls.

Robust safeguards such as anonymization or pseudonymisation as well as technical and organizational security controls are among the go-to safe nets when it comes to shielding profiling data. Also, transparency around profiling methods and data controls in place is important to alleviate users’ concerns.

Ensuring AI systems operate reliably and do not pose risks to users or the environment

According to ISO 42001:2023, AI systems must behave safely under any circumstances without putting human life, health, property, or the environment at risk. To meet these requirements, smart systems shouldn’t operate in silos — they must be weaved into a broader ethical framework that prevents biases in decision-making and mitigates environmental footprint stemming from resource-intensive model training.

Proactive risk management coupled with the explainability of algorithms and traceability in your workloads empowers organizations to shore up capabilities and safeguards instrumental to building trustworthy AI systems that benefit society as a whole.

6 practices to wipe out AI data privacy concerns 

While some companies grapple with AI risk management, 68% of high performers address gen-AI-related concerns head-on by locking risk management best practices into their AI strategies.

Standards and regulations provide a strong ground zero for data privacy in smart systems, but putting foundational principles in action also requires practical strategies. Below, our AI team has curated six battle-tested practices to effectively manage AI and privacy concerns. 

1. Establish AI vulnerability management strategy

Just like any tech solution, an AI tool can have technology-specific vulnerabilities that spawn biases, trigger security branches, and reveal sensitive data to the prying eyes. To prevent this havoc, you need a cyclical, comprehensive vulnerability management process in place that focuses on the three core components of any AI system, including its inputs, model, and outputs.

• Input vulnerability management — by validating the input and implementing granular data access controls, you can minimize the risk of the input vulnerability. 
• Model vulnerability management — threat modeling will help you harden your model by mitigating known documented threats. If you have commercial generative AI models in your infrastructure, make sure to perform close inspection of data sources, terms of use, and third-party libraries to prevent bias and vulnerabilities from permeating your systems.
• Output vulnerability management — strip the output of sensitive data or hidden code to make sure nobody can infer sensitive information and to mitigate cross-site vulnerabilities.

2. Take a hard stance on AI security governance

Along with vulnerability management, you need a secure foundation for your AI workloads, rooted in the wraparound security governance practices. Thus, your security policies, standards, and roles shouldn’t be confined to proprietary models but also extend to commercial and open-source models.

Water-tight security starts with a strong AI environment, amplified with encryption, multi-factor authentication, and alignment to best industry frameworks such as NIST AI RMF. Just like vulnerability management, effective security requires continuous attention to three components of an AI system:

• Input security — check applicable data privacy regulations, validate data residency, and establish Privacy Impact Assessments (PIA) or similar processes for each use of regulated data.
• Model security — make sure you have clear user consent or another reason allowed by law to process data. You can use the PIA framework to evaluate the privacy risks associated with your AI model.
• Output security — revisit the regulations to see whether the regulated data is available for secondary processing. Your AI system should also have a way to erase data on request.

3. Build in a threat detection program

To defend your AI set-up against cyber attacks, you should apply a three-sided threat detection and mitigation strategy that addresses potential data threats, model weaknesses, and involuntary data leaks in the model’s outputs. Such practices as data sanitization, threat modeling, and automated security testing will help your AI team to pinpoint and neutralize potential security threats or unexpected behaviors in AI workloads.

4. Secure the infrastructure behind AI

Manual security practices might do the trick for small environments, but complex and ever-evolving AI workloads demand an MLOps approach. The latter provides a baseline and tools to automate security tasks, usher in best practices, and continuously improve the security posture of AI workloads.

Among other things, MLOps helps companies integrate a holistic API security management framework that solidifies authentication and authorization practices, input validation, and monitoring. You can also design MLOps workflows to encrypt data transfers between different parts of the AI system across networks and servers. Using CI/CD pipelines, you can securely transfer your data between development, testing, and production environments.

5. Keep your AI data safe and secure

Data that powers your machine learning models and algorithms is susceptible to a broader range of attacks and security breaches. That’s why end-to-end data protection is a critical priority that should be implemented throughout the entire AI development process — from initial data collection to model training and deployment.

Here are some of the data safeguarding techniques you can leverage for your AI projects:

• Data tokenization — protect sensitive data by replacing it with non-sensitive data tokens as surrogates for the actual information. 
• Holistic data security — make sure you secure all data used for AI development, including at-rest, in-transit and in-use data.
• Documented data provenance — create verifiable mechanisms to confirm the origin and history of all data used by the models, especially inference data used for model training. Make sure data lineage and data access in non-production and development regions are in check to stave off data manipulation.
• Loss prevention — apply data loss prevention (DLP) techniques to prevent sensitive or confidential data from being lost, stolen, or leaked outside the perimeter.
• Security level assessment — continuously monitor the sensitivity of your model’s outputs and take corrective actions if the sensitivity level increases. Extra vigilance won’t hurt when using new input datasets for training or inference. 

And by no means, do not use data directly as input for commercial pre-trained gen AI models, unless you intend to put sensitive information into the limelight.

6. Emphasize security during AI software development lifecycle 

Last but not least, your ML consulting and development team should create a safe, controllable engineering environment, complete with secure model storage, data auditability, and limited access to model and data backups. 

Security scans should be integrated into data and model pipelines throughout the entire process, from data pre-processing to model deployment. Model developers should also run prompt testing locally in their environment and also in the CI/CD pipelines to assess how the model responds to different user inputs and nip potential biases or unintended behavior in the bud.

Balancing innovation and privacy

To remain top of the game amidst the growing competition, companies in nearly every industry are venturing into AI development to tap its innovative potential. But with great power comes great responsibility. As they pioneer AI-driven innovation, organizations must also address the evolving risks associated with AI’s rapid development. 

Responsible AI development demands from organizations a holistic risk management and data privacy approach, paired with a mix of risk-specific controls. By keeping privacy and ethics in AI development and deployment top of mind, you can enjoy the benefits of AI while prioritizing data privacy and promoting trust and accountability in the use of AI technologies.

The post AI Privacy Concerns: Profiling Through the Risks and Finding Solutions appeared first on *instinctools.

Contents

• What is optimization in AI? Turning your models from good to great
• What problems does AI optimization address?
  • High costs of training and running AI models
  • Slow performance and latency
  • Overfitting 
  • Lack of interpretability
  • ​​Deployment challenges
• Key techniques for AI model optimization
  • Model architecture optimization
  • Data optimization
  • Training optimization
• How much does it cost to optimize an AI model?
• AI optimization is a balancing act

When the honeymoon stage is over, you may find your AI model under-deliver on its promises. Model drift, operational inefficiency, and other hitches creep into the system, nullifying the initial gains. That’s why it’s crucial to pour an equal amount of resources into the development and AI model optimization to maximize the performance of your smart solution and prevent it from going south.

In this article, our AI engineers have curated practical advice about the selection of artificial intelligence optimization techniques along with real-world examples of optimization in artificial intelligence.

What is optimization in AI? Turning your models from good to great

Artificial intelligence optimization is a process of refining the performance and efficiency of existing machine learning models. Although fine-tuning is considered to be an optional, after-the-fact step, it’s the brushstroke that turns your pre-trained AI model from a generalist into a niche expert.

More importantly, optimization algorithms allow your model to bypass data, time, and computational limitations, improving its abilities without eating into your resources.

What problems does AI optimization address?

On a high level, AI optimization aims to slash computational load and memory usage along with improving the model’s performance. Beyond this overarching objective, companies can leverage this technique to tackle a raft of other challenges inherent to AI initiatives.

High costs of training and running AI models

The power of large and complex deep learning models usually comes at a cost as their sophisticated architecture and formidable data requirements make them expensive to train and run. Optimization techniques can slim down the necessary computational resources, helping companies save money — especially on cloud deployments based on pay-as-you-go models.

Slow performance and latency

Low latency is an essential prerequisite for real-time data analysis solutions, such as autonomous vehicles and real-time fraud detection systems. However, increasing prediction accuracy often leads to longer prediction times. AI optimization techniques can enhance model speed and reduce inference time, maintaining both low latency and high prediction precision without compromising either.

Overfitting 

Overfitting often plagues AI models, hindering their ability to perform well when faced with an unseen set of data. The reasons for that are manifold, including high model complexity, small training data size, and dominance of noisy data. 

Optimization techniques such as data augmentation (creating more diverse training data), early stopping (preventing overtraining), and others can offset this defect, improving the model’s ability to generalize to new data. This, in turn, enhances the model’s accuracy for real-world scenarios.

Lack of interpretability

The more advanced your model is, the harder it will be to trace its decision-making process. The black-box nature of sophisticated algorithms also makes models hard to debug and undermines trust in their results.

Although AI model optimization isn’t a cure-all solution, this exercise can usher in more transparency into the opaque nature of artificial intelligence. For example, companies can apply rule extraction techniques, gaining more insight into the behind-the-scenes of the model. Combined with explainable AI, these techniques can peer deep into the model and rationalize specific predictions.

​​Deployment challenges

Large deep learning models guzzle a truckload of processing power and device memory, which makes their deployment challenging for resource-constrained environments such as those involving IoT devices. AI fine-tuning makes smart models leaner by trimming away unnecessary parameters and refining the model’s architecture. 

Some techniques also enable the model to use lower-precision data types instead of the bulky high-precision formats. Unlike their heavyweight counterparts, lightweight AI models have more deployment options and can integrate with new applications and devices.

Key techniques for AI model optimization

To choose the right optimization technique for your model, you need to analyze several factors, including the model’s type, the deployment environment, and your optimization goals. Below, our data science team has broken down the key optimization techniques along with their ideal use cases.

Model architecture optimization

Whenever your model is facing resource constraints, like limited memory or computational power limitations, it’s in for an architecture touchup. Architecture optimization is also beneficial for the deployment efficiency and the accuracy of predictions.

Pruning

Similar to tree pruning, this technique optimizes the model by selectively removing redundant or unimportant parameters, artificial neurons, weights, or deep learning network layers, thus improving the model’s core structure. A proper prune is an investment in the speed and nimbleness of the model. Pruning can also be used as a regularization technique to prevent overfitting.

When to use? Use pruning when you need to reduce computational complexity and inference time. This is typically the case with tight deployment constraints, such as limited memory or processing power on edge devices. For example, mobile phone manufacturers apply pruning to enable on-device image recognition, reducing battery consumption and improving responsiveness.

Keep in mind that this optimization technique can lead to a slight drop in accuracy.

Quantization

Quantization allows you to substitute floating-point weights and/or activations with low precision compact representations. In simple words, this technique transforms a heavyweight AI model, which employs a lot of numbers to make predictions, into a more agile model with a reduced number of bits used for those numbers. 

By paring down the model, quantization makes it faster while also reducing memory footprint. However, the boost in speed may come at the expense of prediction accuracy so you should select this optimization technique for tasks where you can keep the accuracy loss to the minimum.

When to use? Quantization is the go-to option for on-device machine learning tasks, resource-constrained environments, like embedded systems and IoT devices, and cloud-based AI.

For instance, quantization is the secret behind real-time voice interactions on smart speakers that leverage the technique to operate efficiently on low-power hardware.

Knowledge distillation

Knowledge distillation is one of the general-purpose optimization algorithms in artificial intelligence popular for model compression. This technique condenses knowledge from a pre-trained, complex ‘teacher’ model into a simpler and smaller ‘student’ model to improve the performance of the latter. By building on the teacher’s wisdom, the optimized model can achieve comparable performance on the same task, but at less cost.

When to use? Knowledge distillation can benefit multiple AI solutions, including natural language processing, speech recognition, image recognition and object detection. You can use this optimization technique for one of these tasks, provided you have a pre-trained, high-performance model that can then power a smaller model for real-time applications.

For example, knowledge distillation enables a small chatbot model to increase response time and refine its conversation flow by using a large, pre-trained language model as a ‘teacher’.

Data optimization

In a perfect world, all AI models are fed on accurate, high-quality, and balanced data that lays the ground for impeccable performance. In reality, training data often falls short, causing insufficient model adaptability and subpar performance. Data optimization techniques can alleviate issues caused by poor training data while also promoting better results.

Data augmentation

Data augmentation allows data science teams to artificially ramp up the training set by creating new training data from existing data samples. This technique increases the volume, equality, and diversity of training data, allowing the model to bypass the limitations caused by small or imbalanced datasets. Expanded sets of training data, in turn, mitigate overfitting and reduce data dependency.

When to use? Data augmentation helps your model fare well against the backdrop of limited training data, which is a common issue in tasks like image recognition.

For example, data augmentation makes up for limited data variations, which is a well-known hurdle for medical imaging models targeted at rare disease detection. 

Autonomous vehicles also rely on augmented datasets to overcome limitations in real-world data and, ultimately, navigate in diverse weather conditions.

Data distillation

Akin to knowledge distillation for models, dataset distillation helps a smaller model pick the brains of a larger model trained on a massive dataset. This optimization technique allows the smaller model to transfer specific data points, relationships, and internal representations of the data from the larger one, making the smaller model faster and less computationally expensive.

When to use? Data distillation comes to the rescue when you have limited access to a large dataset due to privacy concerns or storage constraints, but you can leverage a pre-trained model, already enriched by a vast dataset on a similar task.  

For example, dataset distillation is a game-changer in the development of customer service chatbots that often lack training data due to the dynamic nature of customer interactions. 

Training optimization

As our experience shows, companies often look for ways to train a model on a dime due to the limited computational resources and budget considerations. Training optimization techniques help strike a balance between the cost and quality of training, without performance trade-offs.

Hyperparameter tuning

Hyperparameter tuning allows you to train your model sequentially with different sets of hyperparameters to increase its training speed, convergence, and generalization capabilities. During this process, the team selects the optimal values for a machine learning model’s hyperparameters, such as learning rate, batch size, number of epochs, and others.

Hyperparameter tuning is an iterative process where data scientists experiment with different hyperparameter settings, assess the model performance, and iterate accordingly.

When to use? You can resort to hyperparameter tuning whenever you need to squeeze out the best possible performance or solve complex optimization problems in artificial intelligence. This optimization technique also allows teams to work around limited datasets and facilitate model adaptation when working with a new dataset. 

Hyperparameter tuning proves beneficial for different ML tasks, including stock price forecasting, fraud detection, drug response prediction, and more. 

Early stopping

This optimization algorithm helps avoid overfitting that typically occurs when training a learner with an iterative method such as gradient descent. Early stopping monitors the model’s performance on a validation set and shuts down training when the validation accuracy starts to degrade. 

When to use? Early stopping helps data science teams deal with complex models or limited training data — when it becomes critical to prevent overfitting.

For example, this fine-tuning technique helps optimize training time for computationally expensive models.

Gradient clipping

During training, the model adjusts its weights based on the calculated gradients. Gradient clipping ‘clips’ the size of the slopes and sets a threshold — maximum value — for them to tackle the exploding gradients problem and improve the stability of the training process. In some cases, this technique can also keep the training process from oscillating and potentially result in faster convergence.

When to use? Gradient clipping is a linchpin technique for any neural architecture that is prone to grow too big.

The application area of gradient clipping is massive, ranging from NLP tasks to reinforcement learning environments and other machine learning techniques. For example, by stabilizing the training process, gradient clipping smooths training for NLP models, enabling them to achieve a nuanced understanding and generation of natural language.

How much does it cost to optimize an AI model?

It’s hard to pin down an exact figure when talking about optimization cost as the final pricing varies greatly based on several variables, including: 

• The complexity of the AI model — sophisticated deep learning models demand more effort and resources to optimize.
• Optimization techniques — some techniques like data distillation require significant computational resources and specific hardware.
• Deployment scale — large-scale deployments increase optimization costs.
• Data storage — large datasets require cloud or on-premise storage solutions.
• Data labeling — manual labeling can increase your optimization costs.

Instinctools’ data science team employs automation tools within our MLOps approach to reduce the time and effort needed for tasks like data pre-processing or hyperparameter search. We also recommend optimizing for the most important metrics, instead of spreading the optimization efforts too thin, to make the entire process easier on the budget.

AI optimization is a balancing act

While artificial intelligence and optimization make a powerful duo, excessive fine-tuning can lead to diminishing returns and drops in model performance. To get AI model optimization right, you also have to strike the right balance between accuracy and computational demand — or else you’ll end up with longer training times, higher costs, and limited deployment options.

The post Polishing Up Your AI Model: Optimization That Drives Success appeared first on *instinctools.

Contents

• Nailing down the terminology first: what is the difference between in-house and outsourcing?
• Advantages of both sourcing approaches proven by real-life projects
• In-house vs. outsourcing: risk profiles
• Examples of insourcing and outsourcing done right
• What’s your ideal sourcing approach to software development?  
• Highs and lows of in-house development and outsourcing in a nutshell

Having the right talent at the right time at a reasonable cost is usually a pipe dream in workforce management. Konstantin Nikitin, *instinctools’ delivery manager, and Yury Eroshenkov, CCMS product owner, reveal how to make this dream your business reality.

Konstantin Nikitin is a delivery manager with a strong software engineering background and 15+ years of proven experience in a managing position.

Yury Eroshenkov is the product owner of DITAworks, an enterprise-grade SaaS software that simplifies technical writing thanks to advanced reuse mechanisms.

Nailing down the terminology first: what is the difference between in-house and outsourcing?

Konstantin Nikitin: Having an internal software development department that stands for in-house development usually doesn’t raise questions about its nature. 

On the contrary, outsourcing software development comes in different forms regarding the hiring distance and the cooperation scope. It can be onshoring within your country, nearshoring to nearby regions, or offshoring to distant locations. Additionally, it can start as IT staff augmentation and be scaled up to a dedicated team and a whole development center.

Advantages of both sourcing approaches proven by real-life projects

Darateja Shymko: Lay it out for me, no sugar-coating: Which option is guaranteed to work?

Konstantin Nikitin: I wish the answer was as straightforward and unambiguous as the question is. But in reality, both in-house software development and outsourcing software projects can fall flat or strike it rich, with plenty of real-life examples. The odds of thriving depend more on choosing the sourcing model that resonates with your business goals and tasks. 

Darateja Shymko: I got my hands on Deloitte’s global outsourcing survey, which states that cost, flexibility, and time to market are the major tech priorities in the post-pandemic business landscape. Let’s reflect on these sore points to highlight the drawbacks and benefits of in-house vs. outsourcing. The most burning question is: How much does it cost to have a fully-packed in-house team and hire a dedicated outsourcing one?

Yury Eroshenkov: I can’t provide exact numbers, but keeping an internal development team really adds up. You have to cover sick leaves, vacations, health insurance, and perks like gym memberships. Plus, you need to invest in ongoing training for your team. And let’s not forget about recruitment costs — in-house hiring may take weeks or even months to spot and enlist necessary experts.  

Konstantin Nikitin: Meanwhile, working with an outsourcing company allows you to kick off the project within several days and you only pay for the work done. All the extra costs related to the outsourced team are on the vendor’s side. Of course, these costs affect hourly rates to a certain extent. 

However, if your company is based in pricy locations, such as the US, Australia, the Nordic countries, or Western Europe, you can still benefit from a global pool of talents in more budget-friendly locations such as Middle Europe. For instance, tech giants of the IT industry like McKinsey and Deloitte set up their R&D centers in Poland for a reason. The country is known for its high-quality IT workforce.

Darateja Shymko: When a company can start a project within a few days with an outsourcing provider, while it takes weeks or even months to find and hire the necessary in-house experts, the choice seems obvious and not in favor of in-house teams. Or is it common among outsourcing providers to promise big but deliver blah?

Konstantin Nikitin: The bone of outsourcing software development is unmatched scalability and flexibility. You can start with a single developer and then expand to a few dozen of them, or set up a core team upfront with a business analyst, software architect, and several engineers, integrating other subject matter experts later on. And when those roles are no longer needed, scaling down is just as smooth and swift, if you cooperate with a reputable outsourcing partner. For instance, when working on an e-health product for Canadian Cardiac Care, we were able to assemble a 13-person project team in under three days.

When time to market is a priority, outsourcing software projects can be your silver bullet. Take another recent project: a SaaS provider needed an MVP for a new, AI-driven product to enhance their tech documentation management software. We built a robust document converter with a fine-tuned LLM at its core in just six weeks. 

Of course, as you noted, not all outsourcing experiences are created equal. It’s essential to avoid vendors who offer lip service rather than solid software development. An unreliable tech partner can deliver a solution that doesn’t match the original idea and your expectations, or, which is worse, misreads customer needs. 

One of our clients – a European venture capital company – reached out to *instinctools after a frustrating experience with an incompetent outsourcing agency. They had a developers-only team, which led to vague project requirements, blurred project vision, and a deep chasm between expectations and reality. Instead of a top⁠-⁠of⁠-⁠the⁠-⁠line digital fundraising platform they envisioned, they got an Excel-like spreadsheet. We stepped in, ran a thorough discovery phase, and got the project back on track. 

Darateja Shymko: What unbeatable strengths of an in-house team make it worth investing in?

Yury Eroshenkov: A high level of direct control over your in-house development team members and the entire project, for sure. Complete control is vital if you develop a specific, complex SaaS product or several related software solutions. With such an approach, you always have in-house employees with a deep understanding of the product’s context. In contrast, in the case of an outsourced team, you’d have to dedicate extra time to contextualize third-party experts. 

Having a core team with at least a business analyst and software architect who understands your system from the inside out also greatly facilitates new feature development.

Darateja Shymko: Are there scenarios where insourcing is the only option?

Konstantin Nikitin: Actually, only one clear-cut case comes to mind. Government sector organizations are likely to prohibit a software development company from cooperating with third-party contractors, even for minor tasks, due to data protection concerns. 

Darateja Shymko: Yury, I’d love to get your take on as a product owner of enterprise-grade SaaS software you’ve built completely in-house. Can you share behind-the-scenes insights into having a strictly internal team?  

Yury Eroshenkov: The tricky part is keeping your highly skilled in-house employees engaged with enough interesting tasks, especially when the project moves into the maintenance phase.

Meanwhile, outsourcing providers have a plethora of various projects that help them retain top-level experts. One month, you might be contributing to building software for an AgTech startup, and two months later, you could be working on custom LMS development for an energy corporation. From the employee standpoint, project diversity can be a considerable advantage when deciding where to work.

Darateja Shymko: Speaking of outsourcing software development, what are the major perks of this approach that we haven’t mentioned earlier?

Konstantin Nikitin: Yury’s point underscores one of the major advantages of outsourced teams — diverse expertise they offer. 

Let’s say you should undergo software modernization and need experts who have chops for both outdated and modern technologies. What if you don’t have in-house aces to ensure a smooth transition to a new tech stack? Nevertheless, hiring in-house developers with specialized skills isn’t cost-effective if you need their support only for a couple of  months.  

Besides, outsourced teams also deserve kudos for their constant proactiveness. I can recall numerous cases when *instinctools, as an outsourcing provider, went the extra mile for their clients. Take, for example, enhancing a client’s testing infrastructure while tackling legacy software modernization, exceeding the minimum number of in-build design templates when creating brand-new product design for lighting controllers, packing an MVP with more features than initially agreed upon — the list can go on and on.  

Cooperation with an outsourcing agency also has well-defined budget and timeline agreements for all development stages, with penalties for downtime on the vendor’s side. This means your contractor is motivated to quickly address any issues that arise. There were numerous cases in my practice when a hybrid development team saved the day, when some of the client’s in-house experts were on vacation, and the vendor’s PM proposed a substitute specialist to maintain the project’s tempo and minimize downtime.  

Darateja Shymko: It sounds like project duration can be a key factor in choosing the right sourcing format, right?

Konstantin Nikitin: Yes, if you only need specialized skills for less than six months, outsourcing software development is a smart move that saves you from the hiring hustle and extra expenses.

Testing a raw hypothesis or clarifying project requirements through a discovery phase is a short-term task with a high level of uncertainty an outsourcing partner can handle effectively. Even if you have an in-house business analyst and software architect, getting the external perspective of top-tier experts intensely contributes to the idea evaluation.   

When it comes to software solutions with a detailed backlog and a clear timeline of three years or more, having core roles such as BA and solution architect internally helps fast-track feature development. 

But here’s the thing: companies don’t have to choose strictly between insourcing and outsourcing. A hybrid approach often works best, and our collaboration with a robotics manufacturing company proves this point.

When the client reached out to us, their internal team was already maxed out, but they needed to present a trailblazing robot model at an industry trade show in less than two months. By quickly augmenting their core team with our outsourced development team, they hit the deadline in just seven weeks without delaying other existing projects. 

In-house vs. outsourcing: risk profiles

Darateja Shymko: Let’s change the angle from benefits to risks. We’ve touched on the chances that the outsourcing team’s promises might turn into a pumpkin. What exactly can go wrong? 

Konstantin Nikitin: I’d say that outsourcing different parts of a solution to different vendors is a highway to hell, covered with extra bills for unforeseen software testing when you try to merge all those parts. Having a single blue-chip tech partner is vital for companies that don’t specialize in software development. For example, at *instinctools, we’ve created a comprehensive ecommerce ecosystem with six solutions for an eyewear company to cover their core business activities, from inventory and resource planning to interactions with customers online and in physical stores.

Another big challenge is the lack of thorough project documentation. Whether you’re working with an in-house team or an outsourcing partner, if you don’t have detailed documentation outlining your vision, scope, competitor analysis, target audience, architecture overview, UX/UI concepts, and so on, you risk losing essential project knowledge. This can make you overly dependent on whoever holds that knowledge, be it in-house developers or external contractors. To avoid vendor lock and be able to rotate personnel within your internal team with minimum inconveniences, you need to set high and clear requirements for project documentation from the start.

Darateja Shymko: Security concerns are another major pain point. Insourcing may seem safer at first, but if we look at companies like Amazon and PayPal, which rely strictly on in-house software development, and giants like Google and Slack, which leverage outsourcing, they all suffer from data leakages nearly every year. Hence, which option would you recommend and why?    

Konstantin Nikitin: I recommend prioritizing data security measures, as your sensitive data can be exposed to the risk of leakage regardless of the sourcing model you choose. A common best practice for data protection is to require mandatory authorization to access the production data. 

You can also obfuscate production data and replace sensitive and personal information with dummy data that looks real but holds no value to intruders.

One of the most demanding cases I’ve encountered was with a bank that aimed to root out all potential loopholes for cybercriminals. They built a closed physical perimeter with a security frame and a guard at the entrance, enforced a no-phones policy, limited access to the Internet, and implemented mandatory audio recording and video monitoring within their office space. The same approach can be applied to a dedicated team or an offshore development center.

So the difference between in-house vs. outsourcing software development in terms of security is that with an internal team, you are the one in charge of creating and maintaining a secure environment, whereas if you cooperate with an outsourcing vendor, this responsibility shifts to them. 

Examples of insourcing and outsourcing done right

Darateja Shymko: Current Fortune 500 statistics show that 90% of these companies leverage both outsourcing and in-house software development. Based on your experience, why do companies that clearly have the budget to expand their internal capacities still prefer to use external contractors for some tasks? Is there an inflection point when managing the development process internally becomes impractical?

Konstantin Nikitin: You’ve hit on a crucial point: “for some tasks” is key here. In my view, you can outsource one-time or non-core tasks. We’ve already mentioned some examples of those. 

The tipping point comes when your in-house development team can’t handle all the existing projects without compromising on quality. One of our recent undertakings was a software modernization initiative for a leading personalization engine company. What started as replacing a rigid legacy tech stack soon expanded to tackling their overall backlog and participating in R&D activities, as the client couldn’t keep up with cascading tasks.

What’s your ideal sourcing approach to software development?  

Yury Eroshenkov: I stand for building the core team internally to retain complete control over the project but with the flexibility to bring in specific skills from the outside as needed. So my vote goes to the hybrid sourcing approach. 

Konstantin Nikitin: As a delivery manager who has managed multiple projects simultaneously, I assure you that you can maintain almost the same level of control over an outsourced development team, considering your outsourcing partner is a reputable service provider with a solid delivery framework and robust security practices. Still, I can’t deny the value of in-house teams, so I agree with Yury and support combining homegrown capabilities with external expertise instead of clashing in-house vs. outsourcing.

Highs and lows of in-house development and outsourcing in a nutshell

We’ve organized the interviewees’ answers in a brief table to lay out bare strengths and weaknesses of in-house and outsourced development, making it easier for you to analyze each option.

The post In-House Development vs. Outsourcing Development | Interview With Experts Who Have Been On Both Sides of the Fence appeared first on *instinctools.

Contents

• How accurate demand forecasting can save the day for your ecom operations
  • Inventory management 
  • Budgeting and financial planning
  • Advertising and marketing optimization
• Three signs your current demand forecasting tool is failing you 
  • Underperforming forecast accuracy
  • Excessive chargebacks 
  • Inventory imbalance
• Five challenges in predicting demand
  • Data blind spots
  • Data management issues
  • Biases in data collection 
  • Inadequate historical records
  • Rapid strategic changes 
• Types of demand forecasting: is it about quality or quantity? 
  • Qualitative methods
  • Quantitative methods
• Demand forecasting techniques: from basic to advanced
  • Spreadsheets
  • BI-enabled forecasting
  • ML-enabled forecasting
• How to approach advanced demand forecasting? 5 crucial steps
  • Step 1: Collect and aggregate data
  • Step 2: Get your data in order 
  • Step 3: Create a forecasting model
  • Step 4: Evaluate the model
  • Step 5: Generate and use forecasts for decision making
• Forecast with precision, navigate fluctuations with confidence

The way ecommerce used to work isn’t working anymore. Traditional supply chain planning technologies and processes fall short of changing gear in the face of black swan events — that now happen at a greater scale, causing intense swings in the global market. In 10 out of 10 cases, outdated forecasts backfire, setting businesses up for lost sales, excess inventory, and dissatisfied customers.

Today, growth-oriented online retailers have to learn the ropes of adaptive demand forecasting in ecommerce that uses real-time demand data to stabilize sales, optimize inventory management, and ensure effective resource allocation even during economic downturns. 

How accurate demand forecasting can save the day for your ecom operations

Ecommerce forecasting tools cannot predict future demand with surgical precision, but that’s not what they’re designed for. By combining historical sales data with market trends, product information, and other insights, ecommerce demand forecasting provides the intelligence needed for business owners to roll with the punches, regardless of the variables.

Inventory management 

Ideally, companies should have a balanced amount of raw materials or manufactured products on hand — enough to accelerate fulfillment, but not too much to increase inventory and warehousing costs. In reality, 70% to 80% of retailers’ cash is tied up in inventory.

Accurate ecommerce inventory forecasting allows merchants to prevent overstocking on slow-moving items, reduce stockouts, and maintain safety stock levels by seeing into demand fluctuations and current inventory levels. Whenever you’re dabbling into new products, forecasting tools can analyze historical sales of similar products and offer granular replenishment recommendations.

Getting a heads-up about peak periods allows companies to get their ducks in a row early and restock on high-demand products. Merchants can allocate fulfillment resources accordingly, ensuring faster turnaround times for customer orders.

Budgeting and financial planning

Building on historical data and consumer preferences, demand forecasting solutions provide a data-driven basis for calculating future sales and predicting expected revenue. Along with incoming cash flows, business owners can leverage demand data to size up upcoming expenses associated with fulfillment and procurement — and in some cases, even negotiate discounts with suppliers. 

Ecommerce sales forecasting tools also outline peak periods and times of cash flow blues, allowing ecommerce businesses to optimize expenses.

Advertising and marketing optimization

Merchants can use the combination of demand forecasting tools and market research to get a handle on the right target audience and focus marketing efforts on customers more likely to purchase their products. A detailed snapshot of seasonal trends and peak seasons also enables companies to optimize their marketing strategies and align campaign timing with commerce demand upticks.

Three signs your current demand forecasting tool is failing you 

In this time and edge, accurate demand forecasting serves as a vital compass, guiding around 43% of ecommerce companies through stormy seas. However, not all tools yield the same forecasting accuracy. Here are three telltale signs your current demand forecasting tool is playing false.

Underperforming forecast accuracy

Here’s the thing with old-school demand prediction tools: they’re heavily reliant on historical data. Although it’s a great starting point, historical data alone doesn’t cut it when it comes to long term demand forecasting — it’s myopic and unable to account for external factors. So unless your tool is supplemented with other forecasting techniques, it has a considerable margin of error.

Excessive chargebacks 

Today’s online shoppers are adamant about shipping times. Whenever there’s a delay in shipping due to inventory issues, a customer doesn’t think twice about opening a chargeback, consigning a merchant to additional costs and potential payment processor restrictions. Excessive chargebacks are a sure sign of a flawed sales planning tool that can’t predict demand right on the money.

Inventory imbalance

Healthy inventory levels are a clear indicator of a smooth-running forecasting system. Without proper demand forecasting, you’ll end up with an unbalanced inventory — too much, too little, or in the wrong place, paired with formidable inventory costs. Unexpected demand surges can quickly throw the inventory off balance, hindering your ability to meet customer demand. If you can relate to the case, chances are your forecasting system is going haywire.

Five challenges in predicting demand

Ecommerce data forecasting is fraught with challenges. Shifting consumer behavior, market volatility, supply chain disruptions, and data hiccups can all cloud the judgment of your forecasting efforts. Let’s look into the most significant obstacles that affect demand forecasting accuracy.

Data blind spots

Demand precision hinges on comprehensive data input, including historical sales data, market trends, consumer surveys, and any other indicators at your disposal. Limited data scope that ignores purchase history, demand patterns, broader economic triggers, and other drivers can lead to unreliable insights. 

Data management issues

Forecasting models collect data that is both high-quality and easily accessible. But when it’s scattered across different teams and systems, forecasters have a hard time producing reliable predictions. Inconsistent data management practices, mergers, and data latency can also throw a few wrenches into data management, causing discrepancies in forecasting.

Biases in data collection 

Biased data selection along with confirmation bias can tip the scales in favor of a particular outcome. For example, sales data tends to be overly optimistic at most times as salespeople, eager to hit quotas, often report idealistic predictions, skewing the forecasts towards a higher number. 

On the same note, customer input often lacks feedback from customers with average experience, while those who’ve had excellent or nightmarish encounters are more inclined to be vocal about it. 

Inadequate historical records

Ecommerce companies that have accumulated consistent historical data can use this data for accurate forecasting — unless it’s incompatible with the forecasting technology, which is often the case. Most businesses just throw this data to the back shelf, with no intention of analyzing it afterward or at least keeping it in check for later. As a result, poorly maintained data complicates reliable predictions, leaving companies with a modest basis for forecasting.

Rapid strategic changes 

Frequent strategy pivots make it challenging to forecast consumer responses as forecasting tools struggle to align customer needs with company actions. In simple words, every time a business calls an audible, they render their historical data ineffective, which can result in meaningless predictions.

Types of demand forecasting: is it about quality or quantity? 

All demand forecasting methods fall into two groups based on the nature of data: qualitative and quantitative. Each category has its strengths and applications — it all comes down to a particular context and available data.

Qualitative methods

Whenever there’s a lack of historical data to build on, companies turn to qualitative forecasting methods that leverage judgment from experts along with market research and other non-numerical data. 

Qualitative methods also fit the bill when a company is entering new markets or debuting products. Examples of qualitative forecasting include such approaches as a Delphi method, customer surveys, and scenario planning.

Quantitative methods

Quantitative methods rely on historical data and statistical techniques to generate long-term forecasts. Hailed as the most precise forecasting approach, these methods are contingent upon the availability of historical data. Time series analysis, regression analysis, and causal analysis all register as quantitative methods, helping companies project future values.

However, these methods are incapable of considering subjective or unpredictable factors, so companies usually employ a combination of quantitative and qualitative approaches to forecast demand with greater accuracy.

Demand forecasting techniques: from basic to advanced

Depending on their technological maturity and availability of external and internal data, ecommerce companies can lean into one of the three forecasting methods stated below — or combine them for better results.

Spreadsheets

Small businesses with limited data and simple forecasting needs usually use spreadsheets like Microsoft Excel and Google Sheets for basic statistical analysis and back-of-the-envelope demand forecasts. As the most fundamental tool for commerce demand forecasting, spreadsheets are intuitive and easy on the companies’ wallets but suffer from limited scalability and narrow analytical capability. 

Based on our tests, Excel can easily manage around 200-300 thousand rows without breaking a sweat, and a single sheet can handle just over a million rows. But, if you want to go beyond that, you’ll need to juggle data across multiple sheets, which is a real hassle and takes up a lot of time.

— Ivan Dubouski, Lead BI Analyst, *instinctools

Also, manual data entry is time-consuming and error-prone, which makes spreadsheet-based strategic planning an ordeal for business owners.

BI-enabled forecasting

Business intelligence tools take ecommerce demand forecasting a step further by providing advanced data visualization, historical data analysis, and reporting capabilities. Unlike spreadsheets, BI tools are fit for processing a spate of data and can pull it from various sources into a unified system. 

Combining internal intelligence like sales data, customer behavior, and inventory with third-party data like ecommerce benchmarks, market trends, and other sources, BI tools deliver a more nuanced look at the current state of demand well-being and its outlook. 

BI-powered forecasting takes over a lion’s share of data collection, processing, and reporting tasks, minimizing the need for human intervention and preventing errors. With BI tools like  Tableau, Power BI, or QlikView, business owners can gain better visibility into crucial business performance and transform complex datasets into straightforward charts, tailored to their specific KPIs.

Power BI is a powerhouse when dealing with large datasets. It smoothly processes up to 50-100 million rows – that’s a whopping 100-200 times more than what Excel can handle! So, if you’re working with massive amounts of data, Power BI is definitely the way to go for efficiency and convenience.

— Ivan Dubouski, Lead BI Analyst, *instinctools

However, business intelligence tools have a significant downside. Their decisions are rooted in historical data and cannot account for unpredictable events or trends. Also, BI tools are designed for structured data and ill-disposed to unstructured insights such as social media posts, images, and videos — which can lead to one-sided predictions.

ML-enabled forecasting

ML-powered commerce demand forecasting steps in whenever ecommerce companies need to go down the rabbit hole of data. ML forecasting doesn’t just estimate demand based on historical and statistical data, it leverages sophisticated analytics models to combine historical and real-time data (both structured and unstructured) and predict more precisely where customer demand is headed.

Thanks to its continuous learning capabilities and broader data scope, ML forecasting excels where other methods struggle. For example, unlike conventional methods, machine learning can predict demand for brand-new products by analyzing sales performance data on similar products. 

And as new data becomes available, the ML model can fine-tune its approach to align predictions with evolving insights. These tools can also streamline the data management workflow by automating the data preparation efforts of analytics teams.

ML-powered forecasting requires commitment though, as companies need significant upfront investment to get started on the ML technology and establish in-house expertise. This type of forecasting is also associated with complexities in implementation and maintenance — but not if you join your efforts with a dedicated ecommerce development company.

Let’s take a look at a comparison chart that outlines the main differences between the three forecasting approaches.

How to approach advanced demand forecasting? 5 crucial steps

If you need to solve the forecasting problem with something more robust than spreadsheets, here’s what your roadmap can look like if you want to set up a smart data-based forecasting process.

Step 1: Collect and aggregate data

An efficient forecasting method is contingent upon a wide selection of relevant and accurate data points. Here’s what data you need to lay the groundwork for a successful prediction model:

• Transaction sales data, including SKUs, location, unit sold, and other information.
• Metadata of an item, including its color, size, and more.
• Price data with timestamps.
• Promotional impact with detailed data on past promotions
• Inventory availability, including in-stock or out-of-stock for each SKU at specific time intervals.
• Location data.

Most forecasting models also call for website traffic data, search term insights, social media data, and weather information. You need to have it both for the past and for the future to supercharge the veracity of your forecasting solution.

Step 2: Get your data in order 

Ecommerce data forecasting models aren’t built from raw data as defects in the input can take a toll on the model accuracy. For example, prediction solutions, with their time-dependent features, are particularly sensitive to missing values as these gaps block the model’s ability to spot patterns and trends over time.

Common tasks involved in data preparation include: 

• Identifying and filling missing entries with standardized values.
• Ensuring data consistency in units, dates, and data types across all datasets.
• Weeding out outliers
• Creating new variables to enhance the model’s prediction capabilities
• Merging datasets to promote more nuanced predictions

Once the data is clean and ready, data science teams divide it into training and testing groups. 

Step 3: Create a forecasting model

Based on the complexity of your problem and the availability of historical data, a forecasting model can be powered by different techniques, such as ARIMA, regression, random forest, and others. The development team then uses the training data to teach the forecasting model to predict the necessary outcome. 

At this stage, developers also experiment with hyperparameters to mold the learning behavior of the model and maximize its performance.

Step 4: Evaluate the model

When the model is up and running, the team evaluates its performance by making predictions on the testing data, comparing the output to the actual values, and calculating the relevant accuracy metrics. The quality of a demand forecast usually hinges on two metrics such as forecast accuracy and forecast bias. While the former determines how a given forecast stacks up against actual sales, the latter describes whether the forecast is over or under the actual sales.

Keep in mind that it’s easier to achieve a high prediction accuracy for products with consistent historical sales and little seasonal fluctuations, while the inherent volatility of trend-driven and weather-dependent products makes it harder to anticipate demand. So, in some cases, models can capture the general trends and potential shifts rather than outlining exact values.

After evaluating the results, developers spot areas for improvement and calibrate the model by adjusting hyperparameters, applying different techniques, or adding more data to the pipeline.

Step 5: Generate and use forecasts for decision making

When the model fits the predefined accuracy threshold, your team deploys it in the cloud and gears it up for forecast generation. Keep in mind that your forecasting model doesn’t operate on autopilot as it needs a constant stream of relevant data — both historical and current — to stay in shape. 

You can export forecasts to the analytic tool of your choice and leverage them for strategic planning.

Forecast with precision, navigate fluctuations with confidence

In today’s turbulent times, businesses have to anticipate demand in a whole new way — with no forecast silos, but with continuous adjustment. A combination of quality data, machine intelligence, and visualization tools are spearheading the way of new-age demand prediction, helping businesses translate forecasts into better business outcomes and stay afloat even in the direst of times. 

At *instinctools, we bridge expertise in data and ML algorithms with a systemic approach and domain knowledge — to help ecommerce businesses improve their forecasting approach and scale it to every business function. Let’s pool our efforts and move your demand planning from reactive to proactive.

The post Going From Chaos to Clarity With Demand Forecasting In Ecommerce appeared first on *instinctools.

Contents

• What is Robotic Process Automation in manufacturing, and how does it work? 
• Four benefits of RPA in manufacturing you can’t ignore
  • Lower operational costs
  • Enhanced regulatory compliance
  • Increased productivity
  • Improved quality and accuracy
• Use cases where RPA in manufacturing pays off 
  • Back-office tasks
  • Production line support
  • Supply chain management
• Empowering intelligent automation amidst the industry 4.0
• RPA journey made easy with these 4 steps to start with
  • 1. Identify and document recurring tasks and procedures that can be automated
  • 2. Determine if cognitive automation is necessary
  • 3. Search for a suitable RPA solution
  • 4. Assess the costs of integrating the solution
• A pure cost reduction exercise or an opportunity to invest in enterprise-wide digital transformation? RPA can do both for your manufacturing business
• FAQ:

Industrial owners are losing a fortune and seeing their productivity plummet due to mundane repetitive manual tasks, inevitable human errors, and employees drowning in spreadsheets. RPA in the manufacturing industry helps to root these issues out.

With ever-present budgetary constraints and tight deadlines, RPA stands out as a relatively cost-effective, fast, and low-risk way to strengthen operations. It’s less complex than other enterprise automation technologies, like smart physical robots within production sites or digital twins that mimic factory floors. 

Organizations report a typical payback of 250% within two or three quarters after RPA adoption. Pretty impressive, isn’t it? Some high-performers have even achieved a 380% return on automation investments.

This is just a small part of why spending on robotic process automation software is forecast to grow at a CAGR of 36.6% from 2022 to 2032. 

Having mastered RPA inside and out, our experts are eager to share hard-won insights distilled from over 20 years on the front lines. Let’s delve into the mechanics of RPA, its transformative benefits, and real-world applications, illustrating how this technology is propelling the industry toward excellence.

What is Robotic Process Automation in manufacturing, and how does it work? 

Robotic process automation (RPA) is a technology manufacturing businesses use to automate high-volume, repeatable tasks.

Using scripts that emulate human actions, software robots are there for you to perform repetitive tasks. They log into enterprise applications, move files, open and send emails, fill in forms, and tackle all that white-collar worker’s daily routine.

The sweet spot for RPA lies in automating rule-based, straightforward, and mostly high-volume tasks. Completing them manually is time-consuming, error-prone, and far from creative yet vital to keeping the factory running. Meanwhile, bots do them all way faster and without lapses.

See it in action:

• Before automation: a manufacturing company receives hundreds of invoices from various suppliers each month. Employees manually input invoice data into the ERP system, verify details, and process payments. The process itself is a bottleneck, often leading to delays and inaccuracies.

• After automation: an RPA bot handles the end-to-end process in seconds, signaling only in case of exceptions where manual validation is required.

There is also a more advanced form of RPA called cognitive automation. It aims to overcome the limitations of traditional RPA by applying more intelligence to tasks, hence adding some recognition and analysis capabilities.

Four benefits of RPA in manufacturing you can’t ignore

RPA turns out to be a game-changer for the manufacturing industry in many ways. By streamlining complex operations, enhancing precision, and slashing costs, it empowers manufacturers to pivot from routine tasks to strategic innovation.

Lower operational costs

Wiping out costly errors and freeing up staff from mundane duties makes RPA an instrumental add-on to your cost-reduction strategy. 

Typical savings on current operational costs range from 25% to 80%, which can translate into tens of millions of dollars annually for some organizations.

Moreover, the speed and accuracy of automation help reduce administrative, prevention, appraisal, and internal/external failure costs — the list can go on and on. 

Enhanced regulatory compliance

Apart from RPA-driven cost reduction, organizations also reap benefits like improved compliance, recognizing the impact of the technology on enhancing efficiency. 

With RPA’s capability to cross-reference raw materials and testing results against regulatory requirements, update compliance documents in real time, and, overall, facilitate adherence to regulations, meeting even stringent standards, such as FDA’s Current Good Manufacturing Practice (CGMP) or equivalent EU legal guidelines, becomes a breeze.

Our client, a leading manufacturer with production plants across the APAC region, stated: “Taking care of compliance and internal checks from the outset of our RPA journey was a brilliant move. Deploying multiple bots has boosted overall process quality and ensured reliable execution.”

All aspects of the manufacturing process can be automatically tracked for compliance with established standards, safety guidelines, legal requirements, and environmental regulations.

Increased productivity

RPA transforms how dozens of routine but critical tasks are handled. The tireless digital workforce completes these tasks in seconds with near-zero error rates. 

Automation can be especially helpful when organizations experience a labor shortage. With RPA in place, all operations run efficiently even during high-volume activity and peak workloads. 

Up to an 85% improvement in time efficiency and saving over 1000 hours or 199+ FTE days can be achieved simply by replacing humans in data entry tasks. Imagine the productivity gains after automating other tiresome back-office routines.

Any way you slice it, the outcome is freeing up thousands of labor hours yearly and increased productivity as workers focus on higher-value, meaningful work.

Improved quality and accuracy

High quality is always a matter of a strong reputation and profitability. Cost of poor quality (COPQ) can amount to 15-20% of sales revenue, with some reaching up to 40% of total operations.

Let’s recall traditional Bill of Material (BOM) management, which often suffers from manual errors. Incorrect quantities or assembly instructions, inconsistent updates, and difficulty tracking changes lead to defective products, wasted resources, and costly rework. Automation software accurately updates BOM data and transfers it into ERP systems, making sure nothing is overlooked, even when changes occur. Zero — that is the number of errors in BOM management you can expect after adopting RPA.

In production lines, RPA maintains stringent quality specifications, reducing defects in final products. Bots consistently perform quality inspections, minimizing the risk of human oversight. After implementing RPA in their quality assurance processes, most of our clients reported up to a 60% reduction in product defects.

Use cases where RPA in manufacturing pays off 

Manufacturers turn to RPA for value-creation opportunities in time-consuming and repetitive processes such as procurement, inventory management, and payment processing. Below, we list examples of RPA manufacturing use cases with the top tasks primed for automation.

Back-office tasks

Purchase orders (PO), status reports, avoiding duplicates, updating invoice records… The manufacturing back office thrives on documentation. Thanks to RPA, a range of operational activities can be completed fast and flawlessly.

Invoice processing

Instead of manually sifting through files and matching numbers, employees can focus on more strategically significant tasks, leaving the meticulous checks to robots.

RPA bots can automate invoice processing by extracting data, validating information, and routing invoices for approval. Here’s a breakdown of what exactly they are capable of:

• Downloading and extracting data from supplier invoices using Optical Character Recognition (OCR) technology
• Verifying data accuracy by comparing it with purchase orders and product catalogsRouting invoices for approval based on predefined rules (for example, amount thresholds)
• Generating automated payment schedules and sending them to the accounts payable department

Leading manufacturing companies using RPA-enabled invoice processing report that over 80-90% of all invoices are handled without requiring manual inspection. 

An invoice automation project, delivered by the *instinctools team for a multinational manufacturer resulted in $0.9 million in annual cost savings.

Order fulfillment

An automated process of order fulfillment is more accurate, as it fends off errors from the outset. What are the common order-related tasks manufacturing companies can automate?

• Data entry of customer orders received through various channels (email, website, etc.)
• Order verification and stock level checks
• Automatic generation of picking lists and documents from shipping processes
• Sending order status updates to customers

Data entry

Repetitive data entry tasks across existing systems are the most obvious target for automation. Such RPA examples include:

• Updating inventory databases with new stock entries (e.g., raw materials)
• Entering production data from machine logs into relevant software
• Transferring customer information from web forms to CRM systems

When a fast-growing beverage distributor faced challenges in managing data flows within their newly implemented enterprise application, they turned to us for a solution. To address the client’s data entry and management hurdles, we deployed RPA software, streamlining data quality checks and automating the input of new product versions into their new ERP system. This strategic move replaced a labor-intensive process with a single, highly efficient bot, amping up operational efficiency and setting the stage for scalable growth without increasing the headcount.

Production line support

RPA solutions enhance productivity along production lines, helping to accelerate operational tasks and phase out manual process flaws.

Quality control checks

Automation tools are used widely in manufacturing tasks aiming to ensure the highest quality possible:

• Collecting data from automated inspection systems, such as vision cameras
• Identifying potential defects in the extracted data based on predefined parameters
• Flagging faulty products and generating reports for further investigation

Generating reports

Automation in the manufacturing sector has long outperformed manual processes in gathering relevant data, visualizing all critical production KPIs, and then delivering final reports to the right stakeholders. RPA speeds up the production planning and reporting by:

• Collecting data from different independent systems on the assembly line, including metrics like machine performance or production output
• Formatting the data into reports with charts and graphs
• Sending reports automatically to relevant personnel, such as production managers, quality control teams, and others

Managing inventory levels

Poor inventory turnover rates, overstocking, and stockouts are nightmares for manufacturing operations managers. RPA bots put them back in control by:

• Monitoring stock levels in real-time based on production data and sales information
• Automatically generating purchase orders for low-stock items
• Tracking incoming and outgoing inventory shipments
• Forecasting & estimating inventory levels

Supply chain management

Due to global geopolitical tensions, a supply chain may become a roller coaster for manufacturers. Automation technologies, including RPA, help companies gain as much control as possible over globally scattered supply chains.

Supplier communication

RPA solutions can streamline repetitive tasks related to supplier communication, including:

• Sending automated purchase orders to vendors
• Monitoring supplier performance based on delivery schedules and quality metrics
• Triggering automated email reminders for overdue deliveries

Order tracking

Automating repetitive tasks in order tracking facilitates transparency in managing the flow of goods from the production line to the warehouse. RPA bots can do the following tasks:

• Tracking the status of shipments from suppliers in real time using tracking numbers
• Generating automated alerts for any delays or deviations from expected delivery times
• Updating internal inventory systems upon receipt of goods

Once again, manufacturing process automation covers various tasks within inventory management, keeping up with regulations, processing documents, streamlining the supply chain, and supporting production needs. The automation potential of these areas delivers the highest business value.

Empowering intelligent automation amidst the industry 4.0

Combined with AI sub-disciplines like machine learning, computer vision, optical character recognition (OCR), and natural language processing (NLP), AI-backed RPA solutions bring more value to the manufacturers’ table. Artificial intelligence helps tackle many complex tasks that RPA alone would struggle to complete.

While RPA can be considered the “doer” of tasks, AI is more about the “thinking” and “learning” sides of things. 

AI-enabled RPA software provides a more advanced level of industrial automation and helps scale your enterprise intelligence. Once you add automation components to certain routines, you can start identifying more complex tasks that require decision-making, pattern recognition, and adaptation to new scenarios. For instance:

• Procurement: predicting pricing trends, analyzing supplier performance, etc.
• Quality control: processing inspection data, identifying defect patterns, predicting equipment failure, etc.
• Production support: optimizing production schedules & inventory management, forecasting demand fluctuations, etc.
• Supply chain management: recommending alternative suppliers, predicting potential disruptions based on historical data, etc. 

RPA journey made easy with these 4 steps to start with

Your RPA implementation should not necessarily start with enterprise-wide automation. Many of our manufacturing clients began with targeted transformations in specific functional areas. Pilot success often fuels wider adoption with far-reaching goals.

Each company moves at its own pace, in its own way. However, there is a set of common steps to ensure all-encompassing RPA implementation regardless of the organization’s maturity level or scalability plans. 

1. Identify and document recurring tasks and procedures that can be automated

Examine your processes and gather feedback from employees across different departments to comprehensively understand their pain points. 

Look closely at your current workflows and identify time-consuming tasks ripe for automation. Pay special attention to repetitive, rule-based administrative tasks with clear steps and straightforward goals. 

You can explore the above-mentioned robotic process automation examples as a starting point or seek professional guidance to identify additional opportunities for your future automated manufacturing.

2. Determine if cognitive automation is necessary

Are RPA bots enough to cover tasks you’ve defined for automation? Or maybe realizing more advanced capabilities, say, human speech recognition, image detection, unstructured data analysis, or AI chatbot integration is needed? 

If yes, then you have to look for additional AI/ML expertise.

3. Search for a suitable RPA solution

Explore relevant manufacturing automation case studies and examine how your peers approach automation. 

While your specific scenario might require fully custom software or customization of ready-made  RPA products, don’t be discouraged by the lack of internal competencies. 

Consider working with technical partners to bridge any expertise gaps. There is often added value in the form of ongoing maintenance and scaling your solution into a corporate-wide initiative as your needs evolve.

4. Assess the costs of integrating the solution

Once the project scope is defined and the team is assembled, it’s time to estimate the expected timeframe and costs. Then, calculate ROI to determine whether the project aligns with your goals and stays on course with profit projections.

A pure cost reduction exercise or an opportunity to invest in enterprise-wide digital transformation? RPA can do both for your manufacturing business

Implementing RPA requires investment and C-level buy-in, but the payoff can be swift and sweet, assuming you have clear goals and engage the right people in the change process. 

The benefits of manufacturing automation go far beyond merely having a cheap and reliable digital workforce for rule-based tasks. Robotic process automation in the manufacturing industry can become a crucial building block of your company’s digital transformation, paving the way for sustainable growth and long-term success. 

FAQ:

The post RPA in Manufacturing: Game-changing Benefits and Use Cases With the Fastest ROI appeared first on *instinctools.

Contents

• Clarifying the terminology: what are LLMs and SLMs?
• Unveiling the differences between an LLM and an SLM: a whole-hog comparison
  • Resource requirements
  • Cost of adoption and usage
  • Fine-tuning time
  • National specificity
  • Capabilities range
  • Inference speed
  • Output quality
  • Security
• The ins and outs of LLMs and SLMs at a glance
• Starting small or going big right away: defining which option works for you

Generative AI and its subset — large language models (LLMs) — keep hitting the headlines, but speaking of their real-world applications in business, the honeymoon phase is over. CEOs start asking the tough questions: is this expensive technology actually worth it, or is there a smarter way to go?

Here’s the inside scoop: new AI models are popping up faster than ever, but a new trend is emerging — small language models (SLMs) that fare well without breaking the bank. Let’s ditch the hype and see which option in the LLM vs. SLM matchup shines brighter and how to turn AI promises into palpable value.  

Clarifying the terminology: what are LLMs and SLMs?

Categorization into small and large language models is determined by the number of parameters in their neural networks. As the definitions vary, we stick to Gartner’s and Deloitte’s vision. While SLMs are models that fit the 500 million to 20 billion parameter range, LLMs hit the 20 billion mark.

Regardless of their size, language models represent AI algorithms powered by deep learning, enabling them to excel at natural language understanding and natural language processing tasks. Under the hood, all transformer models consist of artificial neural networks, including an encoder to grasp the human language input and a decoder to generate a contextually appropriate output.

Unveiling the differences between an LLM and an SLM: a whole-hog comparison

Building and training models from scratch requires significant investments, often beyond the reach of many businesses. That’s why, in this article, we focus exclusively on pre-trained models, comparing notable LLMs such as ChatGPT, Bard, and BERT, with SLMs like Mistral 7B, Falcon 7B, and Llama 13B. 

To feel the cost disparity, consider this: developing and training a model akin to GPT-3 can demand an investment of up to $12 million, and that’s for a version that’s not even the latest. In contrast, leveraging a pre-trained language model costs hundreds of times less, as businesses only need to invest in fine-tuning and inference.

Resource requirements

The history of large language models proves that ‘the bigger the better’ approach has been dominating the AI realm. You can see it in their size — LLMs contain hundreds of billions to trillions of parameters. However, this comes at a cost. High memory consumption makes larger models a resource-intensive technology with high computational power requirements. Even when accessed via API, efficient utilization of a multi-billion large language model requires powerful hardware on the user’s end. 

For instance, if you target GPT, LlaMa, LaMDA, or other big-name LLMs, you’ll need NVIDIA V100 or A100 GPUs, which cost up to $10,000 per processor. These initial resource requirements create a barrier, preventing many businesses from implementing LLMs. 

In contrast, SLMs have significantly fewer parameters, typically ranging from a few million to several billion. They rely on various AI optimization techniques, such as:

• Knowledge distillation to transfer knowledge from the same-family pre-trained LLM. For example, DistilBERT is a lightweight iteration of BERT, and GPT-Neo is a scaled-down version of GPT.
• Quantization techniques to further reduce the model’s size and resource requirements.

Hence, compact model size and lower computational power requirements enable small models to be deployed on a broader range of devices, including regular computers and even smartphones for the smallest models, such as Phi-3 by Microsoft. It turns out that with an SLM as a resource-friendly alternative to LLM, companies can hop on the gen AI train without upgrading their hardware park.

Cost of adoption and usage

To calculate the cost of a language model’s adoption and usage, you should take into account two processes — fine-tuning as a preparatory step to enhance the model’s capabilities, and inference as the operational process of applying the model in practice:

• Fine-tuning helps adapt a pre-trained language model to a specific task or dataset to ensure the quality of its outputs and general abilities match your expectations. 
• Inference calls a fine-tuned language model to generate responses to user input. 

Model fine-tuning can take different forms, but here’s the main thing to remember: its cost is determined by the size of the dataset you want to use for further training. Simply put, the bigger the dataset, the higher the cost.

LLMs don’t need fine-tuning unless you want the model to distinguish the nuances of medical jargon or cover other specific tasks. On the contrary, small language models always call for fine-tuning as their initial capabilities lag behind the larger models. For instance, while GPT-4 Turbo’s ability for reasoning provides satisfying results in 86% of cases, Mistral 7B offers acceptable outcomes only 63% of the time. 

— Chad West, Managing Director, *instinctools USA

To further elaborate, it’s important to understand the concept of tokens, as costs for both fine-tuning and inference are charged based on them. A token is a word or sub-part of a word a language model processes. On average, 750 words are equal to 1,000 tokens.

Discussing the cost of inference brings us to the number of input-output sequences and the model’s user base. If we take GPT-4 as an LLM example, accessing it via API costs, as of June 2024, $0.03 per 1K input tokens and $0.06 per 1K output tokens, totaling $0.09 per request. Let’s say, you have 300 employees, each making only five small 1K-sized requests per day. At a monthly scale, this adds up to around $2.835. And this cost will rise with the size of the requests. 

The substantial cost of LLMs’ usage and the pursuit of cost reduction have driven interest toward smaller language models and fueled their rise. Gartner analyst names SLMs with 500 million to 20 billion parameters a sweet spot for businesses that want to adopt gen AI without investing a fortune into the technology. Deloitte guideline also suggests opting for a small language model within the 5–50 billion parameter range to initiate a hitch-free language model journey. 

Let’s calculate expenses for a specific SLM. Mistral 7B costs $0.0001 per 1K input tokens and $0.0003 per 1K output tokens, resulting in $0.0004 per request. Thus, if we replace GPT-4 with Mistral 7B in the previous example, using this language model will cost you only $12.6/month. 

Rounding up the cost of adoption and usage, we can highlight that both LLMs and SLMs deserve a nod here, as larger models allow you to cut back on the fine-tuning stage, but smaller language models are more affordable for day-to-day usage. 

Fine-tuning time

If you need a gen AI solution familiar with standard medical reports’ structure and a deep understanding of clinical language and medical terminology, you can take a general-purpose model and train it on patient notes and medical reports. 

The logic behind the fine-tuning process is straightforward: the more parameters the model has, the longer it takes to calibrate it. In this regard, adjusting a large language model with trillions of parameters can take months, while fine-tuning an SLM can be completed in weeks. This key distinction in comparing large vs. small language models may play a role in opting for a smaller option. 

National specificity

The lion’s share of the most well-known LLMs originate from the US and China and don’t adequately represent diverse languages and cultures. Studies unveil that LLMs’ outputs are more aligned with responses from WEIRD societies (Western, Educated, Industrialized, Rich, and Democratic).

The current gen AI landscape calls for national-specific language models developed and trained on the data sets in local languages. LLMs try to keep up with the trend and roll out smaller language models targeted at regions with specific alphabets, such as GPT-SW3 (3.5 B) for Swedish, but these cases are one-offs. 

Meanwhile, SLMs take the lead in this direction, with Fugaku (13B) for Japanese, NOOR (10B) for Arabic, and Project Indus (0.5B) for Hindi. 

Capabilities range

Both LLMs and SLMs emulate human intelligence but at different levels. 

LLMs are broad-spectrum models trained on massive amounts of text data, including books, articles, websites, code, etc. Moreover, larger models cover various text types, from articles and social media posts to poems and song lyrics. They are sophisticated virtual assistants for complex tasks requiring broad knowledge, multi-step reasoning, and deep contextual understanding, such as live language translation, generating diverse training materials for educational institutions, and more. At the same time, large language models can also be trained for domain-specific tasks, such as chatbots for healthcare institutions, legal companies, etc. 

But here are the questions to ask yourself as a business owner: How likely are you to need an LLM’s capability to write poems? Or do you want a practice-oriented solution to enhance and accelerate routine tasks? 

SLMs are narrow-focused models designed for specific tasks, such as text classification and summarization, simple translation, etc. As you can already see from the examples, when it comes to the range of capabilities, smaller language models can’t compete with their larger counterparts. 

Using an SLM is like going to a small bakery next door when you need fresh pastry.  But when your shopping list grows to include groceries, you head to a shopping mall — an LLM, offering versatility and breadth. Both solutions are relevant — they just serve different purposes.

Inference speed

LLMs’ power as a one-size-fits-all solution comes with performance trade-offs. Large models are times slower than their smaller counterparts because the whole multi-billion model activates every time to generate the response. 

The chart below unveils that GPT-4 Turbo with 1 trillion parameters is five times slower than an 8-billion Flash Llama 3.  

LLM providers are also aware of this operational efficiency hurdle and try to address it by switching from a dense ML architecture to a sparse Mixture of Experts (MoE) pattern. With such an approach, you have: 

• Several underlying expert models, aka “experts”, with their own artificial neural networks and independent sets of parameters to enable better performance and specialized-knowledge coverage. For example, Mixtral 8x7B incorporates eight experts.
• Gating mechanism that activates only the most relevant expert(s) instead of the whole model for generating the output to increase inference speed.  

Getting back to the chart, see that MoE-based Mixtral 8x7B with 46.7 billion parameters has nearly the same inference speed as a 20-billion-parameter Claude 3 Haiku, narrowing the gap between LLMs and SLMs. 

Output quality

Speed isn’t the only parameter that matters when measuring language model performance. Besides getting the answers quickly, you expect them to be accurate and relevant. And that’s where the model’s context window, or context length, comes into play. It identifies the maximum amount of information within the ongoing conversation a model can consider to generate a response. A simple example is a summarization task, where your input is likely to be big. The larger the context window is, the bigger the files you can summarize. 

A context window also influences the accuracy of the model’s answers when you keep refining your initial request. Models can’t reach the parts of the conversation outside their context length. Thus, with a larger window, you have more attempts to clarify your first input and get a contextually relevant answer.  

Regarding model performance, LLMs clearly beat SLMs. For example, GPT-4 has 32K tokens of context length, GPT-4 Turbo has 128K tokens, which is around 240 document pages, and Claude 3 can cover a mind-boggling 200K tokens with remarkable accuracy. Meanwhile, the average context length of SLMs is about two to eight thousand tokens. For instance, Falcon 7B has 2K, and Mistral 7B and LLama 2 have 8K tokens. 

However, keep in mind that each improvement in the context length boosts the model’s resource consumption. Even a 4K to 8K increase is a resource-intensive step requiring x4 computational power and memory. 

— Chad West, Managing Director, *instinctools USA

Security

While the cost and quality of generative AI solutions have companies scratching their heads, it’s the security concerns that really top the list of hurdles. Both LLMs and SLMs present challenges, making businesses wary of diving in. 

What can companies do to fortify their sensitive data, internal knowledge bases, and corporate systems when using language models? 

We suggest putting a premium on security best practices, including but not limited to:

• Data encryption to keep your sensitive information unreadable even if accessed by outside users
• Robust API to eliminate the risk of data interception
• Access control to ensure the model’s availability only for registered users 

To implement these practices and create a solid language model usage policy, you may need the support of a gen AI-literate tech partner. 

The ins and outs of LLMs and SLMs at a glance

We’ve highlighted the strengths and weaknesses of larger and smaller language models to help you decide between two directions of gen AI adoption. 

Starting small or going big right away: defining which option works for you

After exploring what’s possible, determine what’s practical for your software needs. Both LLMs and SLMs are powerful tools, but they won’t bring the desired benefits on their own. It’s still essential to identify how to effectively integrate them into your business processes, considering industry and national specifics. 

If your resources are limited, you want to test your idea ASAP, or need a model for only a specific type of task, an SLM can help you hit it big without breaking the bank. For scenarios requiring deep textual understanding, multi-step reasoning, and handling massive queries, a broad-spectrum LLM is considered to be a go-to choice.

The post Small Language Models vs. Large Language Models: How to Balance Performance and Cost-effectiveness appeared first on *instinctools.

Contents

• Benefits of AI for software development go beyond automation
  • Cutting costs associated with development efforts
  • Accelerating developers’ productivity and time-to-value
  • Improving code quality
  • Focusing on building and creating vs. repetitive tasks
  • Speeding up the onboarding process and flattening the learning curve for new hires
• 7 ways AI can supercharge your software development process 
  • Writing boilerplate code
  • Code suggestions 
  • Code refactoring and modernization 
  • Code translation
  • Bug detection 
  • Testing
  • Preparing documentation
• Which tasks AI for software development is failing at?
  • Handling a combination of legacy and updated code
  • Contributing to organizational and project context
  • Non-generic architectures
  • Navigating tricky coding requirements
• Choosing an AI software development assistant: open-source vs proprietary tools
• A new era of development empowerment is underway

Today, every company, irrespective of their focus, is a tech-first company. When technology becomes an integral part of everything your organization does, having the means to master continuous delivery is instrumental to fuelling your business growth. In 2024, AI in software development is billed to become that growth engine, embedding software in your products and services at scale.

As an AI-savvy company, we help our clients leverage the power of artificial intelligence in software development, applying it to specific tasks — and we’re sharing our battle-tested expertise and best practices in this guide.

Benefits of AI for software development go beyond automation

When AI made its debut into mainstream technology, organizations approached it with a cautious mindset. These days, 31% of global functional spending on gen AI goes to software development, which bears testament to its rising importance in the field. Let’s see what benefits have turned AI software development from faddish to fundamental.

Cutting costs associated with development efforts

AI-powered software development is one free from time-consuming tasks, such as test cases, exploratory data analysis, small code updates, bug detection, and others. By automating away menial activities, AI tools bring down the number of labor hours, ultimately leading to a more cost-effective development process. Some of our clients wield the sword of machine intelligence to optimize maintenance costs for cloud infrastructure. 

Our client’s team automated repetitive tasks inherent in infrastructure maintenance by employing AI to produce a prewritten set of recommendations for the automated execution of tasks. Bundling the recommendations in a playbook, the team drew up blueprints for repetitive actions, cutting development efforts and associated costs by 30%. 

Accelerating developers’ productivity and time-to-value

Software engineers can join forces with an AI pair programmer to tap auto-complete features like code recommendations and context-aware suggestions. The latter eliminates the need to manually search for code snippets or specific function syntax, minimizing context switching for developers.

AI toolbelts like Github Copilot also keep developers in the zone by offering integrated development environments, summaries for pull requests, and explanations for code blocks.

According to GitHub Copilot, AI-powered developer assistants can deliver an uptick in developer productivity, with results reaching up 55%. 

We’ve seen this firsthand: on average, our clients have achieved a 25% improvement in their development velocity by introducing artificial intelligence into the software development process.

Our client, a prominent bank in the MENAT region, stated: “Embedding AI tools into the development workflows has changed the game for our developers. These tools can produce an entire script based on just a single comment, cutting coding time in half”.

Productivity gains also translate into swifter and more cost-efficient innovation cycles that allow companies to rapidly adapt to shifting customer needs and snap opportunities faster than their competitors.

Improving code quality

AI-powered developer tools leverage natural language processing to dissect context from a user’s existing code and suggest additional lines of code and functions. It means that smart assistants can identify the optimal approach or solution to the development challenge — based on the natural-language prompt from human software engineers. 

AI tools can also improve the debugging process by quickly identifying and rectifying errors, contributing to a higher quality of deliverables. AI-assisted code reviews and static code analysis minimize the code vulnerability surface and improve its consistency. For example, artificial intelligence puts edge cases, which might be hard to spot through manual testing, on sharp display, leading to a more reliable final product. 

Focusing on building and creating vs. repetitive tasks

Over 50% of developers say that artificial intelligence in software engineering allows them to focus on more creative and challenging tasks instead of grinding away at mind-numbing activities like code formatting or documentation updates. Developers then use this headroom to flex their problem solving skills, analyze feedback from end users, and hatch solutions to novel problems.

According to our client, a world-known educational technology company, thanks to AI tools, their software engineers stay in the peak state of productivity instead of wading through code libraries and documentation.

Speeding up the onboarding process and flattening the learning curve for new hires

Apart from being the wingman for experienced software developers, AI systems can also help junior specialists get up to speed. By training intelligent systems on the company’s software stack, developer teams can integrate new hires into the fold faster than usual, accelerating their modus operandi. 

Also, these tools can improve the availability of mentorship and support for junior developers. Unlike team leads, AI tools, with their never-ending bandwidth, can provide instant feedback or relevant suggestions 24/7 to lead rookie developers in the right direction. This creates a valuable tool — artificial intelligence is always there to support junior developers, while senior developers can leverage AI’s insights to provide targeted support when new hires need it most. 

7 ways AI can supercharge your software development process 

According to McKinsey, the direct impact of AI on software development efficiency ranges between 20 to 45% of current annual spending on the function. The value produced by the powerful combo of artificial intelligence and software engineering mainly results from reducing time spent on the following seven activities.

Writing boilerplate code

Boilerplate code is a developer’s long-time nemesis, forcing engineers to spend an eternity writing the same tedious sections of code over and over. Artificial intelligence software development can eliminate the associated drudgery by streamlining the process of boilerplate code generation:

• Pattern recognition — once trained on the existing code base, large language models can identify patterns commonly used in boilerplate code, such as database connections, error handling blocks, and other structures.
• Contextual awareness — intelligent teammates can figure out developers’ intent and fine-tune the boilerplate code to the unique needs of the project.
• Code completion — AI-enabled virtual assistants can suggest code snippets based on the surrounding code and auto-complete the boilerplate structure for a specific function.

Expanding on their previous insights, our client, an EdTech company, shared: “Boilerplate code is the AI’s strong suit — we’ve seen a 25% increase in developer velocity thanks to the granular suggestions provided by AI tools’. 

Code suggestions 

Much like with boilerplate code, AI co-pilots can act as smart code whisperers for developers, offering semantically relevant code snippets based on their deep learning capabilities. These tools can also guide developers toward the right solution by directing them to the relevant libraries and common coding patterns. 

Besides accurate suggestions with comments, developers can pick the AI brains to get their code inline fixed. 

Code refactoring and modernization 

Most codebases get messy over time due to constant fixes and changing project requirements. Generative AI models fed with well-refactored code can highlight code smells in your existing codebase and suggest refactoring techniques or alternative code structures for enhanced system performance. 

Developers can also ask AI tools to explain legacy code — and then feed this explanation, supplemented with additional information, back into the system to modernize the code and facilitate technical debt management.

Code translation

Old-school code translation tools fail to understand the underlying purpose of code due to their focus on the code structure. Conversely, AI-enabled models can discern the code semantics to suggest translations that preserve the original functionality. 

At *instinctools, we also help our clients train AI tools on their existing codebases to augment the model’s translation capabilities and adjust translations to the unique specific coding style, patterns, and libraries used in the project.

Development teams can capitalize on this superpower to streamline code conversion or app modernization projects, such as converting COBOL to Java.

Bug detection 

Development teams can also harness the duo of software engineering and artificial intelligence to automatically catch bugs early in the software development lifecycle. Thanks to built-in pattern recognition and machine learning anomaly detection trained on historical data from similar codebases, AI coding tools can spot subtle anomalies in the existing code that might otherwise go unnoticed.

AI pair programmers can also perform code reviews in the background, bringing potential issues to the fore and suggesting areas for closer inspection. 

Testing

Quality assurance and testing have a significant scope for automation due to inherent redundancy. Although AI can’t take over testing tasks completely, it can significantly improve testing speed and coverage by enhancing the following aspects:

• Test case generation and execution — following software specifications, generative AI can produce test cases to help testers cover a wider range of scenarios.
• Data generation for testing — artificial intelligence proves effective in generating edge cases and corner scenarios that might be challenging to create manually.
• Performance and load testing — AI-powered testing tools can produce load for performance and load testing.
• Test planning — smart coding tools can suggest test cases based on historical data, identify missing test cases, and map out dependencies to identify the right order of test execution.

Artificial intelligence also acts as a collaborative assistant for visual testing, exploratory testing assistance, and maintenance of test cases.

“Before introducing AI in the testing workflow, our developers haven’t had enough time to create unit, functional, and performance tests for their code. Bug slipped through the cracks, causing issues in production with missing tests snowballing into tech debt. AI tools have tackled this problem by automating repetitive testing tasks and increasing test coverage without developers doubling the code’, — noted a representative from a consulting company, partnering with us on software development initiatives.

Preparing documentation

Code is never static — it evolves throughout development cycles, making it challenging for developers to keep up with documentation updates. Based on code comments and function signatures, machine learning models can automatically create a document that accurately outlines system behavior and its functionality. These tools can also account for the implemented tech stack and project-specific functionalities to generate comprehensive documentation aligned with the specific codebase.

Which tasks AI for software development is failing at?

Now the main question is: can AI replace software engineers? Not in the foreseeable future. While AI and software development are a power couple, their duo is still a far cry from a fully automated software development process. Artificial intelligence requires human oversight and collaboration to do its best.

Handling a combination of legacy and updated code

AI software solutions have a hard time clawing through a repository that contains both legacy and new code due to the fragmentation and incompatibility caused by the former. Legacy codebases may not integrate well with common frameworks or libraries and often lack structure and adherence to current best practices — which makes it challenging for AI to understand its functionality. 

Contributing to organizational and project context

Canned AI–based coding tools are know-it-alls, so they are not designed to consider the unique requirements of your project or your business context. It means that unless you train it on your proprietary data, AI output might not meet your specific performance or security needs. 

Non-generic architectures

Artificial intelligence coding tools are capable of mustering basic, high-level design blueprints, while more nuanced architectural solutions remain a near-impossible feat for these tools. Also, the innovative potential of AI is limited by the training data, which means that cutting-edge architectural solutions fall under the domain of human ingenuity, not machine intelligence.

Navigating tricky coding requirements

While AI coding tools are all for answering basic prompts, they hit a brick wall when faced with more complicated coding challenges. For example, AI-based tools wrestle with combining multiple frameworks with disparate code logic or generating a code suggestion based on the big picture.

Choosing an AI software development assistant: open-source vs proprietary tools

Standing at the AI crossroads, companies usually face two options. They can either leverage open-source tools such as OpenCV, PyTorch, and Codestral or invest in proprietary solutions such as Microsoft Github Copilot and IBM watsonx Code Assistant.

Open-source tools that put AI algorithms, pre-trained models, and data sets up for public use are favored by the community due to their transparency and limitless options for innovation. Proprietary tools hide the source code from the public eye, yet offer support from the vendor, and potentially more advanced features.

So which one should you opt for? Here’s a table from our experts that summarizes the key differences:

A new era of development empowerment is underway

As AI continues to push the boundaries of software development, it’s important to remember that it’s not a panacea. Artificial intelligence is an augmentation tool whose true potential lies in its ability to automate menial tasks, enable developers to spend more time on problem-solving and empower them to build high-quality software faster. You can’t expect it to work miracles, but you can embrace the technology to ship software solutions more efficiently than ever before.

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Foraying into software development? Then you’re likely grappling with crucial questions: Should you build an in-house team or opt for outsourcing? Is it better to assemble a cohesive team or rely on individual specialists under your project management? The answers are far from definite and hinge on multiple factors.

As we have seen, a dedicated development team stands out as a top collaboration model, and for good reason. Yet, there are also strong cases for considering other strategies.

Our all-encompassing guide, prepared by a battle-hardened Jedi with two decades in the software development industry, will help you determine when a dedicated team is the optimal choice, and when alternative approaches may be more suitable. We delve into the key players you need on board, and provide strategic insights on selecting the perfect team while effectively managing risks.

What is a dedicated team? The efficiency of outsourcing combined with the commitment of the in-house staff

A team of cross-functional experts that put their backs into your project and go above and beyond to bring it across the goal line — that’s a broad-strokes portrait of a dedicated team. Under this engagement model, companies can bring in full or partial teams that work exclusively on their software projects. 

Just like an in-house team, a dedicated developer team remains skin in the game throughout the entire development process, except you don’t have to stress over the hiring, onboarding, and daily management. You can have as much or as little involvement in the development and management process as you want  — anyway, a dedicated software development team is capable of taking over the entire process of digital product development. 

Such a team dynamically adjusts to the pace and scale of software development, allowing you to fine-tune the team size and composition based on your evolving needs. Developers, designers, QA engineers, and any other roles can be hand-picked on demand when and as you need them. 

Beyond in-house: The power of the dedicated team’s support 

Today, 90% of Fortune 500 companies go for outsourcing in addition to having an in-house team. The reasons for hiring a dedicated development team are manifold — our experts have listed the most significant ones below.

Rapid scalability

Adapting to fluctuating project demands and scalability requirements in-house is a challenging endeavor due to resource limitations and the time required for recruitment and onboarding. A dedicated team model is flexible in roles and skills, allowing companies to build up their capabilities on demand to accelerate time-to-market and support evolving project needs. 

Cost efficiency

Around 59% of companies outsource to cut costs associated with in-house hiring and development. A dedicated app development team eases the financial burden on companies by relieving them of operational overheads and other costs related to sourcing candidates and employee training. Moreover, you can source tech talent in cost-effective regions such as Central and Eastern Europe to bring down the financial outlay of development.

Stress-free hiring

From long hiring cycles to administrative burden, in-house hiring is not for the faint of heart and is definitely not for the employers in a hurry. Conversely, hiring a dedicated application development team is as easy as proving a job specification and approving the candidates cherry-picked to your requirements.

360-degree visibility into the process, away from the grind

In-house hiring and team management are generally associated with the highest level of visibility. But to build it up, company leaders need to constantly have their fingers on the pulse and mother hen their development team. When it comes to an offshore dedicated development team, a powerful combo of dedicated project managers and project management tools can ensure a high level of visibility into the project progress, minus the supervision fuss. 

Access to a wide talent pool

Talent scarcity keeps 4 in 5 company leaders on their toes, stalling new projects and decreasing the odds of securing a niche or highly-coveted talent. By banking on dedicated development team services, companies enter a global talent pool where they can hire skilled experts at a reasonable price. Better yet, this engagement model helps you develop expertise in adjacent fields or completely different ones, including AI, mobile app development, MVP development, and other projects.

What does a dedicated project team structure look like?

The structure of a software development team is determined by a combination of factors. These include the type and complexity of your software product, your timeline, and the allocated budget. Let’s expand on the bare-bones development team structure:

Project manager

A project manager is the point person behind your project responsible for its day-to-day management. Typically, a software vendor assigns a dedicated project manager to lead the entire team, oversee project cost and resource allocation, define project goals, and keep you updated on the project’s progress. 

Business analyst

Business analysts are guardians of business goals and values that bridge the chasm between the business and technical teams. They are instrumental in planning development activities, validating requirements to ensure alignment with business goals, and standardizing processes to maintain a cohesive workflow, facilitating continual improvement based on stakeholder feedback.

Software architect

Equipped with broad technical knowledge, software architects are the great minds behind your software solution architecture and critical technical decisions. They have a final say in the selection of the technology stack, making sure your solution is functional, performant, resilient, and scalable. 

Software developers 

A dedicated backend development team is in charge of building and maintaining the inner mechanisms of your software, including data storage, security, and other server-side functions. Frontend developers focus on building user-facing components of web and mobile applications, making your software easy and engaging to interact with.

DevOps engineers

DevOps engineers are at the wheel of development, testing, and release automation activities. Geared up with special tools and DevOps practices, they enable continuous integration and deployment and ensure smooth collaboration between development and ops teams to speed up development cycles and improve software quality.

QA specialists

Quality assurance engineers inspect every aspect of your software, from code to design, to make sure it meets predefined quality standards and is bug-free. Their turf also spans compliance reviews and security testing to eliminate any potential vulnerabilities in your software solutions.

Depending on your project scope, you may need a different combination of team roles to accommodate your project dynamics.

When a dedicated development team model is the right call

Often, it might be not just one, but a whole set of factors that tip the scales in favor of a dedicated team engagement option.

Specialized skill sets across multiple technologies and business domains

Complex projects that implement a variety of technologies, including cutting-edge ones, are a great mesh for an offshore dedicated team. Most companies lack the internal expertise to proceed with the implementation of such projects, while a software development dedicated team has the varied tech knowledge, skills, and hands-on experience to muster it on their own.

High cost of failure 

Another scenario fit for a dedicated software team is when your project comes with functional or reputational risks — in other words when you bet big on the software quality. Thanks to their experience, dedicated software development teams know how to build a high-quality solution and they usually go by a mature software quality management framework to do so.

Last-chance-to-get-it-right 

When your in-house team is underperforming or your previous vendor has failed to deliver on the promise, dedicated software development experts can pick up the slack in the nick of time. At *instinctools, we’ve seen this scenario many times, providing a team of experts to rescue projects like fintech MVP development and others.

Borrowed expertise

The dedicated team approach also comes in handy when you need to pick the brains of lead software engineers every now and then to identify improvement areas, determine the right development approach, or get actionable advice on the tech architecture. Dedicated experts can provide tech consulting when needed, headache-free. 

Turnkey product development

You can hire a dedicated development team to deliver and launch your custom software product faster and more efficiently. The team focuses solely on your product, selecting the right approach, frameworks, and tools to ensure consistent progress. More importantly, a dedicated remote team is cross-functional, allowing for a one-stop-shop development experience. 

Time to market is critical

If you’re at crunch time for a prototype, an MVP, or a full-fledged product, dedicated teams can offer a shortcut to a successful delivery. Opting for a standby team offers you the double wins of streamlined coordination and specialized expertise, promoting a swift and efficient software development process with no quality trade-offs. So, when time is of the essence, a dedicated team ensures you’re not just fast, but also first-rate.

Complex software development  

When it comes to long term projects with an evolving and highly complicated scope, say you’re building an entire ecommerce ecosystem like one of our clients did, a dedicated team runs circles around other engagement models. If you have both a mobile app and a web solution in your backlog, a dedicated team can move from one project to another, being able to deliver results faster thanks to staying in the loop. 

Vague, unclear, ever-changing requirements 

Last but not least, you can hire dedicated development teams when you don’t have a good grasp of your software product. A dedicated team can work in lockstep with your changing requirements, developing in small increments to incorporate regular feedback. We’re not advocating for working in the dark though: when your vision is blurred, run a discovery session with your team to solidify your project requirements and map out the roadmap to a successful delivery.

Dedicated team is not a cure-all

The dedicated team model brings a wealth of benefits to your table, but only if it marries well with the nature of your project — and this isn’t always the case. For example, if you’re looking to fill specific skill gaps rather than farm out an entire project, then outsourcing software development to a dedicated team is not the right path for you to follow. In this case, staff augmentation will do the trick. 

Staff augmentation does justice to intermittent or short term projects, where you need an extra pair of hands to temporarily augment the capacity of your in-house team, develop a different component of the product, or bridge in-house skill gaps. 

The main difference between hiring a dedicated development team and bringing in augmented experts lies in the level of the vendor’s responsibility, including risk management, process setups, and knowledge transfer.

Vendor’s responsibility	Staff augmentation	Dedicated team
Providing engineering capacity
Onboarding and knowledge transfer
Project documentation
Software development process setup and maintenance
Project, team and risk  management

Keep in mind that neither model can suffice decade-long, strategic initiatives that have very specific requirements in terms of security, tech stack, personnel training, and other essentials. If that’s the case, your company will be better off with an offshore development center — an overseas extension of your company set up and equipped to meet whatever floats your boat. 

How to choose a dedicated team of developers? Six core steps towards collaboration success

You might think that the quality of products and services comes first when choosing a software development company. This hallmark certainly carries the day, but other traits such as transparency, trustworthiness, and understanding of your business context also count for a lot when it comes to remote teams. Let us facilitate matters for you by providing an algorithm that will help you choose the right company and set up your collaboration for success.

Document the use case

Without a use case, an idea just remains an idea, making it hard for you to describe how a software system can be used to achieve your business goals. So taking the time to document use cases before contacting vendors should be your opening move. You don’t have to get into much detail when describing the use case: try to distill it as best as you can, specifying the general requirements for what’s being built.

A dedicated team can fill in the blanks, but provide as much as possible to communicate the need better and allow for a “solution”, not just a body. 

Decide on a location where to hire a dedicated development team

Back in the day, the location used to be a deal-breaker in choosing a dedicated development team, but the rise of remote work has made it less critical. Nevertheless, some outsourcing locations are easier on your budget and have more time zone overlap with your company’s home country.

You should also take into account other considerations, such as cultural fit, regulatory and legal environment, talent pools, and the skills of local developers. As a popular outsourcing destination, Poland checks all the boxes in terms of service quality, time zone compatibility, and cultural fit. It’s also ranked third among the countries with the best programmers in the world.

Make a list of potential vendors

Once you’ve decided on the location, go on a hunt for local software vendors to online directories, review sites, and search engines. You can also ask around for recommendations or visit industry conferences to evaluate potential tech partners. Following your search results, compile a list of potential vendors that have made it through the basic screening. 

Pay attention to work methodology

You can reach out to the shortlisted vendors to get more details about how they handle project development, including their methodologies, communication, and project management infrastructure. Keep in mind that a trusted software vendor should be ready to adjust to your requirements and exercise a collaborative approach where they provide regular progress updates for your team.

Examine expertise and experience

Make sure your dedicated team can deliver on their promise by checking their previous projects and researching client testimonials. You can ask about their experience with the specific technologies and assess their skills in action during technical interviews. Besides technical proficiency, a top-grade vendor should understand your business context and have hands-on domain experience.

Check market reputation 

Finally, you should gauge the market perception and image of your software vendor by analyzing their social media presence, noting the insights from past customers, and looking into their rating on industry-specific platforms like Clutch and Goodfirms. A trustworthy outsourcing company should come highly recommended and have a good name in the market.

Common fears of hiring dedicated teams, allayed 

Before you jump into the unexplored terrain, it’s natural to have your doubts about it. Let’s look into the common fears associated with dedicated teams and how you can overcome them. 

Cultural gaps 

Different countries have varying communication styles, work expectations, and business practices, which can throw a wrench into project quality, communication, delivery timelines, and even the final development cost. 

To mitigate this risk, single out countries that have a strong cultural overlap with your company’s base location. For example, if your company is located in the US, UK, or EU, a country like Poland can offer the combined benefits of skilled talent pools and a similar cultural background to your company’s host country.

No visibility 

The ‘throw-over-the-wall’ approach to outsourcing can leave you in the unknown about the development process, progress updates, and potential stumbling blocks. A dedicated development team that works within a clearly defined and calibrated delivery framework, paired with regular communication, can get this roadblock out of your way.

Communication hurdles 

The lack of consistent communication can get both sides misaligned on the project goals, deliverables, and milestones. To alleviate this risk, a dedicated team should adapt to your communication framework, including your communication channels and meeting cadence. 

Also, your software vendor should have a mature governance and escalation infrastructure in place to proactively handle escalation triggers and keep all the aspects of project delivery in check.

Mismatch in expectations 

There’s a raft of factors, including miscommunication, inadequate project management, and lack of documentation, that can result in the disconnect between your vision and your dedicated team’s perception of your idea. Unclear requirements, more than anything else, can bring on this disconnect, leading to scope creep and wasted budget.

A comprehensive discovery stage paired with thorough requirement-gathering sessions can bring more clarity to your project vision and project’s requirements, ensuring that each stakeholder is looking in the same direction. And if anything goes awry, Agile methodologies and regular feedback can help your team swiftly adapt to your feedback and right the ship.

Hiring a dedicated development team at *instinctools in 3 simple steps 

Building your dream software requires a lean, dynamic team by your side. At *instinctools, we offer a simple, transparent hiring process to help you land your dream team in mere days and jump-start your project.

• Step 1: Discuss your request with a potential vendor 

Our collaboration kicks off with carefully studying your project request and seeing how our expertise can benefit your company. 

If your project vision is limited to a high-level description, our team helps you scope the project by conducting a discovery session. We run discovery workshops with key stakeholders to define the project essentials, including the objectives, deliverables, core technologies, and resources needed. Following the scope, our experts provide accurate time and budget estimations. 

• Step 2: Get a proposal 

Building on a detailed project understanding, we pre-select a team and provide a shortlist of best-fit candidates. You can be as involved in the pre-selection as you want, we always encourage our clients to participate in CV selection and technical interviews to double-check the specialists’ skills. Once our final candidates get your approval, we prepare a Statement of Work that defines the scope of work being provided, project deliverables, timelines, work location, and payment terms and conditions.

• Step 3: Sign a contract and start a cooperation

Before the project launch date, our company finalizes legal agreements and prepares all the final papers. We brief your dedicated team on your project background and help you effectively onboard the specialists. 

Seal the deal on successful projects

Gliding through projects is easier when you have the right development team as your tech wingman. Dedicated teams offer a cost-effective, flexible, and reliable solution for companies seeking tech professionals dedicated to their project’s success. Although this engagement model has a few inherent complexities, you can offset them by teaming up with a mature software vendor well-versed in managing an outsourced team.

FAQ:

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