And then… the people who were supposed to use it don’t.

The feature sits there quietly, gathering digital dust, much like that New Year’s resolution purchase of a treadmill.

This problem is becoming increasingly visible with rapid adoption of AI.

Businesses are spending heavily on artificial intelligence and embedding AI capabilities into existing applications, but access and adoption are not the same thing. On a recent episode of AI Decoded, BBC examined this issue describing it as an ‘adoption gap’ between employees having access to AI tools and using them meaningfully. The program also highlighted a gap between AI training and access to AI tools.

CIO.com is seeing a similar shift at the enterprise level. Its 2026 State of the CIO research found that only 19% of respondents said their AI initiatives had met or exceeded business goals, while 18% said fewer than one-third of their AI use cases were meeting defined expectations. CIO identified unclear ROI metrics, unclear corporate strategy, and lack of in-house expertise among the barriers to scaling AI successfully.

The message for business leaders is straightforward:

The problem may not be that your users don’t want AI. The problem may be that you built an AI feature they don’t need.

The Technology is Not the Product

This is one of the most important distinctions in modern software development.

AI is a technology, and not automatically a product strategy.

Consider a hypothetical customer service application:

A stakeholder might decide that the application needs an AI-powered conversation summarizer because competitors have one.

The development team builds it. The model produces beautifully formatted summaries.

Everyone is impressed … except the customer service representatives.

Why?

Because their actual problem isn’t summarizing conversations. Their problem is finding the customer’s previous commitments, identifying unresolved issues, and understanding what needs to happen next.

The AI summary may be technically excellent and operationally useless.

That is a product-design failure, not a model failure.

Stakeholders Know the Business. Users Know the Work.

This distinction is crucial.

Executives understand strategic priorities, product leaders understand market requirements, IT leaders understand architecture, security and operational constraints, developers understand how to build the solution. But end users understand the thing none of those groups can replicate, what it is actually like to do the job.

Only end users know:

• Which screen(s) they leave open all day.
• Which spreadsheet exists because the official application doesn’t quite do what they need.
• Which reports they export and manipulate manually.
• Which field they enter incorrectly (repeatedly) because the interface makes it confusing.
• Where the five-minute task becomes a 45-minute task, every single time.

This information is gold during software discovery and is often missing from the requirements document.

The AI Adoption Gap Is Really a Workflow Gap

Reuters reported in August that established technology companies are emerging as unexpected beneficiaries of the AI boom because businesses are moving from experimentation toward deployment. But the article also highlights the difficulty of integrating AI with existing software, data and operational processes.

This is an important distinction.

Adding AI to an existing application does not automatically redesign the process surrounding that application.

If an employee previously had to complete five screens, obtain two approvals, and send an email before completing a task, adding a chatbot to the first screen doesn’t necessarily fix anything.

That new chatbot may have simply been added to a bad workflow.

A better question to ask is:

“If we could redesign this process from scratch, what would we actually want the user to do?”

Once you have these answers, only then should you ask where AI can help.

More AI Does Not Automatically Mean More Productivity

There is also growing evidence that AI adoption should not be measured simply by usage.

CIO.com reported in June that organizations are increasingly moving away from measuring success through adoption alone and toward measurable business value. Some organizations have burned through AI budgets while encouraging employees to experiment broadly, prompting IT leaders to focus more heavily on high-impact use cases and cost controls.

Ars Technica recently reported on analysis of 15 million real AI interactions that found most tasks at most jobs were unaffected by AI.

That doesn’t mean AI isn’t useful; it means something more nuanced: AI is useful where it fits the work.

A feature that saves a user 30 seconds once a month is technically automation and likely not a good strategic investment. A feature that eliminates 30 minutes of repetitive work every day for 500 employees is a vastly different conversation.

This is why use-case selection matters.

Build What Is Needed, Not What Sounds Impressive

The correct solution begins with better discovery.

Before approving another AI feature, organizations should ask: “What problem are we trying to solve?”

If the answer is ‘we need an AI feature,’ stop, immediately.

That’s a technology requirement, not a business problem.

Instead ask:

• What task is taking too long?
• What information is difficult to find?
• Where are employees repeatedly entering the same information?
• Where are errors occurring?
• What decisions are being delayed?
• What work is repetitive but still requires human review?
• What part of the workflow creates the most frustration?

Then quantify the problem:

• How many users experience it?
• How frequently?
• How much time does it consume?
• What does that time cost?
• What happens when something goes wrong?

Once you have these answers you will have something worth designing around.

Prototype Before You Build the Ferrari

An often overlooked and useful change is moving more of the investment upstream. Instead of spending months building the complete AI capability, create a prototype.

Put the prototype in front of real users, watch them use it and equally important, watch what they don’t use. That last part can be uncomfortable; users may ignore the feature you spent the most time building.

Good.

You just learned something before spending even more money.

Software development should not be a contest to see who can turn a requirements document into production code the fastest. Instead, it should be a process of reducing uncertainty.

Where a Third-Party Software Developer Can Help

This is where an experienced external software development partner can provide considerable value.

A third-party team can sit between business stakeholders, IT and end users and ask some uncomfortable but useful questions.

• Is this actually the problem?
• Does AI solve it?
• Could an existing application be modernized instead?
• Would an API or middleware layer solve the integration problem?
• Could workflow automation deliver the same result at a fraction of the cost?
• Does solving this problem add or subtract from existing technical debt?
• What happens if we don’t build this?

Those questions can save money; they can also save development capacity. But more importantly, they can prevent a common enterprise technology problem: continuing to spend money on a solution simply because the organization has already spent money on it.

That is the sunk-cost trap.

If users aren’t adopting a feature, the answer isn’t necessarily another round of training.

Sometimes it is better discovery, sometimes it is redesign, sometimes it is fixing the workflow underneath the feature. And sometimes, it’s admitting that the original idea was wrong; that’s not failure, that’s good software engineering.

The Best AI Feature Might Be the One You Don’t Build

There is tremendous pressure on organizations to demonstrate that they are doing something with AI. This pressure can produce some impressive demos; it can also produce software nobody uses and balloon AI technical debt.

The organizations that get the most value from AI will likely be the ones that become disciplined about distinguishing technology possibility from business value.

CIO.com’s 2026 research makes that shift particularly clear: organizations are moving toward structured governance, measurable KPIs and use-case prioritization rather than simply launching more pilots.

The same principle applies whether the feature uses AI, automation, APIs, cloud computing or something that has been sitting in the technology toolbox for twenty years.

• Start with the user.
• Understand the problem.
• Redesign the workflow.
• Choose the technology.
• Build the smallest useful solution.
• Measure the result.
• Scale what works.

Because ultimately, your software doesn’t get a return on investment because it is clever, it gets a return because someone uses it to accomplish something that matters. If your latest AI feature isn’t getting used, don’t immediately blame your users; they may be telling you something much more valuable:

You built the wrong thing.

And that’s a problem worth solving and where a trusted software development partner can help you get there.

We’ve been helping businesses solve their most annoying software challenges for over 20 years, reach out to our advisors for a complimentary 30-minute call before that next feature joins a growing collection of very impressive software nobody opens.

The post AI: We Have a Problem! first appeared on STEP Software.

It is not a medical diagnosis, or a term found in a psychology textbook (yet). Instead, it is the growing feeling of mental fatigue, cognitive overload, and reduced critical thinking that can occur when employees rely heavily on generative AI throughout the workday.

For business leaders, this is more than a cheeky workplace term; it’s becoming a business issue that affects productivity, innovation, employee development, risk management, and budgets.

The irony is hard to miss. The very tools designed to make us work smarter can, if used without balance, make us think less.

What Is AI Brain Fry?

AI Brain Fry has been described as the mental exhaustion and reduced cognitive engagement that some workers experience after spending extended periods interacting with AI tools. Instead of analysing problems, brainstorming solutions, or writing from scratch, employees increasingly prompt AI systems, review responses, make edits, and repeat the process dozens or even hundreds of times each day.

The work may feel easier, but it often requires a different type of concentration.

Microsoft’s 2025 Work Trend Index found that employees are experiencing an ‘infinite workday’ as AI accelerates expectations for productivity, increasing the volume of information and decisions workers process rather than reducing them.

Research from Harvard Business School also suggests that while generative AI can significantly improve productivity for many knowledge tasks, over-reliance may reduce opportunities for learning, experimentation, and independent problem solving, particularly among less experienced employees.

In other words, AI can make people faster, but it does not automatically make them better thinkers.

Why This Is Becoming a Business Problem

Most discussions about AI focus on technology implementation, and how to get it into the budget.

The bigger challenge, however, may be the impacts on human performance.

Employees who spend their day evaluating AI-generated content often report:

• Decision fatigue
• Difficulty concentrating
• Reduced confidence in their own judgement
• Less creative thinking
• Increased mental exhaustion

The World Economic Forum’s Future of Jobs Report 2025 warned that AI adoption will require organisations to invest heavily in human skills such as analytical thinking, creativity, resilience, and lifelong learning because these abilities will become more valuable, not less, as automation increases. We touched on this recently in our blog Humans in an AI Time.

So, what does this mean for businesses?

The effects are company wide:

• Marketing teams reviewing dozens of AI-generated campaigns may struggle creatively
• Developers validating AI-written code may lose confidence
• Legal teams checking AI-assisted contracts may face decision fatigue
• Finance professionals reviewing AI-produced analyses may question their judgement
• Customer service representatives refining AI-generated responses may become mentally exhausted
• Everyone still needs to think critically, but HR gets to deal with the back lash from all the departments

The Hidden Cost of Convenience

One of AI’s greatest strengths is reducing repetitive work. One of its greatest risks is reducing ‘productive struggle.’

Stanford professor Jo Boaler sings the praises of ‘embracing the struggle’ when it comes to learning, psychologists concur on the value of doing things the hard way.

Consider a junior software developer.

Ten years ago, they might spend several hours researching documentation, testing different approaches, making mistakes, and eventually solving a problem.

Today, AI may generate a working solution in seconds.

This sounds like progress, and often it is.

But, if developers never learn why the solution works, organisations risk creating teams that can implement code but struggle to troubleshoot, optimise, or innovate when AI cannot provide the answer. We touched on this back in February in our blog: AI Coding Agents: Yes, Maybe, or No?

These same principles apply across accounting, engineering, marketing, human resources, and operations departments.

Efficiency without understanding creates organisational risk

Why Budgets Should Be Part of the Conversation

At first glance, AI Brain Fry appears to be a people issue, but the financial implications are real.

Consider this:

• If employees require more time to verify AI output, productivity gains shrink
• If poor AI decisions create compliance issues, costs increase
• If employees experience higher levels of mental fatigue, absenteeism and turnover can rise

Gallup estimates that employee disengagement costs the global economy trillions of dollars annually through lost productivity. While AI is not the sole cause of disengagement, organisations should ensure technology improves work rather than simply increases the pace.

Businesses investing heavily in AI must invest equally in developing critical thinking, governance, training and AI literacy.

AI Needs Adults in the Room

Generative AI works best when paired with experienced professionals. It is an assistant not an autopilot.

Successful organisations are establishing practical guidelines encouraging employees to:

• Verify AI-generated information
• Understand outputs before using them
• Continue developing technical and professional expertise
• Use AI to accelerate work, not replace judgement
• Take regular screen breaks to reduce cognitive fatigue

Think of AI like GPS, it is incredibly useful, but, if you follow every instruction without paying attention to the road, eventually you are going to end up somewhere unexpected.

Building a Healthier AI Workplace

Business leaders can reduce AI Brain Fry by creating a balanced approach to AI adoption.

Some practical strategies include:

Invest in AI literacy. Employees should understand both the strengths and limitations of generative AI.

Encourage independent thinking. Not every task needs an AI prompt. Brainstorming, strategic planning, and problem solving still benefit from human collaboration.

Protect deep work. Constant prompting and reviewing AI outputs fragments attention. Dedicated time for uninterrupted work improves focus and creativity.

Review workloads. AI often increases expectations rather than reducing them. Ensure productivity targets remain realistic.

Support continuous learning. Employees should continue developing their technical expertise instead of relying solely on AI-generated answers.

How STEP Software Can Help

Many organisations are discovering that successful AI adoption is less about the technology itself and more about implementation.

Introducing AI without governance, training, or process improvements can create confusion instead of efficiency.

At STEP Software, we help organisations modernise technology while keeping people at the centre of the solution.

Whether developing AI-ready applications, integrating new technologies into existing systems, modernising legacy platforms, or providing experienced software professionals through staff augmentation, our focus is on helping businesses use technology strategically.

Sometimes the best way to improve productivity is not by asking your existing team to do more; its by bringing in experienced specialists who can accelerate projects, mentor internal teams, and provide the expertise needed to implement technology effectively.

Final Thoughts

AI Brain Fry may sound like a trendy phrase, but the challenge behind it is real.

As organisations embrace AI across every department, success will depend on maintaining the balance between automation and human expertise.

Technology should reduce repetitive work; it should not reduce critical thinking.

Businesses that invest in both AI and their people will be better positioned than those relying on technology alone.

After all, artificial intelligence may be getting smarter every month; but the real competitive advantage belongs to organisations filled with people who know when not to ask the robot.

Our advisors are here to help, drop us a line if you are feeling the AI Brain Fry in real-time.

The post This is Your Brain on AI: The Hidden Productivity Problem of Brain Fry first appeared on STEP Software.

Fast forward to 2026 and they have become one of the biggest infrastructure stories in technology.

Driven by the rapid growth of AI, cloud computing, digital transformation, cybersecurity requirements, and high-performance computing, organizations around the world are investing billions in new data center capacity. According to Synergy Research Group, global spending on cloud infrastructure services continues to grow by more than 20% year-over-year, while hyperscale providers continue to expand their global data center footprint.

While this expansion creates enormous opportunities for innovation, it also has significant implications for business budgets, IT strategy, and long-term planning.

For business leaders, the data center boom isn’t just an industry headline, it’s something that will influence technology costs for years to come.

Why Are So Many Data Centers Being Built?

The answer isn’t just artificial intelligence.

Although AI workloads require significant computing power, they’re only one piece of a much larger trend.

Organizations are consuming more digital services than ever before:

• Cloud-hosted business applications
• Hybrid work environments
• Data analytics platforms
• Cybersecurity monitoring
• Video collaboration
• Software-as-a-Service (SaaS)
• Internet of Things (IoT)
• Digital customer experiences

Every one of these services relies on computing infrastructure somewhere.

According to Gartner, worldwide spending on public cloud services continues to accelerate as organizations modernize applications and shift away from on-premises infrastructure.

More Users + More Applications + More Data + More Infrastructure = Unprecedented Demand for New Data Centers

Infrastructure Demand Is Changing Technology Economics

Building a modern hyperscale data center isn’t inexpensive.

Facilities require:

• Massive electrical capacity
• Redundant networking
• Specialized cooling systems
• Physical security
• Backup power generation
• Skilled technical staff

According to the International Energy Agency (IEA), electricity demand from data centers is growing rapidly worldwide, making power availability one of the primary constraints.

This increased demand affects everyone.

Whether your business owns servers or consumes cloud services, infrastructure costs eventually flow through to customers.

What This Means for Budgets

It’s a nuanced reality, moving to the cloud doesn’t necessarily automatically reduce IT spending. As infrastructure providers invest billions in new facilities, organizations should expect ongoing pressure in several areas.

1. Cloud Costs Will Continue to Evolve

Cloud providers continually invest in infrastructure, networking, and security. While competition helps stabilize pricing, businesses should anticipate periodic adjustments as providers expand capacity and introduce premium services.

This makes cloud cost optimization more important than ever.

Organizations should regularly evaluate:

• Idle resources
• Storage utilization
• Licensing
• Reserved capacity
• Data transfer costs

Every dollar saved through optimization is one that doesn’t need to be added to next year’s budget.

2. Electricity is Becoming an IT Budget Conversation

Historically, electricity wasn’t something most CIOs discussed during strategic planning.

This is changing.

With data centers being among the largest consumers of electricity globally, utilities in many regions are investing heavily in grid expansion to support future demand. Businesses operating private infrastructure should expect energy efficiency to become a growing operational consideration.

Even organizations that rely entirely on cloud services can anticipate eventually seeing energy-related costs reflected in service pricing.

3. Hardware Planning Requires Longer-Term Thinking

The demand for servers, networking equipment, storage systems, and supporting infrastructure continues to grow. Although supply chains have improved since the pandemic, enterprise hardware procurement still requires careful planning as highlighted by the 2026 RAM crisis.

Organizations delaying infrastructure refreshes may find themselves competing for limited availability during periods of high demand or facing increased costs associated with ongoing supply imbalances.

Strategic planning is becoming equally as important as purchasing.

Why Business Leaders Should Pay Attention

It’s easy to assume this is an issue only for IT departments.

It isn’t, this is a business problem.

Technology infrastructure supports every business function:

• Sales
• Finance
• Operations
• Manufacturing
• Customer service
• Human resources

When infrastructure costs change, every department feels the impact, eventually.

Forward-thinking organizations are already asking:

• How can we optimize cloud spending?
• Which legacy applications should remain on-premises vs. a hybrid approach?
• Which systems should be modernized?
• Can middleware extend existing platforms instead of replacing them?
• Are we paying for infrastructure we’re no longer using?

These aren’t just technical questions; they’re financial ones.

The Hidden Opportunity

The data center boom isn’t simply about higher costs., it’s also an opportunity to become more efficient.

Organizations that modernize legacy applications, improve software architecture, eliminate technical debt, and optimize cloud environments often reduce infrastructure requirements while improving performance. Modern software typically consumes fewer resources, scales more efficiently, and costs less to maintain than aging applications designed for a different era.

These are just a few of the reasons application modernization has become a boardroom discussion rather than just an IT initiative.

How STEP Software Can Help

Infrastructure investments are only one side of the equation.

Making better use of existing technology is often the fastest and most cost-effective strategy.

At STEP Software, we help organizations maximize the value of their technology investments through:

• Legacy application modernization
• Middleware integration
• Custom software development
• Software and technical audits
• Software performance optimization
• Cloud-Hybrid architecture optimization
• Technical documentation
• Staff augmentation

Rather than replacing systems prematurely, many organizations can extend the life of existing applications while improving performance and reducing operational costs. Strategic management of infrastructure creates flexibility for future planning while protecting previous technology investments.

Looking Ahead

The global data center expansion isn’t slowing down. As businesses continue generating more data and adopting more digital services, infrastructure demand will only increase.

For business leaders, the question isn’t whether the technology landscape is changing. It’s whether your organization is prepared to adapt.

Organizations that actively monitor infrastructure trends, optimize technology spending, modernize strategically, and build flexible IT roadmaps will be far better positioned than those reacting after budgets begin to tighten.

The data center boom isn’t just reshaping the technology industry; it’s reshaping how businesses plan, invest, and compete.

Drop us a line if you are looking for help uncovering optimization opportunities in your organization.

The post The Cost of Doing Data: What the Data Center Boom Means for IT Budgets first appeared on STEP Software.

The assumption was simple: the more technical talent you could attract, the more successful your technology initiatives would be.

While technical expertise remains critical, a significant shift is occurring across the business landscape. Today, many organizations are discovering that their biggest workforce challenges are not purely technical; they are human.

As businesses navigate geopolitical stresses, economic uncertainty, digital transformation, cybersecurity threats, changing workforce expectations, and increasingly complex technology environments, the skills that are becoming most valuable are the ones that cannot be found in a training manual.

Critical thinking. Business acumen. Communication. Adaptability. Leadership. Emotional intelligence.

For business leaders responsible for technology budgets and organizational performance, these ‘soft skills’ are quickly becoming some of the most strategic assets in the company.

A Changing Definition of Talent

The latest Info-Tech Research Group IT Talent Trends 2026 Report points to an important evolution in how organizations should think about technology talent.

While technical skills remain foundational, the report highlights that uniquely human capabilities are becoming increasingly important as workplaces become more interconnected, collaborative, and dependent on rapid decision-making.

This shift is happening because:

• Technology no longer operates in a silo
• Software decisions affect customer experience
• Cybersecurity impacts brand reputation
• Infrastructure investments influence business growth
• Data governance affects compliance and risk management.

Technology professionals are no longer simply managing systems. They are helping shape business outcomes, and it requires a broader set of skills than technical expertise alone.

Why Critical Thinking Has Become a Competitive Advantage

Businesses are facing an overwhelming volume of information, competing priorities, and constant change. The ability to evaluate information, identify risks, challenge assumptions, and make sound decisions has never been more important.

The World Economic Forum’s Future of Jobs Report 2025 identified analytical thinking as the most important skill sought by employers globally; with a whopping 70% of organizations identifying it as a core workforce requirement.

This should not come as a surprise.

Technology projects rarely fail because people lack access to information. More often, they fail because organizations struggle to interpret information effectively and make informed decisions.

Whether evaluating a software platform, planning a modernization initiative, or responding to a cybersecurity incident, critical thinking helps organizations avoid costly mistakes and identify opportunities others may overlook.

The Growing Importance of Business Acumen

One of the most significant changes in technology leadership over the past decade has been the expectation that technical teams understand the business they support.

Successful organizations increasingly need professionals who can answer questions such as:

• How does this project impact revenue?
• What operational risks exist?
• How will customers be affected?
• Does this initiative align with strategic objectives?
• What is the return on investment?

Technical expertise explains what is possible, but business acumen helps determine what is worthwhile.

The organizations gaining the greatest value from technology investments are often those that successfully connect technical decisions to measurable business outcomes.

Emotional Intelligence Is No Longer a Soft Skill

For many years, emotional intelligence was viewed as a desirable leadership trait. Today, it’s increasingly recognized as a business necessity.

Technology projects involve people, competing priorities, difficult conversations, changing expectations, and organizational change. The ability to communicate clearly, build trust, manage conflict, and support colleagues through uncertainty directly impacts project outcomes.

Research published by Harvard Business School Online found that 71% of employers value emotional intelligence over technical skills when evaluating candidates. Their research demonstrates the strong link between emotional intelligence, leadership effectiveness, employee engagement, and team performance.

With organizations being more multigenerational than any other point in history, different communication styles, expectations, and perspectives can create challenges. But when managed effectively, by applying the 4 components of emotional intelligence they can also create opportunities.

The Cost of Ignoring the Human Side of Technology

Workforce challenges are frequently discussed as staffing issues. But the reality is that they are critical business risks.

Overlooking the importance of human capabilities can have real consequences like:

• project delays
• poor decision-making
• burnout
• increased turnover
• innovation drag
• higher operating costs

The European Commission’s State of the Digital Decade 2025 Report identified workforce capability as one of the critical barriers to achieving transformation objectives across member states.

Simply put, technology investments alone are not enough. Organizations also need people capable of maximizing the value of those investments.

How Third-Party Partners Can Strengthen Internal Teams

Many organizations understand the importance of developing these capabilities but struggle with limited time, competing priorities, and resource constraints.

Internal teams are already playing a precarious balancing act between:

• modernization initiatives
• staying ahead of cybersecurity requirements
• providing day to day operational support
• software development on existing, new and legacy systems
• infrastructure management
• evaluating strategic initiative to stay ahead of the competition

This is where trusted third-party partners can provide quick and meaningful value.

Experienced external partners can:

• augment team skills gaps,
• provide specialized expertise and guidance
• support strategic initiatives
• accelerate project delivery
• reduce pressure on internal teams by helping to clear the backlog

Perhaps, more importantly, they bring outside perspective. Because they work across multiple industries and organizations, they often recognize emerging challenges and proven solutions before they become visible internally.

The strongest partnerships do not replace internal talent. They help develop it. By augmenting existing teams, organizations can continue moving forward while building long-term internal capability.

Final Thoughts

Technology will continue to evolve. Platforms will change. Systems will modernize. New tools will emerge.

But with every major technology shift throughout history, one factor has remained constant: people determine success.

Organizations that focus exclusively on technology, risk overlooking the very capabilities that transform investments into outcomes.

• Critical thinking helps navigate complexity
• Business acumen connects technology to strategy
• Emotional intelligence strengthens teams
• Leadership creates resilience during change

These are not new soft skills; but they’re now essential business skills.

In a world where technology is increasingly accessible, the organizations that stand apart will be those that recognize the true competitive advantage has never been their technology but the people behind it.

Drop us a line if need a helping hand building out your competitive advantage.

The post Humans in an AI Time: Why People Are Becoming the Most Important Investment in Technology first appeared on STEP Software.

Things have changed.

In the era of AI, automation, cybersecurity threats, and digital transformation, clean data has become a business-critical asset.

Organizations investing millions into modernization initiatives are discovering a hard truth: bad data quietly destroys software projects, weakens AI systems, increases cybersecurity risk, and drives up operational costs.

For business leaders responsible for technology budgets, the conversation is no longer about whether data optimization matters; it’s about whether the organization can afford to keep ignoring it.

Hidden Costs of Bad Data

Poor-quality data impacts every area of modern business:

• Inaccurate reporting
• Failed software integrations
• Unreliable AI outputs
• Operational inefficiencies
• Compliance risks
• Frustrated employees
• Poor customer experience and engagement

According to IBM’s research on poor data quality, over one quarter of organizations estimate they lose more than $5 million annually due to poor data quality, while 7% report losses exceeding $25 million per year.

Those costs rarely appear as one obvious budget line. Instead, they spread quietly across the organization.

Do any of these issues sound familiar?

• Delayed projects
• Duplicated work
• Manual corrections
• Failed migrations
• Inaccurate forecasting
• Growing technical debt

If you answered yes, welcome to the club. These are the symptoms most companies feel long before they identify the root cause – bad data!

AI Is Making Data Problems Impossible to Ignore

AI has amplified the importance of clean data, dramatically.

Businesses everywhere are deploying:

• AI copilots
• Analytics platforms
• Automation tools
• Predictive models
• Generative AI systems

But AI systems are only as reliable as the data feeding them, the computer science concept of GIGO (garbage in – garbage out) applies now, more than ever.

If customer records are duplicated, AI recommendations become inaccurate. If operational data is inconsistent, forecasting models fail. If legacy systems contain incomplete records, automation workflows break. The cycle continues.

Gartner recently reported that 63% of organizations either lack or are unsure whether they have AI-ready data practices in place. Even more concerning, Gartner predicts organizations will abandon 60% of AI projects unsupported by AI-ready data through 2026.

Those statistics alone should be setting off the executive warning bells. The issues are not always the AI technology itself, its often the condition of the organization’s data infrastructure.

Legacy Systems Making the Problem Worse

Legacy modernization, while it is the goal, it is still a very real struggle for most modern businesses. The challenge of carrying decades worth of fragmented data environments into the modernization roadmap are making the efforts monumentally more difficult.

Older systems were not built for:

• Cloud-native applications
• AI integration
• Real-time analytics
• Modern cybersecurity requirements
• End user UI expectations

Over time, organizations have layered spreadsheets, disconnected databases, custom applications, and duplicate systems on top of aging infrastructure, resulting in fragmented architecture and disparate systems.

The ongoing operational challenges are real:

• Departments maintaining conflicting records
• Inconsistent or inaccurate reporting mechanisms
• Data no one fully trusts
• Growing storage costs, redundant data is not free

IBM notes that poor data quality often stems from:

• Weak governance
• Outdated systems
• Integration failures
• Inconsistent data collection practices

The problems poor data quality causes during software modernization projects are a list all their own. Businesses frequently discover their biggest obstacle is not implementing innovative technology but cleaning the data being migrated into it.

Underestimating The Cybersecurity Risk

Bad data is not just an operational problem, its increasingly becoming a cybersecurity issue.

Fragmented systems and poor governance create:

• Inconsistent access controls
• Unknown data exposure
• Shadow AI usage
• Unknown compliance vulnerabilities

Recent discussions on Reddit, surrounding the new ‘AI usage control’ issued by Gartner, highlight the growing concerns about organizations losing visibility into how company data is being shared with external AI systems.

In other words: poor data governance is now directly connected to cybersecurity exposure and as AI adoption accelerates the risks grow.

Internal Teams Struggle to Solve This Alone

Most IT departments already operate at or below effective capacity levels. We talked about the impacts on employee mental health last week on our blog, but the impacts go beyond this.

Internal teams are balancing:

• Cybersecurity
• Infrastructure support
• Software maintenance
• AI implementation
• Cloud migration
• Simultaneous operational support
• Legacy software and modernization initiatives

Large-scale data optimization projects require:

• Dedicated expertise
• Governance frameworks
• Automation tooling
• Sustained focus without attention fragmentation

Optimization efforts are difficult to achieve internally when they are a team’s sole focus but forget about it when that team is also managing day to day operations. This is why many organizations are turning to third-party firms to champion their optimization efforts.

A Third-Party Optimization Partner is Good Business Sense

For many executives, outsourcing data cleansing sounds like an added expense when it should be viewed as a strategic cost-control initiative.

Experienced third-party firms bring:

• Specialized expertise
• Proven methodologies
• Automation capabilities
• External objectivity
• Additional skillsets to augment support needs (like custom software development)

More importantly, they can identify problems internal teams often normalize after years of working around them.

An experienced optimization partner can help organizations:

• Consolidate fragmented systems
• Improve governance
• Modernize legacy systems and integrations
• Reduce duplicate data
• Prepare environments for AI
• Strengthen security controls

Accelerating modernization efforts while reducing long-term operational costs is a budget win. Because once poor-quality data spreads into modern systems, remediation becomes significantly more expensive.

Getting Budget for Data Optimization

The challenge of getting approved budget for data optimization projects is a common challenge we are hearing from our advisory clients.

The issue is perception.

Data cleansing sounds operational. AI sounds strategic.

But the reality is that AI success depends on data quality.

Without clean, governed, optimized data:

• AI initiatives fail
• Modernization slows
• Cybersecurity risk increases
• Software projects become more expensive
• Employee frustration grows

This is not maintenance work anymore, it’s infrastructure modernization and one of the most important prerequisites for successful digital transformation projects.

Final Thoughts

Businesses are investing heavily in AI, automation, cloud infrastructure, and software modernization. But many are still trying to build those initiatives on unreliable data foundations.

That’s like adding floors to an office building while ignoring cracks in the foundation.

Businesses that succeed over the next decade will not simply be the ones that buy or implement the newest AI tools, they will be the ones that prioritize data health first.

Because in modern business, clean data is no longer an IT luxury, it’s a competitive advantage.

Drop us a line if you are looking for a reliable, experienced partner to help with your data optimization projects.

The post Garbage in – Garbage Out: The 2026 Edition first appeared on STEP Software.

But many businesses are discovering an uncomfortable reality: the faster AI is ‘adopted,’ the faster technical debt can accumulate. And you can’t ask AI ‘what were you thinking?’ because AI doesn’t think. It determines what the next token of code is(most likely) and outputs it (with some random variability) without thinking.

This growing problem, known as AI technical debt, is quickly becoming one of the largest hidden costs in modern business technology, and one of the leading causes of frustration for developers.

What Is AI Technical Debt?

Technical debt traditionally refers to shortcuts in software development that create future maintenance problems. AI technical debt expands that concept beyond code.

It includes:

• Poorly governed AI tools
• Unmanaged data pipelines
• AI-generated code requiring extensive cleanup
• Duplicated automation platforms
• Undocumented workflows
• AI systems layered onto outdated infrastructure

Unlike traditional technical debt, AI debt compounds rapidly because AI systems continuously evolve and rely heavily on interconnected data and infrastructure.

A recent large-scale study analyzing more than 304,000 AI-authored commits found that over 15% of AI-generated commits introduced at least one issue, while 24.2% of those issues persisted into later software revisions.

What AI Technical Debt Looks Like in the Real World

AI debt is rarely obvious at first.

Until …

AI-Generated Code Cleanup

Many organizations are accelerating software delivery using coding copilots and generative AI tools. Companies like Airbnb reports that AI generates roughly 60% of new code, while Anthropic says AI writes more than 90% of its code.

That sounds efficient, right? Wrong!

When engineering teams inherit:

• Inconsistent architecture
• Undocumented logic
• Security flaws
• Code nobody fully understands

The productivity bottleneck doesn’t disappear, it shifts from coding to verification & maintenance.

Lightrun’s 2026 engineering report found:

• 43% of AI-generated code requires manual debugging in production
• 88% of organization need 2-3 redeploys to fix an AI generated change

Register to read the full report here: State of AI-Powered Engineering 2026

The business impact is real and staffing this shift is only part of it.

‘Shadow AI’ Across Departments

Marketing adopts one AI writing tool. HR adopts another. Finance deploys AI forecasting. Operations build internal automations.

Suddenly organizations have:

• Duplicate subscriptions
• Fragmented workflows
• Inconsistent governance
• Unclear ownership

This news is not new, Gartner warned in late 2025 that ‘shadow AI’ was contributing directly to rising technical debt and budget bloat.

Security and Compliance Risks

AI-generated code can introduce cybersecurity problems at scale.

Developers themselves remain skeptical as indicated in the C++ global developer survey where 78% worry about incorrect AI output and 50% cited privacy concerns.

A 2026 Sherlock Forensics report stated that 92% of assessed AI-generated codebases contained at least one critical vulnerability.

At the same time, Google recently warned that cybercriminals are now using AI-assisted development techniques to identify and exploit software vulnerabilities faster.

The cybersecurity risks are real especially for businesses operating in regulated industries, AI debt can quickly become compliance debt.

Which Departments Are Feeling It Most?

IT and Engineering

Technology teams are carrying the largest burden:

• Integrating AI systems with existing software including Legacy systems
• Validating AI-generated code
• Managing cloud costs
• Retraining models
• Refactoring unstable systems
• Managing innovation expectations and team engagement

According to Gartner, worldwide AI spending is expected to reach $2.5 trillion in 2026, with infrastructure spending alone increasing by nearly 50%.

The issue is not simply spending more. It’s spending more while inheriting more complexity.

Finance

Finance teams are struggling with:

• Unpredictable AI related operational costs
• Duplicated SaaS spending
• Unclear ROI measurements

Industry discussions on Reddit are suggesting that technical debt and fragmented infrastructure are among the top blockers preventing CFOs from scaling AI initiatives effectively.

Operations, Support and HR Teams

Operations departments often inherit broken automation chains and disconnected workflows.

HR teams are tasked with doing more with less. Compounded by AI literacy rates being out of alignment with AI Adoption, the burden of finding skilled AI talent falls on HR.

Customer support teams, face AI-system related challenges like:

• Tools that produce inconsistent responses
• Systems requiring constant oversight to ensure customer satisfaction
• Incomplete integration with legacy CRM systems, leading to duplicated effort

Operational friction disguised as automation is leading to decreased employee moral, frustrated customers, decreasing end user satisfaction and challenges filling talent pipelines.

The Legacy Problem

Deploying AI into clean, modern environments is a rare occurrence.

Most businesses are layering AI onto:

• Legacy ERP systems
• Aging databases
• On-prem infrastructure
• Decades-old workflows

Creating immediate integration debt.

Research on AI-enabled systems shows technical debt significantly impacts:

• System understandability
• Scalability
• Security quality

Legacy systems are not inherently bad, in fact, many remain as the backbone of businesses globally.

Having a clean modernization strategy that includes AI can help reduce the friction points for AI adoption and decrease possible DRP black holes.

Why The Right Software Partner Matters

Off-the-shelf AI tools solve isolated tasks. But businesses increasingly require:

• Custom integrations
• API modernization
• Centralized governance
• Architecture tailored to their infrastructure.

The challenge?

It is increasingly difficult to find truly experienced firms capable of handling:

• Embedded AI systems
• Custom software
• Cloud infrastructure
• Legacy modernization

This skills gap is becoming its own business risk and is where an experienced development partner become critical.

With over 20 years experience in all of the above, STEP can help.

Reducing AI Technical Debt

Organizations can curb AI debt by:

1. Auditing all AI tools and workflows
2. Implementing centralized governance
3. Modernizing legacy infrastructure strategically
4. Requiring human oversight for AI-generated code
5. Investing in scalable custom software architecture
6. Prioritizing long-term maintainability over short-term speed

AI debt behaves like financial debt: manageable early, expensive later.

Remember: not every shiny object is right for your business today. Adopting this mindset can save you from budget headaches down the road.

Final Thoughts

AI is already transforming how businesses build software, manage operations, and serve customers.

But speed without structure creates instability.

The organizations that succeed with AI won’t necessarily be the fastest adopters. They’ll be the companies who balance innovation with maintainability, connecting AI, custom software, and legacy modernization into a sustainable long-term strategy.

Because in the AI era, the real cost is not deploying too slowly.

It’s deploying too quickly, without a plan.

Drop us a line if you’d like to chat more about your AI technical debt.

The post AI Technical Debt: The Hidden Cost, Can You Feel it? first appeared on STEP Software.

For business leaders managing technology budgets, edge computing isn’t just a trend. It’s a strategic shift in how infrastructure is designed to support speed, efficiency, and scalability.

Over the last two weeks we have touched on how edge computing correlates with Well-Architected Framework and Embedded Systems; this week we dive into the details of it.

What is Edge Computing?

At its core, edge computing is a distributed computing model that processes data closer to where it’s generated; whether that’s a factory sensor, a retail POS system, or a connected vehicle. Instead of sending all data to a centralized cloud or data center, processing happens at or near the ‘edge’ of the network.

Data By the Numbers:

• According to the latest estimates, 402.74 million terabytes of data are created each day, globally.
• It is estimated that 90% of the world’s data has been generated in the last two years
• 2010 the global data amount was 2 zettabytes
• 2026 is projected to be 221 zettabytes

Why does this matter?

Because modern businesses generate massive volumes of data, and sending it all to the cloud creates latency, bandwidth constraints, and cost inefficiencies. Edge computing solves this by analyzing data locally and only sending what’s necessary upstream.

Why Edge Computing Matters for Businesses

For organizations under pressure to move faster, operate leaner, and deliver better customer experiences, edge computing offers a compelling value proposition.

Real-Time Decision Making

Speed is everything. By reducing latency, edge computing enables near-instant decision-making. This is critical in industries like manufacturing, logistics, and finance, where milliseconds can impact safety, efficiency, or revenue.

Cost Optimization

Processing data locally reduces the amount of data sent to the cloud, lowering bandwidth and infrastructure costs. For organizations managing large-scale IoT deployments, this can translate into significant savings.

Improved Performance and Reliability

Edge computing allows systems to function even when connectivity is limited or inconsistent. This is especially valuable for remote operations or mission-critical environments where downtime isn’t an option.

Enhanced Security and Compliance

Keeping sensitive data closer to its source can reduce exposure and help organizations meet regional compliance requirements.

Enabling AI and Advanced Analytics

Edge computing is increasingly paired with AI (also knows as Edge AI). Edge AI allows businesses to process and act on data in real time without relying on centralized systems.

For businesses in the zettabyte era, edge computing helps them become faster, smarter, and more efficient; without overloading centralized infrastructure.

The Risks and Trade-Offs

Of course, edge computing isn’t a silver bullet. It introduces new complexities that business and technology leaders need to account for.

Increased Complexity

Managing thousands of distributed edge devices is significantly more complex than maintaining centralized systems.
Deployment, monitoring, and updates all become more challenging at scale.

Security Challenges

While edge can enhance data privacy, it also expands the attack surface. More devices mean more potential entry points for cyber threats.

Limited Processing Power

Edge devices often have less compute and storage capacity than centralized data centers, which can limit the types of workloads they can handle.

Integration Complexity

Integrating edge environments with existing cloud and on-prem systems requires careful architectural planning and often new tooling.

The takeaway? Edge computing shifts complexity, it doesn’t eliminate it.

Edge Computing and Legacy Systems

Here’s where things get interesting for many organizations: edge computing doesn’t replace legacy systems; it exposes their limitations.

Legacy infrastructure was (typically) designed for centralized processing. Edge computing, by contrast, relies on distributed, real-time architectures. This mismatch creates friction.

Shared challenges include:

• Data silos that prevent real-time data sharing
• Rigid architectures that don’t support distributed processing
• Limited interoperability with modern edge platforms

For many businesses, edge computing becomes a catalyst for modernization. It forces a rethink of how systems are structured and integrated.

The Role of Custom Software

This is where custom software development becomes critical.

Off-the-shelf solutions often struggle to adapt to the unique demands of edge environments, especially when integrating with legacy systems.

Custom software allows organizations to:

• Build lightweight applications optimized for edge devices
• Enable seamless integration between edge, cloud, and on-prem systems
• Implement tailored security and data governance frameworks
• Scale edge deployments in a controlled, strategic way

In other words, custom software acts as the bridge between where your business is today and where edge computing can take it.

Implementation Challenges for Businesses

Adopting edge computing isn’t just a technical shift, it’s an organizational one.

Business leaders need to be mindful of potential challenges:

Infrastructure Investment

Deploying edge infrastructure requires upfront investment in hardware, networking, and management platforms. Q2 is a great time to review your 2026 budget and pivot if your priorities have changed.

Skills Gap

Edge computing blends cloud, networking, and IoT expertise, skills that are often siloed or missing within organizations. Seeking support from experienced third-party vendors is an excellent way to manage this gap.

Governance and Data Management

With data distributed across multiple locations, maintaining consistency, compliance, and visibility becomes more complex.

Strategic Alignment

Not every workload belongs at the edge. Identifying the right use cases is critical to avoiding unnecessary complexity and cost. If you are unsure where to start, our Advisory team here at STEP offers complimentary 30-minute calls to help get you started.

Final Thoughts: Edge as a Strategic Advantage

Edge computing isn’t about replacing the cloud, it’s about extending it.

The most successful organizations will adopt a hybrid approach, combining cloud, edge, and on-prem systems into a cohesive architecture that supports both real-time performance and long-term scalability.

For business leaders, the opportunity is clear:

• Faster insights
• Lower operational costs
• Better customer experiences

But the path forward requires thoughtful planning, modernization of legacy systems, and often, investment in custom software to tie it all together.

Edge computing is about one thing – getting closer to the action. In today’s business environment, that proximity can make all the difference. Drop us a line if you are sitting on the edge.

The post Edge Computing & Business: Getting Closer to the Action first appeared on STEP Software.

Is it possible to make the app faster for users and cheaper to maintain?”

One of your brightest developers enthusiastically leans forward, and says:

“Yes… but it depends.”

And just like that, you’re in a conversation that includes terms you’re sure are made up, acronyms that you’ve never heard of and concepts you suspect went into the Artemis II launch.

You try and keep up but gapped somewhere after they said, ‘quad trees,’ but before ‘performance optimization budgets.’ You’re certain that they said something involving AI, maybe coding agents? Or was it code splitting?

Either way you’re confident it all sounds impressive and expensive.

It’s tempting to mentally check out at this point. But here’s the reality:

These ‘rabbit hole‘ conversations are exactly where hidden business value is uncovered.

Let’s unpack how and why, business leader can benefit from leaning into these daunting, often humbling conversations.

The Hidden ROI of Technical Rabbit Holes

When developers go deep, they’re not overengineering, in fact they’re identifying levers that directly impact cost, performance, and scalability.

For example:

• Faster apps improve conversion rates and user satisfaction
• Efficient code reduces infrastructure costs
• Better architecture lowers long-term maintenance effort

Google has even tied site performance directly to search rankings through Core Web Vitals, meaning performance improvements can influence revenue.

So that ‘technical tangent’ that lost you might just be the most financially relevant part of your meeting.

Terms You Want to Hear (Yes, Really)

If your team starts using these (high on the current tech radar) terms, don’t panic, instead, lean in.

File Memory Mapping (a.k.a. “Avoid Parsing and hogging memory needs”)

Sadly, this is not a term you will hear often, but when you do, jumping for joy is a reasonable reaction. Memory Mapping can allow for the access and retrieval of data quickly with negligible impact to the CPU or RAM, improving all around performance.

Business impact: Faster load times = better user experience = higher engagement and conversion.

Quadtree (a.k.a. “Culling Data is always welcome”)

Quadtree is a data structure which allows for a large dataset to be accessed quickly from either a macro or micro level. This method is useful in applications that have Mapping or GIS queries and image processing. A neat visual representation of quadtree can be found here.

Business impact: Useless memory + Faster queries = More responsive experience and lower maintenance costs

Optimization Strategies (the umbrella term you love to hear)

This includes:

• Lazy loading
• Caching strategies
• SQL database query optimization
• Efficient API design

Business impact:

Lower infrastructure costs and improved scalability

According to AWS, well-architected framework leads to performance optimization which directly impacts cost efficiency in cloud environments.

The New Player: AI in Performance Optimization

Now for the topic that is coming up a lot around boardroom tables andwill continue to be a re-occurring rabbit holes for tech teams: AI.

The Upside (and it’s big)

AI is increasingly being used to:

• Identify performance bottlenecks
• Suggest code improvements
• Automate testing and refactoring
• Support legacy software modernization efforts

This can significantly accelerate development cycles and reduce manual effort.

Industry reports indicate that AI-assisted development is starting to improve productivity across engineering teams. (proceed with caution)

The Downside (because there’s always one)

AI isn’t magic. It can:

• Introduce inefficient or redundant code
• Miss context-specific optimizations
• Create cybersecurity or compliance risks if not reviewed
• Shadow AI can lead to governance nightmares
• ‘Vibe coded’ solutions can lead to expensive re-writes and questionable data integrity

A topic of concern for IT leaders right now is, ‘AI tech debt’(code that works now but becomes expensive later). If this is not on your radar as a business leader, it needs to be.

Translation for business leaders:
AI can reduce costs today but increase them tomorrow if not governed properly.

Before You Optimize: Start With a Code Audit

Here’s where the smartest teams separate themselves.

Before jumping into a series of optimization efforts, they start with a code audit.

A code audit evaluates:

• Code quality
• Performance bottlenecks
• Security vulnerabilities
• Maintainability issues

Think of it like a financial audit, but for your software. According to OWASP, regular code reviews and audits are critical for identifying vulnerabilities and improving overall system integrity.

Why This Matters for Your Budget

Without a code audit:

• You risk solving the wrong problem
• You may invest in optimizations with minimal impact
• You could miss larger structural inefficiencies

With a code audit:

• You prioritize high-impact improvements
• You reduce wasted spend
• You create a roadmap for sustainable performance gains

While developers will not always outright suggest an audit, they will give you clues of the necessity. If you start to hear terms like code refactoring, data integrity or RPA for legacy software, this is your sign to ask them if it’s time.

In short: you stop guessing and start investing strategically.

How to Stay Engaged (Without a Deep Dive into Reddit)

You don’t need to understand every technical detail. But you do need to have enough knowledge to give yourself a fighting chance of staying in the conversation.

Your secret weapon? Asking the right questions…

Start with: “What’s the impact?”

This helps you translate technical ideas into:

• Cost savings
• Revenue impact
• Risk reduction

If in doubt, ask: “What’s the trade-off?”

Every optimization has one:

• Speed vs. flexibility
• Cost vs. scalability
• Short-term vs. long-term gain

Clarify with: “Is this a now problem or a future problem?”

This helps prioritize:

• Immediate fixes needed a.k.a. company reputation and budget are at risk
• Which strategic investments make it into the pipeline

When all else fails go with … “Can you explain that to me like I am your grandparent?”

You will learn very quickly that developers love to share their knowledge (often in the form of relatable analogies,) and are more than willing to augment your weekend reading list with explanatory You Tube videos or links to free training to improve your AI Literacy.

Why This Matters Now, More Than Ever

Modern software development is moving fast, especially with AI, cloud computing, and increasingly robust user expectations which are driving development pipelines.

Companies that are succeeding are not the ones that avoid complexity; they’re the ones that engage with it just enough to make smarter decisions.

Here’s the simple reality:

• Developers understand the systems
• Business leaders understand the goals

Performance improvements live right in the middle.

Final Thought: Don’t Exit the Rabbit Hole Too Early

The next time your developer says:

“We could improve performance with code splitting, tree shaking, and some AI-assisted optimization…”

Stay with them.

That conversation isn’t a detour, it’s a roadmap to:

• Faster applications
• Lower operating costs
• Better user experiences

You don’t need to follow every tangent, understand every acronym, or even know the latest security threats. You just need to ask the right questions, and recognize that sometimes, the deepest technical rabbit hole is where the biggest business wins are hiding.

If you need help translating technical rabbit holes, drop us a line … our advisors are here to help you bridge the gap between business and software.

The post Down the Software Rabbit Hole: Why Business Leaders Should Lean into Technical Deep Dives first appeared on STEP Software.

This week we dive into LLM’s from the perspective of a software development company who fields questions about with our advisory client’s and uses them to achieve our client’s custom software goals.

 So, What is an LLM?

At its core, an LLM is a type of artificial intelligence model trained on massive amounts of text data to understand and generate human-like language. Models like ChatGPT, Claude Sonnet, and others learn patterns in language including grammar, context, and relationships between words by processing billions (or trillions) of data points.

They don’t ‘think’ or ‘know’ things in the human sense. Instead, they predict what word (or token) should come next based on probabilities learned during training. This distinction matters more than most people realize.

LLMs are built on transformer architectures, first introduced in a 2017 Google research paper, ‘Attention Is All You Need.’ While today’s tools feel new, the underlying ideas have been evolving for decades through natural language processing (NLP), machine learning, and computational linguistics.

Before the Hype: LLMs Weren’t Always Cool

Long before ChatGPT made headlines, early versions of language models were already quietly powering parts of our digital lives. Think autocomplete in email, search engine query suggestions, or basic chatbots.

Companies were using NLP techniques for:

• Customer support automation
• Document classification
• Sentiment analysis
• Fraud detection

What changed wasn’t the existence of the technology, it was the scale. The combination of cloud computing, massive datasets, and improved architectures turned niche tools into more broadly useful systems. This law of neural scaling, seemingly happened overnight.

In other words: LLMs didn’t suddenly appear. They grew up.

What LLMs Are Not

This is where things get interesting, and where many organizations get tripped up.

LLMs are not:

• Sources of truth – They can generate incorrect or outdated information (‘hallucinations’).
• Reasoning engines – While they simulate reasoning, they are fundamentally pattern predictors.
• Plug-and-play business solutions – They require integration, oversight, and governance and general corporate AI Literacy.
• Replacements for human expertise – Especially in regulated or high-risk environments. Assuming there are can lead to long term staffing burdens.

Understanding these limitations and challenges are critical to the decision making process. According to McKinsey’s 2025 State of AI report, while AI adoption is increasing, only a minority of organizations are successfully scaling it across the enterprise; often due to unrealistic expectations and lack of alignment between tools and business processes.

Why LLMs Matter Now

Despite their limitations, LLMs are incredibly powerful when used correctly. They excel at:

• Summarizing complex documents
• Generating code and technical documentation
• Automating repetitive communication tasks
• Extracting insights from unstructured data

For software development teams, this can be a game changer.

GitHub reports that developers using an AI coding assistant completes tasks up to 55% faster in controlled studies. That doesn’t mean fewer developers, it means more productive ones, and the opportunity to focus on higher-value work.

LLMs can help:

• Accelerate legacy system modernization
• Generate boilerplate code and test cases
• Improve documentation quality
• Assist with debugging and code reviews

For organizations dealing with aging infrastructure, that’s a compelling value proposition.

LLM Risks & Concerns

Of course, no conversation about LLMs is complete without addressing the risks.

1. Accuracy & Hallucinations

LLMs can confidently produce incorrect information. This is particularly risky in industries like insurance, healthcare, or finance. Read more about the potential cost for businesses here.

• Data Privacy & Security

Using public LLMs without proper controls can expose sensitive data. Enterprises must consider private deployments or controlled environments.

• Bias & Compliance

Because LLMs are trained on large datasets, they can reflect societal biases. This can create regulatory, reputational  and staff retention risks.

• Over-Reliance

There’s a temptation to treat LLMs as decision-makers rather than assistants. That’s where things go sideways.

Recently, there have been several notable examples of legal cases where lawyers submitted AI-generated filings containing fabricated citations or AI hallucinated cases. The severity of these implications highlight how avoidable repercussion are and the importance of human oversight.

LLMs in the Real World: What’s Actually Working

Beyond the headlines, there are meaningful, practical applications emerging across industries:

• Insurance: Automating claims summaries, improving underwriting insights, enhancing customer service.
• Construction: Analyzing project documentation, improving reporting workflows, reducing administrative burden and improving jobsite safety.
• Manufacturing: Supporting maintenance logs, generating technical documentation, improving knowledge transfer. Check out Rockwell Automation’s State of Smart Manufacturing for more details.
• Financial Services: Assisting with compliance documentation, risk analysis and fraud detection/prevention.

According to McKinsey, gen AI could add between $2.6 trillion and $4.4 trillion annually to the global economy, with significant impact in knowledge-heavy industries.

Why Third-Party Expertise Matters

Here’s the reality: most organizations don’t struggle with access to LLMs, they struggle with implementation.

This is where working with a third-party software partner becomes advantageous.

A strong partner can:

• Integrate LLMs into existing systems (rather than layering them on top)
• Ensure proper data governance and security controls
• Customize models for industry-specific use cases
• Align AI capabilities with real business outcomes

For companies running legacy systems, this is especially important. Retrofitting AI into outdated architecture without a clear strategy can create more problems than it solves.

Industries that benefit most from this collaborative to unlock efficiency gains without introducing unnecessary risk approach are:

• Insurance and financial services (complex workflows, regulatory requirements)
• Industrial manufacturing (data-rich environments with operational dependencies)
• Construction and infrastructure (fragmented systems and documentation-heavy processes)

Final Thoughts: From Buzzword to Business Tool

LLMs are not magic. They are not a silver bullet. But they are also not hype without substance.

They are tools, powerful ones, that can transform how organizations handle information, build software, and operate at scale.

The organizations that win won’t be the ones that adopt LLMs the fastest. They’ll be the ones that understand them the best, and work with trusted partners to create their competitive advantage.

In a time where technology moves quickly but business risk moves slowly, understanding how technology augments real business needs where we excel at STEP, reach out if you are looking to take advantage of power of LLMs.

The post What Is an LLM (Really)? A Practical Guide for Business and Technology Leaders first appeared on STEP Software.

However, according to a global study that included Canadian respondents, Canada ranks among the lowest-performing advanced economies in AI literacy and training, with only about 24% of respondents reporting that they’ve‘received any AI training.’ This puts Canadian businesses at a disadvantage compared to global peers.

This trend isn’t unique to Canada though, similar gaps exist globally. Many employees are using AI tools at work, driven by curiosity or pressure to keep up, but often lack structured guidance or understanding. This creates a paradox: organizations are embracing AI, but most employees don’t feel fully prepared to use it effectively or responsibly. This week our blog focuses on the details of AI Literacy and what businesses need to know to ensure they don’t continue to fall behind the competition.

Whose Responsibility for AI Literacy in Business?

AI literacy isn’t an IT problem, and it’s not an HR problem either, but assigning accountability often gets overlooked. AI literacy is a shared leadership responsibility.

• Executives must define the AI strategy, governance policies, and clear organizational goals for responsible adoption.
• HR leaders should embed AI readiness into training, performance frameworks, and talent development.
• IT leaders must ensure secure, compliant use of AI tools, establish data governance standards, and guide best-practice usage across teams.
• Team managers have a role in enabling hands-on training and ensuring adoption aligns with actual business needs.

When no one “owns” AI literacy, adoption becomes patchy, inconsistent, and risky. Employees improvise on their own, often turning to public forums, social media, or ad hoc online courses that may be outdated, incomplete, or even misaligned with company objectives. We wrote about the risks associated with Shadow AI in October 2025; the risk is no less severe today.

Without corporate-led AI education, organizations risk:

• Unintended security breaches
• Compliance failures
• Unpredictable productivity
• Misuse of AI outputs
• Employee frustration or distrust

Generational Gaps & Workforce Challenges

Generational differences and the gaps they create are influencing how teams adopt AI tools. Younger workers (Gen Z) may be more comfortable experimenting with AI, but often lack the understanding of governance, risk, or organizational requirements. Conversely, more tenured staff (Gen X and Baby Boomers) can be cautious or hesitant to adopt new tools without formal training, resulting in uneven capability across teams.

With Gen Alpha entering the workforce as students this year, and Millennials straddling the adoption line between Gen Z and Gen X; bridging these gaps has never been more critical. This means organizations must build AI literacy programs that are inclusive, scaled, and role-relevant; not just targeted at tech teams or early adopters.

Where Employees Are Getting Their Knowledge

When businesses don’t provide formal AI literacy tools or access, employees seek out alternative sources:

• Online communities and forums (Reddit, LinkedIn)
• Free course aggregators and YouTube tutorials
• Vendor-provided tool guidance
• Peer-to-peer knowledge sharing

These grassroots learning methods can spark curiosity and practical experimentation, but they’re inconsistent and not trackable. They often miss important aspects like governance, ethics, data privacy, and compliance; areas business leaders absolutely must master before tools are deployed at scale.

High-Quality AI Literacy Resources

Fortunately, there are excellent structured resources available right now for organizations looking to build internal capability:

Canada

Amii’s AI Literacy for Everyone Platform
A suite of on-demand courses covering AI foundations, responsible AI, strategy for leaders, and real-world applications. Amii is a Canadian company providing accessible solutions for employees at all levels.

Community Literacy of Ontario Resources
Curated AI literacy resources that can be incorporated into workforce learning programs.

Digital Moment

Helps empower organizations through digital literacy, they also work with students and have mentoring program opportunities for your AI leaders.

US & International

Google AI Essentials & Prompting Essentials (Grow with Google)
Flexible, free online training from Google designed to teach practical AI skills and responsible usage for any role.

Coursera and Udemy AI Courses
New professional courses on AI usage, including tools like Claude, covering both general and developer audiences. Udemy offers AI learning paths specific to job types and include professional certifications.

Generative AI & Cloud Provider Learning Paths
Microsoft Learn, AWS Educate, and Google Cloud offer hundreds of courses in AI and machine learning that can be part of corporate training programs.

These structured programs not only help build skills, but they also introduce best practices in responsible and ethical AI use, a critical concern in regulated business environments.

Business Impact: Why Leaders Should Care

AI literacy directly influences organizational performance and employee morale. Workers with formal training are more confident, more productive, and better equipped to align AI use with strategy and compliance goals.

Businesses that fail to invest risk:

• Productivity losses due to poor or inappropriate AI adoption
• Ethical, legal or compliance issues from misuse
• Missed innovation opportunities
• Difficulty attracting top talent with AI fluency

Conversely, organizations that integrate AI literacy into their talent development framework send a clear signal: they invest in people as well as technology. This is increasingly important in the talent market, where AI skills are no longer niche, they’re foundational.

Final Thoughts

AI literacy can no longer be a side project or a checkbox. It needs clear ownership, strategic planning, and structured learning pathways across the organization. With the right guidance, employees can move from unsure experimenters to confident, responsible users who contribute to innovation safely and effectively.

Investing in AI literacy isn’t just smart, it’s essential for businesses that want to stay competitive, compliant, and agile in today’s rapidly evolving business landscape.

Still not sure where to start with AI? Our Advisory team is here to help, drop us a line for a free 30-minute consultation.

The post AI Literacy Matters, Now More Than Ever first appeared on STEP Software.