DEV Community: Mclean Forrester

How AI Delivers Measurable Returns Without Disruption

Mclean Forrester — Mon, 22 Jun 2026 15:29:37 +0000

The question is not whether artificial intelligence will transform business operations. The question is whether business leaders can articulate precisely where that transformation occurs and quantify its value before committing resources. This distinction separates successful AI adoption from the vast majority of initiatives that generate more noise than net benefit.

When we examine the small and medium enterprise sector, particularly in the St. Louis metropolitan area and across the broader Midwest, a pattern emerges. The organisations that extract genuine value from AI are not those pursuing moonshot projects. They are the ones that have developed a systematic methodology for identifying operational friction, measuring its cost, and deploying targeted automation that integrates seamlessly with existing infrastructure.

The Architecture of Operational Inefficiency
Every business contains hidden inefficiencies that have become so normalised that they escape notice. These are not the obvious structural problems that appear on quarterly reviews. They are the accumulated minutes and hours spent on tasks that require human judgment for verification but not for execution.

Consider the weekly reporting cycle that consumes two hours of a mid level manager's time. The data exists across multiple systems. The formatting requirements are standardised. The distribution list never changes. Yet the process persists because it has always been done this way. When you aggregate this pattern across an organisation, the cumulative effect is startling.

A single workflow consuming two hours per week translates to approximately one hundred hours annually. At a fully burdened cost of thirty five dollars per hour, that single workflow represents three thousand five hundred dollars in direct labour expenditure. The organisation receives no strategic benefit from this expenditure. It merely maintains operational continuity.

Now multiply this across the five or more workflows that exist in most SMEs. The annual leakage approaches twenty thousand dollars before any calculation of error correction, delayed decision making, or employee disengagement. These costs never appear on a profit and loss statement as a discrete line item. They remain embedded in departmental overhead, invisible and unaddressed.

The Automation Principle
The technical foundation for addressing this inefficiency already exists within most St. Louis area businesses. Microsoft environments, which dominate the SME sector, contain Power Automate and Power Apps at no additional cost for most licensing tiers. These tools enable workflow automation without requiring custom development or significant IT intervention.

The implementation pattern follows a consistent trajectory. Identify a workflow with clear inputs, defined outputs, and minimal exceptions. Map the current process including every manual step and decision point. Design an automated alternative that replicates the successful outcomes while eliminating the repetitive elements. Test thoroughly. Deploy incrementally. Measure the results against the baseline.

The transformation is rarely dramatic in isolation. A ten minute automated process replacing a two hour manual task does not generate headlines. But when this pattern repeats across departments and functions, the aggregate productivity gain becomes material. The organisation reclaims capacity without hiring additional staff or requiring existing employees to work longer hours.

Beyond Direct Cost Reduction
The financial arithmetic of automation represents only the visible portion of return on investment. The less tangible benefits often exceed the direct labour savings in strategic value.

Speed of execution improves across the organisation. Customer enquiries receive faster responses. Approvals move through workflows without delay. Sales cycles shorten because information flows freely between systems. These improvements compound over time, creating competitive advantage that competitors cannot replicate quickly.

Accuracy increases when automation replaces manual data handling. Human error in data entry, calculation, and transmission represents a persistent operational risk. Automated workflows eliminate this risk category entirely. The cost of correcting errors, addressing customer complaints, and reconciling discrepancies disappears from the operational budget.

Employee engagement improves when team members can focus on work that requires judgment, creativity, and relationship building. The removal of repetitive administrative tasks does not diminish job satisfaction. It enhances it. People derive meaning from solving problems and serving customers, not from copying data between spreadsheets.

The Measurement Framework
Calculating the return on AI investment requires methodological rigour. The approach we advocate at Blue Llama follows a straightforward protocol that produces defensible numbers.

Select a single workflow for analysis. Document the complete current process including all steps, decision points, and handoffs. Measure the time required for each element. Multiply the total time by the frequency of occurrence. Apply the fully loaded hourly cost for the personnel involved. This produces the current monthly expenditure for that workflow.

Design the automated alternative. In most Microsoft environments, this involves configuring Power Automate flows or building simple Power Apps interfaces. The development effort is modest, often measured in hours rather than days or weeks. Deploy the solution and measure the actual time required for the automated process.

The difference between the manual and automated time investment represents the direct productivity gain. In our experience, reductions of fifty to ninety percent are common for well selected workflows. The reporting example that required two hours manually can often be reduced to ten minutes with automation, reclaiming over ninety hours annually per workflow.

Strategic Capability Development
The most significant benefit of workflow automation extends beyond individual process improvements. Organisations that develop competence in identifying and addressing operational friction build institutional capability that compounds over time.

Teams begin to recognise inefficiency patterns across their work. They develop the vocabulary to describe operational problems in terms of time and cost. They acquire the technical confidence to propose and implement solutions. The organisation transitions from reactive problem solving to proactive process optimisation.

This capability becomes particularly valuable when businesses face capacity constraints or market pressures. An organisation that has automated routine workflows can absorb additional volume without proportional headcount increases. It can respond to competitive threats more quickly. It can experiment with new service offerings without diverting resources from core operations.

Building this internal capability, however, requires more than just identifying problems. It demands a structured approach to learning and implementation. For leaders looking to systematically develop their team's AI proficiency, the AI Learning Path offered by McLean Forrester provides a three-tiered framework that moves participants from foundational literacy to executable strategy, all within the context of their real business operations.

The Integration Imperative
AI and automation succeed when they operate within existing workflows rather than requiring new systems or behaviours. The most common failure mode in technology adoption involves imposing new tools that disrupt established patterns without delivering compensating benefits.

Successful implementation respects the way people work. It identifies opportunities to reduce friction within current processes rather than demanding wholesale process redesign. It provides immediate visible benefits that encourage adoption. It generates data that enables continuous improvement.

For St. Louis area businesses operating within Microsoft environments, this integration is particularly straightforward. Power Automate connects natively to Excel, SharePoint, Outlook, and the broader Microsoft ecosystem. No data migration is required. No new interfaces must be learned. The automation operates invisibly, performing tasks that previously required manual effort.

A Note on External Guidance
Organisations approaching AI adoption for the first time often benefit from external perspective. The internal view tends to normalise inefficiency. What appears to be an acceptable workflow may in fact represent significant productivity leakage.

McLean Forrester provides strategic guidance for organisations navigating this transition. Their expertise in identifying operational friction points and developing appropriate technical responses helps businesses accelerate their automation journey. The combination of external assessment and internal execution capability produces superior outcomes.

For leaders who prefer a structured educational foundation before diving into implementation, the AI Learning Path offers a practical alternative to expensive enterprise programs or shallow community sessions. The live, cohort-based courses are designed specifically for business principals who need to make real AI decisions this quarter, not next year.

The Practical Path Forward
Begin with a single workflow. Measure its current cost. Design and deploy an automated alternative. Measure the results. Document the learning. Identify the next opportunity.

This iterative approach builds momentum while managing risk. Each successful implementation generates data that supports further investment. Each employee who experiences the benefits of automation becomes an advocate for continued adoption. The organisation develops the cultural and technical capabilities necessary for sustained productivity improvement.

The real return on AI investment is not theoretical. It is measurable, achievable, and available to organisations willing to examine their operations with fresh eyes. The technology exists. The tools are accessible. The methodology is proven. What remains is the decision to begin.

The organisations that make this decision today will build competitive advantage that compounds over time. Those that delay will find themselves at an increasing disadvantage as competitors capture productivity gains that remain out of reach. The arithmetic is clear. The only question is when to act.

The Real State of AI in Business for 2026 and Beyond

Mclean Forrester — Thu, 18 Jun 2026 15:31:40 +0000

For a while, the conversation around artificial intelligence felt stuck on repeat. Every conference, every webinar, every sales pitch was hammering the same message: AI is going to rewrite the rules of business overnight. We saw a massive surge in experimentation, with companies rushing to deploy chatbots and copilots just to say they were in the game.

But fast forward to 2026, and the picture has changed dramatically. The initial noise has faded, leaving us in a much more grounded and genuinely exciting phase. This is no longer about flashy demos or impressing the board with buzzwords. It is about delivering real, measurable value. Welcome to the new era of AI, where the focus is on strategic, scaled, and human-centered implementation.

The New AI Value Spectrum
According to industry analysts, the market for AI and machine learning in business is exploding. It is projected to grow from around $330 billion in 2025 to a staggering $1.15 trillion by 2030 . That kind of growth isn't driven by experiments. It is driven by organizations finally moving past the "cool factor" and into the "business factor."

The year 2026 feels like a turning point where we can finally assess which initiatives are delivering and which are stalling . Many companies that jumped headfirst into horizontal, general-purpose AI tools are now in a phase of correction. They are realizing that flashy tech without a solid data foundation and clear operational goals doesn't lead to returns. The real progress is being made by those who understand that success is a spectrum. You have to invest in the proven stuff, like data infrastructure, while also building momentum with more targeted, high-value applications .

The Data Readiness Dilemma
Here is a sobering statistic from 2026: while 97% of organizations report having active AI initiatives, only 5% say their data is truly ready to support them . This is the single biggest hurdle to success that we see every day.

Before you even think about building a sophisticated model, you need to ensure your enterprise data is available, accessible, and fit for use. It is an often-overlooked but critical step. You can't build a skyscraper on a shaky foundation, and you can't build a powerful AI on messy, siloed data. Many companies are finding that their data environments were built for human workflows, not for autonomous AI agents that need to operate continuously across the business .

This is where our expertise at McLean Forrester truly shines. We help you navigate this critical first step to ensure your data is the engine for your AI, not an anchor. This groundwork is essential if you want to successfully scale your AI efforts beyond isolated pilots. You can learn more about how we approach this on our AI and Machine Learning services page.

The Rise of Agentic Workflows
One of the most significant trends we are witnessing in 2026 is the move from generative AI to agentic AI. Instead of just tools that write copy or summarize text, we are now deploying AI agents that can autonomously plan and execute complex tasks. This is rewriting how work gets done.

Companies like ClickUp are going all in, with a 3:1 ratio of AI agents to employees for certain internal workflows . This isn't about replacing humans; it's about fundamentally changing what work looks like. Employees are moving from "doing" and "waiting" to "directing" and "reviewing." They become managers of a digital workforce, overseeing tasks and ensuring quality .

Expectations for this technology are sky-high. Nearly two-thirds of organizations believe agentic AI will free up their employees for more strategic and creative work . But as with any new capability, it needs to be deployed thoughtfully. This is the frontier of intelligent applications, where we build applications that are conversational and intimately know your business and your customers .

The Human Element in AI
Despite all this advanced technology, 2026 has reminded us that the human element is more important than ever. We are seeing a pushback against frustrating, impersonal AI experiences. In fact, Forrester predicts that a third of companies will actually harm their brand by deploying self-service AI chatbots poorly, eroding customer trust .

This tells us one thing: AI isn't a magic bullet. The goal isn't to remove people from the equation but to augment them. Your AI needs to be grounded in your specific domain knowledge and curated data to create a truly helpful experience, whether it is an advanced conversational persona for your customers or a powerful assistant for your employees .

It's interesting to note that the demand for new, human-centric roles is on the rise. We are seeing significant hiring for roles like AI agent operators and AI security and compliance professionals . The workforce is growing and reshaping, concentrating around new capabilities that didn't exist a few years ago. AI is creating demand, even as it changes the nature of existing jobs .

The Path Forward
So, where does that leave us? The path to success in AI is clear, but it requires discipline. It starts with a strong, well-governed data foundation. Then, you can move beyond simple chatbots to create purpose-built intelligent applications or implement Vertical AI that acts like a personal concierge for your customers. For your internal operations, focus on creating an augmented connected workforce that empowers your team with deep organizational knowledge.

The technology is powerful, but the real value comes from integrating it thoughtfully with your existing business processes. It's not about replacing human decision-making but about supporting it, making it faster, more consistent, and more intelligent.

The AI market is maturing, and the winners will be those who focus on operationalization, governance, and creating a clear ROI . The hype cycle is over. The era of high-performance AI has begun.

Ready to Start Your AI Journey?
Navigating the complex landscape of AI and machine learning can be tough. You don't have to do it alone. At McLean Forrester, we are passionate about leveraging the latest AI and ML technology to deliver real business value. Our knowledge of where to apply AI, what type to use, and how to integrate it with your specific business domains is the key to unlocking your competitive advantage.

Let's move beyond the hype and build a practical, powerful AI strategy for your organization. Explore our AI services to see how we can help you turn potential into performance.

Moving AI From Pilot Purgatory to Production Power

Mclean Forrester — Wed, 17 Jun 2026 15:42:21 +0000

Let’s be honest for a second. If you are a CEO or part of an executive team in 2026, the conversation about AI has probably become a source of low grade anxiety. It is the elephant in every boardroom. The pressure to “do something” with artificial intelligence is immense. Your investors are asking about it. Your competitors are talking about it. And your own internal teams are probably experimenting with it, often in silos that don’t line up with your strategic goals.

So the dialogue usually goes something like this. “We need to move on AI,” someone says, and the room nods in agreement. Then comes the pause. “Where do we even start?” The answer is rarely clear. And the most daunting question of all. “What is actually viable for our business?” Not what is cool. Not what is buzzy. But what will fundamentally move the needle.

This is the challenge we live with at McLean Forrester. We see it every single day. Executive teams are paralyzed not by a lack of ambition, but by a surplus of risk and uncertainty. They have read the white papers. They have attended the conferences. They have been pitched by a dozen vendors offering magic pills. The problem is not a lack of information. The problem is a lack of a disciplined, structured path that turns a nebulous idea into a concrete, measurable business outcome.

That is why we developed the AI Value Path. It is a framework designed to do one simple thing. Move leadership teams from the noise of exploration to the clarity of production. It is not another strategy deck that gathers dust on a shelf. It is an execution engine. We do not promise you the moon in a PowerPoint presentation. We promise you a working prototype, built on your data, within weeks. We promise you an evidence based “go” or “no-go” decision, so you can invest with confidence and stop burning cash on speculative bets.

This is the playbook for the AI empowered enterprise in 2026. Let us walk through it together.

Phase 1: Executive Alignment and Opportunity Prioritization: The “Where” and “Why”

The first phase is not about technology at all. It is about people and priorities. We have seen countless AI initiatives fail before they even begin because they were trying to solve a problem that did not actually matter, or because they solved a problem for one department while inadvertently creating one for another.

This phase is a two week forensic dive into your business. It is not a general survey. It is a series of deep, focused workshops with your executive team, but also with the people on the front lines. We want to talk to your operations manager who deals with supply chain headaches every day. We want to sit with your customer service director who knows exactly where the friction points are. We want to talk to your CFO to understand the financial levers that truly matter.

The goal is to build a ranked shortlist of high impact AI initiatives. We are not looking for clever algorithms. We are looking for business solutions. Could we use AI to optimize your pricing in real time based on demand elasticity, potentially increasing margins by a few percentage points? Could we create an intelligent agent that reduces the time your sales team spends on administrative data entry, freeing them up for more valuable client interactions? Could we build a predictive maintenance model that analyzes sensor data to prevent a costly machine breakdown in your manufacturing plant?

Each initiative is rigorously evaluated against a set of defined success criteria. But in 2026, these criteria are not just about technical feasibility. They are deeply integrated with your business objectives. We look at the potential return on investment, but we also look at the cost of inaction. We assess the data readiness. And critically, we examine the cultural and organizational readiness. Is your team prepared to adopt a new tool? What is the training burden?

By the end of these two weeks, you have a clear roadmap. You are not left with a dozen fuzzy ideas. You have a ranked shortlist and a single, selected prototype candidate. You know exactly where you are going to start and, most importantly, why. You have defined what success looks like in concrete terms, perhaps as a percentage increase in revenue, a percentage reduction in cost, or a specific improvement in customer retention.

Phase 2: Prototype Engineering and Validation: The “Show Me” Phase

This is where the magic happens, and it is where we separate ourselves from the consultants who just want to sell you a report. We do not just talk about what is possible. We build it. And we do it in a matter of weeks, not quarters. The pace of business in 2026 is relentless, and a nine month development cycle is a luxury few can afford. We operate in a rapid, iterative fashion.

The prototyping phase is not about crafting a perfect, polished product. It is about answering a single question with empirical evidence. Is this viable? We take the selected initiative and build a functional prototype. And here is the critical difference. We build it on your data. We are not using generic, sanitized public datasets to create a pretty demo. We are ingesting your real world, messy, unstructured, and often imperfect data to see if the model can actually deliver value in your specific context.

This process is full of real life bumps and discoveries. For instance, one of our clients, a global logistics provider, was convinced they needed a complex system to predict shipping delays. When we got into their data, we found that a simpler, more focused model, trained on weather patterns and port congestion data, was more accurate and infinitely more explainable than the elaborate solution they had originally envisioned. The prototype gave them a tool that their operations team could actually trust and use.

We do not just build the model and hand you a code file. We deliver a comprehensive validation report that measures both technical and business performance. We stress test the model. We look at its accuracy, but also its robustness and its potential for bias. We quantify the business impact in dollars and cents. We project what this prototype would mean if scaled to your entire operation. This is not an academic exercise. It is an economic exercise.

At the end of this phase, you have everything you need to make a clear “go” or “no-go” decision. You have seen the prototype in action. You have seen the numbers. You have a complete understanding of the technical debt required to move it to production, the ongoing maintenance costs, and the potential return. You are making a decision grounded in evidence, not in hype.

Phase 3: Production Deployment and Governance Integration: The “How”

If the prototype is greenlit, we do not just drop it over the wall to your IT team and say, “Good luck.” That is where many AI projects, even successful prototypes, die a quiet death. The transition from a lab environment to a secure, production grade system is fraught with peril. The data pipelines might break. The performance might degrade. The security vulnerabilities might become apparent.

The final phase of the AI Value Path is about engineering for scale, security, and governance. We work alongside your internal engineering, security, and compliance teams to ensure the solution is seamlessly integrated into your existing systems. In 2026, this is non-negotiable. Regulatory scrutiny is high, and consumer trust is paramount. You cannot afford a rogue AI.

We put operational controls in place. We build the monitoring dashboards that will tell you how the model is performing in the wild. We establish the feedback loops so the system can learn and improve over time. Crucially, we do not just hand you a solution and disappear. We transfer the knowledge. We conduct rigorous knowledge transfer sessions with your teams so they can own, maintain, and evolve the capability. We help you set up the governance frameworks to ensure the AI remains aligned with your values and your business strategy.

This is not about building a one off application. It is about building a sustainable, scalable capability. It is about moving from a project to a platform. We ensure your scalable requirements, such as the ability to handle peak loads or integrate with new data sources, are built into the architecture from day one.

The Bottom Line

In a world of speculative investments and open ended strategy retainers, the AI Value Path is different. We work in a disciplined, time boxed sprint. We build the prototype so you do not have to guess. And we give you the evidence to make a confident decision, whether that decision is to move full steam ahead, to pivot, or to pause.

For CEOs and executive teams, the hardest part is not deciding what to do. It is deciding when to start. The technology is ready. Your data is waiting. And your competitors are already moving. The question is no longer if you should act, but how you will ensure your actions are disciplined, effective, and measurable. The AI Value Path is how you move from discussion to disciplined execution.

The AI Reckoning: Why Most Companies Are Getting Left Behind

Mclean Forrester — Tue, 16 Jun 2026 15:49:19 +0000

The honeymoon is officially over. For the past few years, the business world has been captivated by the promise of artificial intelligence. We have all seen the headlines about generative AI, the frantic rush to adopt ChatGPT, and the billions of dollars flowing into infrastructure. It has been exciting, a bit chaotic, and honestly, a little bit like the Wild West.

But as we settle into 2026 and look towards 2027, a significant shift is happening. The hype is giving way to a harder, more practical reality. The question on every CEO and CFO's mind is no longer "What can AI do?" but rather, "What is AI actually doing for our bottom line?"

A recent whitepaper from McLean Forrester titled "Maximizing Return on AI Investment: Understanding the Value Curve of AI" captures this challenge perfectly. It outlines a critical framework that I believe will define the next phase of enterprise technology: understanding that AI is not a monolith. Its value is deeply tied to how you implement it. We are entering the era of the "frumpy, but functional" AI, where tangible outcomes are prized over flashy demonstrations.

The Pilot Trap and the 2026 Reality Check
Let's be blunt. 2026 is the year of the great AI reality check. Forrester research suggests that enterprises are expected to delay a quarter of their AI spending into 2027 because the value is just not landing. Only 15% of AI decision-makers report an EBITDA lift from their AI investments in the past year. That is a staggering number.

We have all seen the problem. It is the "pilot trap." Companies launch dozens of small proof-of-concept projects with shiny new tools, get a little bit of productivity gain, and then get stuck. They can't scale. They can't integrate. The value plateaus. This is precisely what the McLean Forrester paper describes with its concept of the value curve. For simple, low-risk tasks, a commercial LLM like ChatGPT works great. You get a quick 5-10% productivity boost, as Gartner suggests. But as soon as your task requires proprietary knowledge or complex reasoning, the returns from these horizontal tools flatten out quickly.

The Shift from Horizontal to Vertical: Where Real Value Lives
This realization is driving the most important trend of 2026: the move from horizontal tools to Vertical AI. Horizontal AI, like a general-purpose chatbot, is for everyone and, consequently, is not optimized for anyone. It is a jack of all trades, master of none.

The real game-changer is Vertical GenAI. This is the third and most powerful level of AI implementation that McLean Forrester highlights. It is not just a tool; it is a custom-built intelligence layer grounded in your organization's specific data, procedures, and workflows.

Think about a financial institution. A horizontal AI can summarize a public report. But a Vertical AI can be an underwriting decision-support agent, reading a loan application, checking for missing documents, and drafting a decision pack, all while staying within strict regulatory and risk guardrails. It moves from being a passive information tool to an active participant in your core business functions. This is where the exponential return on investment lies.

The Middle Ground: The Smart Money is on Hybrid AI
However, jumping straight to a full Vertical AI system is a significant undertaking. It demands data maturity, robust infrastructure, and a clear strategy. For many organizations in 2026, the smartest move is the pragmatic middle ground: Hybrid AI.

McLean Forrester identifies this as a Retrieval-Augmented Generation (RAG) approach. This is the Goldilocks zone of AI investment. You are not retraining a massive model from scratch, which is expensive and complex. Instead, you are connecting a powerful LLM to your own internal knowledge repositories, your policies, your customer data, your historical project documents.

This delivers on a key promise for 2026: data sovereignty. It gives you the accuracy and contextual awareness of a custom system without the prohibitive cost. It reduces the risk of hallucinations because the AI is forced to ground its answers in your proprietary facts. As the enterprise world grows more skeptical of overhyped promises, the ability to deploy a Hybrid AI that immediately improves a customer support team or a sales enablement process is a quick win that builds trust and paves the way for more ambitious projects.

Navigating the Capex Hangover and Governance Demands
This more cautious, value-driven approach is also being dictated by the macroeconomic environment. Big Tech has been spending at a decade-high rate on AI infrastructure, with capex projections for 2026 and 2027 reaching eye-watering levels. This is a massive bet, and investors are starting to ask for tangible returns on this unprecedented investment.

For the average enterprise, this translates into a stricter focus on ROI. CFOs are getting pulled into more AI deals, and finance-gated decisions will slow down the approval of projects without a clear path to profitability. This is a healthy correction. It forces leadership to think like McLean Forrester suggests: treat AI investment as a strategic business decision, not just a technology one.

Furthermore, the era of "move fast and break things" is over. In 2026, AI governance is not a nice-to-have; it is a business imperative. With regulations like the EU AI Act coming into force and a growing awareness of risks like bias and data privacy, we are seeing the rise of the Head of AI Governance in Fortune 100 companies. Any AI strategy that ignores this layer of risk management is simply not viable.

Looking Ahead: A Strategy for 2027 and Beyond
So, what is the strategy for success? The path forward is clear.

First, abandon the obsession with cost. The goal is no longer to find the cheapest, fastest general-purpose model. The goal is to find the solution that generates the highest return for your specific, most valuable business problems.

Second, invest in your knowledge infrastructure. Your proprietary data is your only sustainable competitive advantage in the age of AI. Codify it, structure it, and make it accessible. You cannot have a successful Vertical AI without a solid foundation of data readiness.

Third, adopt a phased and pragmatic roadmap. Start with a high-impact, lower-risk Hybrid AI use case. This will de-risk the technology, build internal expertise, and demonstrate concrete value to stakeholders. From there, you can scale into the more advanced, transformational Vertical AI systems.

The companies that will win in 2027 and beyond are not the ones with the most advanced models or the biggest budgets. They are the ones that understand the value curve of AI. They are the ones who recognize that moving from a horizontal chatbot to a deeply integrated vertical solution is not just a technical upgrade; it is a strategic transformation that aligns AI with the unique intricacies of their organization.

Partnering for Success
Navigating this complex journey from pilot to production requires more than just technical know-how. It demands a partner who understands the strategic, operational, and governance challenges involved. Firms like McLean Forrester specialize in guiding organizations through this progression, from foundational models to highly tailored, domain-specific AI capabilities. Their expertise in enterprise AI integration and strategic transformation can help you unlock the full spectrum of AI value.

The IKEA Blueprint: How SMBs Can Use the AI Transition to Grow Without Layoffs

Mclean Forrester — Mon, 15 Jun 2026 15:30:20 +0000

When IKEA introduced its AI assistant, Billie, the technology did exactly what it was supposed to do. It handled 47 percent of all inbound customer service calls automatically. That saved the company nearly 13 million euros.
For most business leaders, that number signals one thing. Headcount reductions. A recent study of chief financial officers found that 47 percent expect AI to significantly cut their workforce. Only 12 percent feel prepared to manage the shift.
But IKEA did something different. Instead of laying off 8,500 call center employees, the company looked at the other 53 percent of calls. These were the questions that Billie could not answer. Customers needed help designing a room. They wanted advice about taste, context, and personal judgment. The AI could not provide that.
So IKEA retrained those 8,500 employees to become remote interior design consultants. The same people, now doing work the AI could never do. The result was a new revenue stream worth 1.3 billion euros. A cost center became a profit center.
This is what it looks like when a company puts people first. And here is the good news for small and medium businesses. You do not need IKEA sized budgets to follow this example. You just need a better plan.
For a deeper look at how leadership culture drives successful technology adoption, you can explore the resources at Mclean Forrester.
Let us walk through a four phase strategy that any SMB can use to bring AI in without gutting the workforce.
Phase 1: Stop Using AI to Cut Headcount
The most common mistake is also the most damaging. Many leaders see AI as a simple replacement for human labor. They ask, "How many people can we let go?"
IKEA asked a different question. They asked, "What work have we been unable to do because our people are too busy answering simple calls?" That shift in thinking changed everything.
When you automate the low value tasks, you free up your team to find high value problems. The AI handles the volume. Your people handle the complexity. That is where growth lives.
Before you buy any AI tool, sit down with your team and map out the repetitive tasks that eat up their time. Do not ask who you can remove. Ask what you have been missing because everyone is too busy.
Phase 2: Find Your 53 Percent Gap
IKEA discovered that most of the calls the AI could not handle were actually design requests. Customers were asking for help. That help required empathy, taste, and real human judgment. None of that is replaceable by software.
You will find a similar gap in your own business. When you roll out AI, pay close attention to the tasks it cannot do well. Those exceptions are not failures. They are opportunities.
Maybe your AI handles basic customer questions but struggles with returns and exchanges. That tells you something. Your customers are confused about your return policy. Fix that, and you build trust. Maybe your AI handles scheduling but cannot handle last minute changes. That tells you to train your staff on crisis management and upselling.
The key is to see your AI tool as a sensor. It reveals where humans add the most value. Do not ignore that data. Build your retraining plan around it.
Phase 3: Reskill Instead of Replace
IKEA did not fire its call center agents. It turned them into designers. That sounds expensive, but for an SMB it is actually quite practical. You are not retraining thousands of people. You might be retraining five or ten.
Start by looking at adjacent skills. A customer service representative already knows your products. They know the common complaints. They know what confuses people. Teaching them to do basic consulting or sales is a small step, not a giant leap.
Use AI to help with the training itself. There are affordable tools that can coach your employees through role playing scenarios or help them learn technical skills faster. The same technology that scared you can become your best teacher.
More importantly, reskilling builds loyalty. When your employees see that you are investing in their future rather than cutting them loose, they work harder. They stay longer. They suggest improvements. That return on investment is hard to measure but impossible to ignore.
Phase 4: Build a Human Revenue Engine
Once your team is handling the complex work, you need to turn that work into revenue. IKEA started charging for remote design consultations. That one move generated over a billion euros.
You do not have to aim that high, but you should aim for something. Maybe your newly retrained support team can offer setup assistance for a small fee. Maybe they can do custom order coordination. Maybe they can handle premium troubleshooting for a subscription.
The point is this. Your people are now doing work that the AI cannot do. That work has value. Charge for it.
Also, remember that humans provide security. AI can be fooled. AI can leak data. AI can miss context. Your people are the firewall that keeps your customers safe. Highlight that. Your customers will pay for the peace of mind that comes from talking to a real person.
A Final Word for SMB Leaders
The fear around AI is real. Many business owners worry that they lack the digital skills or the budget to keep up. But the cost of doing nothing is even higher. Your competitors will automate. Your customers will expect faster service. Your best employees will leave if you trap them in repetitive work.
IKEA showed us a better way. Use AI to handle the volume. Use your people to handle the value. Save 13 million euros on one side. Earn 1.3 billion on the other. Keep your workforce intact and watch them grow.
You do not need to be a global giant to make this work. You just need to stop asking how many people you can remove. Start asking what valuable work your team has been unable to do because they were buried in tasks a machine should be doing.
That small shift in thinking is the difference between a company that uses AI to shrink and a company that uses AI to thrive.

You’re Overthinking AI. Here’s How to Actually Start.

Mclean Forrester — Thu, 11 Jun 2026 15:51:55 +0000

Let me guess.

You have finally accepted that AI is not just hype. Your board is asking about it. Your competitors are talking about it. And now you are sitting there, maybe with a half-empty coffee, staring at a whiteboard that still says “AI Strategy” in big letters.

The question keeping you up at night? Where do I start with AI?

I have been in that chair. So has every senior leader I know who is not just pretending to have it figured out.

Turns out, the answer is both simpler and harder than you think. Simpler because the first move costs less than a bad hire. Harder because it means ignoring the flashy demos and taking a long, honest look at your own messy data.

Let me save you the next 18 months of recovering from a failed pilot. Here is where you actually begin.

What You Are Really Asking

Here is something the McLean Forrester whitepaper nails. When you search “where do I start with AI for my business,” you are not really asking for a tool list. You are admitting three things without realizing it.

One, you know AI matters. Two, you have no idea what to actually do about it. And three, you are exhausted by articles that say “start with strategy” but never explain how.

Sound about right?

Most AI advice is written for people who already have a team of PhDs on staff. The rest of us? We have got spreadsheets, legacy systems, and that nagging feeling that someone in marketing is already feeding customer data into a free ChatGPT account.

Be Honest: Where Are You Really?

Let me give you a quick gut check from the paper.

Most organizations are sitting at Stage 1, what they call “ad-hoc literacy.” That means people across your company are using AI tools. Some are paying for them personally. Some are not. Productivity gains are happening, but nobody is measuring them. And yes, data is leaking into consumer tools without anyone tracking it.

Sound familiar? Do not feel bad. That is just reality.

The real mistake is trying to jump straight from this controlled chaos to a custom AI model that runs your whole supply chain. That is like learning to swim by jumping off a cruise ship. What you actually need first is Stage 2: an enterprise license with proper controls, basic training, and a simple acceptable-use policy.

Boring? Yep. Absolutely necessary? Also yep.

Here Is Where I Push Back a Little

The paper cites that 95% of generative AI pilots fail to deliver measurable value. And look, that number is worth paying attention to. But I think we are measuring the wrong thing.

Here is what actually happens.

Your marketing writer finishes a first draft in 30 minutes instead of two hours. Great, right? Except then she spends that “saved” time on three more rounds of revisions. The final output looks exactly the same as before. Your metrics show zero improvement.

Does that mean the AI failed? Not really. It means the workflow did not change.

And this is where the paper gets really good. It says organizations that redesigned their work around AI were nearly three times more likely to see real business value. That is the hidden variable. Not the fancy model. Not the perfect data architecture. Just the messy, human process of changing how things actually get done.

Two Places to Go That Will Actually Help

Instead of giving you more generic advice, let me point you to two specific spots on the McLean Forrester website.

First, go look at their homepage. Find the AI Learning Path, Tier 1. It launched June 10. This is not theory. It is built for exactly where you are right now. And here is a detail I love: the CEO, Heather McLean, spent 20 years in the Air Force. That means she cares about practical results, not buzzwords. If you are tired of consultants who have never run a real operation, that is your signal.

Second, go back to the full whitepaper itself. There is a self-assessment in there, Stage 0 through Stage 5. Print it out. Pass it around the leadership team. Be honest about where you actually sit. That single exercise will save you from the most expensive mistake out there: trying to build an advanced solution when you have not even finished the basics.

The Three Levels, Plain and Simple

Let me translate the paper’s technical framework into something you can explain over lunch.

Level 1 is commercial AI. Think ChatGPT Enterprise, Microsoft Copilot, that whole category. You are looking at $20 to $60 per seat monthly, plus maybe $50,000 to $200,000 to roll it out properly. It will not transform your business overnight. But it will teach your people what AI is actually good at and where it lies. Skip this level, and you will be the CEO who approves a $2 million project without really understanding it.

Level 2 grounds AI in your data. Think customer service bots that actually know your products or internal search that finds the right contract. This is where things get interesting. It is also where they get expensive, roughly $75,000 to $400,000 for a single pilot, and messy. The paper admits openly that “RAG sounds clean in a slide deck and is messy in practice.” I love that honesty.

Level 3 is proprietary AI. These are custom models built on your secret sauce. We are talking millions of dollars. Most of you should not even think about this for years.

Here is the pattern I see over and over. Companies at Stage 1 try to build Level 3. Or they buy Level 2 without doing Level 1 first. Or they invest millions in technology without redesigning a single workflow. Do not be that company.

What to Actually Do This Quarter

Enough theory. Here is my opinion, shaped by the paper but pushed into action.

First, authorize an enterprise AI license within the next 90 days. Not because it is transformational. Because your people are already using consumer tools, and the risk of data leakage is higher than the cost of the subscription. Pick one vendor. Deploy it. Train everyone. Write a one-page policy. Done.

Second, codify the knowledge in your top performers’ heads. Your best account manager knows how to triage complaints. Your senior engineer knows which suppliers to escalate. Most companies have never written this down. AI forces you to do it. And that work pays off forever, with or without the technology.

Third, start where your data is cleanest, not where the headlines are. For manufacturers and financial firms, that is the back office. For retail and media, it might be customer-facing personalization. Ignore the generic playbooks. Look at your own spreadsheets instead.

The Risks Nobody Warns You About

Everyone talks about hallucinations. And yes, those happen. But that is not the main risk anymore.

Data leakage is the real problem. Every time an employee pastes customer information into a free AI tool, you are gambling with your reputation. Vendor lock-in is another sleeper. The model you build on today might be gone in 18 months. Quality drift means systems that worked last quarter suddenly behave differently this quarter.

The paper puts it perfectly: “The risks that get publicized are not the risks that most often realize.” Stop worrying about Skynet. Start worrying about your data governance.

Here Is the Bottom Line

You do not need a perfect strategy. You need basic literacy this quarter, a targeted pilot next quarter, and a real commitment to redesigning workflows, not just buying software.

The McLean Forrester whitepaper is worth your time because it is written for someone with a budget, a board, and a burning desire to stop feeling behind. It will not give you easy answers. It will give you honest ones.

And honestly? That is where every successful AI journey starts. Not with a grand vision. But with the courage to admit you are at Stage 1, buy the enterprise license, and do the unglamorous work of documenting how your business actually runs.

The AI revolution is not coming. It is already here, probably in the personal accounts your employees are not telling you about. Your only real choice is whether you lead it or just clean up the mess afterward.

Start smart. Start now. Head over to McLean Forrester and begin.

From AI Pilot to Production: Why Your Data is the Real Obstacle

Mclean Forrester — Wed, 10 Jun 2026 15:30:36 +0000

We have all heard the promises. Artificial intelligence will revolutionize your workflows. It will unlock hidden insights. It will finally make your unstructured data useful.

Then reality hits.

You feed a folder of contracts, research papers, or internal memos into a model. What comes back looks confident. But is it right? Can you trace a single claim back to its original document? And what happens when two sources say completely different things?

That is the dirty secret of enterprise AI. The models are powerful, but they are not magic. They cannot fix broken data. They cannot audit themselves. And they definitely cannot explain why they made a particular decision.

One firm decided to stop talking about these problems and start solving them.

Building a Product That Ships, Not Just Demos

Heather McLean from McLean Forrester recently shared a real example of how her team moved from AI theory to AI practice. They built a product called UnicornIQ from start to finish.

Here is what it does. You point it at a folder of documents. It reads every single file. It extracts the factual claims inside each one. Then it checks every claim against everything it already knows. It flags conflicts. It consolidates duplicates. It scores what survives based on evidence.

Now imagine you have a new draft report or a fresh proposal. You can run it through the same system. UnicornIQ will tell you how well that new draft holds up against what the organization already knows to be true.

That is powerful. But the real genius is in the trust layer.

Every fact links back to its source document. Every change keeps a revision record. Every confidence score rolls up from evidence you can actually drill into. In a regulated environment like finance, healthcare, or legal services, that paper trail is not a nice to have. It is the whole product.

You can learn more about how that works on the UnicornIQ.

Two Levels of AI, One Small Team

What makes this story even better is how McLean Forrester built the product. They used AI at two levels.

First, AI is the engine inside UnicornIQ. It does the reading and the reasoning. That is the obvious part.

Second, they wove AI through their own development process. They used it to ground their planning in their own quality standards. They paired it with engineers on the daily work. They ran automated reviews before a human ever got involved.

That second part is rare. It is also why a small team can carry a platform this size without the quality slipping.

None of it is luck. It is the same lesson they tell every client. The model is not the product. The thinking layer around the model is the product. And whether a human can trust and audit the result is the entire point.

Why Most AI Pilots Never Ship

Let me be blunt. Most AI pilots fail because they skip the hard work. They assume clean data. They ignore conflicts. They treat source documents as optional.

Then comes the pilot review. The demo looks great. The team gets excited. But when someone asks “Can we trace this answer back to an original file?” the answer is usually no. Or worse, it is a handwavy “We are working on it.”

That is not production ready. That is a science project.

Real enterprise AI needs three things. It needs to handle your messy, real world data. It needs to give you auditable, verifiable outputs. And it needs to do all of this without a team of PhDs holding it together.

UnicornIQ was built to meet those three requirements from day one. It does not assume your data is clean. It assumes your data is a mess. Then it goes to work.

A Better Way Forward

If you are tired of AI pilots that never ship, there is a better path. You do not need a bigger budget or a fancier model. You need a different approach.

Start with the data problem. Build for auditability. Design for human trust. That is what McLean Forrester did with UnicornIQ. And it is what they would do for you.

You can see their broader approach to applied AI on the McLean Forrester.

The Bottom Line

Here is the truth. The difference between a firm that talks about AI and a firm that has shipped it is not hype. It is not a larger engineering team. It is a willingness to do the unglamorous work of verification, auditing, and conflict resolution.

UnicornIQ is proof that a small, focused team can build enterprise grade AI. But only if they keep their eyes on what actually matters. Not the model. Not the buzzwords. But whether a human can trust the result.

That is the standard you should hold any AI vendor to. And it is the standard McLean Forrester holds itself to every single day.

So go ahead. Ask the hard questions. Demand the paper trail. And if a vendor cannot show you exactly where their answer came from, walk away.

Because in the real world, confidence without evidence is just a guess. And guesses do not belong in your source of truth.

You Know You Need AI. You Just Don't Know Where to Punch.

Mclean Forrester — Tue, 09 Jun 2026 15:13:21 +0000

Let me guess. You have heard the word "AI" so many times in the last 18 months that it has started to sound like static.
ChatGPT this. Generative that. The robots are coming for your invoices.
You are a business owner. You have a team to pay, customers to chase, and probably a leaky faucet in the breakroom that no one has fixed. You do not have time to become a computer scientist. But you also aren't stupid. You see your competitors getting faster. You see the headlines. And there is a little voice in the back of your head that says, "If I don't figure this out, I am going to be the last person at the party."
That is a stressful place to be.
Here is the truth. Most small business leaders are stuck right where you are. They believe AI will matter. They want to start. They just don't know where to punch. What tool? Which process? How do you use it well enough to get real results, not just a cool party trick?
You need a map. Not a theory. Not a hype speech. A map. And you can find one at the AI ROI for Small Business Workshop on June 10.
The Problem Isn't Willingness. It's Window Shopping.
Right now, you are probably window shopping. You open a free account on an AI tool. You type a few funny prompts. You ask it to write a poem about your cat. And then you close the laptop and think, "Cool, but how does that help me close a sale next Tuesday?"
That is the gap. The gap between "playing with a toy" and "getting a return on investment."
You cannot browse your way into productivity. You need a framework. You need to know exactly which three tasks in your business are screaming to be automated. You need to know how to write a prompt that doesn't produce garbage. And you need to know how to get your team to actually use this stuff without them thinking you are trying to replace them.
Because here is what usually happens. A business owner gets excited. They buy a subscription. They send a company wide email that says, "Everyone use AI!" And then… nothing. It dies on a shelf. It becomes another abandoned software license.
That is expensive. Not just the money. The momentum.
Where the Actual ROI Lives
Let me save you some time. The ROI for small business AI is not in the flashy stuff. It is not in generating weird art or writing philosophical essays. It is in the boring, repetitive, soul crushing work that you and your team hate doing.
Drafting the sixth version of a proposal. Summarizing a two hour client call into bullet points. Writing the first draft of that social media post you have been avoiding. Cleaning up messy data in a spreadsheet.
These are not big problems. They are death by a thousand paper cuts. And AI is very, very good at paper cuts.
A focused four hour session can show you exactly where to cut. You do not need a week long seminar. You do not need a consultant to bill you for six months. You need someone who has actually done this. Someone like Larry McLean at McLean Forrester, who can walk you through a real business, not a Silicon Valley fantasy, and say, "Here is where you put the lever. Here is how much time it saves. Here is the exact button to push."
The Three Questions You Actually Need Answered
You do not need a glossary of terms. You do not need a history of neural networks. You need answers to three specific questions.
First, how does this actually apply to my specific business? A retail shop uses AI differently than a law firm. A construction company uses it differently than a marketing agency. The general advice is useless. You need the specific.
Second, where would I even start tomorrow morning? Not next month. Not after a big software overhaul. Tomorrow. What is the single highest leverage activity you do every week that AI could handle in 90 seconds? Find that, and you build confidence.
Third, how do I use it well enough to get real results? Most people are terrible at talking to AI. They type two words and get a bad answer. They think the tool is dumb. The tool is not dumb. The operator just doesn't know the syntax. There is a right way and a wrong way. The right way saves you hours. The wrong way wastes your time.
And the bonus question. How do I bring my team along without it turning into a shelf project? This is where most leaders fall down. You cannot just announce change. You have to invite them in. Show them how AI handles the tasks they hate. Make them the hero. If you force it, they will resist. If you free them from the boring stuff, they will thank you.
The workshop on June 10 is built to answer every single one of these questions. No fluff. Just a straight shot to getting your time back.
Why a Workshop Beats Another YouTube Deep Dive
You have already watched the YouTube videos. You have read the LinkedIn posts. You have saved the threads. And you are still stuck.
Because information is not implementation.
A live workshop is different. It is four focused hours with a human who has walked this path. You get to ask your dumb questions. (They are not dumb. Every other person in the room has the same ones.) You get to see the actual keystrokes. You get a plan that fits your actual business, not a template from a tech blogger who has never run a payroll.
You also get accountability. When you sign up, you are making a decision. Not "someday." Not "when things calm down." Things never calm down. You are deciding that June 10 is the day you stop window shopping and start using.
The facilitator, Larry McLean, brings over 40 years of experience. He has led digital transformation for the U.S. military and teaches AI strategy at Washington University. But more importantly, he works with small businesses every day through McLean Forrester. He knows your constraints. He knows your budget. He knows you don't have a data science team.
Your Move
Look. You already believe AI will matter. That belief is not the problem. The gap is the how to.
You can spend another six months reading headlines and feeling behind. Or you can spend four hours on June 10 getting a map.
The tools are not waiting for you. Your competitors are not waiting for you. The faucet in the breakroom will probably still be leaking. But at least you will finally know where to punch.
Register for the AI ROI for Small Business Workshop on June 10. Come with your real business problems. Leave with a real plan. No hype. Just results. And if you have questions before you sign up, reach out to the team at McLean Forrester. They would rather have a quick conversation than have you register and not get value from it.

Ditch the Digital Rust, Embrace the Intelligent Core

Mclean Forrester — Mon, 08 Jun 2026 15:50:07 +0000

Remember the old days. Not the 1990s, but just a few years back. In 2023 and 2024, everyone was talking about cloud migration as if it were the finish line. You moved your servers. You felt proud. Then you realized something. Your applications still felt clunky. They were just expensive, clunky problems now living in a shiny data center.

That world is over. We have entered a new phase. Welcome to 2026. The question is no longer if you should modernize. The question is how fast you can evolve from fragile legacy systems into what we call the Intelligent Core. This is a living, learning, and autonomous heart of your business.

So what does real modernization look like this year and beyond? It is not just a lift and shift. It is a fundamental rethink. And companies like McLean Forrester have been quietly building the roadmap for this exact moment. Their approach is worth a second look, but through a 2026 lens.

First, let us talk about the biggest shift. Speed is no longer just an advantage. It is survival. But the speed we need today is not about writing more code faster. That is a trap. The real speed is in decision making. Can your portfolio tell you, in real time, which applications are draining your budget and which are fueling growth? Can it adapt to a sudden supply chain shift without a six month rewrite?

This is where the old methods fail. The old rationalization process took months. You would hire consultants. They would spreadsheets. They would argue about "business value" for weeks. By the time they finished, the market had changed twice.

That is ancient history. The modern standard, the one outlined in forward thinking services like Application Rationalization 360, uses live data and AI driven analysis. It is a holistic snapshot. But let me explain what "holistic" actually means in 2026. It means your applications, your data pipelines, your security protocols, and even your user behavior streams are analyzed together. Not in separate silos. Together.

Imagine a tool that does not ask you what you think your systems do. It watches what they actually do. It maps every data connection, every hidden dependency, that one old payroll script that nobody remembers but somehow the entire CRM relies on. That is the real value. You stop guessing. You start knowing.

We call this Rapid Portfolio Discovery. And the best teams are now doing this in days, not months. They use intelligent agents that scan your environment 24/7. They find the rot. They find the gold. Then they present a clear picture.

Now, let me be real with you. Finding the problems is the easy part. The hard part is deciding what to do. Do you rewrite everything? Do you buy a new SaaS product? Do you just leave that one weird inventory app alone because it technically still works?

This is the moment where most modernization efforts die. They get stuck in analysis paralysis. Or worse, they try to boil the ocean. They attempt a "big bang" rewrite that costs millions and fails spectacularly.

The smarter move in 2026 is a ruthless, dynamic rationalization. You need a Customized Rationalization Roadmap that treats your portfolio like a living ecosystem. Some applications get retired immediately. They are the digital weeds. Some get retained but isolated. They are the old oak trees that are too rooted to move, so you build a safe path around them. And some get transformed.

That transformation is where the magic happens. But again, the methods have changed. You have heard of refactoring. Everyone has. But the old way was manual. It was expensive. It was slow. A developer would spend weeks untangling a single spaghetti code module.

No more. We are now in the age of agentic refactoring. This uses collaborative AI tools that do not just suggest changes. They execute them under your supervision. They can refactor, rebuild, or even rearchitect entire systems 40 to 60 percent faster than just two years ago. That is not a small improvement. That is a paradigm shift.

What does that mean for your team? It means your best developers stop being maintenance janitors. They start being architects of the future. They focus on new features, on customer experience, on the moonshot ideas. The AI handles the tedious work of breaking up that monolithic legacy app into modern, cloud native microservices.

And the benefits? They compound. Security improves because you are not patching a 15 year old framework. User experience improves because your app does not lag anymore. But the biggest win is agility. You can release updates weekly, even daily. Your time to market shrinks from months to hours.

So what comes after 2026? Let me offer a prediction. We are moving toward the autonomous application portfolio. A system that self heals. A system that automatically scales resources up and down based on real demand. A system that even suggests its own refactoring opportunities before they become urgent problems.

The companies that thrive in 2027 and 2028 are not the ones with the most code. They are the ones with the cleanest, smartest, and most adaptable application landscape. They are the ones who stopped treating modernization as a project and started treating it as a continuous discipline.

Do not let your legacy systems hold you back another quarter. The cost of waiting is higher than the cost of changing. Every day you delay, your technical debt grows. Every day you delay, your competitors get leaner.

Take a fresh look at your portfolio. Ask yourself the hard question. Is your application stack helping you lead, or is it quietly holding you back? The tools and the expertise exist right now. The only missing piece is the decision to start. Make that decision today. Your future Intelligent Core is waiting.

Stop Chasing Shiny Objects. Start Integrating Emerging Tech Like You Mean It.

Mclean Forrester — Thu, 04 Jun 2026 15:53:45 +0000

Let me be honest with you.

Most "emerging technology" talk is just noise.

Someone says AI. Someone else says IoT. Then a third person throws in automation. And suddenly everyone feels behind. Everyone feels like they need to buy something new.

But here's my opinion. The problem isn't a lack of new technology. The problem is that most companies have no idea how to actually integrate it.

You don't need another pilot project that dies in six months. You need a real strategy. You need to weave these new tools into the bones of your existing IT infrastructure without breaking everything in the process. That's what we actually mean by Emerging Technology Integration.

And if you're not thinking about this right now? You're falling behind. Fast.

The Future Proof Myth

Everyone promises to future proof your business. It's a great marketing line. I get it.

But here's what nobody tells you. You can't predict the future. No one can. What you can do is build a system that bends without breaking. One that adapts when the next big wave hits.

That's the only real insurance policy in tech. Not prediction. Adaptability.

So when we talk about future proofing your business , we mean something very specific. We mean giving you the flexibility to absorb whatever comes next. Not a crystal ball. A shock absorber.

Why I Actually Trust McLean Forrester (And You Should Too)

Full disclosure. I don't write nice things about just anyone.

But this team does something different. They don't chase trends. They don't slap an AI label on an old product and call it innovation.

Here's what I've seen them do.

They live and breathe the latest technology.
Not in a performative way. They research. They experiment. They break things on purpose so they know how to fix them. Then they bring only the most advanced, battle tested solutions to their clients. You don't have to be a tech expert. You just have to show up. They handle the rest. Check out their technology solutions page to see what I mean.

They actually understand AI and Machine Learning.
This is rare. Most consultants use "AI" as a buzzword to sound smart. McLean Forrester uses it to deliver value. We're talking generative AI that writes, creates, and predicts. Machine learning that gets sharper every single day. Their work in AI driven automation and predictive analytics isn't theoretical. It's running in real businesses right now. And it's making decisions that actually matter.

Their Advanced Decision Making tool is a cheat code.
Okay, let me get opinionated again.

Most decision making software is fine for small stuff. What color should the button be? Which email subject line works better?

But for large, complex, operational decisions? The kind where accuracy, transparency, and reliability are absolutely non negotiable? Most tools fall apart.

McLean Forrester built something different. An advanced reasoning solution that delivers 100% accuracy. I don't throw that number around lightly. It's a genuine game changer. If you're making high stakes calls, you need to look at their advanced decision making platform.

They build capabilities fast. Really fast.
Another opinion. Most agencies take way too long.

They disappear for months. They build something in a dark room. Then they emerge with a solution nobody asked for.

McLean Forrester does the opposite. They deliver value as early as humanly possible. Then they listen. They use client feedback loops and co creation sessions to iterate like crazy. The result? Solutions that actually work because you helped design them. Read about their rapid capability development process.

Robotic Process Automation that doesn't suck.
Let me tell you what RPA should be.

It should handle the boring, repetitive garbage that eats your team's soul. Data entry. Form processing. Report generation. The stuff that makes smart people feel like robots.

McLean Forrester's RPA solutions do exactly that. They streamline tasks. They improve efficiency. They reduce human error. And yes, they cut operational costs.

But here's the real win. Your team gets to focus on high value, human centric work. The creative stuff. The strategic stuff. The stuff that actually grows your business. That's the whole point of robotic process automation . Not replacing people. Freeing them.

Intelligent Applications people actually want to use.

This is my favorite part.

Most customer facing AI is terrible. It's clunky. It's repetitive. It feels like talking to a toaster.

McLean Forrester takes a completely different approach. They build intelligent applications on top of your domain knowledge and your curated, grounded data. Not generic training data from the open internet. Your actual, reliable, trustworthy information.

The result? An interactive, conversational application your customers will genuinely be excited about. Something that helps them. Something that doesn't make them want to throw their phone across the room.

That's the future of customer experience. And you can see examples on their intelligent applications page .

My Final Take

Here's the truth.

Emerging technology isn't going to save you by itself. Neither is a bigger budget or a fancier title.

What saves you is smart, strategic integration. The boring, difficult, absolutely essential work of making new things work with old things. Of building systems that scale. Of making decisions with confidence instead of guesswork.

McLean Forrester does that work. They don't sell hype. They sell solutions that actually run.

So stop chasing shiny objects. Stop buying tools nobody knows how to use. And start integrating like you mean it.

Get started with McLean Forrester today . Your future proof business is waiting.

Stop Wasting Time on AI Fluff. You Need a Real Path.

Mclean Forrester — Wed, 03 Jun 2026 15:43:36 +0000

I have watched smart business leaders waste months on AI training that delivers nothing. They attend free webinars. They scroll through LinkedIn AI influencers. They buy books that are outdated before they ship.

And still. Still they cannot answer the basic questions. What do I do on Monday? Where do I invest first? How do I separate real capability from shiny demos?

The problem is not you. The problem is the training market. It is broken into two useless extremes.

The Two Broken Buckets
Let me name them clearly.

Bucket one: Free community content. You get inspiration. You get statistics. You get a warm feeling that AI is important. You do not get frameworks. You do not get hands on practice. You do not get anything you can execute when your feet hit the floor on Monday morning.

This bucket is not training. It is entertainment. It makes you feel informed without making you capable.

Bucket two: Enterprise programs. These cost thousands. They assume you have a Chief AI Officer. They assume a seven figure data budget. They assume your organization runs like a Fortune 500 tech giant.

If you are a founder, a mid market executive, or a growth leader, these programs are not for you. They were built for someone else. Someone with deeper pockets and larger teams.

So where does that leave you? Stuck in the messy middle. Too advanced for beginner fluff. Not ready for enterprise excess.

You need a third option. You need a learning path built for real businesses making real decisions this quarter.

What Real AI Learning Looks Like
I believe most AI training gets the fundamentals wrong. It teaches you what AI is. It does not teach you how to lead with AI. There is a difference.

Real AI learning starts with vocabulary and frameworks. You cannot lead what you cannot name. You cannot execute without a mental model. The first step is building the language of AI. Types of AI. The AI value curve. Agentic AI. RPA. Not as buzzwords. As tools in your decision making toolkit.

But vocabulary alone is empty. You need hands on practice. Real reps with real tools. You need to actually prompt. Actually build. Actually fail and iterate in a safe environment. This is how learning sticks. Not through slides. Through doing.

And then you need strategy. The ability to look at your specific business, your specific pain points, your specific opportunities, and build a plan that works. This is the step most training skips entirely. They teach you the what. They never teach you the how for your unique context.

The Human Side No One Talks About
Here is my honest opinion. The technology is the easy part. The hard part is the people.

Most AI initiatives stall because leaders ignore change management. They buy the tool. They train the skills. Then they wonder why no one uses the new capability.

Real AI leadership requires frameworks for adoption. Kotter’s 8 Stages. The Gleicher Formula. These are not academic exercises. They are practical tools for moving human beings through change.

If you are not managing the people side, you are not managing AI. You are just collecting software.

Why Small Cohorts Matter
I am skeptical of massive online courses. Thousands of students. No interaction. No feedback. No accountability.

Real learning happens in small groups. Capped at twenty seats. Live sessions with a real facilitator. Real Q&A. Real attention to your specific situation.

This is not scalable in the Silicon Valley sense. That is the point. Deep capability is not built through mass production. It is built through focused, human centered instruction.

You need someone who can look at your business and say, "Here is where you should start. Here is what to ignore. Here is the mistake you are about to make."

That requires a live expert. Not a recorded video.

The Facilitator Makes the Difference
I would not trust my AI learning to someone who has only studied AI. I want someone who has done AI. Who has led transformations. Who has sat in the hard chair and made decisions with real consequences.

A professor who teaches graduate courses in IT strategy and digital transformation. A leader with forty years across commercial and public sectors. Someone who directed IT services for a U.S. Combatant Command. Who led an Enterprise Data Management Office. Who served as a deployed squadron commander following 9/11.

This is not a trainer. This is a practitioner. And that distinction matters enormously when you are trying to separate real capability from vendor hype.

What You Should Actually Expect
Let me tell you what a real AI learning path should deliver.

You should walk away with an actionable AI strategy tailored to your business. Not a template. Not a generic plan. A specific strategy for your specific context.

You should get real hands on practice with the tools. Not a demo. Not a walkthrough. Your own hands on the keyboard, building something real.

You should receive frameworks for identifying opportunities and selecting investments. A clear process for saying yes to the right projects and no to the wrong ones.

And you should understand execution risk. The real reasons AI initiatives fail. The change management required. The organizational dynamics you cannot ignore.

If a course does not promise these outcomes, walk away. You are wasting your time and money.

The Opportunity Cost of Doing Nothing
Here is my direct opinion. The biggest risk is not choosing the wrong AI training. The biggest risk is choosing no training at all.

Every week you delay is a week your competitors get ahead. Not because they are smarter. Because they started. Because they built capability while you waited for perfect information.

Perfect information does not exist. The AI landscape moves too fast. You learn by doing. You learn by trying. You learn by building and iterating.

Waiting is a decision. It is the decision to fall behind.

Your Move
You have a choice. Stay stuck in the messy middle. Keep attending free webinars that leave you unequipped. Keep telling yourself you will get to AI next quarter.

Or take a different path. A path built for real leaders. Live online. Small cohorts. Hands on practice. Frameworks you can use on Monday morning.

The first step is simple. Visit the AI Learning Path page. Read the details. See if it fits your situation.

But do not wait forever. Do not let perfect be the enemy of started. Your journey from AI curious to AI capable begins when you decide that fluff is no longer acceptable.

Decide today.

Autonomy at Scale: Why Agentic AI Changes Everything

Mclean Forrester — Tue, 02 Jun 2026 15:40:35 +0000

Let's be real. Chatbots that just talk back to you are old news.

If you've been following tech at all in 2026, you've probably heard the term "Agentic AI." Here's the simple truth. Generative AI, think ChatGPT, is like that really smart friend who can write you a poem or summarize a meeting. Agentic AI is the one who actually does your chores. (If you want a deeper dive into what Agentic AI actually is and how it works under the hood, check out this breakdown here.)

And that's the big shift happening right now. We aren't just asking AI questions anymore. We're assigning it tasks. So what does that actually look like? I'll show you, no buzzwords.

The Big Shift: From Answering to Doing Here's what really changed in 2026. Autonomy. A standard chatbot waits for your prompt, spits out an answer, and stops. Memory of a goldfish.

Agentic AI works differently. You give it a high-level goal, something like "Research the top three project management tools for my team and make a comparison spreadsheet," and it just goes off to work. It doesn't just list ideas. It opens a browser, searches for reviews, compares prices, maybe even scans your team's past Slack messages to see what everyone complained about. Then it writes the report. That's the whole trick. It can actually reason, remember, and grab stuff from other tools along the way.

The Three-Layer Cake of 2026 AI Here's something most people miss. Companies aren't using one AI. They're using three, stacked together.

Predictive AI (The Fortune Teller): This one guesses what will happen. Banks use it to spot fraud. Retailers use it to figure out how many jackets they'll sell next week.

Generative AI (The Creator): This is your text and image generator. It writes the draft email or draws the logo.

Agentic AI (The Executor): This is the new kid on the block. It actually runs the workflow.

Take sales teams in 2026. Here's how it actually plays out. Predictive AI scores which customers are likely to buy. Generative AI writes a personalized email for each of them. Then Agentic AI hits send, checks who opened it, schedules follow-ups, and updates the CRM. All without you lifting a finger.

What's Actually Happening in the Real World Right Now We're finally past the pilot phase. Last I checked, almost 60% of professionals have agents actually running in production. Here's where they're showing up.

Software Engineering (the biggest win): We aren't just using Copilot to autocomplete a line anymore. Agentic systems now handle what's called "incident response." If a server crashes, an agent can inspect the logs, identify the bug, write a potential fix, and open a pull request for a human to review.

Healthcare: Forget typing notes. Agents now handle "prior authorization" – you know, that nightmare of paperwork before you can actually get a procedure done. The agent gathers records, fills out the forms, and even faxes (yes, fax) the insurance company. Then it tracks the request until it's approved.

Manufacturing: In factories, AI agents analyze vibration data from machines. If something sounds weird, it doesn't just alert a human. It predicts the failure, orders the spare part, and reschedules the maintenance crew. Problem solved before anyone even knew there was one.

The "How" Is Harder Than It Looks So if it's that good, why isn't everywhere using it yet?

Simple answer. Building one that actually works is a nightmare. There's a massive gap right now. Tons of companies want this stuff, but only a few have the right architecture to pull it off. Here's what separates a slick demo from the real deal in 2026.

The Loop: Agents work on what's called a "ReAct" loop, Reason plus Act. They think, "I need data X," then use a tool to get it, observe the result, and think again. The tricky part? Making sure they don't get stuck buying a thousand rolls of toilet paper on Amazon. (That actually happened.)

Tool Design: You can't just give an agent a button that says "Search." You have to give it guardrails. For example, "Use web search, but don't use it if the info is already in the document I just gave you." This saves money and prevents obvious stupidity.

Memory: Agents need three types of memory: short-term (what just happened), long-term (vector databases), and episodic (remembering they messed up last time). Most beginners forget episodic memory, which is why their agents make the same mistakes over and over again.

The Reality Check: Risks and Vibes Look, it's not perfect. Executives are drooling, but the people doing the actual work? Way more cautious. There are real risks in 2026.

Cascading Errors: If an agent makes a small wrong decision at step one, it can ruin steps two, three, and four before you even notice. It's a "garbage in, garbage out" nightmare on steroids.

The Permission Problem: How much access do you give an AI? If you give it access to your email, Slack, and financials, and it gets confused, you've got a data leak on your hands. Which is why you basically have to assume nothing can be trusted.

Cost: It turns out that thinking costs tokens, and tokens cost money. Running a deep-research agent for an hour can burn through API credits faster than you'd believe.

The Bottom Line
Look, 2026 is when stuff finally started getting done. Generative AI wrote the headline. Agentic AI is doing the work.

We're moving from "Software as a Service" to software that works for you. The technology is moving faster than our ability to govern it, but the direction is pretty clear. In the next 18 months, you won't be navigating menus on a screen just to file an expense report. You'll just text an agent, "File my receipts and tell me if I went over budget." And it'll just happen.

That's what's different this time. It doesn't just know things. It does them.

Getting Agentic AI right takes more than just hooking up an API. If you're thinking about bringing this into your organization, visit McLean Forrester to see how they help businesses go from pilot to production without the headaches.