DEV Community: Mclean Forrester

From Chaos to Clarity: A Structured Approach to AI That Actually Delivers

Mclean Forrester — Thu, 02 Jul 2026 15:32:58 +0000

A leadership team had been wrestling with artificial intelligence for over a year. They had launched three separate pilots. They had hired a data science consultant. They had attended the conferences and read the analyst reports. But when I asked what had actually moved the needle on their business performance, the silence in the room spoke volumes. They were burning fuel in every direction with no clear vector. All thrust and no vector. And the answer was not to buy more AI tools.

The Real Gap in AI Adoption

This scenario is becoming the norm. Organizations feel the pressure to act. Competitors are deploying AI. Boards are demanding a strategy. Investors are asking tough questions about AI readiness. So companies start moving, often without a clear destination, and they end up with a collection of disconnected experiments that never scale to production. The failure rates are sobering. Research suggests that the majority of generative AI projects never make it out of the pilot phase. That is not because the technology does not work. It is because the conditions for success were never put in place.

Most organizations are trying to layer AI on top of foundations that simply cannot support it. Their data is scattered and inconsistent. Their application portfolios have grown organically over decades, creating technical debt that slows everything down. Their teams are spending too much time maintaining legacy systems and not enough time on innovation. When you point an AI model at that kind of environment, you do not get transformation. You get a faster way to surface existing problems.

The Discipline of Starting with “Not Yet”

The rule Heather McLean brings to every engagement is simple. The team does not move into build mode until the vision genuinely becomes theirs. When they can see what the client sees, they attack the solution with real focus, driving outcomes in customers’ hands rather than chasing outputs. And sometimes that means telling a client “not yet” before they ever write a line of code.

This is exactly what the AI Value Path from McLean Forrester was designed to do. It is a structured, low-risk path from AI exploration to full-scale deployment, organized across three phases with measurable outcomes at every step.

Phase One: Executive Alignment and Opportunity Prioritization

The first phase is not about technology. It is about people and priorities. For two weeks, the team conducts a forensic dive into your business. They sit with your executive team, but also with the people on the front lines who deal with operational headaches every day. They talk to your operations manager who understands where the supply chain friction points are. They sit with your customer service director who knows exactly where customers get frustrated. They work with your CFO to understand which financial levers truly matter.

The goal is to build a ranked shortlist of high-impact AI initiatives. The team is not looking for clever algorithms. They are looking for business solutions. Could AI optimize pricing in real time and potentially increase margins? Could an intelligent agent reduce the time your sales team spends on administrative tasks, freeing them up for more valuable client interactions? Could a predictive maintenance model prevent costly machine breakdowns?

Each initiative is rigorously evaluated against defined success criteria. The team looks at potential return on investment, cost of inaction, data readiness, and critically, 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 know exactly where you are going to start and why. You have defined what success looks like in concrete terms.

Phase Two: Prototype Engineering and Validation

This is where McLean Forrester separates itself from consultants who just want to sell you a report. They do not just talk about what is possible. They build it. And they do it in weeks, not quarters.

The prototyping phase is about answering a single question with empirical evidence. Is this viable? The team takes the selected initiative and builds a functional prototype. The critical difference is that they build it on your actual data. They are not using generic, sanitized public datasets to create a pretty demo. They are ingesting your real world, messy, and often imperfect data to see if the model can actually deliver value in your specific context.

This process is full of discoveries. For one logistics client who was convinced they needed a complex system to predict shipping delays, the team 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 team does not just build the model and hand you a code file. They deliver a comprehensive validation report that measures both technical and business performance. They quantify the business impact in dollars and cents. They project what this prototype would mean if scaled to your entire operation. At the end of this phase, you have everything you need to make a clear go or no-go decision, grounded in evidence, not hype.

Phase Three: Production Deployment and Governance Integration

Building a prototype that works on a laptop is one thing. Deploying a secure, reliable system across an entire enterprise is a completely different challenge. Many AI projects die in the transition from successful pilot to full-scale production.

The final phase is about engineering for scale, security, and governance. The team works alongside your internal engineering, security, and compliance teams to ensure the solution is seamlessly integrated into your existing systems. In today’s environment, this is non-negotiable. Regulatory scrutiny is high, and consumer trust is paramount. You cannot afford a rogue AI.

The team puts operational controls in place. They build the monitoring dashboards that will tell you how the model is performing in the wild. They establish feedback loops so the system can learn and improve over time. Most importantly, they do not just hand you a solution and disappear. They transfer the knowledge through rigorous knowledge transfer sessions so your teams can own, maintain, and evolve the capability.

The Learning Path: Building Internal Capability

Beyond the AI Value Path, organizations serious about AI adoption need to build internal capability. The AI Learning Path from McLean Forrester is designed to take business principals from basic AI literacy to an executable, AI-enabled strategy.

The program is delivered across three tiers. Foundations covers core concepts and gets you hands-on with prompt engineering. Application moves from literacy to action, helping you build an actual AI-powered workflow during the session itself. Strategy walks you through a modern strategy framework, helping you build a portfolio of ranked AI projects with value propositions and ROI analysis.

The businesses winning with AI are not necessarily the biggest ones. They are the ones led by people who decided to get capable instead of staying curious.

The Bottom Line

In a world of speculative investments and open-ended strategy retainers, a disciplined path is essential. McLean Forrester works in time-boxed sprints. They build the prototype so you do not have to guess. And they 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.

Sometimes, the most valuable thing we can do is help a client stop chasing every direction at once and focus on a clear vector forward. That is the difference between burning fuel and making progress. That is the difference between activity and achievement. And that is exactly what McLean Forrester delivers for every client, every engagement, every time.

The AI Imperative: A Pragmatic Framework for Getting Started

Mclean Forrester — Wed, 01 Jul 2026 15:42:28 +0000

As Heather McLean of Forrester aptly notes, this question is the modern rallying cry of the small business owner. It signals a crucial awareness: AI is not a futuristic novelty, but a present-day competitive lever. However, this recognition often collides with the paralyzing fear of misstep, especially when resources are scarce and the margin for error is thin.

The answer, counterintuitively, is not to dive headfirst into strategy. Strategic planning, while vital, can become a trap, a quagmire of analysis that stalls momentum before it begins. The intellectual starting point for any AI strategy for small business is not a grand blueprint, but a clear-eyed assessment of your current reality. For a deeper understanding of this foundational step, you can explore the AI Value Path framework offered by Heather’s firm.

Step 1: Diagnose Your Organizational AI Maturity

Before you can chart a course, you must know your coordinates. Heather introduces a powerful diagnostic tool: the AI Maturity Model. This is not a vanity metric, but a strategic compass. It allows you to benchmark your organization against a clear spectrum:

Level 0 – The Analog Holdout: No AI use is present. Operations are entirely manual or rely on traditional software without any AI components.

Level 1 – The Ad-Hoc Experimenter: AI use is inconsistent and driven by individuals. Employees might use a public tool like ChatGPT for brainstorming or drafting emails, but there is no formal policy or coordination.

Level 2 – The Structured Adopter: Your business has invested in enterprise licenses for commercial large language models (LLMs). More importantly, you have established governance, training, and usage policies.

Level 3 – The Data Integrator: You have deployed at least one production AI system that is integrated with your proprietary business data. This is where AI starts creating defensible value.

Level 4 – The Process Re-Engineer: You have multiple production AI systems, and their adoption has led to a fundamental redesign of business processes, not just a digital facelift.

Level 5 – The AI-Native Differentiator: AI is not an add-on; it is a core component of your value proposition. Domain-specific AI models are used to create a distinct competitive advantage.

This diagnosis accomplishes two things. First, it demystifies the path forward by providing a tangible goal for the next step. Second, it instills a sense of progress. Moving from Level 0 to Level 1 is a significant and defensible victory.

Step 2: Shift from “Where” to “Why” – Identify Pain Points

Once you know where you are (Maturity Level), the question transforms from “Where do I start?” to “What is hurting me most?”

Strategy, in this context, is about deliberate focus. Avoid the temptation to look for generic “use cases.” Instead, conduct a systematic audit of your daily operations. Ask your team and yourself:

What recurring tasks consume an inordinate amount of time?

Where do bottlenecks occur in our workflow?

What manual processes are most prone to human error?

Where are we burning resources on low-value, repetitive work?

These pain points are your most fertile ground for AI adoption. They represent immediate, tangible problems that AI is uniquely equipped to solve. By starting with a problem, rather than a technology, you ensure that your investment directly drives efficiency and reduces friction. To further refine your approach to identifying these opportunities, you can review the structured guidance available on the main site.

Step 3: Evaluate Opportunities Through a Dual Lens of Effort and Value

With a clear list of pain points, you now have a roster of potential “opportunities” to apply AI. The final step is prioritization.

This is where intellectual rigor meets practical execution. For each use case, you must conduct a rapid, honest assessment against two critical dimensions:

Level of Effort: What is the technical, financial, and personnel cost to implement this solution? Does it require simple prompts (Level 1 effort), or does it require integration with a CRM and database (Level 3 effort)?

Value of Outcome: If successful, what is the quantifiable impact? Will it save X hours per week, reduce errors, increase revenue, or improve customer satisfaction?

The aim is to identify the “quick wins,” opportunities that sit in the high-value, low-effort quadrant. These are the projects that will build momentum, generate buy-in, and fund the next phase of your journey.

Conclusion: The First Step is the One You’re On

As Heather aptly puts it, the starting point is not a destination, but a decision. It is the act of moving from passive observation to active assessment. By first understanding your organization’s AI maturity, then focusing on your most acute pain points, and finally prioritizing based on a clear effort/value analysis, you replace analysis paralysis with deliberate action.

This is the beginning of a true AI strategy for small business, one not built on hype, but on a grounded, rigorous, and pragmatic path to business transformation.

Army AI and Autonomous Robot Boats: Redefining Pacific Logistics Through the AI Value Path

Mclean Forrester — Tue, 30 Jun 2026 15:48:25 +0000

The vastness of the Pacific Ocean has always been the U.S. military’s greatest logistical headache and its most significant strategic vulnerability. Distances are measured in thousands of nautical miles, not hundreds, and the “tyranny of distance” has historically constrained operational tempo. That is why the recent statements from Maj. Gen. Gavin Gardner regarding the 8th Theater Sustainment Command’s use of Artificial Intelligence and autonomous watercraft represent more than just a technological upgrade; they signify a fundamental philosophical shift in how the Army plans to fight and sustain itself in the 21st century.

Gardner’s assertion that “if you can work in the Pacific, you can work anywhere in the world” is not hyperbole; it is a recognition that the Pacific is the ultimate proving ground for modern military logistics. However, the true brilliance of this approach lies in the realization that the private sector is already pioneering the solutions the military desperately needs. By explicitly stating he is leveraging commercial partners for warehouse management and supply chain timing, Gardner acknowledges a crucial truth: military logistics is fundamentally a business problem, albeit one with life-or-death stakes.

The Algorithm of War

The Army’s move to use AI for demand analysis is a significant step away from the “just-in-case” logistics that have historically clogged supply chains. Gardner pointed out the reality that resources are finite and “we just can’t afford to stock everything.” This is where Artificial Intelligence becomes the decisive factor. By utilizing AI to forecast demand over “time and space,” the Army is shifting from reactive resupply to predictive sustainment.

This is where the concept of the AI value path becomes critical. In the context of military logistics, an AI value path isn’t just about processing data; it is about creating a tangible, operational advantage. It involves mapping the journey from raw intelligence and consumption data to a deliverable action that saves time and lives. The commercial world has perfected this for inventory management, but applying it to a contested theater with electromagnetic interference, weather variability, and a near-peer adversary is a different beast entirely. The AI value path here must account for variables that Wall Street algorithms never see. By applying a rigorous value path to its logistics AI, the Army can ensure that the algorithms are not just smart, but also trustworthy and resilient against adversarial manipulation.

The Fleet of the Future is Uncrewed

While the AI processing data in a headquarters is powerful, the physical manifestation of this modernization is the autonomous watercraft. The news that the Army is fielding vessels over 100 feet long, capable of carrying up to eight 20-foot containers, is a game-changer. The reliance on the aging Landing Craft Mechanized-8 fleet has been a known vulnerability. The new Maneuver Support Vessel (Light), while manned, offers a glimpse of the future: speed and shallow draft. However, the focus on autonomy suggests an understanding that the future battlefield is too dangerous for manned ships in the primary logistics role.

Gardner’s vision of having “between 30 and 100” medium autonomous vessels berthed across the Indo-Pacific is the kind of distributed logistics network that makes a peer adversary sweat. Such a fleet would allow the U.S. to preposition supplies and perform “rapid insertions” of assets like the HIMARS and Marine NMESIS without putting a large, valuable target (like an LPD or LSD) in harm’s way.

The Regulatory Bottleneck

However, the biggest hurdle identified in this push is not technological; it is legal. Gardner correctly points to the current U.S. maritime laws requiring a minimum crew size, which limits autonomous operations to pilot programs. This is a critical friction point. For the AI value path to reach its full potential in the Pacific, the output of the AI must be the ability to act swiftly. If an AI calculates that a re-supply vessel needs to arrive at a specific island chain within a narrow window, but the ship is delayed because of a law requiring a crew that doesn’t exist or a port that won’t accept an unmanned vessel, the entire value of the AI is negated. The AI value path is broken at the last mile. Gardner’s push for the Coast Guard to accept “unmanned systems enter into ports” is a necessary step to ensure that the “value” generated by the algorithm can be physically realized on the battlefield.

The McLean Forrester Insight

In the current AI sphere, there is a dangerous emphasis on the “cool” factor of algorithms rather than their utility. As McLean Forrester notes, the conversation around AI is often fragmented, focusing on the model rather than the mission. The Army’s approach in the Pacific is a textbook example of how to do it right: identify the mission (logistics in a vast theater), identify the friction points (distance, resources, law), and apply AI specifically to solve those problems.

The success of the 8th TSC’s endeavor will hinge on their ability to treat the entire operation as a cohesive “AI value path.” If they can successfully integrate commercial AI for demand analysis, pair it with experimental autonomous vessels, and simultaneously lobby to change the regulatory landscape, they will have created the most efficient logistics network in military history.

The Army is right to see the Pacific as the ultimate test. It is an unforgiving environment where the rules of engagement are different. But as Gardner suggests, if the Army can untangle the knot of logistics in this region using AI and robotics, it will hold a capability that is unmatched anywhere else. It is no longer about simply moving boxes; it is about moving them with the precision and speed of an algorithm and the resilience of a robot.

AI Revolution 2026: Claude Surpasses ChatGPT, IBM Breaks 1nm Barrier, and What It Means for Your Business

Mclean Forrester — Mon, 29 Jun 2026 15:51:30 +0000

The past seven days in artificial intelligence have delivered a cascade of announcements that feel less like incremental progress and more like a fundamental shift in how technology will integrate into every corner of our lives. From the way video is produced to the inner workings of the human brain, the progress is breathtaking and, frankly, a little disorienting.

Perhaps the most significant narrative this week is the changing of the guard at the top of the AI industry. For years, OpenAI and its flagship product, ChatGPT, have been the undisputed leaders in the public consciousness. That story is evolving. A major report from Sensor Tower revealed that while ChatGPT recently became the fastest app to reach 1 billion monthly active users, its market share has fallen below 50 percent for the first time. The real story is who is gaining ground. Anthropic's Claude is not just closing the gap; it is becoming the preferred choice for many businesses. The report shows that Claude generates significantly more revenue per user and converts a higher percentage of its users to paid subscriptions. Another index found that Anthropic has surpassed OpenAI in business adoption, with more U.S. companies reaching for Claude first when a new project starts. This is a stunning reversal. Businesses are choosing Claude over ChatGPT, and the reason seems clear: Claude is viewed as more reliable, trustworthy, and better suited for the demands of enterprise production work.

This trend points to a broader maturation of the AI market. The era of experimentation and novelty is giving way to a focus on practical, measurable results. This is precisely the environment where a company like McLean Forrester thrives.

We have built our firm on the principle that AI must be implemented securely, simply, and with an unwavering focus on real business outcomes. The news this week confirms our core philosophy: the winners in this new era will not be the ones with the flashiest demos, but the ones who can successfully integrate AI into their operations to deliver tangible value.

This is where our experienced team excels. With over 30 years of enterprise technology experience, we understand the complexities of modern organizations. We cut through the hype and focus on what works, turning the overwhelming potential of AI into a clear, actionable plan for our clients.

The week's news also saw a significant development in the field of video creation, representing a complete transformation of the production process. This aligns perfectly with our focus on collaborative innovation. We believe that the best solutions are born from close partnership with our clients. By working together, we help them identify how these new generative capabilities can be harnessed to create new value and streamline their content creation workflows, moving beyond theoretical possibilities to practical applications.

At the same time, groundbreaking research is allowing scientists to see the human brain like never before. Meanwhile, IBM has crossed the sub-1nm chip barrier, a monumental achievement that will underpin the next generation of even more powerful and efficient AI systems. This highlights how the entire ecosystem is advancing in parallel. The software is becoming more capable, the hardware is becoming more powerful, and the science is deepening our understanding of intelligence itself. However, this incredible power also introduces new risks. A bombshell report finding that AI chatbots show a left-wing bias is a stark reminder that these systems are not neutral. They reflect the data they are trained on and the choices made by their developers. As these tools become more influential, understanding and mitigating such biases is not just a technical challenge but an ethical imperative.

Japan has also firmly entered the AI race, releasing a model that performs on par with leading systems. This is a crucial development, diversifying the global AI landscape and ensuring that the technology is shaped by a wider range of perspectives and values. As AI becomes a tool of national and economic strategy, this kind of healthy competition is vital. On the other end of the spectrum, China's delivery robots are becoming a part of daily life, offering a glimpse into a future where intelligent machines handle logistics and mundane tasks, allowing humans to focus on higher-level work.

It is this principle that guides everything we do at McLean Forrester. Our approach is grounded in insight and real-world experience. Our team brings 60 years of combined military and government experience, which instills a "mission first" mindset and a deep understanding of security, risk, and operational efficiency. We view AI adoption as a strategic logistics problem, ensuring that we automate the right processes in the right way to deliver a decisive advantage.

As we process these developments, it is also wise to keep a critical perspective. The reported left-wing bias in chatbots is a powerful reminder that these systems are not neutral arbiters of truth. They are products built by people with inherent, if unconscious, biases. As leaders and decision-makers, we must be aware of these tendencies to ensure the technology is used responsibly and equitably. The news of Claude's rise over ChatGPT also underscores the need for a multi-model strategy. The market is not a zero-sum game. Many enterprises are running both models simultaneously to leverage their respective strengths. This speaks to the need for an agile and informed approach to AI adoption, one that a accessible and direct partnership with a company like ours can provide.

ChatGPT's upcoming voice mode sounds truly incredible, promising to make interactions with AI even more seamless and natural. As interfaces evolve, the ability to adapt and integrate the best tools for the specific job will be crucial. We are built to help our clients navigate this complex and fast-moving landscape, providing them with the clarity and speed needed to stay ahead.

Built on Vision. Driven by Innovation. McLean Forrester started with a simple conversation in a kitchen. A group of experienced professionals came together with a shared vision to build something extraordinary beyond the limits of corporate and government structures. United by expertise and a passion for innovation, we set out to create a technology company focused on people and real customer value. To learn more about our team and our approach, visit our About page and discover how we are making AI work for businesses.

This was another major week in AI. The progress is undeniable, but it is the application of this technology to solve real-world problems that will define its ultimate value. That is the gap McLean Forrester is built to bridge. We provide the strategic edge, the technical expertise, and the commitment to results that allows our clients to turn the promise of AI into tangible performance. Visit our homepage to see how we can help you navigate the future with confidence.

Cloud Migration Done Right: A Phased Approach to Minimizing Risk and Maximizing ROI

Mclean Forrester — Thu, 25 Jun 2026 15:31:20 +0000

The conversation around cloud migration has fundamentally shifted. It is no longer a question of "if" but "how" and "why." As we navigate 2026, moving to the cloud is a strategic imperative for businesses aiming to reduce operating costs, increase efficiency, and unlock new possibilities for growth. For organizations still tethered to legacy systems, the pressure to modernize is immense. By 2026, the global cloud migration services market is estimated to be worth a staggering USD 383.04 billion, underscoring how central this transition has become to business strategy. This guide explores the key drivers, emerging trends, and a robust strategy for a successful cloud migration, highlighting how expert guidance can turn complexity into a competitive advantage.
Why 2026 is the Year for Confident Cloud Adoption
For over a decade, the promise of the cloud has been clear, yet many organizations have hesitated due to security, governance, and compliance concerns. In 2026, these barriers are finally being overcome. A convergence of automation, regulatory clarity, and advanced cloud-native visibility is enabling businesses to pursue migration with unprecedented confidence.
The primary drivers for this acceleration are multifaceted. Cost optimization remains a powerful motivator, with enterprises reporting 20% to 30% operational expenditure savings after moving workloads to the cloud, primarily by eliminating costly hardware refresh cycles and right-sizing resources. However, the scope has broadened. AI readiness has emerged as a significant catalyst. Organizations understand that legacy systems cannot effectively support advanced AI and automation initiatives. To unlock the value of these next-generation technologies, businesses must migrate to scalable, flexible cloud-native environments where data can be easily accessed and processed. Additionally, the need to support remote and hybrid work models continues to drive cloud adoption, as organizations migrate collaboration suites and identity services to ensure consistent and secure user experiences everywhere.
Building a Strategic Cloud Migration Roadmap
A successful cloud migration is not a single event but a carefully planned journey. Rushing into the process without a clear strategy is a common pitfall, often leading to budget overruns and missed opportunities. Experts suggest that the most successful migrations follow a phased, business-outcome-driven approach. Here is a step-by-step guide to building a robust strategy.

Define Clear Business Objectives and KPIs Before any technical work begins, it is crucial to define the "why." What specific business outcomes are you aiming for? Is it cost reduction, improved resilience, faster time-to-market, or enhanced customer experience? Establishing clear Key Performance Indicators (KPIs) from the outset ensures that the migration is measured against real business value, not just technical milestones. This alignment also helps in building a strong business case to secure stakeholder buy-in.
Conduct a Comprehensive Discovery and Assessment A thorough understanding of your existing IT estate is the bedrock of a successful migration. This involves cataloging every application, database, service, and their interdependencies. Often, organizations discover they lack a complete inventory, which can lead to costly surprises and downtime. Automated discovery and assessment tools are essential to build a realistic view of your portfolio and map out dependencies. This phase helps classify workloads into those that are cloud-ready, those needing refactoring, and those best kept on-premises due to regulatory or performance constraints.
Choose the Right Migration Strategy Not all workloads are created equal. The "7 Rs" framework provides a taxonomy for evaluating the best approach for each application. Rehost (Lift-and-Shift): Moving workloads with minimal changes. This is often the fastest path but may not fully leverage cloud benefits.

Replatform: Making minor optimizations, such as moving to a managed database service, to gain some cloud advantages without major re-architecting.

Refactor / Re-architect: Re-architecting the application to be cloud-native, which can be up to 20 times longer than rehosting but yields the greatest long-term benefits in agility and cost.

Repurchase: Replacing the application with a Software-as-a-Service (SaaS) alternative.

Retire: Decommissioning obsolete systems.

Retain: Keeping a workload on-premises for now.

Most large-scale migrations will use a mix of these strategies, prioritizing workload complexity and business criticality.

Execute a Phased Migration Instead of a "big bang" approach, a wave-based migration is far more manageable and less risky. Starting with low-risk, non-critical development and test environments allows teams to gain confidence and refine their processes. After that, they can move to less critical production workloads before finally migrating business-critical applications and sensitive data. This phased approach minimizes disruption and allows for a controlled learning curve. During this phase, the expertise of partners like McLean Forrester can be invaluable, guiding you through each step of the cloud migration process to ensure a smooth and secure transition.
Prioritize Security, Governance, and FinOps from Day One Security and governance should never be an afterthought. A zero-trust architecture, robust identity and access management (IAM), and data encryption are foundational elements that must be built into the cloud environment from the start, not retrofitted later. The shared responsibility model of the cloud requires organizations to be proactive. Similarly, FinOps, or financial operations, is no longer optional. With 84% of organizations struggling to manage cloud spend, it is essential to adopt FinOps principles early. This involves implementing robust tagging for cost allocation, using budgets and alerts to track spending, and continuously optimizing resource utilization to eliminate waste. Considering that estimated wasted cloud spend sits at around 29% of total budgets, effective governance is critical.
Plan for Post-Migration Optimization Cloud migration is not the finish line; it is a new beginning. The focus must then shift to continuous optimization. This includes right-sizing resources, taking advantage of committed-use discounts, and establishing a Center of Excellence (CoE) to codify best practices and promote innovation. The goal is to create a sustainable operating model that delivers ongoing value, agility, and cost-efficiency. The Role of a Cloud-Agnostic Partner Navigating the complexities of a modern cloud migration often requires expertise that is not available in-house. The best partners offer a cloud-agnostic approach, meaning they are experts across all major cloud provider platforms and can recommend the solution that best aligns with your existing ecosystem and strategic goals. They help assess cloud readiness, develop a customized migration roadmap, and provide the specialized skills needed to manage the process effectively. As highlighted by McLean Forrester, their decades of experience in software architecture and cloud migration ensure your systems are in excellent hands. They can act as a trusted guide, using advanced technology to uncover blockers and smartly migrate systems to the cloud where the ROI makes the most sense. Conclusion The cloud migration journey in 2026 is defined by strategic intent, technological maturity, and a focus on business outcomes. It is a powerful lever for reducing costs, enhancing security, and unlocking the innovation potential of AI and modern digital solutions. By adopting a phased, well-planned strategy and partnering with experienced experts, organizations can navigate the common challenges and realize the immense benefits of a successful cloud transformation. It is time to move beyond hesitation and build a scalable, resilient, and efficient future in the cloud.

How Smart Companies Are Driving Revenue, Cutting Costs, and Winning with Artificial Intelligence

Mclean Forrester — Wed, 24 Jun 2026 15:28:45 +0000

The conversation around artificial intelligence has officially shifted. We are no longer asking if AI can do something. The question for 2026 is whether your business can afford to be left behind while the rest of the market forges ahead. The AI experiment is over, and the age of performance has arrived.
NVIDIA's annual "State of AI" report provides a comprehensive pulse check on this transformation. Drawing from over 3,200 responses across financial services, retail, healthcare, and other critical sectors, the data is a loud and clear signal to every executive hesitating on their strategy. This isnt just about technology anymore. It is about the fundamental economics of your business. The report confirms that AI is no longer a speculative investment. It is becoming essential infrastructure for driving revenue, cutting costs, and boosting productivity across every industry.
For companies that have been waiting for the right moment, the message from this data is unmistakable. The time to move is now. At McLean Forrester, we help organizations cut through the noise and implement AI strategies that deliver tangible results. We understand that adopting AI across your organization is now easier than ever, provided you have the right partner to guide you.
The Verdict is In: AI Delivers ROI
For years, one of the biggest hurdles to AI adoption was a simple question. Does it actually work? The NVIDIA report puts that doubt to rest. An overwhelming 88% of respondents said that AI has had an impact on increasing annual revenue in some or all parts of their business. That is not a marginal gain. Nearly a third of those organizations saw a revenue increase of more than 10%.
The story is just as compelling on the cost side. Overall, 87% of respondents reported that AI helped reduce annual costs. In the retail and CPG sector, this effect was even more pronounced, with 37% of companies cutting costs by more than 10%. This is a powerful validation for CFOs who have been demanding a clear return on investment.
We see this firsthand. A Fortune 100 retailer, Lowe's, used AI-powered digital twins of its stores to streamline operations and generate 3D models at a cost of less than one dollar per model. This is the kind of efficiency gain that directly impacts the bottom line. As Michael O'Rourke, SVP at Nasdaq, said, "AI has the ability for us to unite all the different businesses and products". This ability to unify data streams and create a holistic view is the engine of modern revenue generation. To understand how practical business transformation like this can be achieved, exploring AI consulting services is a wise first step.
Beyond the Bottom Line: How AI is Reshaping Work
The financial data is impressive, but it only tells part of the story. The real magic of AI is happening at the workflow level, where it is fundamentally changing how work gets done. The report found that the top two goals for AI implementation are creating operational efficiencies and improving employee productivity.
This isnt about replacing people. It is about empowering them. Over half of respondents said that improved employee productivity was one of the biggest impacts AI had on their operations. In the telecommunications sector, a staggering 99% of respondents said AI helped improve employee productivity.
Consider the example of manufacturing. Siemens is helping companies like PepsiCo create high-fidelity 3D digital twins of their factories. These "digital twins" allow them to simulate changes and identify up to 90% of potential issues before any physical modifications occur. The result was a 20% increase in throughput and a 10–15% reduction in capital expenditure. This isnt just an efficiency gain. It is a complete reimagining of the production lifecycle. For organizations looking to achieve similar outcomes, learning about AI strategy development can provide a clear roadmap.
The Shift to Open Source and Agentic AI
As AI matures, so do the strategies for deploying it. Companies are moving away from one-size-fits-all solutions. The report shows that 85% of respondents consider open source and open weight models moderately to extremely important to their AI strategy. This allows businesses to fine-tune models with their own proprietary data, creating highly specific applications that solve their unique challenges.
We are also seeing the dawn of a new era. Agentic AI. In 2025, companies began to experiment with advanced AI systems designed to autonomously reason, plan, and execute complex tasks. The report captures this experimentation phase, with 44% of companies either deploying or assessing agents last year. This is no longer a future concept. It is happening now in 2026, touching everything from code development to legal and financial tasks.
In healthcare, for example, Mona by Clinomic acts as a medical onsite assistant in intensive-care units. It has produced a 68% reduction in documentation errors, enhancing patient records while helping clinical professionals realize a 33% reduction in perceived workload. This is a powerful example of how agentic AI can augment human expertise in high-stakes environments.
The Challenge is Real, But So is the Opportunity
Despite the overwhelming momentum, challenges remain. Lack of AI experts and data scientists was cited as a primary challenge by 38% of respondents. Many companies are still grappling with their data. Having sufficient data and other data-related issues were cited as the top challenge by 48% of respondents.
This is precisely where strategic guidance becomes critical. The industry is moving beyond the "pilot phase," and companies need a clear path to production. At McLean Forrester, we specialize in bridging the gap between vision and execution. As our CEO, Heather McLean, has noted, "There is a significant gap between understanding that AI is important and knowing how to implement it effectively". The gap between intention and execution is where many promising AI initiatives stall.
Looking Ahead: The New Competitive Landscape
The data makes one thing clear. The companies that successfully deploy AI are pulling ahead. Larger companies with more than 1,000 employees are seeing broader adoption and greater ROI, simply because they have the capital and expertise to move quickly. However, the tools are becoming more accessible. The power of open source and the availability of specialized AI programs mean that size is no longer the only differentiator.
Almost all respondents said their AI budgets will increase or at least stay the same in 2026. This is a clear indication that the market views AI not as an expense, but as an investment in the future. The winners will be those who use AI to drive new business opportunities, optimize their existing workflows, and empower their workforce.
The state of AI is strong. It is driving revenue, cutting costs, and boosting productivity. The question is no longer whether to adopt AI, but how quickly you can implement it to secure your competitive advantage. We are ready to help you answer that question. Taking the next step with AI business solutions can turn artificial intelligence from a promising concept into a performance engine for your organization.

How Generative AI Automates Council Planning: A Practical Guide

Mclean Forrester — Tue, 23 Jun 2026 15:44:11 +0000

I have been thinking a lot about that article on how the UK government is using Google Cloud generative AI to speed up council planning. It is a perfect example of how this technology can cut through red tape and help people get things done faster. The story is compelling. They are dealing with mountains of paperwork that delay housing developments and frustrate citizens. The goal is to cut decision times in half by using AI to handle the boring stuff, like parsing PDFs, summarizing public comments, and drafting reports, so human planners can focus on the big complex projects.

It got me wondering though. What would it take for a regular small business or even a local government here to get the same kind of benefit? The challenge for most organizations is not a lack of ambition. It is a lack of a clear practical path forward. Many business leaders are AI curious but not yet AI capable. They get stuck in what is called the pilot trap, running endless experiments without ever achieving a measurable return on investment. They know AI matters, but they do not know where to start, and they are drowning in generic advice or enterprise programs that cost a fortune and do not fit their needs.

This is where McLean Forrester steps in. They have built a structured human centered approach to bridge that exact gap. They are not just consultants who hand you a report. They are educators and practitioners who roll up their sleeves and work with you. Their philosophy is rooted in the belief that the bottleneck to AI adoption is not technology. It is trust and translation. They focus on answering the real practical questions that small business owners and government leaders are asking.

Will this actually save us money? They push for task level accounting and a clear cost migration map so you can see exactly where the savings come from. How do I keep my brand voice from sounding like a robot? Instead of just better prompts, they focus on fine tuning AI on an organization's proprietary data to preserve its unique personality and local knowledge. Who is liable when the AI gets it wrong? They provide practical frameworks for managing risk, including a human in the loop review process that is a legal requirement for many, and they emphasize the need for proper insurance and documentation. What is the one thing I should automate first? They cut through the paradox of choice by recommending a low risk high impact starting point, like meeting summarization and follow up. It is a simple way to save hours a week and build confidence for bigger projects.

That last question is the perfect lead in to the AI ROI Workshop they are offering right now. It is a hands on four hour virtual session that moves participants from AI awareness to building their own actionable AI strategy. And get this. Early bird pricing is only 99 dollars, and the cohort is capped at 20 people. That is a far cry from the 5,000 to 25,000 dollar enterprise programs out there.

It is led by Larry McLean, their Chief Growth Officer, who brings a unique mix of 40 years of leadership experience spanning both the commercial sector and senior federal roles, including leading the Enterprise Data Management Office at U.S. Transportation Command. He is also a professor at Washington University in St. Louis. So the person guiding you has been in the trenches, advising on some of the most complex IT and data environments imaginable.

The workshop is built around a simple but powerful idea. You do not just learn about AI. You use it. You get hands on practice with tools like Claude using their CRAFT prompt engineering framework. You learn to identify where AI delivers real ROI in customer experience, marketing, or internal decision making. And you leave with a draft AI strategy tailored to your business, not a generic template. They even include frameworks for managing the people side of change, which is where most AI efforts quietly stall. It is practical. It is affordable. And it is designed for real people running real organizations, just like the planning officers in the UK government who are overwhelmed with paperwork.

The workshop is not a one off thing. It is the first step on what McLean Forrester calls the AI Value Path, a structured framework to move organizations from exploration to execution. This is the roadmap they use for bigger more complex engagements, whether it is helping a small bakery automate inventory forecasting or deploying Enterprise Secure AI for a government agency that requires air gapped private deployments for the most sensitive workloads.

They understand that for government entities, security is not a nice to have. It is a non-negotiable requirement. They are a woman and veteran owned small business that seems to genuinely care about building solutions that fit the unique constraints of the public sector and the real world needs of the people who work there. You can learn more about their commitment to the public sector on their IT Strategy and Assessment page which highlights their work with municipalities and educational organizations.

The UK government is aiming to roll out their tools nationwide by 2027. That is the scale of what is possible when you take this seriously. But you do not have to wait for some massive government program to start seeing benefits. Cities and counties across the United States are dealing with the same problems. The same backlogs. The same frustration. And they are starting to realize that the solutions exist. They just need the right partner to help them implement them.

McLean Forrester is that partner. They are already working with municipal governments. They are already delivering real savings. And they have a model that focuses on people first, which in the public sector is exactly what you want. The future of government is not about less human involvement. It is about making that involvement more meaningful. Giving people the tools to do their best work. And building systems that serve communities better. That is what generative AI can do. And that is what companies like McLean Forrester are making possible.

Think about a planning officer who is spending half their time reviewing simple applications like house extensions or loft conversions. That is not why they got into this work. They became planners because they wanted to shape communities. Help them grow. Make them better places to live. By automating the administrative parts, you free those people up to work on the big projects. The housing developments. The commercial districts. The infrastructure that actually changes a city.

We are in a moment where technology is finally catching up to the problems governments have faced for decades. The cloud makes it possible to process massive amounts of data without building expensive data centers. Generative AI can understand and organize information in ways that used to require armies of human reviewers. But technology alone is not enough. You need people who understand both the tech and the context. Who know how to work with governments, understand their constraints, and build solutions that actually fit their needs.

McLean Forrester has positioned themselves right at that intersection. They are small enough to care about each client and agile enough to actually get things done. Their Google Cloud partnerships mean they have access to the same tools the UK government is using, but they are building solutions for American cities and schools. The coordination between public ministries and external technical partners establishes a structured division of labor for enterprise software engineering. Public ministries define the policy guidelines and statutory boundaries, while external technical partners engineer and deploy the underlying model architectures.

The successful integration of these systems demonstrates the feasibility of hosting advanced language models within a secured public cloud infrastructure to process core administrative workloads and modernize public service delivery. This is not some distant future scenario. This is happening right now. And McLean Forrester is making it accessible to organizations that might otherwise be left behind. They are proving that with the right approach, anyone can harness the power of AI to work smarter, not harder.

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.

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.