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Why Most AI POCs Stall and How to Build an AI Operating Model That Works

26 Aug
Why Most AI POCs Stall and How to Build an AI Operating Model That Works

A few months ago, I was speaking with a leadership team that had invested heavily in Generative AI. They had done everything right, at least on paper. They hosted hackathons, launched AI innovation days, encouraged teams to experiment with tools like ChatGPT, Cursor, Claude, and Gemini, and generated dozens of exciting proof of concepts (POCs). The demos were impressive. The excitement was real. Then I asked a simple question: “How many of these solutions are actually running in production and creating measurable business value?” The room went quiet.

If you have been involved in AI initiatives, you have probably seen the same pattern. Teams produce innovative AI prototypes, leadership gets excited, and everyone talks about transformation. Yet months later, very few of those ideas become dependable, secure, and widely adopted business solutions.

The problem isn’t the technology. Today’s AI models are incredibly capable. The problem is that most organizations are trying to scale AI without building the operational framework needed to support it. In my experience, the companies that successfully scale AI don’t necessarily have the best models. They have the best operating model.

One of the first challenges organizations encounter is what I call the “happy path” problem. Most AI POCs are built using clean data, predictable scenarios, and carefully controlled environments. Real-world systems rarely behave that way. Customers enter incomplete information, business rules change, systems fail, and unexpected edge cases appear. Many teams react by trying to perfect the solution before launch, spending months solving hypothetical problems. However, I believe that the smarter approach is the simpler one: release a focused solution to a targeted audience, gather feedback, learn from real-world usage, and improve iteratively. The fastest way to build a production ready AI product is to get real users into the loop as early as possible, not to spend months and years to perfect a solution.

Even when organizations overcome that hurdle, they often run into a second challenge: data and integration issues. Most AI failures are not actually AI failures. They are data failures, process failures, or integration failures. A POC can operate on a carefully curated dataset, but production systems need secure data pipelines, governance controls, monitoring, observability, auditability, and integration with existing business workflows. I have seen organizations spend months debating which model to use while completely overlooking the fact that their underlying data ecosystem wasn’t ready. Thus, companies should invest in building a stronger data pipeline, improving their integration testing capabilities, and creating an operational framework that can accelerate pace of innovation. After all, the AI isn’t the problem, the foundation is.

Another common trap is measuring activity rather than outcomes. These days, organizations proudly report the number of tokens used, experiments conducted, prompts executed, or AI tools adopted. Those metrics may indicate engagement, but they don’t necessarily indicate value. The questions leaders should be asking are much simpler: Did customer satisfaction improve? Did operational costs decrease? Did revenue increase? Did employee productivity improve? AI creates value only when it is connected to measurable business outcomes. Everything else is experimentation. That’s why I recommend that organizations regularly review AI adoption, engagement, and ROI through existing governance forums such as Monthly Product Reviews (MPRs), Monthly Business Reviews (MBRs), Engineering Operations Reviews, and Level 10 (L10) meetings. These forums help connect AI initiatives to measurable business outcomes, making it easier to understand what’s creating value, what’s falling short, and where organizations should focus their next investments.

As organizations mature, AI governance becomes increasingly important. Unfortunately, AI governance has developed a bad reputation and is often viewed as the enemy of innovation, since AI models are evolving at an unprecedented pace, heavyweight governance processes often struggle to keep up. In reality, governance is what allows innovation to scale safely. Whether it’s privacy, security, compliance, responsible AI, or human oversight, organizations need clear and repeatable processes that help teams move quickly while managing risk responsibly. The Air Canada chatbot incident serves as a reminder that companies remain accountable for what their AI systems do, regardless of how autonomous those systems may appear. Thus, I recommend that organizations rethink their decade-old governance models. Since AI is evolving rapidly, innovators should not have to navigate multiple approvals and disconnected stakeholders just to get an AI use case reviewed. Instead, organizations should establish a centralized governance forum where teams can bring ideas, ask questions, receive timely guidance, and understand the rationale behind governance decisions. The goal is not to reduce oversight, but to streamline it. By creating a transparent, well-socialized, and operationalized governance framework with regular follow-ups and clear communication, organizations can strengthen governance while simultaneously accelerating the pace of innovation.

Another common mistake organizations make is failing to invest in their people. Like any major transformation, AI augmentation is ultimately a transformation of how people work. Simply providing access to AI models or GenAI tools does not guarantee success. Organizations need to invest heavily in AI literacy, change management, coaching, upskilling, and adoption strategies, just as they would for any other enterprise-wide transformation. Leaders cannot assume that because AI tools are available, employees will automatically use them effectively or that productivity will magically improve. Without the right training, guidance, and support, most employees will either underutilize AI or use it only for basic tasks. Employees need to understand where AI can help, where it cannot, and how to integrate it into their daily workflows responsibly and effectively. After all, if people continue to view AI as nothing more than a chatbot, the vision of building AI-powered assistants, autonomous workflows, and agentic AI solutions will remain out of reach. This is why leaders must build an operating model that reinforces the right behaviors, creates opportunities for learning, and encourages adoption through the right incentives. Ultimately, AI transformation is not a technology transformation. It is a people transformation.

Finally, organizations need to answer one critical question: When AI fails, who is accountable? As tools like Claude, Gemini, Cursor, and GitHub Copilot become part of everyday software development, accountability can quickly become blurred. My view is straightforward. AI can generate content, code, and recommendations, but it cannot own responsibility. The engineer who deploys the code remains accountable for its quality. The product manager who launches the feature remains accountable for its business impact. Human ownership must remain intact, regardless of how much AI contributed to the solution.

The biggest obstacle preventing companies from realizing value from AI isn’t model performance or innovation. It’s the absence of an AI operating system that connects governance, measurement, accountability, data readiness, and talent development into a repeatable framework. AI doesn’t reduce our need for leadership, governance, operational excellence, or execution discipline. It increases it.

 
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Posted by on August 26, 2026 in 21st Century, AI Leadership, Leadership, Management, technology

 

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The Most Expensive Cost-Cutting Decision in Technology

13 Aug
The Most Expensive Cost-Cutting Decision in Technology

Over the last two decades, I have had the opportunity to work with startups, growth-stage companies, and some of the world’s largest enterprises. Across these organizations, I have helped product, engineering, design, and quality teams improve the way they operate. Sometimes the challenge was a lack of strategic alignment. Sometimes teams were working hard but prioritizing the wrong things. Other times the problem was execution, where ownership, planning, and delivery were disconnected from one another. Regardless of the company or industry, one lesson has remained surprisingly consistent: success is rarely determined by great ideas alone. It is usually determined by the operating system that helps teams turn those ideas into reality.

Unfortunately, that operating system is often the first thing leaders cut when revenue falls short and budgets need to be reduced. I have seen this happen more times than I can count. A company misses its financial goals and leadership begins reviewing costs. Product teams are viewed as essential because they drive innovation and define the future roadmap. Engineering teams are viewed as essential because they build the product and deliver features. Design teams are viewed as critical because they shape the customer experience. Then attention turns toward Technical Program Managers (TPMs), Project Managers, Delivery Managers, and PMO organizations. Since these teams are not directly writing code or defining product features, they are often perceived as overhead. The conclusion becomes simple: remove the program management layer and allow product and engineering leaders to absorb the responsibilities.

On paper, the decision sounds reasonable. However, it often creates far more problems than it solves. I have personally witnessed this firsthand in multiple startups I supported throughout my career. In two of the companies I worked at, leadership reduced a significant portion of the TPM and program management function during periods of financial pressure. The assumption was that product managers and engineering leaders could take over the coordination work while continuing to execute their existing responsibilities. Leaders hoped this would reduce costs without affecting delivery speed or business outcomes.

The opposite happened. Within a few months, product managers found themselves spending less time talking to customers and more time tracking dependencies across teams. Instead of validating market opportunities, refining roadmaps, and measuring customer outcomes, they were coordinating schedules, chasing updates, preparing reports, and managing delivery risks. At the same time, engineering leaders became consumed with sprint planning, program reviews, portfolio reporting, PI planning sessions, and other operational activities. Rather than focusing on architectural improvements, engineering productivity, DevOps optimization, developer experience, and technical mentorship, they were forced to fill an operational gap that nobody had anticipated. The work itself did not disappear. It simply moved to people who were hired to do something else.

As a result, execution slowed down, planning became less predictable, dependencies fell through the cracks, and leadership visibility decreased. While the company saved money on a spreadsheet, it lost efficiency across the organization.

One reason this happens is because great program management is often invisible. When TPMs and PMO teams are performing well, most people do not notice the work they are doing. They see smooth planning cycles. They see aligned roadmaps. They see risks identified early. They see stakeholders staying informed. They see teams moving together toward common objectives. Because the outcomes appear seamless, it becomes easy to underestimate the effort required to create and maintain that alignment.

The best way I can describe it is this: program management is the operating system of an organization. Just as Windows/Mac operating system allows hardware and software to work together efficiently, TPMs and PMO leaders create the frameworks, governance, communication channels, and execution mechanisms that allow product, engineering, design, and QA teams to work effectively together. When you remove the operating system, the individual components still exist, but they stop functioning together at the same level of efficiency.

This becomes particularly important as organizations scale. In small teams, coordination can happen organically. People can walk across the room, have a quick conversation, and resolve issues immediately. However, as organizations grow, dependencies increase. More stakeholders become involved. Priorities compete for attention. Teams become distributed across locations and time zones. Without strong operational frameworks and cross-functional governance, execution naturally becomes more difficult.

Many leaders today argue that Artificial Intelligence (AI) will eliminate the need for large PMO organizations. There is certainly some truth to that argument. AI is already helping organizations automate reporting, generate dashboards, summarize meetings, identify risks, and track work more effectively than ever before. I personally believe these innovations will continue to transform how TPMs and PMOs operate over the next several years.

However, automating tasks is not the same as replacing a function. AI can generate a status report. It cannot always drive organizational alignment. AI can identify a delivery risk. It cannot negotiate priorities across competing stakeholders. AI can provide project insights. It cannot build trust among leaders, challenge assumptions, facilitate difficult decisions, or create accountability across teams. The most valuable work performed by modern TPMs is not administrative. It is strategic. It is helping organizations make better decisions, focus on the highest-value work, resolve complex dependencies, and improve how teams operate together.

The irony is that companies often spend enormous amounts of money trying to improve engineering productivity, accelerate software delivery, improve customer outcomes, and increase organizational effectiveness, while simultaneously reducing the very teams responsible for enabling those outcomes. They invest heavily in building products but underinvest in the systems that make product development successful.

In my experience, the highest-performing organizations understand that operational excellence is not a luxury. It is a competitive advantage. They recognize that product managers should spend most of their time understanding customers, validating roadmaps, and measuring business outcomes. They recognize that engineering leaders should spend their time improving architecture, strengthening DevOps capabilities, mentoring engineers, and increasing delivery efficiency. They also recognize that somebody needs to own cross-functional execution, strategic prioritization, governance, dependency management, and organizational effectiveness. That “somebody” is often your TPM and PMO organization.

Are there program managers who simply act as messengers between teams? Absolutely. Every profession has people who contribute more value than others. But judging the importance of an entire function based on its weakest examples is a mistake. The best TPMs create leverage across the entire organization. They help companies prioritize strategic bets, improve product development processes, streamline software delivery, increase engineering productivity, strengthen Agile execution, and drive better business outcomes.

As technology continues to evolve and AI becomes increasingly capable, I believe the TPM role will evolve as well. Routine administrative work will continue to be automated. Reporting will become easier. Data collection will become smarter. But the need for leaders who can align strategy with execution, drive cross-functional collaboration, optimize operating models, and help teams deliver value faster is not going away anytime soon. If anything, it is becoming more important.

The next time an organization considers reducing its TPM, Project Management, or PMO function as a cost-saving measure, I would encourage leaders to pause and ask a simple question: Who will own the operating system that makes execution possible? Because building great products is only half the challenge. Building an organization that can repeatedly deliver those products at scale is what separates companies that survive from those that truly win.

 
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Posted by on August 13, 2026 in 21st Century, AI Leadership, Leadership, Startup

 

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How AI Transforms Program Management: From Reporting to Strategic Partnership

20 May
How AI Transforms Program Management: From Reporting to Strategic Partnership

Early in my career at a couple of Fortune 500s, program management excellence often meant one thing: being able to produce a clean, defensible status report. Green boxes built credibility. Red ones triggered escalation. The irony was that by the time something turned red, everyone already felt the pain, the report simply made it official.

Fast forward to today, use of AI often exposes an uncomfortable truth: much of what we call program management has been information movement, not decision support. Startups figured this out long ago. They don’t have the luxury of formal status cycles; they rely on shared situational awareness. AI finally allows large organizations to do the same without collapsing under scale.

What changes is not visibility, but interpretation. AI is extremely good at synthesizing fragmented signals into a coherent story. That’s something PMOs have historically tried to do manually, often under time pressure and political constraints.

In most of these big tech giants, I have often seen programs where risk doesn’t emerge explosively, it creeps. A dependency slips a sprint. A scope assumption quietly changes. A team compensates heroically. None of this is “red,” but all of it matters. AI excels at spotting these slow burn patterns precisely because it doesn’t get tired, defensive, or distracted by hierarchy.

Thus, I have been extensively using AI into my day-to-day activities by replacing weekly status decks with weekly sense‑making narratives. Instead of asking teams to explain why something is red or green, I have been using Rovo and Cursor to ask questions like: What’s drifting from plan but not yet obvious? What commitments are most vulnerable if nothing changes? These questions provoke far better conversations, provide helpful insights to the leadership team, and help the core project team to maneuver challenges.

The practical change required to implement this workflow is surprisingly small. You just need to enable Rovo agent in JIRA, work with your teams to fix JIRA hygiene challenges, and connect Cursor with your Atlassian suite. Once you do the groundwork, you can then feed AI your existing artifacts like Jira updates, roadmap changes, sprint notes, and ask it to generate insights rather than summaries. You can then review these insights and share it with your teams. This workflow and its visibility will fundamentally change how your teams operate. Over time, teams will stop optimizing for optics and start optimizing for coherence.

So, I strongly believe that AI won’t make program managers irrelevant, but it will make them more like strategists and less like couriers. The PMO of the future won’t be judged by how accurate its reports are, but by how early it helps leaders see reality and help them win through data driven decision making.

 
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Posted by on May 20, 2026 in 21st Century, AI Leadership, Leadership, Management, technology

 

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New Operating Model for Product & Engineering Ops in the World of AI

12 May
New Operating Model for Product & Engineering Ops in the World of AI

When I first saw teams experimenting with AI before it became cool, the behavior felt really familiar. It reminded me of how teams treated Agile in the early days, something you “use/follow” rather than something that fundamentally reshapes how work flows. Teams were excited, curious, and well intentioned, but almost everyone was underestimating the change in front of them.

At larger enterprises like GE and Schneider, operations always lived a layer below the visible product surface. Customers never see the spreadsheets, the JIRA workflows, the dependency maps, or executive readouts, but those invisible systems determined whether strategy delivered the outcomes that we were looking for. AI is now inserting itself directly into that invisible layer.

Most teams today are using AI as a chatbot. They paste in meeting notes, ask for summaries, maybe generate a PRD draft or clean up status language. That’s fine, but it’s also like using a high performance engine only to power the radio. The real power shows up when AI becomes part of how decisions get made, not just how words get written.

I saw a version of this contrast clearly when working with startups versus large enterprises. Startups rarely debate whether a process is “ready.” They automate thinking early because speed leaves no alternative. In contrast, large organizations often wait for certainty, governance, and sign‑off, which delays leverage. AI flips this dynamic. For the first time, large companies can gain a startup‑like operational awareness without burning people out.

The biggest mental shift is this: AI is not just another productivity tool, it is an operating layer that sits between data and action. Every organization already has raw inputs: roadmaps, sprint plans, incident logs, metrics, emails, Slack threads. What most lack is synthesis at scale. Humans do this manually, inconsistently, and too late. AI changes that.

One of the most effective early experiments I have seen is when we stop asking AI to “do work” and start asking it to “explain the system.” Thus, I often ask questions like: What changed this week that mattered? Where are we accumulating hidden risk? What assumptions are we acting as if they are true, but haven’t validated recently? These questions help me sharpen my approach and provide insights that I can quickly review and validate with my organization’s strategy.

Another practical way to begin is to deliberately wire AI into your operating cadence. For example, before every weekly program review, I feed my model JIRA updates, dependency map, and roadmap changes. After that I ask it for a narrative and compare it against my overall understanding of the program. This approach has helped me cut down my manual tasks by almost 40%. Recently, I have started providing some additional context to my model and started asking it what tradeoffs it will make based on the information and using that to improve program’s execution. If you follow this approach, then overtime AI will become your partner and help you expedite decision making. 

I believe that the teams that win with AI won’t be the ones who generate content faster. They will be the ones who design systems where insight appears earlier, decisions happen cleaner, and surprises shrink. This isn’t a tooling upgrade, it is an operating model shift.

 
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Posted by on May 12, 2026 in 21st Century, AI Leadership, Leadership, Management

 

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The Importance of Program Management for Start-ups: Driving Success and Scalability

10 Jul
The Importance of Program Management for Start-ups: Driving Success and Scalability

In the fast-paced and budget-conscious world of start-ups, many founders prioritize immediate product development and customer-centric improvements over establishing a program management structure within their organization. While this approach may work for some, it’s crucial to recognize the immense value that program management can bring to start-ups. From streamlining operations and fostering focus to connecting cross-functional teams and managing dependencies, program management plays a vital role in driving the success and scalability of start-ups. In this blog, we will explore several key ways in which program management can benefit your start-up.

Helping you with focus: Start-up founders are often driven by their passion for their products and the impact they can create. However, maintaining focus amidst competing priorities can be challenging. This is where program management proves invaluable. By acting as a thought partner, program managers help founders direct their efforts towards areas that truly matter. Whether it’s defining growth strategies, aligning cross-functional leaders, or focusing on outcomes instead of outputs, program management ensures that everyone is working towards common goals.

Connecting the dots: During the hyper-growth phase of a start-up, the work culture may appear chaotic with various teams forming rapidly and contributing to business growth. However, without proper coordination, teams can end up working in isolation, causing delays and inefficiencies. During that time, program management can step in as the glue that connects these cross-functional teams, ensuring smooth operations and effective collaboration. By bridging communication gaps and facilitating information flow, program managers can enable teams to work cohesively towards shared objectives.

Dependency management: As start-ups scale, dependencies between different domains within the business become more complex. Timely delivery of critical components can heavily rely on the execution of interconnected tasks. In these times, program managers can help the team by identifying and managing these dependencies. By collaborating with cross-functional leaders and aligning priorities based on business impact, they can facilitate efficient execution of initiatives, reducing turnaround times and enabling sustained growth.

Standardizing workflows: Efficiency is paramount for start-ups aiming to scale rapidly. One of the key roles that program managers can play in a start-up environment is in standardizing workflows and establishing practices that drive efficiency gains. Whether it’s implementing agile methodologies, coaching teams on best practices, or facilitating collaboration across departments, program managers can help start-ups speak a common language. This standardization fosters better coordination, enhances productivity, and enables seamless scaling of teams.

Recognizing the benefits: While the aforementioned benefits highlight the value of program management for start-ups, the scope of its impact extends beyond these aspects. Program managers can help execute critical cross-functional initiatives, provide prioritization frameworks, and support organizational growth. If you’re unsure about the benefits, it’s advisable to seek advice from industry leaders before making a decision. Embracing program management could be a game-changer for your start-up’s success.

For start-ups seeking to navigate the challenges of growth, program management is not a luxury but a necessity. It enables founders to stay focused, promotes effective collaboration, manages dependencies, and standardizes workflows. By embracing program management, start-ups can drive their success, achieve scalability, and make significant strides in their respective industries. So, if you’re a start-up founder, take a moment to consider the immense benefits that program management can bring to your organization. Don’t hesitate to explore this invaluable resource and give your start-up the best chance to thrive.

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Posted by on July 10, 2023 in 21st Century, Leadership, Management, small business, Startup

 

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Driving Success: How TPMs Help Resolve Dependencies Between Teams

08 Mar
Driving Success: How TPMs Help Resolve Dependencies Between Teams

In today’s fast-paced and competitive business environment, cross-functional teams are becoming increasingly common as companies seek to innovate and stay ahead. However, with multiple teams working on various projects, dependencies between them can be a significant challenge, resulting in delays, miscommunication, and even project failures. Technical Program Managers (TPMs) can play a crucial role in resolving dependencies between cross-functional teams. This blog post will explore how TPMs can help facilitate effective communication, collaboration, and coordination between teams to ensure that programs are delivered on time and achieve organizational goals.

Facilitating communication: TPMs coordinate communication between teams, identify gaps, and ensure that everyone is on the same page. Regular meetings or stand-ups can be set up where teams provide dependency updates, identify roadblocks or dependencies, and prioritize tasks. These sessions can be used to share progress reports, discuss risks and mitigation strategies, and align on timelines and deliverables.

Identifying dependencies: TPMs often have a high-level view of all the programs executed within the organization. Thus, they can identify dependencies between teams and track their status to ensure they are being addressed in a timely manner before they become a blocker for achieving the company’s objectives.

Prioritizing and tracking dependencies: TPMs have a deep understanding of intra-team and inter-team dynamics, given the cross-functional nature of their role. Thus, TPMs can manage inter and intra team dependencies to ensure that one team’s work is not blocked by the lack of progress on the part of another team. They can prioritize the work of different teams based on their impact on dependencies and the company’s objectives, and identify and mitigate risks associated with dependencies across different teams.

Building relationships and trust: TPMs can help build relationships and trust between cross-functional teams to ensure they can work effectively, particularly when dealing with dependencies. This can be achieved by sharing information, being transparent, conducting joint brainstorming sessions, breaking down silos, defining ownership, setting clear expectations, and strengthening personal relationships by driving offsite events.

Improving transparency: TPMs can provide the necessary transparency to the team to drive efficiency in resolving dependencies. They can communicate deadlines to the right stakeholders to adjust plans accordingly and use tools like JIRA, Asana, Microsoft Project, Wiki, and collaboration platforms like Slack or Microsoft Teams to facilitate communication through shared dashboards and/or weekly reports.

In summary, TPMs play a vital role in managing dependencies between teams by facilitating effective communication, tracking and prioritizing work, building relationships, and improving transparency. They ensure that teams work together effectively and that all dependencies are identified and addressed in a timely manner to ensure program success.

 
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Posted by on March 8, 2023 in Leadership, Management, small business

 

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