Build smarter with AI: A blueprint for senior leaders stuck between AI ambition and execution

Why senior leaders feel stuck after two years of AI investment

Most executives we speak with are living the same scene. The board says, do something with AI. The pilots start. Demos are produced. Hackathons are run. Licenses are bought. Two years pass.

They have receipts for activity. They have nothing in production.

Searches for "AI tool" have gone vertical since ChatGPT launched in 2022. Corporate spending on AI software has done the same — Ramp's data on US corporate cards shows the average AI software contract value moving from negligible in 2023 to serious enterprise commitments in 2025. The activity is real. The impact gap is widening into a crisis of execution.

The pattern repeats across every conversation we have at the SVP and VP level: pilots scattered across teams, point solutions without holistic impact, no structured way to convert spend into something that compounds. The investment thesis is sound. The OKR pressure is real. The system to translate one into the other is missing.

The diagnosis below comes from a joint masterclass with Gianluca Mauro, founder of AI Academy, and John Vetan, co-founder of Design Sprint Academy. AI Academy knows how to build the rocket. Design Sprint Academy knows how to point it. This article combines both.

What is the AI Stack Map?

The AI Stack Map is a 2x2 framework created by Gianluca Mauro to help leaders decide what to buy, what to build, and what to forget when investing in AI. It plots every AI use case across two axes: value to the business and specificity to your business.

The axes are simple by design.

• Value: If you solve this challenge, how much impact does it create? A small productivity nudge, or a meaningful shift to revenue, cost, or strategic position?
• Business specificity: Is this a problem every organization shares, or one that only your business has a real incentive to solve?

Four quadrants emerge. Each quadrant calls for a different AI strategy.

Quadrant 1: Low value, low specificity → Use generic AI tools

This is meeting summaries, email rewriting, document drafting. Useful, but not transformative. Every company has the same need.

Here, generic tools win: ChatGPT, Claude, Gemini, Copilot. They are the Microsoft Excel of the AI era — used by everyone, from marketing campaigns to finance reporting. Their return on investment depends almost entirely on how proficient your people are with them. Without training and change management, the tools sit idle.

What to do: Buy licenses. Invest heavily in training and change management. Measure adoption, not just deployment.

Quadrant 2: High value, low specificity → Use vertical AI tools

This is contract review, financial reporting, marketing content, legal research. The need is high-stakes, but the underlying problem is shared across companies in the same function.

Here, generic tools fall short. Specialized vertical AI tools — Legora for legal, Jasper for marketing content, Epiphany for instructional design — are built for the specific workflow and outperform ChatGPT on the same task. Could you use ChatGPT to review legal contracts? Yes. Should you, if you are a serious financial institution? No more than you would run your CRM in Excel.

What to do: Let business units own the selection. Coordinate from the AI team or AI Lab. Test thoroughly — there are many thin wrappers in this category that add little real value.

Quadrant 3: High specificity, low value → Build internally with an experimentation mindset

This is the quadrant most companies skip. Internal back-office processes that are unique to how your business operates, but where the upside is incremental rather than transformative.

This is the perfect place to build agents and small AI products with low stakes. Your team learns the craft of building. They develop the judgment to know what works. A thousand small agents powering internal workflows compounds into transformation. And occasionally, a small idea built here turns out to be a big one.

What to do: Build with no-code tools (Make, n8n, Zapier) or vibe coding. Let teams experiment fearlessly. Treat this as capability building, not product development.

Quadrant 4: High value, high specificity → Build strategically, with the right direction first

This is the quadrant that moves the needle on your business. AI products built around your competitive advantage. Workflows redesigned with AI embedded where the value is most concentrated.

And this is the quadrant where most enterprise AI investments fail. Modern AI tools make building easier than ever. The failure mode is direction — pointing the rocket at the wrong target.

What to do: Invest disproportionately in the scoping phase. Get the problem right before you build. The methods that follow in this article are designed for this quadrant.

Should I buy or build AI tools?

The buy-vs-build debate is one of the most common questions executives ask, and it is the wrong question.

Most teams arguing about buy versus build have not yet defined what problem they are solving. That is like arguing about rocket engines before you have decided where you are going.

The honest answer is that you will need to do all three.

• Buy generic tools to lift productivity across the organization (Quadrant 1).
• License specialized vertical tools for high-value, common workflows (Quadrant 2).
• Build custom agents and AI products where your competitive advantage lives (Quadrants 3 and 4).

The right question is not which AI should we use. It is which part of our business deserves it.

Why is AI activity high but AI impact low in most enterprises?

Because organizations are trying to solve a strategic problem with a technical volume knob. They believe that if they throw enough use cases at the wall, one will stick and transform the company.

Four misconceptions keep AI from delivering value. Each shows up at the SVP and VP level after roughly 12–18 months of scattered investment.

1. AI does not fix dysfunction. It magnifies it.

You cannot automate alignment. You cannot optimize confusion. And you cannot prompt your way out of a broken culture.

Many teams treat AI as a band-aid for bad processes. The result is predictable: disconnected prototypes multiply, demos look impressive, and results stall. AI is a mirror. It exposes the dysfunction already inside the organization.

When organizations skip clarity and go straight to coding, AI becomes an accelerant for chaos.

2. The buy-vs-build debate is a distraction from the real question

Teams retreat to a technology debate to avoid the harder strategic one. The procurement conversation feels productive. It isn't. It keeps leaders busy comparing vendors while skipping the step that determines whether any of this matters: defining what to actually solve.

3. The most experienced people use AI the most — and trust it the least

A 2025 field survey found that engineers and managers with eight or more years of experience used AI more than anyone else, and trusted it less than anyone else. They have seen every silver bullet become a maintenance problem.

One respondent put it cleanly: AI generates code faster. But faster code never fixed a poor understanding of why the code exists.

These veterans are the people asking the right question — where does AI actually add meaning, not just speed? They know the difference between using tools and building capability.

4. AI will not save your meetings. It will expose misalignment.

AI can generate a thousand answers in a second. It cannot facilitate a single difficult conversation. If your meetings are slow, unstructured, and ego-driven, AI exposes that weakness faster.

Speed doesn't kill. Misalignment at speed kills.

Facilitation has stopped being a soft skill. It is now an AI survival skill.

Why do most enterprise AI initiatives stall at level one or two?

When organizations do start moving on AI, adoption tends to follow four levels. Most enterprises are stuck somewhere between the first two.

Level 1: Individual. Everyone experiments. People build their own copilots, prompt templates, mini agents. Some of it relates to the business. Some of it doesn't. It looks productive on the surface.

Level 2: Team. Standard prompts emerge. Some agents get plugged into team tools — Slack, Google Sheets, Jira. The team starts moving in the same direction.

Level 3: Organizational. The company has an approved AI stack, governance, security checks, and a portfolio of validated AI use cases. Capability is institutionalized.

Level 4: Ecosystem. AI connects across partners, clients, and customers. Shared data. Open APIs. Agents from different organizations talking to each other across the value chain.

Most enterprises are between Level 1 and Level 2. Strong individual usage, occasional team workflows, and a wall.

The wall is not technical. It is structural.

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What goes wrong at Level 1

A business user builds a useful agent. The moment it needs to scale beyond their own desk, compliance, data privacy, security, and governance arrive at the door. None of those have been thought through. The agent dies on the vine.

A second pattern: someone builds a useful workflow on their own machine. Their small team uses it. Nobody else does. When that person leaves the company, the knowledge leaves with them.

What goes wrong at Level 2

Managers realize that ten teams are building ten versions of the same agent. There are no standards, no shared learning, no ownership clarity. Everyone is busy. The organization is not transforming.

What goes wrong at Level 3

Teams have no clear, repeatable path from we want to do this with AI to this is in production. Every team invents its own approach. The work looks chaotic from the top. Leaders cannot prove ROI on dozens of pilots that aren't connected to business value. Faith in the program erodes.

The failures look new. They are not. They are the same organizational tensions that existed before — misalignment, unclear priorities, lack of validation, broken collaboration, slow decision-making — amplified by speed. Companies learned to live with them. With AI moving faster than adoption, they cannot anymore.

What is the missing middle in AI transformation?

The missing middle is the gap between AI strategy at the top of the organization and AI execution at the bottom. It is where most enterprise AI investments get stuck.

At the top, leadership has an AI ambition — often vague. Let's do something with AI. AI is a priority next year. We need to be more strategic about this.

At the bottom, teams need to put agents into production, scale them, govern them, and prove they work.

Between the two sits a layer where teams are told to do something with AI but no one defines what it means, why it matters, or how to start. Without alignment, without clear use cases, without validation, teams stall.

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Closing the missing middle requires two complementary skill sets:

• Strategic skills: framing the problem, aligning stakeholders, validating solutions, defining what value means.
• Applied AI skills: turning that clarity into something tangible — agents, workflows, working products.

AI Academy and Design Sprint Academy bridge this gap together. Strategic clarity from one. Applied building from the other.

What is the system that closes the missing middle?

A repeatable three-stage system that takes a vague AI ambition and converts it into a working, validated AI product. It is built as a muscle the organization owns — not a one-off engagement.

The three stages:

Stage 1: AI Problem Framing

A 1-day workshop with 6–8 cross-functional decision-makers. The team aligns on which AI use case is worth pursuing — connecting it to a real business goal and a real customer or employee need.

The output is an AI Use Case Card: who, what, why, how, constraints, success metrics. A build/pause/kill decision on a specific AI opportunity.

This is where most enterprise AI failures get prevented. Without this stage, teams ship solutions to problems that were never validated.

Stage 2: AI Workflow Sprint

A 2–4 day workshop that takes the validated use case and produces either a working AI agent MVP or a clear no-build decision, validated with real users.

The sprint redesigns the workflow first, then decides what AI should do inside it. Day 1 maps the current state. Day 2 designs the AI-enabled version. Day 3 builds an MVP with an AI Build Trio (AI Engineer, UX/Product Designer, Subject Matter Expert). Day 4 tests it with five users and produces a scale, iterate, or stop decision.

In the past, design sprints existed because we didn't want to spend six months building something nobody wanted. With AI, the question is sharper — we can build prototypes in days. The reason we still need the sprint is to make sure we are prototyping the right thing.

Stage 3: Building the Agent

The validated use case becomes a working AI agent MVP, built with no-code or low-code tools, ready to be hardened by IT and scaled. Teams who run this stage themselves — not via a consultant — learn the muscle.

This last stage is where AI Academy's Agentic AI Bootcamp slots in: a structured, hands-on environment where non-technical teams build their first production-ready agent.

What does it take to make this system work?

Three ingredients. The system is straightforward; the environment around it is what most companies get wrong.

Ingredient 1: The right people in the room at the right time

This is sequenced top to bottom.

• Leadership sets direction at the strategy level. Where can AI create the biggest value?
• Middle management and domain experts translate the direction into specific AI use cases via AI Problem Framing.
• Cross-functional execution teams — designers, AI engineers, business owners — turn use cases into working AI agents inside their actual workflows.

Most companies break this sequence. They run team-level experiments without leadership direction. Or leadership writes a strategy that never lands with the people who could execute it. Or middle management builds use cases without ever talking to the people who will use them. Each break creates the missing middle.

Ingredient 2: Facilitation

The collaboration bar in most enterprises has dropped lower than leaders realize. Endless meetings that produce alignment on having another meeting. Workshops where people play with sticky notes and three weeks later nothing has changed. Difficult conversations avoided to protect egos.

Facilitation is the antidote. It gives teams a shared language, forces real decisions, and keeps everyone aligned to the goal. It is the role that turns a workshop from theater into a decision-making system.

This is why facilitation has become an AI survival skill. The work that AI exposes — alignment, prioritization, decision quality — is exactly what facilitation produces.

Ingredient 3: Structure

AI Problem Framing, the AI Workflow Sprint, and the agent-building process are time-boxed, step-by-step methods. Recipes. They can be codified into playbooks and toolkits, which means they can be run consistently and at scale.

The argument that structure kills creativity does not hold up in practice. Structure is what makes creativity work at scale. Inside the structure, there is plenty of flexibility for the team to think.

Where to start

Before your team builds another prototype or licenses another tool, ask one question:

What's the most important problem in our business — and how can AI help us solve it?

Not which AI tool is hot this quarter. Not whether to buy or build. Not what the competition is doing.

The problem. The customer. The business value.

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📺 Prefer to watch? Watch the full masterclass on YouTube (60 minutes).