Skin in the Game, Dear Business: Why AI Value Demands Shared Ownership

This article is Part 2 of our Value-First AI Series. In Part 1, we introduced the Value-First AI Framework and argued that every AI use case should begin with a clear value hypothesis. In this follow-up, we go one step further: even with a strong hypothesis, AI will only create real impact if both business and technology teams share ownership. In other words, the business must have skin in the game.

In our previous article, AI Without a Value Hypothesis is Just an Experiment, we talked about why every AI use case should start with a clear value hypothesis. But what we’ve learned through experience is that even when you nail the value piece, success is still far from guaranteed.

The truth about AI use cases is that they’re inherently transformational. They don't stay neatly contained in their own little corner. Each use case ripples outward, reshaping processes (how work is organized and decisions are made), influencing people (their roles, skills, and behaviors), and stretching the limits of technology (data, systems, and infrastructure).

But the catch is that most of those ripple effects don't happen inside the AI team. They happen in the business. The processes that need to change? Those belong to business teams. The people who need to adapt? They report to business managers. The systems that need updating? Business departments usually own them.

This creates a significant challenge: how can an AI team truly articulate a realistic value hypothesis if they don't have buy-in from the business teams who actually control whether that value gets realized? The short answer is: they can’t.

The myth of "just build it and they'll come"

Many organizations fall into the same trap. They think that if their data and AI teams “just deliver great models,” the value will automatically follow. Business teams throw requirements over the fence and expect the technology side to do the rest.

But this mindset completely misses how value actually gets created. Value doesn't emerge from the model sitting on a server somewhere. It emerges when AI capabilities meet real business context.

Defining a robust value hypothesis therefore requires co-creation. Business leaders know how processes really work, what levers actually drive change, and what's realistic given current operations. AI teams understand technical possibilities, scalability challenges, and innovation. You need both perspectives to create a hypothesis that's not just compelling on paper, but, but actually executable in practice.

Even the best model in the world won’t move the needle if it’s not embedded seamlessly into workflows, the people expected to use it resist or ignore it, or processes stay exactly the same, forcing the AI to operate in isolation.

Value doesn’t emerge from technical excellence alone. It requires business teams to advocate for change and back that advocacy with tangible contributions of budget, people, and time.

Also read: The Hidden Skill Every Data Team Needs: Effective Stakeholder Communication

Why the business teams need skin in the game

Having business teams act as collaborators, is still not enough. They can provide information, clarify requirements, and attend meetings all day long. But to truly unlock AI’s value, the business must go one step further: they need to actively shape, adopt, and sustain AI use cases. They need skin in the game and a shared sense of responsibility for outcomes, not just inputs.

What does this look like in practice?

Process adaptation: means business teams need to be willing to rethink and redesign how work gets done. AI can't simply be bolted on as an afterthought. It needs to be embedded where it can actually drive impact. That means changing workflows, updating procedures, and sometimes completely reimagining how things work.
People engagement: requires clear communication, proper training, and the right incentives to help employees trust and adopt AI-driven changes in their daily work. People need to understand not just how to use new tools, but why those tools matter and how they'll help.
Technology integration: often means legacy systems and scattered data need to be adapted or restructured so AI can scale sustainably. This work doesn't belong to the AI team alone. Business teams control many of these systems and need to champion the changes required.

Without this level of business commitment, even the most technically brilliant AI solutions remain end up underused, delivering prototypes instead of transformation, and potential instead of performance.

Value-first also means process- and people-first

If “value-first” is the guiding principle for AI, then process and people come first too. Technology may be the enabler, but it’s processes and people that determine whether value ever materializes. Your model can be 99% accurate in the lab, but if it doesn’t fit into workflows, if employees don’t trust it, or if the organization isn’t ready to adapt, that accuracy is meaningless.

This isn’t just theoretical. Most AI initiatives don’t fail because of flawed models but because of human and organizational barriers. Misaligned processes, resistance from employees, lack of cross-functional ownership. In other words: if you don’t get the business context right, even the smartest AI will stall.

This requires three things that business teams need to own:

Change management: AI adoption is ultimately a change journey, not a technical rollout. Employees often worry about what AI means for their jobs. Without clear communication, fear quickly turns into resistance. Leaders need to explain not just how to use new system, but why they matters and how they’ll help. This means aligning incentives, offering reskilling opportunities, and providing space for feedback. A good idea, badly implemented, goes nowhere.
Holistic thinking: AI rarely lives in a silo. Each initiative ripples across the operating model. A customer service chatbot, for example, doesn’t just affect call handling. It influences employee training, escalation processes, data governance, and even compliance. Leaders need to ask: How does this AI change the way our business operates end-to-end? Without this systemic view, AI initiatives risk becoming isolated pilots that never scale.
Shared accountability: Too often, data teams are measured on model accuracy while business teams measure success in revenue, cost, or customer satisfaction. The gap between those metrics is where AI initiatives go to die. True value emerges when both sides commit to the same success criteria: adoption rates, process improvements, customer outcomes. This requires cross-functional ownership, with business leaders and AI teams jointly responsible for results.

When leaders put process and people first, they reframe AI from being “a tool the tech team builds” to “a capability the organization uses.” And that shift from tech-first to human-first is what separates pilots that fade from transformations that stick.

The call to business leaders

Data and AI leaders cannot shoulder this responsibility alone. Business leaders need to recognize that realizing value from AI value requires their full participation. Without active engagement from the business side, even the most elegant technical solution risks becoming expensive shelf ware.

This means moving from a “spectator” mindset to a “co-owner”. Business leaders can’t simply sponsor an AI initiative and hope value emerges. They need to help shape, enable, and sustain it.

In practice, this looks like:

Championing AI as business transformation: Position AI initiatives not as tech projects, but as core enablers of strategic goals. Like improving customer experience, driving efficiency, or unlocking new revenue streams. When business leaders talk about AI as part of the company’s growth narrative, they legitimize it across the organization.
Allocating resources for change, not just models: Funding a model build is the easy part. The harder and often overlooked investment is in adapting processes, retraining people, and modernizing systems so AI can actually live inside the business. Leaders must be willing to fund these “last mile” efforts, which often determine whether value is realized.
Owning adoption and impact: Business teams are where adoption happens. Leaders must hold their own organizations accountable for using and improving AI solutions. That means setting clear expectations, measuring adoption, and rewarding behaviors that drive impact. If adoption lags, business leaders should treat it as seriously as they would underperformance in any other strategic initiative.

AI success isn’t delivered to the business; it is co-created with the business.

Shared skin, shared success

AI has the potential to deliver enormous value, but only when both technology and business teams lean in together. Data scientists and engineers can design world-class models, but those models won’t change outcomes unless business leaders adapt processes, support their people, and share responsibility for results.

In other words: no skin, no game. AI value requires more than technical brilliance. It requires organizational transformation. That transformation only succeeds when the business puts its weight behind it.

The Value-First AI Framework goes beyond being a technology playbook. It’s a blueprint for collaboration. It’s a reminder that business and tech must share skin in the game. When both sides align around outcomes, commit resources to change, and co-own accountability, AI stops being an experiment and becomes a true engine of business impact.

For leaders, the reflection is simple but profound: Are we treating AI as a tool for the data team, or as a shared journey of transformation? The answer will determine whether AI remains a promising idea or becomes a source of lasting competitive advantage.