How to close the AI value gap

Today, organizations increasingly venture into AI, allocating considerable resources to initiatives that range from exploratory pilot projects to full-scale implementations. While the promise is clear—greater efficiency, innovation, and competitive edge—the outcomes often don’t match the ambition. 

This illustrates that while AI capabilities are accelerating, the ability to translate those capabilities into business value remains a challenge for many. This gap between what leaders feel AI should achieve and how prepared their teams and processes are to deliver on that promise, is referred to as the AI value gap.

AI has swiftly moved from a niche product created by experts to a widely accessible feature deeply embedded across the organization. This shift underscores a fundamental principle: achieving meaningful change with AI starts not with the technology itself, but demands transformative change across multiple layers:

• Strategy & governance: This is where the journey starts. Strategy defines the why—the goals, opportunities, and value the organization seeks to unlock with AI. Governance ensures that this vision is pursued responsibly and sustainably.
• Process design: Once the strategic direction is clear, organizations must identify how they will achieve it. This means redesigning processes and operations to align with AI capabilities and ensuring that data flows in ways that support intelligent decision-making.
• Technology: With redesigned processes in place, the right technology stack becomes essential. AI cannot be operationalized without the infrastructure to support it.
• Process implementation: This is where plans become action. It includes configuring tools, integrating AI into workflows, updating procedures, and training teams on how the new processes should function. It’s the operationalization of the strategy, process design, and technology decisions made earlier in the pyramid. But implementation alone doesn’t guarantee impact.
• Adoption: At the top of the pyramid is the most critical—and often overlooked—layer. Adoption is about people actually using the new processes and tools in a meaningful, consistent way. It’s the behavioral shift—when employees not only know how to use AI, but also choose to use it, trust it, and embed it into their daily work. It’s when AI becomes part of the organizational culture, not just a technical rollout.

Many companies channel their energy into developing AI tools and building robust data infrastructures, yet they underestimate the importance of cultivating an organizational culture that embraces these changes. Employees need to feel empowered, confident, and engaged to incorporate AI into their decision-making processes and workflows effectively.

While it has become easier for organizations to access off-the-shelf AI tools and build technical capabilities, they struggle with adopting these solutions in a way that creates real business value.  AI is no longer confined to specialized domains—it influences nearly every area of an organization. This broad integration underscores the importance of addressing the human element, emphasizing that everyone within the organization has a role to play in understanding AI’s potential, mitigating its risks, and navigating its ethical implications.

These changes can cause uncertainty, making it essential to manage the transition thoughtfully. Some reasons that are consistently present across industries have a distinct human root cause:

1. Resistance to change: Employees fear disruption to their roles, uncertainty about AI's impact, and lack trust in new technologies.
2. Lack of leadership alignment or support: Without a clear vision and commitment from leadership, AI remains a side project, disconnected from business strategy.
3. Cultural inertia: Existing processes, habits, and mindsets resist the shift towards data-driven and AI-augmented ways of working.
   

Successfully embedding AI in an organization requires more than ambition—it demands structure. Without a clear roadmap, even the most promising AI initiatives risk stalling in pilot mode. Recognizing this, KPMG supported Digital Flanders in the creation of the AI Playbook: a practical guide designed to help public entities move from experimentation to scaled, responsible AI adoption.

The Playbook consolidates our experience into a structured, step-by-step approach. It offers actionable activities, best practices, and real-world examples that bridge the gap between vision and execution. Whether you're just starting or scaling up, the AI Playbook provides the clarity and confidence needed to move forward with purpose. Preparing for AI means preparing for change—and those who succeed in doing so are the ones who transform AI's potential into tangible value.

Dive deeper into each pillar of the AI playbook and discover actionable steps to prepare your organization:

• Vision & Strategy - Is there a shared understanding of what AI can (and cannot) do for your organization?
• Innovation – Does your organization have a good view on the real problems and challenges, or end users’ needs?
• Responsible AI - Are legal, ethical, and social implications integrated into the AI development lifecycle?
• People & Organization – Are your teams ready for new roles, responsibilities, and ways of working?
• (Coming soon in MVP 2) AI architecture - Is your data and IT landscape supportive, secure, and scalable?

While every sector has its own unique challenges, the foundations of successful AI adoption are remarkably consistent. Whether you're in the public sector or private industry, structured guidance can make the difference between stalled ambition and sustained impact.

Download the guide:  Het AI Playbook | Vlaanderen.be.

For organizations seeking tailored support, KPMG’s Lighthouse Data and AI teams are ready to help. From strategic visioning to architecture design, orchestration, and delivery—we partner with you to turn AI into measurable value. 

Michelle Claessens, Advisor & Bart Van Rompaye, Principal