Davos 2026: The Year AI's Execution Gap Became Undeniable

After five years on the World Economic Forum's Digital Leaders board, I've learned to watch for convergence. When CEOs across energy, financial services, retail, and manufacturing start using the same language—unprompted—something real is happening.

This year, that language is clear: execution, trust, governance.

Not capability. Not models. Not innovation.

The AI debate at Davos has fundamentally shifted.

The Same Problem, Everywhere

Earlier this week, Mohamed Kande, Global Chairman of PwC, told a CEO and board-level audience that more than half of large organisations are seeing little or no measurable value from their AI investments.

The reasons he cited were consistent:

• Unclear ownership
• Weak data discipline
• Lack of operational clarity
• Insufficient governance

These aren't technology problems. They're infrastructure problems.

In private conversations across industries—energy, industrial manufacturing, mobility, professional services—the same pattern emerged. Everyone has powerful models. Everyone has board-level ambition. Everyone has pilots that look impressive on paper.

And yet value keeps stalling in the same place: the last mile.

Why the Last Mile Keeps Breaking

The last mile is where AI meets enterprise reality:

As Satya Nadella has framed it clearly: long-term advantage will come from deploying AI reliably and securely inside real organisations, at scale, over time.

Not from building smarter models. From building trusted execution.

The Missing Infrastructure Layer

Here's the uncomfortable truth from Davos: AI is moving into the core of organisations faster than decision rights, accountability models, escalation paths, and audit mechanisms are being redesigned.

That imbalance carries risk—even when the technology performs well.

Think about what this means practically.

Every AI agent that touches a production system needs:

• Clear boundaries on what it can and cannot do
• Audit trails for every action it takes
• Cost visibility so experiments don't become budget black holes
• Escalation paths when it hits the edge of its competence

Most enterprises have none of this in place.

They've built the intelligence. They haven't built the infrastructure for trust.

Enter Protocol Standardisation: MCP as the Foundation

The solution isn't more governance committees. It's standardisation at the protocol layer.

This is where the Model Context Protocol (MCP) becomes critical.

MCP standardises how AI agents connect to tools and systems. It's the equivalent of what TCP/IP did for networking, or what Kubernetes did for container orchestration—a common language that lets different systems work together reliably.

Without protocol standardisation, every AI integration is bespoke. Every connection is a custom build. Every audit is a forensic exercise.

With MCP, you get:

• Consistent tool interfaces across your entire stack
• Predictable behaviour that can be governed at scale
• Interoperability between agents, tools, and systems from different vendors

But protocol standardisation alone isn't enough.

The Governance Layer: Where Trust Gets Built

Standardised protocols need a governance layer that enforces the rules enterprises actually care about.

This is where Palma.ai fits.

Palma sits between your AI agents and your enterprise systems. It doesn't make the AI smarter—it makes the AI accountable.

Here's what that looks like in practice:

The executives at Davos aren't asking for more AI capability. They're asking for the infrastructure that lets them say yes to the capability they already have.

That's the governance layer.

The Architecture That Actually Works

The winning stack for enterprise AI in 2026 looks like this:

Intelligence at the top. Standardisation in the middle. Governance before the edge.

This architecture answers the question that kept surfacing at Davos:

"How do you turn intelligence into reliable action, at scale, inside the messy core of the enterprise?"

You don't do it with better models. You do it with better infrastructure.

What This Means for 2026

Davos this year felt less like a technology showcase and more like a governance reckoning.

The signal is clear:

The companies making progress aren't chasing ever more sophisticated models. They're doing the harder work of redesigning how decisions are made, how work flows end to end, and how humans and machines actually complement each other in practice.

That's not an AI problem. That's a leadership problem.

And leadership problems need infrastructure solutions.

The Bottom Line

The AI gap is no longer technological. It's operational.

Protocol standardisation through MCP gives you the foundation. Governance platforms like Palma.ai give you the control. Together, they give you what every executive at Davos is actually asking for:

The ability to say yes to AI—without losing control of the enterprise.

The future of AI will be shaped not only by technical capability, but by leadership choices, organisational discipline, and the ability to absorb change without losing control.

That sits at the heart of the work happening now.

And it's where the most consequential decisions of 2026 will be made.