Davos 2026: Why AI Adoption Still Falls Short and What Truly Drives Enterprise Scale 

By Raj Vattikuti, Chairman and Founder, Calibo

TL;DR: Enterprises don’t lack AI ambition or experimentation. They lack a trusted, practical path from experimentation to production. AI scales when businesses can experiment safely in a governed sandbox, deliver bite-sized use cases, and move proven intelligence into production in an automated way—with trust and governance built in.

As global leaders prepare to convene at Davos, one question continues to surface in boardrooms and executive discussions: Why is AI still struggling to deliver at enterprise scale?

After decades of building and scaling technology companies and working closely with global enterprises, I’ve seen multiple waves of innovation promise transformation. AI is no different in ambition. What is different is the widening gap between investment and impact. Despite significant spending, only a small portion of AI initiatives translate into sustained business adoption.

This is not a technology problem. It is an execution problem.

AI without adoption is not innovation

Across industries, AI programs are rich with ideas, pilots, and proofs of concept—yet many never reach production. The reasons are consistent.

Business teams often lack a trusted environment to experiment with their own data. Production systems are complex and siloed, making experimentation risky. And pilots frequently remain disconnected from real operational workflows, limiting ownership and confidence.

When AI lives outside the business, adoption slows. When it cannot move safely into production, value remains unrealized.

Businesses need their own data-AI experimentation sandbox

At Calibo, our work is anchored in a simple premise: 

Businesses need their own data-AI experimentation sandbox built around bite-sized use cases—separate from today’s complex and siloed production environments.

This separation gives business teams the freedom to experiment and learn without disrupting mission-critical systems. And when the sandbox runs inside the customer environment, it enables strong cybersecurity, controlled access, and faster readiness—while giving teams the confidence to experiment safely.

Once intelligence is proven, it can move into production in an automated manner—with trust and governance built in from the start.

From intelligence creation to trusted production

Real adoption happens when businesses can answer three questions clearly:

1. Does this intelligence solve a real problem?

2. Can we trust the results?

3. Can we operationalize it safely and repeatedly?

A governed experimentation sandbox creates clarity before scale. It ensures explainability before automation—and gives enterprises confidence that intelligence moving into production is ready for real-world impact.

Talent is the multiplier

Technology alone does not drive transformation. People do.

AI adoption accelerates when business-ready talent learns and builds inside real, governed experimentation environments. When teams understand both business context and engineering discipline, innovation becomes repeatable and scalable.

This is how organizations move beyond one-off pilots and begin building lasting AI capabilities.

A practical AI agenda for Davos

As conversations at Davos increasingly focus on AI governance, productivity, and economic impact, the discussion must move beyond promise to practice.

The enterprises that succeed will be those that enable business-led experimentation, invest in trusted and governed foundations, and build talent ecosystems that know how to move intelligence from sandbox to production.

Innovation is not about doing more experiments. It is about building the discipline to scale what works.

I look forward to continuing this conversation in Davos with leaders who are focused on turning AI into a true engine of business value.

Raj Vattikuti
Chairman and Founder, Calibo