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WHITE PAPER

Moving agentic AI from promise to performance

The dual transformation required to turn agentic AI into measurable business value.

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Agentic AI is creating new possibilities for how organizations serve customers, empower employees and operate their businesses. Yet for many organizations, the path from potential to measurable business impact remains unclear.

In-depth interviews with enterprise leaders reveal a common challenge: realizing value from agentic AI requires a dual transformation. Organizations must innovate what they offer and renovate how they operate.

This report explores:

  • Why agentic AI initiatives should begin with a business outcome, not a technology deployment.
  • How organizations must go beyond traditional proofs of concept by testing real-world conditions, validating readiness and creating evidence for scale.
  • The role of proprietary data signals, enterprise context and institutional knowledge in creating competitive advantage.
  • Why governance is critical to maintaining trust as agent authority evolves.

Drawing on interviews with leaders responsible for AI, data, analytics, strategy, innovation and transformation, the report identifies the conditions that enable organizations to move from promise to performance.

Download the report to explore the full findings and uncover what drives successful agentic AI adoption at scale.

Most companies similar to us are in a phase of mainstream experimentation–learning and experimenting with how to adopt AI and agentic AI systems. Lots of POCs, but how much is used as a mainstream business process and operating model is very little.

Technology United Kingdom

FAQ

What governance framework supports agentic AI?

As agents take on greater responsibility, governance must evolve alongside them. The research highlights the importance of clear authority levels, business ownership, traceability, escalation paths, auditability and recourse. Effective governance does more than manage risk; it enables organizations to expand agent authority responsibly while maintaining trust, accountability and human oversight.

What does a successful agentic AI pilot look like?

A successful pilot does more than prove technical feasibility. The research found that effective pilots test real-world conditions, evaluate production readiness, validate business value and generate evidence to inform decisions about scaling, refining or discontinuing a use case. The goal is not simply to demonstrate that AI works, but to reduce uncertainty around a production decision.

How can companies move from AI pilots to full-scale implementation?

Organizations that successfully move from experimentation to scale begin with a clear business outcome and work backward from there. The research found that scaling requires more than technology. It depends on testing under real-world conditions, building the right data and context foundations, establishing governance and renovating the workflows, approvals and ownership needed to turn the agent’s output in to business value.

How do enterprises build and implement an agentic AI roadmap?

An effective agentic AI roadmap starts with alignment around a clear business outcome. Based on interviews with enterprise leaders, successful implementation requires defined use cases, success metrics and the right data and context foundations. It also depends on governance and workflow changes that help move agentic AI from experimentation to measurable business value.

Why do companies need agentic AI implementation consulting?

Many organizations are experimenting with agentic AI, but struggle to move promising pilots into other business workflows. Implementation consulting can help organizations translate this ambition by defining the business outcome, aligning teams around the operating changes required and determining what it will take to scale responsibly.

What challenges can AI implementation consulting help organizations solve?

Enterprise leaders identified several barriers that can keep agentic AI pilots from reaching production, including undefined success metrics, fragmented data, unclear ownership, workflow bottlenecks and governance gaps. Consulting can help organizations uncover and address these issues early, so agentic AI solutions are tested against real conditions and connected to measurable business outcomes.

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