Despite widespread corporate enthusiasm for AI agents, most organizations are not equipped to deploy them effectively. A new report from Celonis shows that 76% of companies say their current operations and infrastructure cannot support agentic AI. This gap persists even though 85% of organizations want to adopt the technology within three years.
The Readiness Gap
The disconnect between ambition and execution stems from a fundamental mismatch in how businesses approach AI. Many organizations simply layer AI agents onto existing human-centric workflows rather than redesigning their operating models from scratch. Prasun Shah, global CTO for workforce consulting and chief AI officer at PwC UK Consulting, compares this approach to adding sticky tape to a broken operating model. He says embedding AI agents into a human operating model prevents firms from unlocking the full value of agentic AI.
AI agents can execute entire workflows with limited human input. They coordinate complex tasks, make independent decisions, adapt to changing conditions, and iterate performance. Early deployments in customer service, HR, and sales suggest that AI agents could accelerate business processes by 30% to 50% and reduce low-value work time by 25% to 40% at scale. But realizing those gains requires enterprise-wide change, not piecemeal additions.
Why Sticky Tape Fails
Shah warns that layering AI onto existing systems creates conditions where disillusionment sets in. The full value of agentic AI lies in its capacity to act as connective tissue between different parts of an organization. That means rethinking workflows, decision rights, and performance management systems. Without that redesign, AI agents become mere point tools rather than active participants in value creation.
Enterprises need to pivot from linear processes to a model where AI agents operate at machine speed across multiple systems simultaneously. This requires a technology stack designed for AI agents, not human operators. Leaders must prioritize access to multiple datasets and applications to build tacit knowledge. Shah calls this architectural shift the next competitive battleground for enterprises.
A New Framework for Change
Enterprise agentic AI platform Ema, in partnership with HFS Research, coined the term agentic business transformation to fill a gap in the existing lexicon about AI agents. CEO and founder Surojit Chatterjee argues that current vocabulary fails to capture the scope of the change. Digital transformation moved from paper to software. AI transformation added intelligence to existing processes. Co-pilot focuses on assisting human tasks. But agentic business transformation is about integrating AI agents into the fabric of the organization.
The framework rests on three pillars: the technology stack, the workforce, and the metrics used for success. Each pillar must be redesigned for an environment where AI agents are primary actors. When a new business requirement emerges, Chatterjee says, organizations that have made this shift can configure an AI employee quickly instead of waiting months for a software update.
Why This Matters
For enterprises racing to adopt agentic AI, the stakes are high. Without fundamental organizational redesign, companies risk wasting investment and falling behind competitors that embrace the change. The ability to deploy AI agents at scale could reshape entire industries by slashing processing times and freeing human workers from routine tasks. But the window for acting is narrow. As more firms attempt the transition, those that treat agentic AI as just another software layer will find themselves stuck with a broken operating model and mounting disillusionment.
The message from experts is clear: agentic AI demands a new way of working, not just a new set of tools. Organizations that ignore that reality will struggle to compete.
