BlogsSep 22, 2026
Where Agentic AI Transformation Can Go Wrong
By Walid Negm
Human attention and judgment were expensive, so organizations often turned work into sequences, queues, assignments and handoffs even when the underlying work was more dynamic. What we already can see is that AI agents can remain attentive, reason about changing conditions and coordinate in ways we never thought possible. That makes it practical to reconsider not only who performs the work, but how the work itself is organized.
- CEO
- Large AI investment with little change in growth, capacity or competitive position; fragmented transformation efforts; scaling complexity faster than value
- CFO / Finance
- AI costs outpacing realized savings; weak ROI; hidden coordination/review costs; duplicated spend; benefits that never reach the P&L
- CHRO
- Workforce disruption without productivity gains; roles degraded into exception-handling and oversight; skill mismatches; lower adoption and morale
- COO / Functional leader
- Faster tasks feeding the same bottlenecks; more handoffs and exceptions; throughput not improving; service reliability falling; operational complexity increasing
- Engineering / CIO
- Agent sprawl; integration and maintenance burden; brittle dependencies; rising technical/agentic debt; unreliable autonomy; probabilistic behavior in places that require deterministic control