Published
Four leadership decisions that determine whether AI scales
We partnered with HFS Research to bring you this agentic AI report based on a survey of 545 senior executives across 11 industries (plus Fortune 2000 interviews). It explains why the hardest part of scaling agentic AI is organizational readiness: accountability, measurement, workforce clarity, and process design.
Move from assisted AI to governable autonomy
Read the reportWhat's inside the report:
Why enterprise gen AI delivered productivity gains but stopped short of transforming execution
How agentic AI changes the enterprise risk profile when systems coordinate tasks and trigger actions across workflows
Where organizations are actually deploying agents today, and why most remain in supervised modes
How to design governable autonomy with clear escalation paths, decision rights, and evidence capture
Four questions leaders need to answer
1. Who owns outcomes when agents act?
Agentic systems are advancing faster than enterprise confidence in them. Only 22% of enterprises are comfortable authorizing domain‑level or broad autonomy, reflecting unresolved accountability when AI actions have real consequences.
2. What does agent success look like?
Expectations for agentic AI are high: 71% expect it to deliver ROI faster than any previous technology wave, yet 67% still rely on productivity metrics designed for earlier automation. Without agent-native metrics, enterprises struggle to prove value, defend investments, and decide when when autonomy should expand.
3. Is the human impact accounted for?
Agentic AI reshapes not just tasks but also decision authority: 44% of enterprises expect flatter organizational structures and 36% expect specific roles to be eliminated rather than augmented. Acceptance improves when organizations define decision rights, oversight responsibilities, and escalation paths.
4. Do workflows support autonomy?
Automation inside broken workflows produces brittle autonomy, so agentic AI compounds value only when workflows are redesigned end to end. Processes need to be rebuilt so agents can own outcomes safely.
Autonomy compounds only when all four decisions are resolved
View details on each card
Download the report for the full analysis
Read the reportFrequently asked questions (FAQ)
Agentic AI refers to AI systems (often called AI agents) that can plan, coordinate tasks, and take actions across tools and workflows rather than only generating outputs for a human to execute. In enterprise settings, agentic systems may route work, trigger transactions, resolve exceptions, and make operational decisions under defined guardrails.
Enterprises may trust a model to generate content, but granting autonomy means trusting a system to act in ways that can create legal, operational, and reputational consequences. The report shows the trust gap is largely an accountability gap: leaders need clear ownership, escalation paths, explainability, and evidence capture before expanding agent permissions.
Trust and accountability: Define who owns the agent and who is responsible when it fails
Measurement: Replace productivity-only metrics with agent-native KPIs that reflect autonomous execution
Workforce clarity: Make decision rights, oversight responsibilities, and intervention points explicit
Process design: Redesign end-to-end workflows so autonomy is governable and scalable
Agentic AI ROI should include not only productivity and cost but also execution metrics such as workflows completed end to end without escalation, decisions removed from human queues, independent exception handling, reduced handoffs, and improved cycle time for outcomes (not just tasks). The report explains why many organizations need new KPIs to capture the value of autonomous, decision-driven systems.
Human-in-the-loop typically means a person reviews or approves outputs and actions routinely. Supervised autonomy means agents can execute within predefined guardrails, escalating only for specific risks, exceptions, or thresholds. This is often a practical middle step for scaling autonomy while preserving control and accountability.
- CIOs, CTOs, CDOs, and heads of AI/automation scaling AI agents from pilots to production
- COOs and functional leaders responsible for end-to-end execution (finance, customer operations, IT, supply chain)
- Risk, compliance, and governance leaders defining accountability for autonomous decisions
- Transformation leaders redesigning operating models, workflows, and performance measurement
