Agentic AI is not another hype cycle but the dawn of goal-driven systems that deliver measurable ROI. The shift, he argues, is from pilots and prompts to processes and profits.
When I discuss Agentic AI, I don’t describe it as just another wave of hype. For me, the future of enterprise AI is not about writing better prompts; it is about creating systems that understand context, make informed decisions, and act across real business workflows. Agentic AI marks a shift to goal-driven systems that can plan, utilize tools, act autonomously, and continue learning. The promise is not more pilots, it is measurable ROI in consulting, finance, compliance, cyber, and enterprise operations.
What Agentic AI Really Means
Agentic AI is the natural next step after rule-based systems, machine learning, and generative AI. Unlike GenAI, which creates content but waits for prompts, agents are goal-driven. You give them a business objective, and they perceive, reason, act, and learn. They can retrieve context from transactional and vector databases, browse the web or internal systems, and execute actions often without waiting for human input.
As I put it: “You set the goal; the system perceives, reasons, acts, and learns.”
Why Now: From Curiosity to CAGR
Adoption is accelerating. Nearly a third of enterprises expect agent-based capabilities to be embedded in their apps, and large firms , including KPMG , are already rolling out agent stacks for clients. Yes, we may be near the peak of the hype cycle, but this time the economics are different. Agents can replace or streamline parts of a process, and the ROI is defensible in CFO terms, not just on slide decks.
Use Cases That Land– At KPMG, we are already putting Agentic AI into practice:
- Consulting: Agentic engagement managers co-pilot consultants as they parse RFPs, draft proposals, flag risks, and trigger downstream actions.
- Compliance: Policy bots ingest new regulations and suggest edits to enterprise policies while risk engines rescore exposure.
- Cybersecurity: Our “virtual SOC analyst” integrates threat intelligence, SIEM logs, and dark web signals, escalating or auto-resolving incidents with human oversight.
- Enterprise Operations: Agents handle employee queries, triage IT tickets, and initiate procurement or HR workflows seamlessly across systems.
Build for Outcomes, Not Demos
My message is simple: start with low-friction, high-impact use cases. Define hard KPIs upfront, such as cycle time, cost-to-serve, error rate, and revenue lift, and measure them continuously. If you cannot state the outcome, you cannot defend the investment. Don’t reinvent platforms. Experiment quickly with what already exists, scale what works, and avoid bespoke builds that drain momentum.
Foundations First: Data, Guardrails, and Talent
Agents are only as intelligent as the context in which they work. This involves unifying data across silos, ensuring data quality, and monitoring model behavior. Governance is non-negotiable. At KPMG, our Trusted AI Council ensures that privacy, security, and regulatory controls are enforced, always with humans in the loop for sensitive actions. Cyber vigilance is critical as agents browse, fetch services, and execute steps that could be exploited. Talent is another bottleneck, not just for data scientists, but also for product managers who align agent behavior with business value, and engineers who can ship safely at scale.
Rethink the Business, Not Just the Stack
My strongest advice is this: don’t try to add AI to your business; rethink your business with AI. Utilize agents to redesign processes around outcomes, such as shorter collection cycles, faster policy updates, and lower mean time to resolution in security. Sprinkling assistance into old workflows is not a transformation.
Agentic AI will separate enterprises that count pilots from those that count profits. The winners will pair fast experiments with CFO-grade metrics, treat data and governance as product features, and keep humans firmly in the loop where stakes are high. The technology is ready. The real question is whether operating models and incentives are prepared, too.