What is agentic AI — and why it matters now

Agentic AI goes beyond chatbots and copilots: it chains reasoning, tools and actions to deliver outcomes (e.g., generate options ➝ analyse data ➝ trigger workflows). Analysts place agentic AI among 2025’s top trends, but also caution that many initiatives stall without ROI discipline and strong controls.

The value thesis. Generative/agentic AI is already tied to multi-trillion value pools; banking alone could see hundreds of billions annually as agents automate service and operations. Your job is to localise that value into your P&L: revenue, margin, working capital and risk.

CEO mindsets that separate winners from pilots

1. Outcomes > demos. Fund use-cases with P&L-visible targets (e.g., +2–3 pts service NPS; −20% handling time; +5% conversion). Anchor every agent to a KPI and time-to-value. (McKinsey: value materialises where processes are redesigned, not just tool-tipped.)

2. Operate like a product company. Treat agents as products with roadmaps, SLAs and owners—avoid scattered proofs of concept that never scale. (Gartner’s cancellation warning is largely an operating-model failure.)

3. Govern by design. Build trust, safety and auditability in from day zero: adopt ISO/IEC 42001 for an AI management system; align risks to NIST AI RMF; and track EU AI Act applicability and dates.

4. Human-in-the-loop where it matters. Use approval gates for high-impact actions (financial transfers, pricing, customer remediation) and log everything for review.

A two-year roadmap (board-level)

Phase 1: 0–6 months — Prove value safely

• Pick 3 agentic use-cases with fast payback and bounded risk (e.g., sales proposals, collections dunning, supplier onboarding orchestration).

• Stand up the operating model: executive sponsor; AI product owners; model risk committee; data governance; security reviews.

• Controls baseline: adopt ISO/IEC 42001 (policy, risk register, incident playbooks), map risks to NIST AI RMF, and confirm EU AI Act exposure (GPAI vs high-risk).

• Target metrics: cycle-time ↓30–50%; cost per transaction ↓10–20%; NPS/CSAT ↑; first-pass yield ↑.

Phase 2: 6–12 months — Industrialise

• Platform choices: standardise on an agent framework and tool-use pattern (actions, memory, retrieval, orchestration).

• Service reliability: observability, rollback, sandboxed actions, API quotas.

• Workforce enablement: redefine roles (agent supervisor, prompt/flow designer) with training pathways.

• Compliance runway: EU AI Act dates—prohibitions and literacy obligations apply from 2 Feb 2025; governance and GPAI rules apply 2 Aug 2025; full applicability by 2 Aug 2026 with a longer runway for embedded high-risk systems to 2 Aug 2027.

Phase 3: 12–24 months — Scale & new business

• Portfolio expansion: 10–20 agents across functions (pricing ops, claims adjudication, field-service scheduling, financial close).

• New revenue: productise agents as features (autonomous advisory, proactive service).

• M&A and partnerships: buy vs build vs partner decisions based on time-to-value and compliance debt.

• External assurance: consider ISO/IEC 42001 certification to signal maturity to customers and regulators.

Operating model: how you’ll run this

• Leadership & funding. One accountable exec (CPO/CTO/CIO) with a capital envelope tied to KPI lift, not activity. Quarterly stage-gates.

• Product squads. Each agent has a product owner, tech lead, data lead, and risk lead; they own metrics, safety cases and runbooks.

• Risk & compliance.

  • ISO/IEC 42001: AI policy, lifecycle controls, supplier oversight and incident management.

  • NIST AI RMF: map risks across govern, map, measure, manage.

  • EU AI Act: track category (prohibited / GPAI / high-risk), technical documentation, transparency and AI literacy obligations on the published timeline.

Where value is showing up already

• Financial services. Early UK/EU pilots point to autonomous money-management and service automation; regulators are watching closely—governance and senior-manager accountability matter.

• Enterprise ops. Analysts note big efficiency uplifts when agents automate complex, cross-system workflows—beyond traditional RPA/IVR.

FAQs

Q1. What is agentic AI?
AI that can plan, decide and act (with constraints) to accomplish goals—automating multi-step work and integrating with your systems. Analysts classify it as a 2025 strategic trend. PagerDuty

Q2. Where should CEOs start?
Pick a few high-value, low-regret workflows; assign product owners; set KPI targets; and stand up governance aligned to ISO/IEC 42001 / NIST AI RMF while mapping EU AI Act obligations. ISO | NIST

Q3. Biggest pitfalls to avoid?
Unscoped pilots without KPIs, weak operating models, and neglected compliance—drivers behind Gartner’s forecast that >40% of agentic initiatives could be cancelled. Gartner