Half of UK businesses now treat AI as the front line of operational resilience
Operations leaders are shifting their playbook. New research shows 49% of resilience investment is going to technology and AI - more than triple the spend on supply chain diversification (14%) or talent and training (13%). And 87% of leaders feel pushed by regulation to rethink how resilience works end to end.
The message is clear: AI has moved from "pilot" to "protection." It's not just automating tasks - it's helping teams anticipate disruption, speed up decisions, and keep pace with stricter controls.
Key numbers you can use
- 49% of resilience spend flows to technology and AI.
- 87% of leaders say regulatory pressure is forcing a reset of operational resilience.
- Supply chain (14%) and upskilling (13%) are now far behind AI in spend.
- 25% rank agentic AI (autonomous, goal-driven systems with guardrails) as a top priority.
- 50% are rolling out AI agents across multiple functions.
- Sector gap: science/tech/research leads (36% high focus; 56% scaling across areas). Retail (15%) and education (13%) lag.
- Ownership is fragmented: 37% IT/engineering, 29% CoEs, 28% hybrid models, 27% data teams.
- 75% lack a unified AI strategy.
- Reported gains: 43% faster decisions, 39% better compliance, 38% cost savings.
Why Ops is reweighting toward AI
Operational uncertainty and tightening oversight make reaction-only models expensive. AI helps shift to earlier warning, faster triage, and tighter control loops. That's why agentic AI is moving from experiment to standard practice in larger enterprises where compliance, scale, and speed matter most.
The agentic AI moment (and what it means for you)
Agentic systems can pursue defined goals, coordinate tasks, and act within limits. Think incident triage that opens tickets, drafts comms, and escalates based on policy - without waiting for a human to click every button. Early movers are wiring these agents into core workflows, not side projects.
The catch: scattered ownership slows results
With AI split across IT, data, and "innovation" teams, many firms lack a single operating model. That fragmentation creates duplicated tooling, policy gaps, and unclear accountability. Until Ops, Risk, and Tech share one plan, consistency will suffer.
What Ops leaders should do in the next 90 days
- Pick two critical outcomes: e.g., cut mean time to resolve by 30%, improve SLA adherence by 5 points.
- Stand up a lightweight AI governance lane: policy owners, model approval flow, data access rules, human-in-the-loop points.
- Name a single owner in Ops who partners with Risk, Security, and IT. Create one backlog and one funding view.
- Run two agentic pilots tied to those outcomes (see use cases below). Ship in 4-6 weeks, not quarters.
- Instrument everything: baseline metrics, control groups, and post-implementation reviews.
- Close the loop with regulators: map controls to your operational resilience framework and audit trails.
High-impact use cases for Operations
- Incident triage and resolution: classify, route, propose fixes, draft comms, and escalate by policy.
- Regulatory change tracker: monitor rule updates and generate control-impact summaries for owners.
- Vendor risk monitoring: scan financial, cyber, and news signals; trigger playbooks on thresholds.
- Capacity and backlog planning: forecast demand, rebalance workloads, and suggest schedule changes.
- Frontline assistant: suggest next-best actions, pull SOPs, and prefill forms from case context.
Data, risk, and compliance basics (keep it simple, make it traceable)
- Define allowed data sources, redaction rules, and retention by use case.
- Require human approval for actions with customer or regulatory impact.
- Log prompts, outputs, decisions, and overrides for audit.
- Test for bias, error rates, and failure modes before scaling.
- Align with UK guidance: see the FCA's operational resilience policy PS21/3 and the ICO's AI resources here.
Metrics that matter
- Detection and decision time; mean time to acknowledge/resolve; change lead time.
- SLA/OLA adherence; first-contact resolution; backlog age.
- Compliance findings closed; audit exceptions; model override rate.
- Cost per ticket/case; automation rate by step; customer effort score.
Team and operating model
- Form an Ops-led AI squad with Security, Risk, and IT embedded. One backlog, shared OKRs.
- Create living SOPs that include AI steps, controls, and fallbacks.
- Offer skills paths for prompt design, agent orchestration, and control testing. If you need structured upskilling by role, explore role-based AI courses.
Bottom line
AI has become the primary lever for resilience investment. Early adopters are reporting faster decisions, better compliance, and lower costs - while many others stall due to scattered ownership and unclear strategy.
The edge goes to teams that rebuild operations with AI at the core: clear goals, tight controls, fast pilots, and measurable outcomes. Start small, move with intent, and make it auditable.
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