McKinsey: Banks move past AI pilots to full-scale process overhaul

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Financial AI is moving from pilots to full process overhaul

McKinsey & Company's latest research points to a shift: banks and financial institutions are moving past isolated AI pilots and rebuilding end-to-end processes. The conclusion comes through its Global Banking Annual Review and its 2025 State of AI survey of 2,000 companies across finance, technology, retail, and more.

The signal is clear. Value shows up when AI is embedded in the workflow, not when it sits in a lab. Leaders are redesigning processes, controls, and teams around AI, instead of treating models as side projects.

Why pilots stall

• Fragmented data and manual handoffs slow delivery and blunt model impact.
• Unclear ownership: models "thrown over the wall" without product-minded teams.
• Risk and compliance uncertainty freezes deployment beyond small scopes.
• Success metrics focus on model accuracy, not business outcomes or adoption.

What scaling actually looks like

• Process-first design: Start from a target customer or risk outcome, then rebuild the workflow around it.
• Data products: Curated, governed datasets with clear contracts, lineage, and refresh SLAs.
• Unified AI platform: Feature store, model registry, monitoring, and CI/CD to ship safely and often.
• Two-speed risk management: Fast path for low-risk changes, deep reviews for material models.
• Human-in-the-loop: Clear escalation, feedback capture, and policy-aligned guardrails.
• Adoption-first metrics: Targets for straight-through processing (STP), cycle time, loss avoided, and unit cost.

Where finance leaders are finding value first

• Onboarding and KYC: Document extraction, screening, and decision support to cut cycle times.
• Underwriting and pricing: Risk segmentation and decisioning with explainability baked in.
• Fraud and AML: Transaction scoring, network analysis, and alert triage to focus analyst effort.
• Collections and recovery: Propensity models and tailored contact strategies to improve cures.
• Service and sales: copilot tools, next-best-action, and self-service that actually resolves intent.
• Finance and treasury: Cash forecasting, anomaly detection, reconciliations, and close acceleration.
• Operations: Document processing, QA automation, and AI agents for back-office tasks with controls.

Operating model changes leaders make

• AI product teams: Cross-functional pods (product, engineering, data science, design, risk, ops).
• Central enablement, federated build: Shared platforms and standards; business units own outcomes.
• Model lifecycle discipline: Versioning, drift monitoring, champion/challenger, and decommission rules.
• Frontline incentives: Adoption targets in performance plans, with training and clear playbooks.

Risk, compliance, and control stack

• Traceability for data, features, prompts, and outputs, with audit-ready evidence.
• Explainability proportionate to model materiality; clear use restrictions for sensitive attributes.
• Red-teaming and bias checks at pilot, pre-launch, and post-launch intervals.
• GenAI safeguards: retrieval boundaries, prompt/content filtering, and PII protection.

Technology posture that supports scale

• Data: Event streaming, data contracts, feature store, and access controls with row/column policies.
• Models: Registry, automated testing, shadow mode, and A/B frameworks.
• GenAI: Retrieval-augmented generation for docs, vector search, prompt templates, and content moderation.
• Agents: Orchestration with clear permissions, human checkpoints, and activity logging for audit.

90-day plan to graduate a pilot into production

• Pick 1-2 processes tied to clear P&L impact and measurable bottlenecks.
• Map the end-to-end workflow, controls, latencies, and decision rights.
• Stand up a cross-functional pod with a single accountable product owner.
• Baseline data readiness; create a minimal data product with lineage and SLAs.
• Ship a thin slice to production with human-in-the-loop and strict guardrails.
• Instrument adoption and business KPIs; publish weekly dashboards.
• Close the loop: frontline feedback improves prompts, features, and policies.
• Pre-negotiate review paths with model risk and compliance to avoid stalls.
• Lock a 3-sprint roadmap to remove the next two biggest constraints.

KPIs that prove value (and survive scrutiny)

• Customer: STP rate, cycle time, resolution rate, CSAT/NPS for assisted channels.
• Risk: Loss avoided, false positive/negative rates, alert productivity, stability/drift.
• Operations: Unit cost, capacity per FTE, rework rate, SLA adherence.
• Governance: Model uptime, incident MTTR, review lead time, evidence completeness.

People and capability build

• Roles: Product owner, analytics translator, ML engineer, data engineer, model risk specialist, and designer.
• Training: Teach teams to write effective prompts, read model diagnostics, and interpret guardrails.
• Change: Job aids, coaching, and incentives that reward usage and outcomes, not activity.

Helpful references

For additional context on industry direction, see McKinsey's overviews of its banking and AI research:

• McKinsey insights on financial services
• McKinsey AI and analytics research

Build skills and pick the right tools

If you're standing up AI in finance and want a curated view of practical tools and training:

• AI tools for finance
• Courses by job function

The step that matters most now: pick a process, commit a team, and ship something small to production with controls. Then let the metrics guide your next move.