A six-dimension diagnostic to benchmark your bank, insurer or asset manager's AI maturity. Answer each question honestly — most institutions discover they are 12–18 months behind where they think they are.
6
Dimensions assessed
30
Diagnostic questions
2.8
Median FS score
Most FS institutions score themselves 1.5 levels higher than independent assessment reveals. The median composite score across 20+ engagements is just 2.8 out of 5.
The six dimensions
Enterprise.AI's FS AI maturity model evaluates your organisation across six dimensions, each scored 1 (Initial) to 5 (Leading). The composite score reveals whether you are an AI laggard, follower, fast-follower, leader or differentiator.
1Strategy & Vision
Board-endorsed AI ambition and value pools.
2Governance & Risk
Policies, committees, three-lines model.
3Data & Platform
Data foundations, MLOps, GenAI tooling.
4Talent & Culture
AI literacy, hiring, change capacity.
5Use Cases & Value
Pipeline, scaling, measured ROI.
6Responsible AI
Ethics, fairness, regulatory readiness.
Quick assessment
Pick the statement that best describes your institution today. Your score updates live below.
Composite score
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Detailed level descriptions
Level 1: Initial (1.0–1.9) · Laggard
Characteristics: No formal AI strategy, scattered experiments, no governance, minimal platform investment, few models in production. Significant regulatory exposure.
Strategy: No board discussion or executive alignment on AI. No AI mention in enterprise strategy.
Governance: No AI policy. No committees. Risk management applies traditional frameworks to AI, missing key risks.
Data & Platform: Each project builds its own ML infrastructure. No shared model registry, monitoring or validation function.
Talent: Pockets of AI expertise; no systematic hiring or training. Knowledge leaves with people.
Use Cases: Fewer than 5 models in production. Success is sporadic and not measured.
Responsible AI: No fairness testing, explainability or regulatory alignment. Shadow AI running rampant.
Recommended action: 6–8 week engagement to establish AI foundations (policy, governance, inventory, quick wins).
Level 2: Developing (2.0–2.9) · Follower
Characteristics: AI is on the agenda, some governance in place, but execution is inconsistent. Competing visions of how to organize. Starting to invest in platform.
Strategy: AI mentioned in strategy document or investor calls. No detailed roadmap or board ownership.
Governance: AI policy exists but is lightweight. Committee meets irregularly. Risk tiering vague.
Data & Platform: Some teams have MLOps capabilities. Data lake or warehouse in early stages. Model monitoring is basic.
Talent: Dedicated AI hiring in place, but recruitment lags demand. Training offered but adoption is low.
Use Cases: 5–15 models in production, mostly proofs of concept. ROI inconsistently measured.
Responsible AI: Awareness of fairness and regulatory issues, but no systematic program. GenAI use uncontrolled.
Recommended action: 3–6 month engagement to consolidate governance, prioritize use cases, and build enterprise platform roadmap.
Level 3: Defined (3.0–3.9) · Fast Follower
Characteristics: AI is core to business strategy, governance and platform are operational, scaling phase. On par with peer institutions. Risk management reasonably mature.
Strategy: Dedicated board-level AI strategy. Clear roadmap. Competitive positioning understood. Value pools identified.
Governance: Board-approved AI policy, governance committee meets monthly. Risk tiering applied consistently. Three-lines model extended to AI.
Data & Platform: Enterprise MLOps platform operational. Data warehouse/catalog in use. Model monitoring real-time for critical systems.
Talent: Established AI hiring, reasonable retention. Mandatory training for tech and risk staff. AI fluency growing in leadership.
Use Cases: 15–30 models in production with measured ROI. Use case prioritization framework in place. Clear portfolio strategy.
Responsible AI: Responsible AI program operational. Fairness testing on critical models. Regulatory compliance program starting.
Characteristics: Industry-leading AI operating model, significant competitive advantage, regulator-grade governance, high model production throughput. Ahead of peers.
Strategy: AI is core competitive thesis. Board drives AI strategy with clear KPIs. 3-year roadmap actively managed and communicated.
Governance: Regulator-grade AI governance. Board AI committee, AI Risk Committee, formal three-lines model. Annual external audit of controls.
Data & Platform: Proprietary data fabric with real-time metadata. Feature store with high reuse. Advanced monitoring (fairness, drift, explainability).
Talent: AI fluency embedded in hiring & promotion. Thought leadership externally. Talent retention excellent. Continuous learning culture.
Use Cases: 30+ models in production with rigorous ROI tracking. Portfolio-level optimization. Rapid, disciplined scaling.
Responsible AI: Responsible AI program operating at scale. Fairness embedded in SDLC. Regulator-aligned compliance framework. GenAI governance mature.
Recommended action: Selective advisory on differentiation, talent, platform innovation. Consider Enterprise.AI partnership for thought leadership.
Level 5: Leading (4.6–5.0) · Differentiator
Characteristics: AI is a source of sustained strategic advantage. Best-in-class operating model, culture, and outcomes. Potential thought leadership & external engagement.
Strategy: AI is central to competitive thesis and margin expansion. Forward-looking investment in emerging techniques and use cases.
Governance: Proactive regulatory engagement. Leading practice framework. Potential regulator feedback on AI governance.
Talent: Magnets for top talent. Strong external thought leadership. AI fluency throughout organization.
Use Cases: 50+ models in production; many driving P&L. Continuous innovation at scale. Clear realized value > plan.
Responsible AI: Industry-leading responsible AI program. Proactive research. Potential regulator engagement on best practices.
Recommended action: Partnership on thought leadership, emerging risk areas, and external engagement. Consider co-branded content or speaking engagements.
Improvement roadmap by dimension
Once you've completed this assessment, use the recommended roadmap below for your specific maturity level and dimension:
Strategy & Vision roadmap
Level
Near-term (3 months)
Medium-term (6–9 months)
Long-term (12–18 months)
1 (Initial)
Board workshop on AI strategy & value. Define risk appetite. Identify top 5 use cases.
Board-approved AI strategy document. Clear 3-year roadmap with investment profile.
Quarterly board reporting on AI KPIs. Competitive positioning analysis.
2 (Developing)
Refine strategy based on business model. Identify data & platform investments needed.
Board AI committee. Quarterly strategy reviews. Regulator engagement begins.
Regulator feedback loop. Strategy adjusted based on market dynamics.
3 (Defined)
Extend roadmap to 5 years. Identify emerging use cases. Build innovation pipeline.
Establish AI innovation lab. Board visibility on emerging AI trends. Competitive intelligence on peer AI strategies.
Thought leadership engagement. Speaking, publishing. Industry participation.
4+ (Leader)
Continuous roadmap updates based on market. Investment in next-generation capabilities (Gen AI, causal, RL).
Continuous improvement of governance model. Emerging risk monitoring (prompt injection, data poisoning, etc.).
Proactive regulator engagement on governance benchmarking. Thought leadership on responsible AI.
Potential industry working group participation. Share lessons learned.
Only 3% of FS institutions reach Level 5 (Leading). The jump from Level 2 to Level 3 is where most institutions stall — it requires a shift from project-based to platform-based AI delivery.
Peer benchmark data (confidential aggregate)
Based on Enterprise.AI engagements with 20+ FS institutions (banks, insurers, asset managers), here is the distribution of current maturity:
Level 1 (Initial): 15% of institutions. Typically regional banks, smaller asset managers with recent AI interest but low execution.
Level 2 (Developing): 35% of institutions. Most mid-tier banks and growing insurance companies. Momentum but execution gaps.
Level 3 (Defined): 35% of institutions. Large banks, major insurers in Western Europe. Mainstream capability.
Level 4 (Managed): 12% of institutions. Select global banks, leading fintech-adjacent players. Demonstrable competitive advantage.
Level 5 (Leading): 3% of institutions. Only the most advanced AI-native fintechs and leading global banks. Potential thought leaders.
Median composite score for FS: 2.8 (Developing to Defined). Most FS institutions are 12–18 months behind where they perceive themselves.
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