AI Centre of Excellence: Building Capabilities That Scale With AI Adoption

In this AI Centre of Excellence (AI CoE) series, we’ve established why Boards need an AI CoE, explored its eighteen essential functions, mapped your multi-speed AI reality, and designed governance structures to manage it all. Now comes the critical question: how do you build the capabilities that transform governance frameworks into business results? The answer lies not in technology deployment, but in systematic capability development across the Five Pillars capability areas.

Businesses are making significant investments in AI, sophisticated technology platforms, and top talent recruitment, yet adoption remains confined to use-cases which rarely make it out of production. The reason is consistent - technical capabilities race ahead whilst governance, operations, value realisation, and cultural readiness lag behind. This imbalance between pillars goes some way to explain why 70-90% of AI pilots fail to reach production.

The fact is that organisations are pouring resources into AI technologies, while neglecting the foundational capabilities that enable sustainable adoption. The Five Pillars framework prevents this imbalance by ensuring systematic capability development aligned with your actual AI maturity.

From Assessment to Action: Your Capability Building Roadmap

The AI Stages of Adoption (AISA) simulator presented in the third article in this series revealed a multi-speed reality: customer service at Optimising, manufacturing at Adopting, finance still Experimenting. While the fourth article provided a governance structure created to manage this complexity, now you need capabilities that match each function’s maturity while building toward enterprise transformation.

The challenge isn’t just building capabilities - it’s building the right capabilities at the right maturity level for each AI stage of adoption. Experimenting functions need basic governance and sandbox environments. Optimising functions require sophisticated MLOps and value tracking. Transforming initiatives demand culture change programmes and ecosystem partnerships.

Your AI CoE orchestrates this complex capability development, ensuring investments align with actual needs rather than aspirational targets. It prevents the common mistake of building Scaling-level capabilities for Experimenting functions or constraining Transforming initiatives with Adopting-level governance.

This orchestration role transforms the AI CoE from cost centre to value accelerator. By building capabilities systematically, you reduce the 70-90% AI pilot failure rate whilst accelerating time-to-value for successful initiatives.

Building Pillar 1: Governance & Accountability Capabilities

Governance remains the most misunderstood pillar. Too often, Boards create elaborate frameworks that stifle innovation whilst failing to address actual risks. Building effective governance capabilities requires starting with your shadow AI reality and progressively building sophistication that enables rather than constrains.

Every organisation has shadow AI - unofficial experiments lurking in spreadsheets, on phones, in cloud accounts, and on corporate credit cards. Rather than shutting them down, transform this hidden risk into visible innovation through a Shadow AI Discovery initiative aligned with the People, Culture & Adoption pillar. This approach transforms hidden experiments into governed innovation that creates Well-Advised value across Innovation and Responsible Business Transformation. It’s simple: register your experiment, receive governance support, face no penalties. This ‘ask for forgiveness’ approach, combined with anonymous surveys and procurement monitoring, creates a living AI asset register that captures innovation wherever it emerges.

The key to effective governance lies in matching sophistication to organisational readiness. At the basic level, governance should fit on a single page - a simple AI ethics checklist asking essential questions: Who could be harmed? What biases might affect outcomes? How do we maintain human oversight? As maturity grows, add governance accelerator tools that automatically determine risk tiers and route approvals, reducing weeks to days. At advanced levels, build governance directly into infrastructure with continuous compliance checks and real-time alerts, making governance friction disappear.

This governance approach directly enables Well-Advised outcomes - lightweight governance for Experimenting stages preserves Innovation, whilst robust controls for Transforming stages ensure Responsible Business Transformation. The multi-speed nature of AI adoption creates unique challenges. Customer service might operate sophisticated chatbots whilst HR experiments with basic CV screening. A well-designed governance routing engine adapts to this reality:

Success requires practical tools teams actually use. An AI initiative canvas capturing objectives, risks, and success metrics on one page. Dynamic RACI matrices that evolve with your AI maturity. Governance decision trees guiding common choices. These instruments transform governance from bureaucratic burden to innovation enabler, helping teams move faster with clear guidance than with unclear freedom.

Building Pillar 2: Technical Infrastructure Capabilities

Technical infrastructure extends beyond “just cloud” to AI-specific capabilities that enable sustainable adoption. Most organisations discover they possess more AI-ready infrastructure than they realise - IoT platforms become AI experimentation environments, compliance data lakes transform into ML training grounds, and disaster recovery compute provides model training capacity. The lesson? Audit before you build.

Infrastructure choices shape Well-Advised value delivery. Building for Operational Excellence might prioritise stability, whilst Innovation demands experimental flexibility. The AI CoE Simulator helps identify which value dimensions matter most for each function’s AISA stage. Rather than pursuing comprehensive AI platforms that take years to deliver value, build modular capabilities in 90-day sprints. Start with a data foundation focused on accessibility over perfection - simple catalogues helping teams find data, quality scorecards showing AI fitness, and lightweight pipelines using existing tools. Follow with experimentation modules featuring pre-configured environments with cost controls, standard notebooks, and template projects. Each module delivers immediate value whilst building toward architectural coherence.

The build versus buy versus partner decision shapes your AI future. Build proprietary algorithms and data pipelines for competitive differentiation. Buy commodity ML platforms and development tools for rapid deployment. Partner for specialised capabilities like computer vision that would take years to develop internally. This hybrid approach balances control with speed, investment with capability access.

The centralisation debate misses the point - implement “federated coherence” instead. Centralise shared platforms, common tools, and security standards for efficiency. Federate infrastructure for unique needs, tool selection within guidelines, and edge deployments for flexibility. The key lies in creating standard, composable components that teams assemble into unique solutions, enabling both Operational Excellence through standardisation and Innovation through flexibility.

Plan for scale from day one. Infrastructure debt accumulates faster in AI than traditional IT - models trained on inadequate infrastructure require complete rebuilds, experimental pipelines collapse under production loads. Build “production-ready pilots” that start small but start right: cloud-native architectures, cost allocation from inception, multi-tenancy for resource sharing, and built-in observability. Think beyond immediate needs - that experimental model might soon handle millions of requests, that gigabyte dataset could grow to terabytes. Making architectural choices that don’t require fundamental revision as adoption grows isn’t over-engineering - it’s intelligent design that supports Well-Advised value creation across all dimensions.

Building Pillar 3: Operational Excellence Capabilities

Operational excellence transforms AI from impressive demos to sustainable business capabilities. MLOps represents more than DevOps for machine learning - it’s a fundamental reimagining of how AI systems operate sustainably when models degrade mysteriously in production, data changes break predictions, and performance metrics tell incomplete stories.

Operational maturity varies by AISA stage as revealed by your AI CoE Simulator assessment. Experimenting functions need basic documentation supporting Innovation, whilst Optimising functions require comprehensive MLOps delivering Operational Excellence. The journey begins with comprehensive versioning that strengthens Governance & Accountability pillar maturity - not just code, but data, models, configurations, and environments. This capability enables organisations to demonstrate Responsible Business Transformation to regulators and stakeholders by recreating any model’s exact training conditions months later. Automation follows naturally: model training triggered by data updates, deployment pipelines promoting models safely, and rollback procedures reverting problematic updates. Start simple with basic output validation, then add behavioural tests, bias detection, and performance validation incrementally.

AI fails differently than traditional software - subtly rather than obviously. Comprehensive monitoring must cover data drift, concept drift, performance degradation, and business impact. Making this accessible to business users proves crucial - “customer satisfaction correlation” resonates more than “F1 score degradation.” Transform tribal knowledge into systematic playbooks for incident response, performance optimisation, and proactive maintenance that prevent operational heroes from becoming single points of failure.

Human-AI collaboration defines modern operations and directly supports Customer Value & Growth through enhanced experiences. Establish explicit decision boundaries: high-confidence predictions execute automatically, medium-confidence seeks validation, low-confidence defaults to human judgment. When humans override AI recommendations, capture their reasoning to train next-generation models. This bidirectional learning - AI systems learning from human expertise whilst humans learn from AI insights - creates capabilities beyond either alone.

Embed governance throughout operations rather than treating it as separate oversight:

Different AISA stages require different operational intensity. Your AI CoE provides templates calibrated to each stage - lightweight documentation for experiments, basic automation for adoption, comprehensive MLOps for optimisation, adaptive operations for transformation. This prevents over-engineering experiments whilst ensuring production readiness for scale.

Building Pillar 4: Value Realisation & Lifecycle Management

Traditional ROI calculations tell incomplete AI stories. When personalisation AI shows negative first-year returns, deeper analysis reveals customer lifetime value increasing dramatically over years, with improved retention, basket size, and brand loyalty. AI creates value that conventional business cases can’t capture.

Building comprehensive value frameworks requires collaboration between finance, strategy, and AI teams using Well-Advised value tracking systems that measure AI impact across all five strategic priorities - Innovation, Customer Value, Operational Excellence, Responsible Business Transformation, and Revenue/Margin/Profit. Immediate metrics still matter - cost reduction, revenue increases, risk mitigation, time savings. But strategic value reveals AI’s transformative potential: option value from new capabilities, learning value from experiments, platform value as narrow applications become enterprise capabilities, and innovation value from previously impossible opportunities. Multi-disciplinary workshops where teams design frameworks together capture both financial returns and strategic impact aligned with Well-Advised priorities.

Creating frameworks means nothing without tracking mechanisms. Build systems that connect AI initiatives to measurable outcomes through A/B testing, holdout groups, statistical controls, and business experiments. Make impact visible at every level - executives see portfolio value creation across Well-Advised dimensions, managers track specific initiatives and their contributions, teams monitor model performance. Replace quarterly reviews with continuous monitoring where value indicators track automatically and anomalies trigger immediate investigation.

Shift from project to portfolio thinking to multiply value through strategic coordination. Portfolio synergy reviews multiply Well-Advised value creation - where Operational Excellence improvements in one area enable Innovation in another:

Dynamic resource allocation responds to performance - high performers get more investment, underperformers face pivot-or-terminate decisions, emerging opportunities access contingency funds. Reviews reveal hidden connections where one team’s work benefits another, multiplying portfolio value across Well-Advised dimensions.

Manage AI systems actively from conception through retirement. Pre-launch rigour prevents later disappointment. Launch gates terminate non-viable pilots early. Growth phase monitoring identifies scaling opportunities. Maturity optimisation sustains value delivery. Sunset discipline prevents zombie AI. Throughout, value engineering teams act as force multipliers - optimising existing models, identifying cross-functional opportunities, and sharing amplification strategies that compound returns over time.

Building Pillar 5: People, Culture & Adoption

People and culture determine AI success more than any technology choice, yet this pillar receives the least investment. When sophisticated AI platforms sit unused whilst employees continue manual processes, the truth becomes clear: perfect technology fails without human understanding, trust, and embrace.

Executive AI immersion proves most critical - not conference presentations but hands-on experience building models, encountering biased data, struggling with edge cases. This experiential learning transforms AI from magic or threat into reality: powerful but imperfect, requiring human wisdom. Strategic workshops connect insights to business implications whilst peer learning reveals common challenges. Functional academies follow, teaching role-specific applications whilst addressing fears directly - AI augments rather than replaces skills, relationships matter more when routine tasks automate, creativity becomes the differentiator. Using real company data for actual problems means participants leave with working solutions and confidence.

Building communities multiplies impact. AI Champions Networks become transformation catalysts aligned with Well-Advised priorities. Rather than appointing advocates, volunteers passionate about potential translate AI benefits into functional language - the supply chain manager explaining how predictive maintenance prevents weekend callouts (Operational Excellence) carries more weight than executive mandates. Practice communities organised around capabilities share knowledge organically: data scientists reviewing code, ethicists wrestling with bias (Responsible Business Transformation), business teams identifying use cases (Innovation), engineers advancing MLOps. Internal conferences, wikis, mentorship programmes, and innovation challenges prevent reinvented wheels whilst focusing energy on real problems.

Address resistance through radical transparency, not platitudes:

Transform culture systematically beyond training. Leadership models continuous learning - CEOs sharing AI failures alongside successes. Organisational structures evolve from hierarchies to cross-functional teams. New roles emerge like AI translators and algorithm auditors. Decision frameworks clarify human versus AI authority. Performance metrics reward adoption and experimentation. The message becomes clear: AI fluency drives career advancement.

Build talent through multiple channels supporting all Well-Advised dimensions. Develop internally by identifying high-potential employees and providing sponsored education. Acquire externally through university partnerships and flexible arrangements. Multiply through ecosystem partnerships requiring knowledge transfer. This multi-channel approach accesses more capability than any organisation could build alone, transforming AI from threat to opportunity whilst delivering value across Innovation, Customer Value, and Revenue/Margin/Profit.

The AI CoE as Capability Orchestrator

Your AI CoE transforms from governance body to capability accelerator through sophisticated orchestration across all Five Pillars - not through central control but intelligent coordination that enables rather than constrains.

The hub-and-spoke model makes this work elegantly. While the central hub maintains standards and shares best practices, spokes embedded within business functions adapt capabilities to local needs. This balance ensures enterprise coherence without stifling the innovation that emerges naturally at the edges of your organisation.

Prioritising capability investments becomes an art of balancing multiple tensions. AISA stage gaps reveal where immediate action prevents failure - that Adopting function lacking basic operational capabilities faces far greater risk than an Optimising function missing advanced features. Meanwhile, your Well-Advised priorities shape strategic direction, determining whether Innovation demands experimenting capabilities or Operational Excellence requires robust adoption infrastructure. Risk considerations add urgency to the mix, as functions handling sensitive data need governance before technical infrastructure, whilst customer-facing applications demand operational excellence before any advanced features.

The AI CoE Simulator from Week 3 transforms this complexity into clarity. Beyond revealing your multi-speed reality, it highlights capability gaps across functions and pillars through its maturity assessments. Red indicators demand immediate investment, yellow signals populate your roadmap, whilst green areas become sources of expertise for lagging functions. This visual approach creates a shared language that aligns stakeholders around priorities whilst maintaining momentum toward your AI transformation goals.

Practical Implementation: Your 90-Day Capability Sprint

Theory without action delivers no value. This 90-day sprint, structured around your AI CoE Simulator results, builds capabilities systematically across the Five Pillars whilst delivering Well-Advised value from day one.

Days 1-30: Foundation Setting

Begin with governance fundamentals aligned to your AISA stages. Establish ethical guidelines that teams can actually follow. Create approval processes that enable rather than obstruct. Build basic risk registers that capture without paralysing.

Assess infrastructure reality using AI CoE Simulator insights. Identify existing assets that can accelerate AI adoption. Highlight critical gaps that block progress. Design minimal viable infrastructure for immediate needs whilst planning strategic platforms.

Map current capabilities across all Five Pillars. Use maturity assessments to establish baselines. Identify quick wins that build momentum across Well-Advised dimensions. Engage stakeholders to ensure buy-in.

The first 30 days should produce an “AI Readiness Dashboard” showing capability status across functions. Red-yellow-green indicators make gaps visible. Drill-downs reveal specific needs. Executive alignment accelerates dramatically with this clarity.

Days 31-60: Pilot Enablement

Build operational frameworks for sustainable pilots that deliver Well-Advised value. Create monitoring approaches that track what matters. Establish feedback loops connecting technical and business teams. Design integration patterns for common scenarios.

Launch initial skills programmes targeting immediate needs. AI literacy for executives ensures informed governance (Responsible Business Transformation). Practitioner basics for pilot teams enable execution (Operational Excellence). Change management for affected groups smooths adoption (Customer Value).

Select lighthouse pilots that demonstrate value whilst building capabilities. Choose initiatives spanning different AISA stages to test multi-speed governance. Ensure each pilot strengthens specific Five Pillars capabilities.

Launching three complementary pilots works well: a chatbot (Adopting), fraud detection (Optimising), and relationship management (Transforming). Each builds different capabilities whilst sharing common infrastructure and governance.

Days 61-90: Scale Preparation

Develop value frameworks capturing multi-dimensional benefits across all Well-Advised priorities. Move beyond simple ROI to comprehensive value creation. Build tracking systems that connect AI investments to business outcomes. Create portfolio views enabling strategic resource allocation.

Design culture programmes for broader adoption supporting Innovation and Customer Value. Executive AI immersion builds leadership commitment. Practitioner communities enable peer learning. Success story repositories inspire continued adoption.

Establish capability development pipelines ensuring continuous improvement. Training programmes build skills systematically. Recruitment strategies access missing capabilities. Partnership models leverage external expertise.

The 90-day sprint should culminate in an “AI Capability Expo” where teams demonstrate achievements. Pilots show business value. Infrastructure teams reveal platforms. Culture champions share transformation stories. Board members leave with confidence that AI investments will deliver returns across all Well-Advised dimensions.

Measuring Capability Maturity

The AI CoE Simulator provides ongoing measurement, tracking how capability improvements in each pillar advance your AISA stage progression and Well-Advised value creation. Systematic measurement ensures capability investments deliver intended outcomes. Maturity indicators make progress visible and gaps apparent.

For Governance & Accountability, initial maturity shows basic ethical guidelines and manual approval processes. Developing maturity adds risk frameworks and clear accountability. Advanced maturity delivers automated governance and real-time compliance. Leading maturity shapes industry standards and regulatory frameworks.

Technical Infrastructure maturity progresses from sandbox environments through production pipelines to enterprise platforms and ecosystem integration. Operational Excellence evolves from basic documentation through systematic monitoring to self-optimising operations.

Value Realisation advances from learning metrics through benefits tracking to portfolio optimisation and business model innovation. People, Culture & Adoption matures from awareness programmes through functional skills to transformation leadership and ecosystem orchestration.

Assessment tools and scorecards make maturity tangible. Regular reviews track progress. The AI CoE Simulator visualises gaps. Benchmarking reveals relative position. Quarterly maturity assessments can show significant progression across pillars, with pockets of advanced capability emerging in critical areas.

Connecting capability maturity to business outcomes proves investment value aligned with Well-Advised priorities. Higher governance maturity correlates with fewer AI incidents (Responsible Business Transformation). Infrastructure maturity reduces deployment time (Operational Excellence). Operational maturity improves model performance (Revenue/Margin/Profit). Value realisation maturity increases ROI across all dimensions. Culture maturity accelerates adoption (Innovation).

From Capabilities to Transformation

Systematic capability building transforms AI from risky experiment to sustainable competitive advantage. By developing all Five Pillars in harmony with your multi-speed reality, you create foundations for long-term success.

The journey requires patience and persistence. Capabilities build incrementally, not through dramatic leaps. Early investments in governance and culture may seem slow but prevent costly failures later. Technical infrastructure without operational excellence creates impressive demos that never reach production. Value frameworks without people capabilities generate plans that never materialise.

Your AI CoE orchestrates this complex development, ensuring balanced progress across all pillars whilst adapting to different functional needs. It transforms governance from constraint to enabler, infrastructure from cost to platform, operations from overhead to excellence, value tracking from afterthought to driver, and culture from resistance to acceleration.

Your AI CoE Simulator assessment revealed where you are. The Five Pillars show what capabilities you need. Well-Advised priorities guide where to focus for maximum value. Together, these frameworks transform random capability building into strategic AI acceleration.

Next week, we’ll explore how to launch your first 90 days with Well-Advised value focus, turning these capabilities into tangible business results. You’ll learn how to select initiatives that build capabilities whilst delivering value, create momentum through early wins, and establish patterns for sustainable success.

The organisations succeeding with AI aren’t those with the most advanced technology or largest budgets. They’re those building capabilities systematically across all Five Pillars, adapting to their multi-speed reality, and maintaining strategic alignment through effective governance. Your capability building journey starts now.

About the Author

Mario Thomas is a transformational business leader with nearly three decades of experience driving operational excellence and revenue growth across global enterprises. As Head of Global Training and Press Spokesperson at Amazon Web Services (AWS), he leads worldwide enablement delivery and operations for one of technology's largest sales forces during a pivotal era of AI innovation. A Chartered Director and Fellow of the Institute of Directors, and an alumnus of the London School of Economics, Mario partners with Boards and C-suite leaders to deliver measurable business outcomes through strategic transformation. His frameworks and methodologies have generated over two-billion dollars in enterprise value through the effective adoption of AI, data, and cloud technologies.