AI Centre of Excellence: Scaling Beyond Pilots to Enterprise Transformation

The initial success of AI pilots across various functions creates both opportunity and challenge for organisations. Whilst individual departments may report impressive results from their AI initiatives - whether in customer segmentation, predictive maintenance, or process automation - these isolated victories often mask a more complex reality. The fundamental question facing AI Centres of Excellence (AI CoE) is how to transform these discrete successes into systematic, enterprise-wide capabilities without sacrificing the agility and innovation that enabled early wins. This requires adapting and extending the AI CoE’s capabilities to support a broader scope of activity, building on the solid foundation established during the first 90 days to enable sustainable scale whilst maintaining governance and quality.

When Scaling AI, Most Organisations Fail

The pattern is familiar across industries and geographies: organisations successfully complete AI pilots with promising results, yet struggle to achieve production scale. Recent data shows that over 88% of AI pilots never make it to production, a dramatic failure rate that stems from fundamental misconceptions about what scaling AI actually requires.

Most organisations approach scaling as a multiplication problem, assuming that if one pilot succeeded, surely ten pilots will deliver ten times the value. This linear thinking ignores the exponential complexity that emerges when moving from controlled experiments to enterprise deployment, where technical debt accumulates rapidly, governance frameworks strain under increased load, cultural resistance intensifies across departments, and knowledge silos proliferate faster than they can be broken down.

The fundamental problem isn’t ambition or even execution, but rather approach. These organisations confuse replication with scaling, activity with progress, and projects with platforms. True scaling requires fundamental shifts in how you think about and manage AI initiatives, moving from artisanal crafting of individual solutions to industrial-scale production of AI capabilities.

Moving From Project to Platform Thinking

Successful scaling begins with a critical mental model shift from project thinking to platform thinking, and this isn’t merely about technical architecture but represents a fundamental reimagining of how AI capabilities develop and deploy across your organisation. Project thinking treats each AI initiative as unique, requiring bespoke solutions for data access, model development, governance oversight, and value measurement. Whilst this approach works for pilots where learning trumps efficiency, it becomes unsustainable at scale, creating redundancy, inconsistency, and ultimately ungovernable complexity that strangles progress.

Platform thinking recognises that whilst AI use cases vary dramatically in their business application, the underlying capabilities they require remain remarkably consistent across initiatives. By building these as platforms rather than project-specific solutions, you create leverage that transforms both the economics and governance of AI at scale. The transformation happens when organisations stop asking “How do we do more AI projects?” and start asking “What platforms would make any AI project faster and safer?”

This shift in questioning leads to identification of critical platform components: unified data access layers that handle permissions and lineage transparently, model development environments with built-in governance checkpoints, deployment infrastructure ensuring consistent monitoring and performance management, measurement systems aligned to business KPIs rather than just technical metrics, and knowledge sharing platforms that capture lessons and accelerate organisational learning. The impact proves transformative in practice, accelerating new AI initiatives that previously required months from conception to launch now deploying in weeks, and business units stop viewing the AI CoE as a bottleneck and start seeing it as an accelerator for their own transformation efforts.

Managing a Multi-Speed Portfolio

As you scale AI initiatives across the enterprise, your portfolio becomes increasingly complex and diverse. Different parts of your organisation are at different stages of the AI Stages of Adoption (AISA), creating a multi-speed reality that traditional programme management approaches cannot address. Whilst your digital teams might be approaching the Transforming stage with sophisticated AI applications, your finance function might still be Experimenting with basic automation, and your operations teams could be somewhere in between, Adopting proven use cases but not yet Optimising them for maximum impact.

Effective portfolio management for scaled AI starts with clear segmentation that acknowledges these different speeds and maturity levels. Building on the portfolio approach I’ve previously discussed, organisations need to balance their AI initiatives across three categories: quick wins that deliver immediate value and build momentum, capability building initiatives that create foundational infrastructure and skills for future success, and transformative initiatives that reimagine core business processes or create new revenue streams.

As you scale from pilots to production, this portfolio balance becomes even more critical. Quick wins continue to demonstrate value and maintain stakeholder support whilst capability building initiatives ensure you have the depth of maturity needed to support broader deployment. Transformative initiatives, which require maximum AI CoE attention and board-level oversight, represent your bets on future competitive advantage.

This portfolio approach solves a critical scaling challenge by matching governance intensity to initiative type and maturity, enabling speed for quick wins whilst ensuring appropriate oversight for transformative initiatives.

Building Repeatable Patterns and Evolving Governance

Scaling successfully requires transforming unique project experiences into repeatable patterns that accelerate future initiatives whilst maintaining quality. This is where playbooks become critical, not as rigid prescriptions but as flexible frameworks that capture hard-won wisdom whilst remaining adaptable to context. Effective AI playbooks synthesise technical patterns that have proven successful, governance approaches that balance speed with safety, and change management practices that drive adoption by addressing human factors systematically.

The challenge lies in finding the productive middle ground between uselessly generic guidelines and overly prescriptive rules. Successful playbooks emerge from systematic capture and synthesis of actual project experiences, documenting not just what worked but why it worked and under what conditions. They include technical architectures proven robust across deployments, data requirements discovered through experience, governance checkpoints that prevent real problems, and measurement frameworks connecting technical metrics to business value.

Perhaps even more challenging than creating playbooks is evolving governance structures originally designed for pilot oversight into frameworks that enable enterprise transformation. Traditional governance focuses on control through review gates and approval processes, but this approach becomes a bottleneck at scale. Scaled governance requires a fundamental shift from control to enablement, embedding governance into platforms and processes rather than relying on discrete review events.

This evolution demands rethinking each of the Five Pillars for scale. Governance & Accountability evolves from project review boards to embedded policy engines that automatically enforce standards. Technical Infrastructure shifts from bespoke solutions to platform services with governance built in by default. Operational Excellence moves from manual processes to automated workflows with continuous monitoring. Value Realisation transforms from periodic reviews to real-time dashboards with automated alerting. People, Culture & Adoption advances from discrete training programmes to embedded learning systems providing just-in-time upskilling.

However, rather than static rules embedded in platforms, leading organisations deploy AI-powered governance systems that continuously monitor model behaviour, automatically detect drift or bias, and adapt policies based on emerging patterns. These AI governance assistants can interpret context, assess risk dynamically, and even predict which initiatives might face compliance challenges before they arise. The irony isn’t lost - using AI to govern AI - but it’s becoming essential as the complexity of scaled AI operations exceeds human oversight capacity.

Knowledge Transfer and Cultural Transformation

One of the most underestimated challenges in scaling AI is knowledge transfer at the speed required for enterprise transformation. The lessons learned in pilots must propagate across the organisation faster than new challenges emerge, but traditional training approaches fail when applied to rapidly evolving AI capabilities. Successful knowledge transfer at scale requires multiple reinforcing mechanisms creating a learning ecosystem rather than isolated training events.

Communities of Practice form the backbone, bringing together practitioners across functions to share experiences and solve challenges collaboratively through dynamic digital spaces rather than just monthly meetings. Learning must be embedded into platforms themselves, with solutions becoming features and effective approaches captured as playbook modules. The creation of AI Champions throughout your organisation provides distributed expertise that scales beyond what any central team could provide: business leaders who grasp AI’s potential and limitations, becoming local evangelists and first-line support.

Scaling AI also intensifies cultural challenges exponentially as contained experiments now threaten established ways of working across the entire enterprise. Fear of displacement escalates from abstract concern to immediate threat, and resistance intensifies from passive scepticism to active opposition. Successful cultural transformation requires acknowledging these fundamental human concerns with honesty and respect, demonstrating through action how AI augments rather than replaces human capability.

The path forward involves showing concretely how AI eliminates drudgery rather than jobs, freeing humans for higher-value work requiring creativity, empathy, and judgment. Most critically, involve affected employees directly in designing AI solutions for their work, giving them agency in shaping their AI-augmented future. Create clear career pathways showing how employees can grow alongside AI, developing comprehensive reskilling programmes and celebrating early adopters as pioneers rather than threats.

The Platform Portfolio and Measurement Evolution

Whilst cultural and organisational changes enable scaling, technical platforms make it practically possible at the speed and quality required for competitive advantage. The core challenge in AI platform design is identifying which elements require standardisation for governance and efficiency whilst preserving flexibility where innovation happens.

Essential platform components form an integrated ecosystem: Data Platforms providing unified access with built-in governance augmented by AI-powered access controls and anomaly detection, Development Environments standardising tools whilst using AI to suggest appropriate governance checkpoints based on use case characteristics, Deployment Infrastructure ensuring consistent model serving with AI-driven monitoring for drift and performance degradation, and Knowledge Repositories centralising reusable artefacts with AI-powered search and recommendation engines that surface relevant patterns and lessons.

As AI initiatives scale, portfolio management must evolve from static annual planning to dynamic orchestration responding to emerging opportunities in real-time. Traditional planning cycles cannot accommodate AI’s pace of change and potential for unexpected breakthroughs. Dynamic portfolio management starts with clear success metrics aligned to the Well-Advised strategic priorities, measuring not just model accuracy but business impact across innovation, customer value, operational excellence, responsible transformation, and financial performance.

Traditional project metrics fail at scale because they measure activity rather than impact. As I’ve outlined in my AI business case approach, effective measurement requires a balanced scorecard that captures the full spectrum of AI value creation. At scale, this means tracking leading indicators that predict future success (such as capability development across the Five Pillars and user engagement metrics), lagging indicators that confirm delivered value (like achieved business outcomes across the Well-Advised dimensions), and predictive indicators that signal emerging opportunities or risks (including progression through AISA stages and pipeline strength).

For scaled AI initiatives, this balanced scorecard approach ensures you’re measuring what matters: how effectively you’re delivering value across all five Well-Advised strategic priorities whilst building sustainable capabilities across the Five Pillars. Leading indicators might include the number of business units progressing through AISA stages or technical infrastructure readiness scores. Lagging indicators confirm actual business impact through revenue growth, cost reduction, and customer satisfaction improvements. Predictive indicators help anticipate future success by tracking factors like the depth of AI literacy across leadership teams and the maturity of governance frameworks. This comprehensive measurement approach guides resource allocation decisions and provides early warning of scaling challenges before they become critical.

Your Scaling Roadmap

Scaling AI successfully requires sustained effort over multiple quarters with clear milestones. Quarter 1 focuses on foundation building: selecting successful pilots for scaling, identifying platform requirements, developing initial playbooks, and establishing communities of practice. Quarter 2 emphasises platform development: building your first shared component, adapting governance for scale, launching knowledge transfer programmes, and expanding active initiatives. Quarter 3 drives acceleration: completing core platforms, implementing dynamic portfolio management, achieving enterprise-wide transformation, and establishing sustainable funding. Quarter 4 achieves maturation: platforms handling most initiatives, automated governance, self-sufficient business units, and AI embedded in strategic planning.

You’ll recognise successful scaling not through metrics alone but through fundamental shifts in organisational behaviour. Business leaders naturally consider AI options when facing challenges, teams instinctively share lessons and leverage platforms, governance enables rather than constrains, and value delivery is systematically measured and celebrated. This cultural embedding emerges from consistent execution of scaling strategies - platform building democratising innovation, governance evolution enabling speed, knowledge transfer spreading capability, and portfolio management demonstrating value.

The journey from pilot to scale tests every aspect of your AI Centre of Excellence. Master these transitions, and you join the 12% of organisations that successfully scale AI from promising pilots to transformative capabilities. The principles are clear from repeated experience, the patterns proven through successes and failures. The only question is whether you’ll embrace the fundamental changes required or remain trapped in pilot purgatory.

Your board is watching for evidence of real transformation beyond pilot success. Your competitors are moving to capture AI-enabled advantages. Your opportunity window is finite before AI capability becomes table stakes rather than differentiator. Scale now, scale smart, and scale with purpose. The future belongs to organisations that transform AI from isolated experiments into enterprise capability embedded in culture and operations.

Next week’s final article will explore how to future-proof your AI Centre of Excellence, ensuring that the capabilities you’ve built can evolve with advancing technology and changing business needs. The journey from scaling to sustaining requires different strategies and structures, but builds naturally on the foundation you’ve created through successful scaling.

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.