A Complete AI Adoption Framework: AISA, Five Pillars, and Well-Advised

Since publishing the AI Stages of Adoption (AISA), the Five Pillars, and Well-Advised, I’m regularly asked ‘How can I practically put them together to help me accelerate AI adoption in my business?’ While each mechanism provides valuable insights individually, their real power emerges when used together as a framework. In this article, I explain how to use AISA, the Five Pillars, and Well-Advised to address the six concerns of the board, and catalyse AI transformation in your business.

The Multi-Speed Nature of AI Adoption and Board Implications

The fundamental challenge boards face today stems from AI’s inherently parallel adoption pattern. Unlike previous technology transformations, AI initiatives emerge simultaneously across different business functions, each with distinct characteristics that demand nuanced evaluation and governance approaches.

Different AI applications create dramatically different value-investment profiles. Some initiatives, like large language model-powered content generation, deliver quick wins with minimal investment, showing returns within days or weeks. Others, such as custom foundation models or complex manufacturing AI systems, require substantial commitment across multiple dimensions before realising strategic value. This isn’t simply about timeline differences, it reflects the fundamental economics of how AI creates value.

Implementation complexity varies enormously across applications. Customer service chatbots might reach production deployment within weeks, leveraging existing infrastructure and requiring minimal process changes. By contrast, predictive maintenance systems demand extensive data preparation, sensor integration, and fundamental process redesign that can span months. Each initiative operates on its own complexity curve, creating natural variation in adoption speed.

Departmental readiness adds another layer of variation. Marketing teams often embrace AI for content creation because the technology directly addresses their creative productivity challenges with limited risk exposure. Finance departments, however, approach automated decision-making with appropriate caution, given regulatory requirements and fiduciary responsibilities. These different risk tolerance levels naturally create different adoption velocities across the organisation.

The investment dimension reality further complicates this landscape. AI initiatives require commitment across financial resources, people development, data preparation, process redesign, and time allocation. Different business areas have varying capacity across these dimensions, creating natural constraints that influence adoption speed. A department with strong technical infrastructure but limited change management capacity will progress differently than one with abundant resources but cultural resistance to automation.

This multi-speed reality creates unprecedented evaluation and governance challenges that map directly to the six key areas of board concern for AI governance.

Integrating the Three Mechanisms

The power of integrating the three mechanisms emerges when boards understand how AISA, the Five Pillars, and Well-Advised create a comprehensive framework that addresses AI’s unique characteristics whilst maintaining strategic coherence. Each mechanism serves a distinct but complementary purpose, together forming what I consider the complete AI adoption framework.

AISA answers the fundamental question “Where are we now?” by assessing current AI maturity across the adoption journey. Its key insight lies in deliberately using ‘investment’ rather than ’time’ as the assessment dimension, reflecting the multiple dimensions organisations must commit to AI initiatives. These include financial investment in direct costs like compute resources and software licences, people investment through training existing staff and hiring AI specialists, data investment in cleaning and preparation activities, process investment in workflow redesign and governance establishment, and time investment spanning both calendar duration and person-hours across the organisation.

This investment focus reveals why different AI initiatives create varying value-investment profiles. Some initiatives might require substantial upfront commitment before realising value, whilst others deliver quick wins with minimal investment. Understanding these profiles helps boards make informed portfolio decisions rather than applying uniform investment criteria across fundamentally different initiative types.

The Five Pillars capability areas address the question “What capabilities do we need?” by identifying requirements for successful stage transitions through AISA. Rather than viewing capabilities as static requirements, the Five Pillars recognise that different AISA stages demand different capability maturity levels. This dynamic relationship ensures that organisations build appropriate foundations for their current stage whilst preparing for future advancement.

The Five Pillars encompass Governance and Accountability mechanisms that ensure appropriate oversight and ethical deployment, Technical Infrastructure that provides the foundation for AI system deployment and scaling, Operational Excellence processes that ensure reliable and consistent AI system performance, Value Realisation and Lifecycle Management approaches that track and maximise return on AI investments, and People, Culture, and Adoption initiatives that build organisational readiness for AI transformation.

The Five Pillars approach recognises that capability requirements evolve with organisational maturity. Basic governance frameworks sufficient for early experimentation become inadequate for business-critical AI systems. Similarly, foundational technical infrastructure that supports pilot projects requires enhancement to enable enterprise-wide scaling. This graduated approach prevents both under-investment in early stages and over-engineering for simple use cases.

Well-Advised completes the integration by answering “How do we know we’re on the right track?” through evaluating strategic value and alignment across business dimensions. This mechanism ensures that AI investments advance broader business objectives rather than becoming isolated technology initiatives. Well-Advised examines value creation across Innovation and New Products/Services, Customer Value and Growth, Operational Excellence and Efficiency, Responsible Business Transformation, and Revenue, Margin, and Profit.

This balanced approach prevents boards from overweighting single value dimensions whilst ensuring comprehensive strategic alignment. AI initiatives that deliver across multiple Well-Advised pillars typically create more sustainable value than those focused narrowly on single outcomes. This multi-dimensional perspective becomes particularly important in multi-speed adoption environments, where initiatives create value through different mechanisms and timescales.

Using AISA, Five Pillars, and Well-Advised

Successfully integrating AISA, the Five Pillars, and Well-Advised requires a systematic approach that respects the multi-speed nature of AI adoption whilst maintaining strategic coherence. The three-step process provides boards with a practical methodology for applying these mechanisms together as a framework rather than in isolation.

The first step involves mapping the multi-speed landscape and aligning capabilities with strategy. Mapping the multi-speed landscape begins with conducting comprehensive AISA assessments across business areas, recognising that different functions will demonstrate varying investment-value profiles. This assessment must analyse the multi-dimensional nature of AI investments, encompassing financial commitments, people development requirements, data preparation needs, process redesign obligations, and time allocation demands. Critically, this step involves identifying which initiatives offer quick wins versus those requiring strategic long-term value development, creating a portfolio perspective that guides resource allocation decisions.

Aligning capabilities and strategy requires evaluating Five Pillars capability gaps across the multi-speed landscape using specific transition indicators rather than generic checklists. This evaluation must consider how capability requirements vary across different business areas operating at different AISA stages. Simultaneously, boards must map initiatives to Well-Advised pillars, ensuring balanced strategic value creation rather than concentration in single value dimensions. The output involves creating comprehensive matrices showing business functions, their current AISA stages, capability gaps, and value-investment profiles.

The second step focuses on establishing appropriate governance structures whilst selecting high-value initiatives for development. This step leverages the AI business case approach I’ve set out in previous articles, to evaluate and select initiatives based on comprehensive value creation potential rather than simple ROI calculations. The prioritisation process must address all six board concerns across the multi-speed environment, ensuring that governance scales appropriately with different initiative risk levels and maturity requirements.

Central to this step is leveraging an AI Centre of Excellence (AI CoE) as the operational hub for coordinated governance. The AI CoE must be positioned to report directly to the board via the risk committee, ensuring appropriate visibility and authority for managing multi-speed adoption realities. This positioning enables the AI CoE to coordinate across different business areas whilst maintaining strategic alignment and appropriate risk management.

Boards should oversee AISA assessments, align capabilities, and monitor progress to ensure strategic and risk oversight, adapting governance to multi-speed adoption patterns.

The third step, involves launching coordinated pilots whilst monitoring multi-speed progress. Implementation must focus on high-value initiatives that build shared capabilities rather than isolated solutions. This approach maximises the platform value created by early initiatives, enabling acceleration of subsequent projects through leveraged capabilities.

Monitoring multi-speed progress requires sophisticated tracking mechanisms that recognise different advancement patterns across business areas. Progress measurement must account for varying value-investment profiles, different risk tolerance levels, and distinct capability development needs. The monitoring framework should track both individual initiative progress and cross-functional capability development, ensuring that shared investments deliver expected portfolio benefits.

Refinement based on multi-speed feedback loops ensures continuous improvement of both individual initiatives and the overall integration approach. This iterative methodology recognises that AI adoption involves continuous learning and adaptation rather than linear progression through predetermined stages.

Practical Application: Streamlined Scenarios

Understanding how the integrated framework applies across different organisational contexts helps boards appreciate its practical value whilst recognising implementation approaches that match their specific circumstances. The following scenarios illustrate how AISA assessment, Five Pillars evaluation, and Well-Advised alignment create different strategic approaches depending on organisational AI maturity.

Early-stage organisations typically demonstrate most business areas operating in the Experimenting stage, characterised by different departments conducting independent experiments with varying success rates. The parallel reality involves uncoordinated AI exploration across functions, often including significant shadow AI usage alongside formal pilot projects. Five Pillars gaps generally encompass weak governance across all areas, limited technical infrastructure for enterprise deployment, and minimal cross-functional coordination mechanisms. Well-Advised priorities focus on Operational Excellence quick wins that demonstrate immediate value whilst building foundational capabilities. The key action involves establishing an AI Centre of Excellence with appropriate board reporting whilst launching operational pilots that can provide rapid ROI demonstration and shared capability development.

Mixed-maturity organisations represent the most common scenario, with advanced areas operating at Optimising or Transforming stages whilst emerging areas remain at Experimenting or Adopting levels. The parallel reality might involve marketing departments scaling AI content generation systems whilst finance departments conduct initial fraud detection pilots. Five Pillars gaps typically show strong technical capabilities in advanced areas contrasted with uneven governance frameworks and cultural readiness across the organisation. Well-Advised priorities require balancing continued innovation in advanced areas with systematic capability building in emerging functions. The key action focuses on leveraging learnings from advanced areas to accelerate progress in emerging ones whilst standardising successful approaches across the organisation.

Advanced organisations demonstrate multiple areas operating at Transforming or Scaling stages, with sophisticated understanding of how to manage parallel progression effectively. The parallel reality involves different business areas leading in different AI domains whilst collaborating on shared capabilities and strategic initiatives. Five Pillars gaps typically show strength across most capability areas but require ecosystem-level capabilities for industry leadership and comprehensive scaling. Well-Advised priorities emphasise Innovation and new revenue stream development whilst maintaining operational excellence across all functions. Key actions focus on driving industry standards development and establishing ecosystem partnerships that extend organisational AI capabilities beyond traditional boundaries.

Overcoming Integration Challenges

While integrating three mechanisms might initially seem complex, organisations can overcome potential challenges through systematic approaches that leverage existing structures whilst building incrementally toward comprehensive capability.

The most common concern Boards express involves complexity management; how to integrate sophisticated frameworks without creating bureaucratic overhead that stifles innovation. The solution lies in leveraging your AI CoE as a simplifying hub rather than attempting to implement all framework elements simultaneously across the organisation. Starting with a single high-value pilot allows organisations to test integration approaches whilst building confidence and capability incrementally.

This pilot-first approach enables practical learning about framework integration whilst delivering immediate value that justifies continued investment. The AI CoE can coordinate framework application for the pilot, refining approaches based on practical experience before expanding to additional initiatives. This methodology transforms potential complexity into manageable learning opportunities whilst maintaining momentum toward comprehensive integration.

Common integration mistakes provide valuable guidance for avoiding predictable pitfalls. Sequential thinking represents the most fundamental error—assuming all business areas must progress through AISA stages uniformly. AI’s parallel nature means different areas can and should progress at different speeds, with governance frameworks that accommodate this reality rather than forcing artificial uniformity.

Capability gaps create another frequent failure mode when organisations attempt to advance between AISA stages without building required Five Pillars foundations. Moving from Experimenting to Adopting without establishing basic governance frameworks creates unnecessary risks, whilst advancing from Optimising to Transforming without cultural transformation capabilities typically results in strategic initiatives that fail to achieve sustainable change.

Strategic imbalance emerges when organisations overweight single Well-Advised pillars, particularly focusing exclusively on Operational Excellence whilst neglecting Innovation or Responsible Transformation dimensions. Balanced portfolios deliver superior long-term value, especially when managing multiple parallel initiatives with different strategic contributions.

Governance silos represent perhaps the most dangerous mistake, where organisations under-invest in shared capabilities that benefit multiple initiatives. The parallel nature of AI adoption makes shared infrastructure, governance frameworks, and expertise development even more valuable than in sequential technology adoptions. Investment in comprehensive AI CoE capabilities pays dividends across multiple simultaneous initiatives whilst reducing overall implementation risk.

Making It Practical: Implementation in Three Steps

The integrated framework transforms from theoretical understanding to practical value through systematic three-step implementation that leverages your AI CoE whilst building capabilities incrementally.

The first step, Assess and Align, uses your AI CoE to conduct integrated AISA, Five Pillars, and Well-Advised evaluations across your multi-speed landscape. This comprehensive assessment creates baseline understanding of current AI maturity, capability gaps, and strategic alignment opportunities whilst identifying specific initiatives that offer the greatest potential for both immediate value and long-term strategic impact.

The second step, Prioritise and Govern, applies your established AI business case framework to select high-value initiatives whilst establishing governance structures that address all six board concerns across your multi-speed environment. This prioritisation process must balance portfolio considerations, ensuring appropriate mix of quick wins and strategic investments whilst building shared capabilities that benefit multiple initiatives.

The third step, Implement and Iterate, launches coordinated pilots that build shared capabilities whilst monitoring progress through your AI CoE and refining approaches based on multi-speed feedback loops. This iterative approach enables continuous learning and adaptation whilst maintaining strategic momentum toward comprehensive AI transformation.

The beauty of this three-step approach lies in its recognition that AI adoption involves continuous learning rather than linear progression through predetermined phases. By building learning mechanisms into implementation processes, organisations can adapt to changing circumstances whilst maintaining strategic direction and appropriate governance oversight.

Conclusion: Your Complete AI Governance System

AI’s multi-speed reality requires integrated governance approaches that no single framework can address comprehensively. Together, AISA, Five Pillars, and Well-Advised systematically address all six board concerns whilst providing practical guidance for managing parallel adoption patterns that characterise modern AI transformation.

Your AI Centre of Excellence becomes the operational engine for this integrated approach, coordinating framework application across business areas whilst maintaining strategic alignment and appropriate governance standards. This positioning transforms potential complexity into manageable coordination challenges whilst building organisational capabilities that enable sustainable AI transformation.

The business case foundation you’ve established provides the prioritisation mechanisms needed to select initiatives identified through integration analysis. Rather than replacing existing investment frameworks, the integrated approach enhances them with AI-specific considerations that improve decision-making quality whilst maintaining financial discipline.

Our upcoming six-article series will dive deeper into each board concern, starting with Strategic Alignment for multi-speed initiatives. Each article will provide detailed guidance for applying integrated framework approaches to specific governance challenges whilst building comprehensive AI transformation capabilities.

AI’s multi-speed reality requires integrated governance that matches its complexity and potential. Through your AI CoE, assess your landscape today and launch one high-impact pilot to build momentum for transformation that balances innovation opportunity with governance responsibility.

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.