Orchestrating Multi-Speed AI: The Complete AI Framework as Guiding Policy

Stanford’s AI Index shows 78% of organisations using AI, yet McKinsey finds only 21% have redesigned workflows to integrate it. Last week’s article explained that gap through the Six Concerns as diagnosis – Boards understand these concerns are interconnected, yet governance mechanisms address them sequentially, creating cascading vulnerabilities across multi-speed adoption. Diagnosis reveals the problem, but organisations need guiding policy that shows how to govern these concerns as a system while managing the reality that different functions naturally advance at different speeds.

The Complete AI Framework I introduced earlier this year provides this guiding policy through three complementary mechanisms that create leverage, not just understanding. The Five Pillars define what capabilities organisations must build across data and analytics, tools and technologies, skills and capabilities, processes and governance, and strategy and vision. The AI Stages of Adoption (AISA) maps where functions stand in their maturity journey from Experimenting through Adopting, Optimising, Transforming, and Scaling. Well-Advised explains why to invest by capturing multi-dimensional value beyond ROI – including revenue potential, innovation opportunities, risk mitigation, customer value, and operational improvements.

Together as the Complete AI Framework, they enable Boards to target resources where they create compound value, pulling laggards forward rather than constraining leaders to match the slowest pace.

The architecture of systematic governance

The power of the Complete AI Framework lies not in its individual components but in how they create leverage against the diagnosed challenges. Consider how a global retailer might evaluate an AI-powered inventory optimisation initiative. Traditional governance would assess the business case in isolation – projected cost savings, implementation timeline, risk factors. The Complete AI Framework reveals how to concentrate force for maximum impact.

Through the Five Pillars lens, this initiative becomes a lever for building shared capabilities. Data and Analytics infrastructure built for inventory optimisation also enables demand forecasting and customer analytics. Tools and Technologies integrated for supply chain systems create platforms other functions can leverage. Skills developed in one team become trainers for others. This concentration of investment in shared capabilities creates the leverage Rumelt advocates – each pound spent generates value across multiple initiatives rather than serving a single project. This shifts from isolated business cases to concentrated investments that compound across initiatives.

The AI Stages of Adoption assessment reveals where to focus for maximum acceleration. The marketing team, already Optimising sophisticated demand prediction, becomes a learning laboratory for inventory management still Experimenting with basic models. Customer service, Transforming through automated systems, provides proven governance frameworks others can adapt. This isn’t failure – it’s strategic advantage. Advanced functions pull others forward through shared learning, concentrating organisational energy where it creates most value.

Well-Advised evaluation uncovers value leverage points beyond simple cost reduction. When inventory optimisation reduces stock-outs (operational improvement), it also increases revenue through better availability. When it enables predictive restocking services (innovation), it deepens customer relationships (customer value). When it improves supply chain resilience (risk mitigation), it protects the entire enterprise. This multi-dimensional value creation is how the framework channels action toward maximum impact – every initiative selected for its ability to generate compound benefits.

This three-dimensional analysis transforms Board evaluation from isolated project approval to strategic orchestration. The framework doesn’t just help Boards understand initiatives better – it shows them how to create leverage by concentrating resources on capabilities that accelerate the entire organisation. Advanced functions become force multipliers. Shared infrastructure becomes a competitive moat. Multi-dimensional value becomes compound advantage.

Multi-speed adoption: From weakness to leverage

The Complete AI Framework’s strategic insight is recognising multi-speed adoption not as governance failure but as leverage opportunity. World Economic Forum data confirms this reality: “Industries with tons of good data could have AI adoption rates around 60-70%. Meanwhile, sectors without much data might struggle with less than 20%.” Rather than fighting this pattern, the framework exploits it for competitive advantage. This directly addresses the velocity tensions I set out in the second article in the series, turning mismatched paces into mutual acceleration rather than governance chaos.

Marketing teams racing ahead with generative AI aren’t creating fragmentation – they’re discovering what works, developing governance models, and building capabilities that finance can later adopt with reduced risk. Manufacturing’s methodical approach to AI implementation isn’t holding the organisation back – it’s ensuring robust processes that become templates for mission-critical applications. Each function’s natural velocity becomes a strategic asset when properly orchestrated.

The Five Pillars framework reveals why forcing uniform progress destroys value. A function with mature data infrastructure will naturally adopt AI faster – forcing it to slow down wastes its advantage. Teams still digitising paper processes need foundation building before AI adoption – pushing them faster creates unmanaged risks. The framework concentrates resources where they generate maximum leverage: advanced functions get innovation support to push boundaries, developing functions get capability building to establish foundations, and the organisation captures learning from both.

This approach directly addresses the shadow AI paradox diagnosed previously. With 90% of employees using personal AI tools while only 40% of companies have official programmes, suppressing shadow AI doesn’t work – it drives innovation underground where it creates more risk. The framework instead channels this energy as strategic advantage. Shadow AI users become innovation scouts, identifying valuable use cases. Their experiments reveal what actually works versus what sounds good in vendor presentations. The AI Centre of Excellence (AI CoE) transforms this ungoverned creativity into governed innovation, avoiding the governance-reality gaps that undermine transformation.

The leverage principle extends to competitive dynamics. While competitors struggle to synchronise their entire organisation, companies using the Complete AI Framework accelerate where they can while building where they must. They don’t wait for perfect alignment before capturing value. They don’t constrain innovation to match their slowest function. They concentrate force where it creates advantage, using multi-speed adoption as a feature, not a bug.

The AI Centre of Excellence as force multiplier

Frameworks create potential; operational mechanisms deliver results. The AI CoE emerges as the force multiplier that transforms policy into practice, concentrating organisational energy where it generates maximum impact rather than dispersing it across disconnected initiatives.

Unlike traditional IT governance structures that often become bottlenecks, the AI CoE operates as an accelerator. IBM research demonstrates how effective AI CoEs reduce costs through centralised capabilities while speeding deployment through shared learning. The key is concentration of force – rather than each department building separate capabilities, the CoE creates shared platforms that benefit all.

Through the Five Pillars lens, the AI CoE identifies and builds leverage points. Instead of twenty departments each hiring one AI specialist, the CoE builds a centre of expertise that supports all twenty with deeper capability. Rather than duplicate data lakes, it establishes enterprise platforms that scale efficiently. This concentration doesn’t just reduce costs – it accelerates capability building by focusing resources where they create compound value.

AISA guides how the AI CoE creates stage-appropriate leverage. Functions not yet Experimenting need awareness – the AI CoE provides education that prevents costly mistakes. Experimenting teams need sandboxes – the AI CoE offers environments that accelerate learning. Adopting departments need scaling support – the AI CoE supplies frameworks proven elsewhere. This targeted support concentrates resources where they unlock progress rather than spreading them uniformly.

Well-Advised principles ensure the CoE captures and multiplies value across dimensions. When customer service’s chatbot reduces call volumes, the AI CoE doesn’t just document operational savings – it identifies how this capability could qualify sales leads (revenue multiplication) or enable new service models (innovation leverage). Every successful initiative becomes a lever for broader transformation.

Most critically, the AI CoE transforms the shadow AI challenge from risk to leverage. Those 90% of employees already using personal AI tools aren’t rebels – they’re innovators showing where value exists. Through AI amnesty programmes, the AI CoE channels this energy into governed experimentation. Shadow users become official pilots. Personal tools that work get enterprise licenses. Underground innovation surfaces as competitive advantage. This isn’t just risk management – it’s leveraging existing momentum for acceleration.

Beyond frameworks: Strategic integration with standards

The Complete AI Framework gains additional leverage through integration with formal standards like ISO 42001, but not in the way most Boards expect. Think of ISO 42001 as the governance skeleton – defining what must be governed – while the Complete AI Framework provides the operational muscles that turn compliance into competitive advantage.

When ISO 42001 requires risk assessment, organisations typically create another compliance checklist. The Complete AI Framework transforms this into leverage by showing how risk frameworks developed in cautious functions become accelerators for bold experimentation elsewhere. The same pattern repeats across all requirements: performance evaluation becomes multi-dimensional value intelligence through Well-Advised, documented processes become reusable capabilities through the Five Pillars, and data privacy requirements become the foundation for customer trust that enables entirely new service models.

This isn’t about making compliance less burdensome – it’s about recognising that in AI, the constraints themselves become competitive moats. When your algorithmic fairness processes open markets that competitors can’t enter, when your governance frameworks enable speed rather than constraining it, you’ve transformed regulatory requirements from costs into assets. The organisations that understand this integration will build advantages that non-compliant competitors can never match.

Metrics that matter: Creating measurement advantage

Most Boards measure AI through backward-looking financial metrics, discovering too late that their initiatives have failed to scale. The Complete AI Framework transforms this by creating three-dimensional measurement that provides foresight rather than just hindsight.

Leading indicators act as early warning systems before problems cascade. When experiment velocity slows, it signals innovation throttling months before competitive impact appears. When AI initiatives drift from strategic objectives, it predicts the fragmentation that destroys value. These metrics don’t just document – they enable course correction while it still matters.

But forward-looking metrics alone aren’t enough. Lagging indicators confirm which patterns actually generate value, turning successful approaches into templates for acceleration. When one function’s governance model enables faster deployment, it becomes the blueprint for others. When specific capability investments consistently yield higher returns, they guide resource concentration. Every measurement becomes intelligence for future advantage.

The real breakthrough comes from predictive indicators powered by AI itself. Machine learning models now predict which pilots will successfully scale based on early adoption patterns, enabling Boards to channel resources toward winners before results confirm success. Natural language processing analyses employee sentiment to identify resistance before it crystallises into failure. This is measurement as strategic weapon – using AI to govern AI.

Predictive foresight emerges as the pivotal lever: AI-powered models forecasting pilot success channel resources from failures to winners, preventing cascades while amplifying compound value.

McKinsey’s research underscores why this matters: only 17% of organisations currently attribute more than 5% of EBIT to AI, yet 87% expect significant revenue growth within three years. That gap between current reality and future expectation makes three-dimensional measurement essential. Without leading indicators showing whether you’re on track, lagging measures confirming what works, and predictive metrics revealing where to concentrate resources, Boards are essentially gambling on transformation rather than governing it.

Portfolio thinking: Orchestrating compound value

Traditional portfolio management treats AI initiatives as independent investments to be balanced for risk. The Complete AI Framework reveals something different: portfolios as systems where each initiative multiplies the value of others through strategic sequencing and shared capabilities.

Consider how this works in practice. Quick wins that leverage existing data infrastructure don’t just deliver immediate value – they build organisational confidence that unlocks funding for larger transformations. Foundation investments in governance frameworks don’t just enable compliance – they become the platforms that let multiple future initiatives scale faster. When marketing’s rapid experiments discover what works, manufacturing doesn’t have to repeat those failures. When finance develops robust governance for high-risk applications, sales can adopt those frameworks to move faster with confidence.

This is where multi-speed adoption transforms from challenge to advantage. Functions moving at different velocities aren’t creating fragmentation – they’re creating a learning system where each function’s natural pace serves the whole. Conservative functions develop the ethical frameworks that enable bold experimentation elsewhere. Fast-moving teams discover the techniques that slower-adopters can implement with reduced risk. The portfolio doesn’t try to synchronise these speeds but orchestrates them for mutual reinforcement.

The result directly addresses the cascade failures diagnosed in the previous article. Instead of Strategic Alignment in one area undermining Ethical Responsibility in another, portfolio orchestration ensures they reinforce each other. Risk Management in critical functions doesn’t constrain Innovation but enables it by providing proven frameworks for experimentation. Financial discipline in core operations generates the resources that fund breakthrough initiatives. The portfolio becomes an engine for compound value creation rather than a collection of competing projects.

From policy to action

Guiding policy means nothing without implementation that creates leverage. The Complete AI Framework shows Boards how to channel organisational energy where it generates maximum advantage, turning multi-speed adoption from apparent weakness into strategic strength.

The first step requires honest assessment through all three framework lenses to identify leverage points. Where do Five Pillars capabilities already exist that others could build upon? Which functions at advanced AISA stages could pull others forward? What Well-Advised value dimensions could multiply across initiatives? This isn’t just analysis – it’s reconnaissance for strategic advantage.

The second step establishes the AI CoE as a force multiplier, not another bureaucratic layer. The AI CoE needs authority to concentrate resources where they create compound value, capability to transform shadow innovation into governed advantage, and credibility to orchestrate multi-speed adoption without forcing false synchronisation.

The third step launches carefully selected initiatives that build leverage systematically. These aren’t just pilots – they’re strategic investments chosen for their multiplication potential. Each should consciously build capabilities others can use, advance multiple functions through shared learning, and capture value that compounds across dimensions.

Success requires accepting strategic realities that traditional governance resists. Not all functions will or should move at the same pace – variation creates leverage. Some initiatives will fail – but systematic learning from failure accelerates future success. Shadow AI will persist – suppressing it just drives it deeper, as 90% usage proves. Channel it for acceleration instead. The Complete AI Framework doesn’t eliminate these realities but transforms them into competitive advantages.

The strategic imperative

Boards confronting AI face a strategic choice with compound implications. Continue treating AI as disconnected projects, hoping fragments cohere while competitors build systematic capability. Or adopt frameworks that create leverage, concentrating force where it generates maximum advantage while maintaining governance coherence.

The Complete AI Framework provides guiding policy that transforms multi-speed adoption from problem to leverage, shadow AI from risk to innovation accelerator, and governance requirements from constraints to competitive moats. By integrating Five Pillars capability building, AISA maturity recognition, and Well-Advised value multiplication, Boards gain mechanisms for creating compound advantage rather than incremental improvement.

The diagnosis from last week revealed the challenge: Six Concerns creating cascading failures when addressed sequentially. This week’s guiding policy provides the solution: integrated frameworks that create leverage by concentrating resources where they generate compound value. Next week’s article completes the strategic journey with coherent actions – specific steps that transform policy into competitive advantage.

Strategy without action is merely intention. But action without leverage is merely activity. The Complete AI Framework provides both the blueprint and the force multipliers. Those Boards that commit to systematic implementation will transform AI from expensive experimentation into compound competitive advantage. Those that don’t will accumulate disconnected pilots while competitors who embraced leverage-based approaches capture not just the value they’re leaving on the table, but the value they didn’t know existed.

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.