Most AI investments fail to scale because organisations focus on technology whilst ignoring culture. The Accelerator addresses the relationship dynamics and organisational silence that determine whether AI empowers teams or causes skills atrophy and risky behaviour.
AI Adoption Accelerator
Build sustainable AI capability across your organisation
A research-informed programme that addresses why 70% of AI transformations fail—by focusing on culture, not just technology.
The race is on
The Challenge
Your organisation faces pressure to "do something" about AI. The supplier landscape changes every day, pilots don't lead to sustainable outcomes.
You've probably tried:
- Vendor training that doesn't stick
- Pilot projects that never scale
- Policies that create shadow IT rather than adoption
- Consultants who leave you dependent on their expertise
The missing piece isn't technology. It's the organisational learning infrastructure that allows people to develop capability together. The disconnect between how people actually experience AI and how organisations design adoption creates the gap where transformation efforts fall apart.
The Archetypes
How People Actually Experience AI
My research revealed four distinct relationship patterns with AI:
The Tool: AI as capability enhancer. You maintain oversight, make strategic decisions, and use AI to handle information processing whilst you focus on judgment and context. The relationship is instrumental but not dependent—you're getting better at your job, not outsourcing your thinking.
The Teacher: AI as coach helping you develop expertise. You use AI to learn new skills, understand complex topics, and build capability in areas where you're developing competence. The relationship is developmental and time-limited—as you build expertise, you rely on AI less.
The Sparring Partner: AI as creative collaborator challenging your thinking. You use AI for ideation, exploring alternatives, and pressure-testing ideas. The relationship is dialogic—you bring judgment and context, AI brings breadth and different perspectives.
The Trap: AI as dependency that erodes capability. What starts as efficiency gradually becomes inability to function without the system. Skills atrophy, contextual knowledge fades, and you've traded short-term productivity for long-term vulnerability.
Whether your teams use AI as an empowering tool or fall into the trap isn't an individual choice—it's determined by organisational design.
Organisations that successfully adopt AI create conditions where people can:
- Maintain independent thinking whilst leveraging AI's capabilities
- Learn through structured experimentation rather than trial-and-error
- Share insights across teams without fear of appearing incompetent
- Build skills that compound rather than atrophy
The approach
Multi-Level Interventions
To create lasting change it's crucial to approach AI adoption across all levels. Governance and innovation are enabled by awareness, skills, and knowledge-sharing.
Individual Awareness
Recognise your dominant archetype patterns and develop deliberate switching capability based on task demands. Build AI literacy that enhances rather than replaces critical thinking. Maintain independent problem-solving skills whilst leveraging AI.
Team Capability
Weekly experimentation cycles with structured reflection, not resource-intensive hackathons. Cross-team learning sessions that spread insights rapidly. "Think first" practices that prevent skills atrophy whilst improving AI outputs. Documentation infrastructure that captures learning and builds shared capability.
Organisational Culture
Leadership development for managers enabling adoption, not just C-suite strategy. Psychological safety foundations that reduce organisational silence. Breaking silos between technology, L&D, risk, and business functions. Measurement systems tracking capability development, not just efficiency gains.
Evidence based
Grounded in Converging Research
The programme design synthesises findings across multiple research streams
Progressive encapsulation: As AI systems expand their functions, work processes that once required human coordination and contextual judgment gradually become invisible inside automated systems. This isn't inevitable—deliberate intervention can maintain human capability whilst leveraging AI's strengths.
Cognitive debt accumulation: Neuroscience research tracking brain activity found AI users showed significantly reduced neural connectivity compared to non-users. Skills atrophy research extends across professions—mathematics teachers showing 91% variance in problem-solving ability explained by AI dependency, medical specialists experiencing detection rate declines, accountants unable to function when systems failed.
Organisational silence: The disconnect between leadership enthusiasm and frontline overwhelm kills 70% of AI transformations. People don't disclose when AI makes errors, don't ask questions revealing knowledge gaps, don't share failed experiments that could help others. This structural silence between strategy and execution is the hidden failure point.
The 70-20-10 principle: Research on successful AI adoption mirrors leadership development findings from decades ago—organisations succeeding in AI allocate 70% of resources to people and processes, 20% to technology and data, 10% to algorithms. Laggards invert this ratio and consistently fail to scale beyond pilots.
The manager multiplier effect: When managers adopt a "pilot mindset"—high agency and optimism about AI—their direct reports are 3x more likely to develop it themselves. Yet middle managers receive the least support whilst facing the greatest pressure to enable adoption.
My research identified the four archetypal relationships with AI, providing practical language for organisations to discuss how people actually experience AI adoption. This framework connects individual experience to organisational design choices, making visible the patterns that determine success or failure.
Beyond usage stats
Measurable Outcomes
Unlike generic AI training, we track what actually matters
Integration depth: Movement from surface use → functional workflows → embedded processes → strategic redesign. This measures whether AI is actually transforming how work gets done, not just adding steps to existing processes.
Psychological safety: Increased team willingness to discuss AI failures and uncertainties. This measures whether people feel safe learning in public—the foundation for everything else.
Organisational voice: Better information flow between leadership and frontline. This measures whether the structural disconnect that kills most transformations is actually improving.
Capability retention: Teams maintain independent problem-solving skills whilst leveraging AI. This measures whether you're building sustainable competitive advantage or creating hidden dependencies.
Learning velocity: How quickly teams experiment, share insights, and adapt approaches. This measures organisational learning capability—the actual competitive differentiator in rapidly evolving technology landscapes.
Creating Change
The Accelerator
The programme typically runs 3-6 months, tailored to your organisational context. Core components include AI Amnesty (creating psychological safety), team experimentation cycles, leadership development, organisational infrastructure, and continuous measurement. Investment reflects the 70-20-10 principle: most value comes from enabling your people, not purchasing technology.
This works best for organisations serious about scaling AI beyond pilots, who recognise their barriers are cultural rather than technical. Particularly valuable for HR/L&D leaders, operations leaders, technology leaders frustrated by slow adoption, and C-suite executives addressing the strategy-execution gap.
Book a free discovery call to discuss your AI adoption challenges and explore whether this approach fits your organisation.
I also offer speaking and workshops on AI adoption, executive coaching for leaders navigating transformation, and research collaborations with organisations contributing to ongoing AI adoption research.