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Krista Parker
Published on 8 June 2026
Building an AI strategy through culture, co-design, and organisational narrative
Many organisations are guilty of approaching AI adoption as a technical or operational challenge, hinging on tooling, productivity, and governance. However, for their teams, AI can raise more human questions about value, creativity, and autonomy.
The disconnect is already visible. According to Harvard Business Review, 76% of executives report that their employees feel enthusiastic about AI adoption, yet only 31% of individual contributors express enthusiasm for adopting AI. Recent research from the ONS also shows that many people remain concerned that AI will threaten future job security or reduce their income over time.
These concerns are not necessarily due to resistance to change. When leaders talk about efficiency, automation or transformation, people may hear deeper, more worrying questions:
- Will my creativity still be valued?
- Will my judgment still matter?
- What will the future of this organisation look like?
These are not factual, black-and-white questions with clear, objective answers. They're philosophical and cultural questions that can even be unique to an organisation's industry, size, or history. What leaders often view as resistance to AI is actually resistance to an implied story about human value, rather than to AI itself.
Successful AI adoption cannot rely solely on strategy documents. Organisations need cultures that welcome participation, approaches that co-design AI solutions with teams rather than imposing those solutions on them, and narratives that help people to envisage themselves in the future of work being created.
In this blog, I'll explore why culture, co-design and narrative are becoming central to effective AI adoption, and how we're shaping this approach at The Adaptavist Group.
Why many AI implementations fail
It's all too common for AI implementations to centre around governance boards, capability programs and centres of excellence. While these approaches might be technically sophisticated, they can feel culturally disconnected from the rest of the organisation, especially because they often focus too strongly on efficiency and regulation, rather than values and meaning.
AI people quickly become separate from the business. AI becomes something owned by specialists, rather than something wider teams are invited to shape collectively. As a result, many people begin to feel like passive recipients of change rather than active participants.
When AI initiatives are communicated through the language of optimisation, automation, or productivity, teams can begin to question what the organisation truly values, and whether human judgement, creativity, and experience still have a meaningful place within it.
Ultimately, AI adoption succeeds or fails because of both the quality of the technology and the quality of the organisational storytelling surrounding it. People need to understand not just what is changing, but why it matters, what role they still play, and what will remain fundamentally human in the organisation's future. Keeping communication channels open is crucial.
The importance of narrative
The most successful organisations rely on systems and stories to drive meaningful change.
AI adoption can stall when leaders treat the more philosophical questions as technical ones. People who are concerned about their future don't necessarily want to hear about how new AI tools will improve efficiency. They want to understand how they can continue doing meaningful work while maintaining their identity and autonomy.
To feel genuinely involved and engaged in AI adoption, people need a clear, human-focused narrative that explains:
- What is happening
- Why it matters
- What will be preserved
- What the organisation aspires to in the future
Creating opportunities for learning, experimentation, and knowledge sharing can help teams build confidence while ensuring AI is used responsibly.
One approach is to establish dedicated learning pathways that give employees a strong foundation in AI fundamentals before introducing more advanced use cases. Teams can then make more informed decisions about where AI can add value, recognise potential risks, and identify opportunities to improve ways of working.
At The Adaptavist Group, we took this approach through AI Essentials, a training programme originally designed to help clients build AI literacy. As demand for AI knowledge grew, the programme was adapted for internal use, combining digital learning with live workshops. The experience reinforced that investing in AI education early on can help both employees and leaders engage with new technologies more confidently and effectively.
Why co-design is crucial
Co-design is more than just a delivery method. It should actually become rooted in your organisational culture.
It's not unusual for technical teams to work in isolation when implementing AI, separated from other departments and end users. Such a siloed approach limits creativity, leads to misaligned outcomes, and increases the risk of AI implementation failure.
Co-design breaks down these barriers by bringing diverse stakeholders into the AI implementation process from the beginning. Treating your teams not as obstacles to transformation, but as interpreters of it, can transform your entire AI adoption process. After all, there is no single "correct" future of work. Organisations need participation from an array of stakeholders to shape it.
The table below outlines how a traditional, centralised model can evolve into an inclusive, co-designed delivery model.
| Centralised model | Co-designed model |
|---|---|
| AI rollout | AI participation |
| Governance and compliance | Ownership and responsibility |
| Efficiency narrative | Meaning narrative |
| AI as a tool | AI as a practice |
| Training users to use AI | Shaping a culture of AI use |
Providing safe spaces for questions and discussion can help teams to feel much more involved, engaged, and most importantly, eager to experiment.
For example, running dedicated office hours sessions and opening up discussion forums via Slack or Teams are both effective ways of encouraging people to raise questions or concerns, and share their knowledge.
We’ve found success with this approach, encouraging both technical and non-technical people to share their questions and insights via AI office hours sessions, as well as initiating their own transformation within their current workflows, based on what’s most important to their day-to-day work.
Reframing centres of excellence
Centres of excellence play an important role in AI implementation. However, the role of a CofEs should not be to own AI adoption, but to enable it through experimentation, support and shared learning.
While traditional CofEs are viewed as command centres or governance authorities, the most successful organisations are reframing them as facilitators, translators and capability builders. Rather than acting as gatekeepers of AI knowledge, these teams help bridge the gap between technical possibility and everyday reality. They support teams to safely explore AI in ways that are relevant to their own context, challenges, and expertise.
Successful AI adoption rarely happens through top-down instruction alone. Teams are far more likely to engage positively with AI when they feel they have permission to experiment, ask questions, challenge assumptions, and contribute to shaping how tools are used in practice.
In this model, the CofE becomes less about centralised control and more about creating confidence, trust, and shared understanding.
As part of our own approach to this, we’ve made the AI Essentials course available to all employees of The Adaptavist Group in our Learning and Development catalogue, giving everyone the opportunity to build up their AI skills and knowledge, not just technologists.
People then have the freedom and knowledge to experiment with tools, and share their learnings or questions during AI office hours sessions, or via dedicated Slack channels.
Why the most successful organisations use AI as a mirror
AI should force organisations to confront the more philosophical questions they might've previously avoided:
- What do we actually value?
- What should remain human?
- How should decisions be made?
- What kind of culture are we trying to build for the future?
Successful AI adoption isn't always about using the most advanced AI models and agents, or the most sophisticated tooling. While technological capabilities are no doubt important, the true differentiator for a lasting transformation lies in the human elements of the process. By prioritising reflection, participation, and cultural adaptation, you can ensure your AI journey remains human-centric and aligned with your organisation's core values.