An indicative roadmap for AI adoption

As AI continues to evolve at pace, organisations are rightly asking not just whether to adopt it but how to do so in a structured, scalable, and sustainable way. What begins as a personal productivity tool can quickly evolve into a transformative force across the organisation, but only if the right foundations are laid. We set out a practical, staged, and non-linear approach to rolling out AI, from initial tools that enhance individual performance to advanced models that reshape how your organisation operates and competes.

When talking about AI adoption, it is important to be clear about the difference between terms that are often used interchangeably but mean very different things. We have used many of these terms below, so here is a simple way to think about them.

• Artificial intelligence: this is an umbrella term that covers the development of machines and systems that can perform tasks that typically require human intelligence, such as reasoning, learning, decision-making or pattern recognition. It is the foundation for the other terms below.
• Generative AI: this is a subset of AI focused on creating new content such as text, images, code or ideas based on data on which it has been trained and patterns it has learned. It is reactive and relies on human prompts to produce its outputs. There is a correlation between the quality of the training data and user prompt, and the quality of the answer it generates. For example, using GPT-4 to draft a business case or an ERM Framework.
• AI assistants: these are applications built on generative AI and other technologies and are designed to help people complete specific tasks more quickly and easily. They are reactive, act on demand, and improve productivity within the need to change underlying processes. For example, using intelligent meeting recap to summarise a Teams meeting and draft follow-up emails.
• AI Agents: they use AI to achieve multi-step goals and can act independently and autonomously once given an objective. They can be designed to manage tasks without needing human instructions at every step. For example, using a customer service agent to review, categorise and draft the response to a claim or complaint.
• Agentic AI: the exact meaning has evolved in more recent years. In this context, we mean AI agents that can orchestrate and coordinate multiple other agents or tools – like a conductor or manager. They break down a goal into sub-tasks, delegate the tasks to other AI agents, APIs or tools, monitor, adapt and resolve conflicts. Examples could be a multi-agent logistics system.

Successful AI adoption is not about rushing into the most advanced tools or use cases. It’s about progressing with clarity, confidence, and control, building capability and trust at every step. Organisations will have different starting points, the roadmap is not linear. Not every organisation needs to build their LLMs, and not every process should be agent-led. Understanding the roadmap empowers you with the knowledge around several key areas.

1. Language to frame AI maturity internally.
2.  Identifying how to prioritise investment and classify capability gaps.
3.  Structure to balance innovation with risk and control.
4.  A path to embed AI sustainably, not just tactically.

By treating AI adoption as a journey rather than a one-off deployment, organisations can unlock value early while preparing for deeper transformation ahead.

If you’re planning to introduce or scale AI in your organisation, ask yourself: What stage are we truly at, and what needs to be true for us to move forward with impact and integrity?

AI adoption isn’t one big leap; it’s a series of intentional, intelligent steps. Start where you are and be clear about where you’re heading. The goal isn’t to chase shiny tools but to build lasting value.

As AI evolves, the organisations that thrive will be those that move from curiosity to capability, with clarity, structure, and purpose. 

To evaluate your business's preparedness for AI, complete the AI maturity assessment, for a personalised analysis and practical next steps. Contact Buki Obayiuwana for an AI readiness review.