Four choices that determine whether AI succeeds
Practical frameworks, checklists, and actionable guidance to help organisations adopt and use AI with greater control and confidence.
Playbook
What you’ll learn
- Why AI initiatives can become trapped in compliance paralysis and how to keep them moving.
- How to reduce Shadow AI by making the approved approach the easiest one to follow.
- Why quality and control don’t always have to be a trade-off.
- How to effectively monitor AI usage, costs, and business value.
Two insights to get you started
The biggest challenge is rarely the technology
What slows AI down is often unclear guardrails rather than poor models. When people know where it’s safe to experiment, what data they can use and how usage is monitored, AI can scale with control.
Shadow AI emerges when the right way is too difficult
When people turn to their own AI tools, it’s rarely out of defiance. It happens when the approved option isn’t simple or useful enough. Make the right way the easiest way, and Shadow AI decreases.
What’s in the guide?
Four decisions for successful AI adoption
Create the right conditions
Clear governance for confident AI adoption.
Make AI easy to use
Trusted tools that reduce Shadow AI.
Balance quality & control
The right model for the right task.
Make AI measurable
Connect AI usage to cost and value.
AI success starts before you scale
AI initiatives don’t always stall because the technology isn’t ready. They can stall because organisations haven’t created the conditions needed to adopt AI safely and effectively.
Questions around data, ownership, approved tools, model choice, cost and business value can make it difficult to know what to do next.
This playbook provides a practical framework for addressing four decisions that shape how organisations adopt, govern and scale AI.
The value of AI is not determined by how widely it is used, but by how intentionally it is applied. When organisations can connect AI activity, costs and outcomes, they can turn AI from experimentation into measurable business value.
Six questions before saying yes
- What problem or business task is AI expected to solve?
- Does this initiative belong in the safe testing zone or the restricted zone?
- What is the potential impact if the output is incorrect, shared with the wrong audience or
used in the wrong context? - What data is required, and can it be used in the intended environment?
- Is there a sandbox where non-technical users can experiment safely?
- What should success be measured by: time saved, quality, risk reduction, cost or user value?
