AI Strategy
Mar 25, 2026
Eight Questions Every Leader Should Be Asking About AI in 2026
Written by: Ashleigh Greaves, CEO, simplefy.ai
2026 AI Predictions - Part 4 of 4
Over the past three weeks, I have written about the trends shaping how businesses adopt AI in 2026: the gap between conversation and implementation, the people and change management challenges that technology alone cannot solve, and why governance is becoming a competitive advantage.
All of it comes back to one thing: the quality of questions leaders are asking.
The organisations that are making genuine progress are not the ones with the biggest AI budgets or the most tools deployed. They are the ones asking harder, more honest questions about what AI actually changes and acting on the answers.
I pressure-tested these questions with leaders across the UK and Australia. They surfaced honest conversations about where organisations were stuck, what they were avoiding, and what needed to change.
Here are eight questions worth bringing to your next leadership conversation.
1. If our AI tools disappeared tomorrow, what behaviours would actually break?
If nothing breaks, AI is still optional rather than embedded.
This question reveals the difference between adoption and integration. Most organisations will find that AI sits on top of existing workflows rather than inside them. That is not necessarily a problem today, but it tells you how far you still have to go.
2. Where does trust sit in our AI stack, and who owns it?
Trust is not a feature of the technology. It is an executive accountability problem.
Someone in your organisation needs to own the question of "how do we know this AI output is reliable, safe, and appropriate?" If that ownership is unclear, every AI deployment carries unmanaged risk. This is not an IT question. It is a leadership question.
3. Are people using AI because it is allowed, or because it is genuinely better than how they worked before?
Policy creates compliance.
Value creates behaviour change.
If your teams are using AI because they have been told to, or because a licence has been deployed, you are measuring adoption by inputs rather than outcomes. The real question is whether AI has actually improved how someone works and whether they would choose to use it even if it were not mandated.
4. Which decisions in our organisation still take days that should take minutes, and why?
AI exposes decision friction faster than any process review ever could.
When information that used to take a team two days to compile can be synthesised in minutes, the bottleneck shifts from gathering to deciding. If decisions are still slow, the problem is not a lack of data. It is a lack of clarity about who decides, with what authority, and on what basis. AI makes that visible.
5. Do our leaders understand AI well enough to challenge it, not just approve it?
Approval without judgment increases risk, not safety. If your leadership team is approving AI initiatives based on vendor presentations and internal enthusiasm without the ability to ask hard questions about risk, reliability, data handling, and governance, that is a vulnerability. Leaders do not need to be technical. They need to be informed enough to challenge assumptions and ask "what could go wrong?"
6. If we had perfect organisational memory, what uncomfortable patterns would surface?
This is the question most leaders avoid. Repeated delays, avoided decisions, misaligned incentives, and recurring friction all become visible when context is no longer fragmented. AI is beginning to make perfect organisational memory possible. The question is whether leadership is ready for what it reveals. The answer is often more confronting than the technology itself.
7. Are we designing AI for efficiency, or for better judgment?
Efficiency scales output. Judgment determines whether that output creates real advantage. Most AI deployments are optimised for speed: faster drafts, faster summaries, faster analysis. That is valuable, but it is a floor, not a ceiling. The organisations that pull ahead will be the ones using AI to improve the quality of decisions, not just the speed of execution. That requires designing AI into how judgment is formed, not just how tasks are completed.
8. Does your organisation have the governance muscle to scale AI, or are you building capability on top of an accountability gap?
Without governance foundations, every AI initiative carries compounding risk. You might not feel it on the first project. You will feel it at scale.
How to use these questions
These are not meant to be answered in a single meeting. They are designed to surface honest conversation about where your organisation actually stands.
Pick three that resonate. Bring them to your next leadership discussion. Do not treat them as an audit. Treat them as a starting point for the conversations that need to happen before your next AI investment, your next vendor decision, or your next board update.
The organisations making real progress with AI in 2026 are not the ones with the best technology. They are the ones asking better questions and being honest about the answers.
If any of this has resonated across this series, I would genuinely welcome the conversation. Whether you are just starting, already deep in it, or somewhere in between -- reach out, we're happy to help.