In the age of algorithmic everything managers are increasingly turning to AI to support decision making in anything from forecasting sales to streamlining hiring.
With our focus on well-formed decision-making, we think there’s a watch-out; AI is only as good as the guardrails around it.
Without a clear framework it can amplify bias, obscure accountability or lead teams astray with overconfidence in machine-generated outputs.
It’s so easy to simply take what AI says as correct but you only need to play around with it and feed in different information to realise the danger of automatic trust. Our favourite suggestions so far are using glue to make your pizza stick together, using petrol in cooking, eating rocks and the benefits of chewing tobacco 😱
Let’s pause here before we blame AI for all our crazy decision-making. We know it’s generative and it’s continually learning, it’s not a consistent recall system and it’s not meant to be so let’s not make it all AI’s fault!
We’d rather take a look at how a manager can get started on this journey to speed up some inputs and where we’d recommend slowing down, on purpose. For example; you could use AI in lower-risk areas to speed up automating reports or pulling together data but slow down the conversation about what that data means for you or your team.
What we think is exciting is it will give us more time to ensure there is plenty of cross-functional chat between the various data teams and frontline leaders. Less time spent in the gathering of data means it would be common sense to spend the remaining time in better conversations.
The most important outcome of AI is managers need to treat it not as a shortcut or a quick fix but as an assistant for deeper thinking.
So, let’s focus on how to help managers harness AI for derision making, without outsourcing their judgment? It starts with those five failsafe parameters we mentioned. They are simple, sturdy, and most importantly, non-negotiable.
Managers Must Use The Five Failsafe Parameters
Decisions need to be well formed, and well informed.
Managers need to be skilled in making decisions. This needs a structure to move from discussion and debate, through to an outcome. Structure provides support for managers to make the right choice, not the easiest one.
Human Oversight Is Mandatory
AI can suggest but it must never decide alone. Managers should treat AI as a strategic advisor not a final authority. Every recommendation needs a human checkpoint especially when decisions affect people, ethics, or long-term strategy.
Transparency Over Black Boxes
If you can’t explain how the AI reached its conclusion don’t act on it. Managers should prioritise tools that offer clear logic, traceable data inputs, and understandable outputs. This builds trust and enables well informed scrutiny.
Bias Audits Are Routine Not Optional
AI systems inherit the biases of their training data. Managers must regularly audit for skewed outcomes especially in areas like hiring, performance reviews, or data gathering. If the system favours one group over another it’s not just a tech issue, it’s an input error and the biased decision becomes a leadership failure.
Context Is always King
AI thrives on patterns but it doesn’t understand nuance. Managers must layer in context, market shifts, team dynamics, cultural factors; in fact anything that AI can’t grasp. A good decision blends data with lived experience, intuition, and strategic foresight.
At We Are BRAVE we know that paying attention to your team’s critical decision-making and discussion skills, alongside proper insight and those sturdy parameters or guard rails means AI can assist you with better human decision making.
Our Decide BRAVE module works perfectly as that elusive piece of the puzzle to offer the human input to support an artificial process.
If you’d like to learn more, then reach out and start the conversation.
We’d love to help.
Rhi
