Part 2 - Make AI Actually Pay
AI doesn’t create business value by itself. Leaders decide where it belongs.
Australian small and medium businesses using AI are growing 2.8 times faster than those that are not. But using AI and getting value from it are not the same thing.
AI activity is easy to create. Business value is harder.
Before investing more time, money or attention, leaders need to decide what success looks like, where the biggest business constraint sits and what AI should not be used to do. This is where AI moves from experimentation to a disciplined business decision.
Decision 4: What success looks like
AI’s possibilities are easy to describe. Success is harder.
Before committing resources, define the business outcome the initiative should improve and how progress will be measured. That outcome might be:
Greater productivity
A better customer experience
Lower costs
Faster growth
Increased team capacity
What matters is that the result is specific enough to guide priorities and investment.
Where leaders get it wrong
Launching initiatives without defining success
Measuring usage instead of outcomes
Pursuing too many projects without clear priorities
Make the call
Choose one current or planned AI initiative. Write down the single business outcome it is expected to improve. Then ask: how will we know we have been successful in six months? If you cannot answer clearly, redefine the objective before moving forward.
Ask yourself
If every AI initiative had to prove its value within six months, which ones would you still back?
Decision 5: Your biggest constraint
Every business has something slowing it down. It might be:
Slow decisions
Inefficient processes
Poor customer response times
Limited team capacity
Repetitive manual work
AI creates the most value when it is aimed at the constraint having the greatest effect on performance. The goal is not to automate everything. It is to remove the bottleneck creating the biggest drag on the business.
Where leaders get it wrong
Trying to solve too many problems at once
Choosing projects because they are exciting
Automating a poor process instead of fixing it
From the seat
The clearest AI result I saw did not come from the most exciting use. It came from the most boring constraint. For a financial services client, the drag was call centre volume. We aimed real time and AI transformation at that one bottleneck and cut call centre volume 40%, with overheads down 22%. Nothing else on the list would have moved the business that far.
Make the call
Ask your leadership team: if we could remove one bottleneck from the business over the next six months, which one would create the greatest impact? Agree on the answer before exploring how AI could help.
Ask yourself
Are you using AI to solve your biggest business challenge, or simply the easiest task to automate?
Decision 6: What AI shouldn’t do
Just because AI can do something does not mean it should. Some work depends on:
Empathy
Context
Trust
Relationships
Accountability
AI may be able to assist, but that does not mean it should lead. One of leadership’s most important responsibilities is deciding not only where AI belongs, but where it does not.
Where leaders get it wrong
Automating work that depends on human judgement
Treating efficiency as more important than experience
Failing to set boundaries around AI use
Make the call
Create two lists with your leadership team:
AI should help with…
This work should stay human led…
Use the lists to set clear expectations before AI becomes part of everyday work.
Ask yourself
If your customers knew exactly where AI was being used, would they agree that you had made the right choices?
Next in the series: Build a team that can actually use AI
Value is not the AI working. It is the business moving because of it.
Business value only becomes real when your people know how to use AI clearly, consistently and responsibly.
