A business case assumes fewer people. Should it?
AI has released capacity. Should you take the saving or reinvest it?
Roles are changing. Which capabilities can you afford to lose?
A global AI plan is ready. Will the same decisions make sense in every market?
Audentier helps boards and leadership teams work through decisions like these before they become expensive to reverse.
What can genuinely come out of the cost base? What should be retained? Where could that capacity be worth more somewhere else?
Which capabilities become more important? What does the organization risk losing? Where could an apparently efficient decision create a longer-term constraint?
What are the assumptions? What are the organizational consequences? What alternatives exist before the workforce response is set in motion?
How do differences in market conditions, capability, economics and operating context change the decision?
Management sees $70M in potential savings from AI. Before taking it out of the cost base, leadership needs to know how much represents work that genuinely disappears, what capability would disappear with it, and where released capacity could create greater value elsewhere.
Here are three examples of where Audentier can help leadership decide what to do.
Management estimates AI can release $70M of annual workforce cost. Finance wants the saving reflected in the operating plan. Before approving it, leadership wants to know whether the entire $70M should actually be removed.
Audentier examines the assumptions behind the productivity case, the choices available to leadership, and the commercial and organizational consequences of each.
How much should we harvest, how much should we reinvest, and what should we retain or redesign?
What Audentier believes leadership should do, and the reasoning behind it.
What needs to be true for the recommendation to hold, and where the evidence remains uncertain.
The capabilities, options or organizational strengths leadership could unintentionally remove.
The recommendation, economics, consequences, uncertainties and conditions for reconsideration, distilled for executive discussion.
As AI moves into day-to-day operations, decisions about productivity are becoming decisions about cost, capability and people. The transition between the two is longer and harder than most programs account for.
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Photo: Kunmi Owopetu
Teju Ajani's career has sat at the intersection of technology, commercial ambition and operating reality.
Across leadership roles at Google, YouTube and Apple, including as Managing Director of Apple Nigeria, she has built businesses and technology ecosystems across markets with very different economics, capabilities and operating conditions.
Audentier works directly with boards and leadership teams facing material decisions arising from AI adoption.
teju@audentier.com