Why AI strategy fails when it starts with productivity instead of purpose
In this ADAPT Insider episode, Dr. Jon Whittle, ADAPT Advisor and former Managing Director of Data61 at CSIRO discusses why organisations struggle to scale AI, what boards keep getting wrong about productivity, and how purpose changes the quality of AI use cases.Most organisations approach AI through an efficiency lens and are surprised when value fails to scale.
According to Dr. Jon Whittle, ADAPT Advisor and former Managing Director of Data61 at CSIRO, AI must be a purpose led transformation.
He argues that sustainable impact comes from aligning AI to organisational intent, leadership capability, and human outcomes rather than narrowly defined productivity gains.
Listen to the full episode on Apple Podcasts, Spotify, and YouTube.
Key takeaways:
- Efficiency is not value. AI delivers lasting impact only when aligned to purpose, not just productivity metrics.
- Leadership sets the ceiling. CEOs and boards must define the why of AI or risk fragmented, low impact adoption.
- Culture scales AI faster than technology. Change management, governance, and human outcomes determine success.
Productivity is a weak primary driver for AI
Task efficiency is easy to see.
Organisational value is harder to earn. AI can save time at the individual level while failing to lift performance across the business, because that time is often absorbed by more meetings, more tasks, or higher expectations.
That is why productivity on its own is a weak organising principle for AI strategy.
Jon makes this point directly, arguing that the evidence so far shows personal productivity gains do not necessarily aggregate to the organisational level, and can even contribute to burnout when leaders respond by pushing more work into the space AI creates.
Purpose-led AI unlocks different use cases and outcomes
The quality of AI use cases depends on the quality of the question leaders ask at the start.
Organisations that begin with speed and savings tend to optimise existing activity.
Organisations that begin with purpose tend to identify barriers that are stopping them from delivering what they exist to do.
Jon argues that this shift has to come from the CEO and board. His dental practice example shows the difference clearly.
An efficiency lens focuses on getting more patients through the chair. A purpose lens focuses on reducing fear, increasing early intervention, and improving health outcomes.
As he puts it, the use cases become “quite different” once purpose comes first.
AI adoption is a leadership and culture challenge
Most organisations do not stall on AI because the models are weak.
They stall because the organisation is not aligned strongly enough to absorb the change.
Governance, stakeholder management, executive backing, and change management determine whether AI moves beyond experimentation or gets trapped in committees and theatre.
Jon is explicit that adopting AI is “fundamentally not a technical problem” and instead goes back to culture and change management.
He also warns that committees without a clear sense of purpose and top down buy in will just keep spinning the wheels, and that leaders in charge of AI need fluency across technology, governance and ethics, and business rather than depth in only one of those areas.