AI ambition is running well ahead of execution. While investment is rising and most enterprises can point to at least one live use case, far fewer can show repeatable value across the business.

In ADAPT’s survey of 200 Australian CIOs, 70% planned to increase investment in generative AI, yet only 25% had automated workflows in place. Just 13% considered their AI efforts successful.

For Alan, that gap is not mainly a technology problem.

It reflects weaker alignment between AI programs, business outcomes, and the disciplines needed to make adoption stick.

 

Business alignment is what gives AI a reason to scale

AI programs gain momentum when they are attached to a clear commercial or operational problem.

Without that anchor, organisations end up with experimentation that looks active but struggles to convert into repeatable value.

That is the pattern Alan sees in his conversations with executives, and it lines up closely with ADAPT’s data.

Many organisations have one visible use case in market, but far fewer can show value across ten or more.

His view is that the strongest programs begin with a defined problem, whether that sits in procurement, financial analysis, or another practical workflow, then build from there.

The real differentiator is whether the initiative is connected to a real business need and a measurable result.

 

Education turns AI access into usable capability

Giving people access to AI does not guarantee better work.

Teams still need the confidence, context, and practical understanding to apply it well inside real workflows.

Alan compares the challenge to early dashboard adoption.

Dashboards only became valuable when organisations taught people how to use them meaningfully, and he argues that AI follows the same pattern.

Prompt access on its own is not enough.

The organisations making stronger progress are building broader business fluency, helping teams understand where AI fits, how it should be used, and what good usage looks like in practice.

That matters because adoption slows quickly when employees are left with a tool but no structure around how to turn it into impact.

 

ROI discipline decides whether AI earns long term support

The first AI projects are often the easiest to justify.

The harder task comes later, when leaders need to show that value is being sustained, expanded, and translated into outcomes the business actually cares about.

That pressure is becoming harder to ignore.

In ADAPT’s data, 50% of CIOs struggle to measure ongoing AI benefits, while 40% of CFOs remain sceptical about whether technology investments are delivering the value promised.

Alan’s point is that successful organisations treat validation as part of the program from the start.

They track impact, stay close to the business outcome, and avoid mistaking activity for return.

That discipline becomes critical when AI moves beyond early pilots and into broader investment decisions.

Organisations move further when they align programs to business priorities, teach people how to use the tools well, and keep proving the value as adoption grows.

 

Key takeaways:

  • AI success depends on leadership, not just technology: Successful AI initiatives are not about having more data scientists or better tools, but about leadership aligning teams around clear, measurable business outcomes; a point reinforced by the 87% of CIOs who say their AI efforts are still not delivering expected results.
  • Proving AI’s long-term value is a major hurdle: 50% of CIOs struggle to measure ongoing AI benefits, and 40% of CFOs believe deployed technologies often fail to deliver expected value, underscoring the need for better ROI tracking and validation frameworks.
  • Strategic alignment, not scale, drives success: Only 13% of CIOs consider their AI efforts successful, with winning organisations focusing on aligning AI with business goals and embedding it effectively, rather than relying solely on technical resources or executive ownership.
Contributors
Alan Jacobson Former Chief Data and Analytics Officer at Alteryx
Formerly the chief data and analytics officer (CDAO) of Alteryx, Alan Jacobson drove key data initiatives and accelerating digital business transformation for... More

Formerly the chief data and analytics officer (CDAO) of Alteryx, Alan Jacobson drove key data initiatives and accelerating digital business transformation for the Alteryx global customer base. As CDAO, Jacobson led the company’s data science practice as a best-in-class example of how a company can get maximum leverage out of its data and the insights it contains. He was responsible for data management and governance, product and internal data, and use of the Alteryx Platform to drive continued growth.

Prior to joining Alteryx, Alan held a variety of leadership roles at Ford Motor Company across engineering, marketing, sales and new business development; most recently leading a team of data scientists to drive digital transformation across the enterprise. As an Alteryx evangelist at Ford, Alan spent many years leveraging the Alteryx Platform across the company and witnessed first-hand the impact a culture of analytics can have on the bottom line and what it takes to succeed as a data-driven enterprise. Alan will extend his role as an evangelist to customers, helping data workers and business leaders alike foster a culture of analytics and deepen their investments in digital transformation strategies.

Alan was recognized as a top leader in the global automotive industry as an Automotive Hall of Fame Leadership & Excellence award winner and an Outstanding Engineer of the Year by the Engineering Society of Detroit and works with the National Academy of Engineering and other organizations as an advisor on data science topics.

Alan earned his bachelor’s degrees in engineering from the University of New Hampshire and received his master’s degree in mechanical engineering from Virginia Tech.

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leadership data modernisation