Enterprise discipline will decide who captures AI value

Weak economics, unchanged workflows, and limited organisational readiness are holding back enterprise AI value.

Joey Meynink, Head of Strategy at ADAPT, drew on ADAPT research across Australian and New Zealand executives to show where organisations need to strengthen execution: commercial measurement, workflow redesign, and leadership capability at CIO Edge.

Key takeaways:

• Measure value before expanding investment: Track cost per successful outcome, revenue, risk, and CFO-backed financial measures rather than relying on adoption or deployment metrics.
• Redesign three to five high-value workflows first: Focus on high-volume, high-friction processes where agents can materially change execution, decisions, and cost.
• Build leadership and workforce capability alongside the technology: Judgement, adaptability, accountability, and commercial acumen shape whether organisations can turn new capability into enterprise value.

AI economics have to survive the business case

Adoption and deployment metrics show whether the technology is being used.

They do not show whether it creates financial value.

38% of CIO Edge respondents track AI usage or adoption without tracking value, while just 1% consistently measure enterprise-level AI value with CFO involvement.

Cost per successful outcome exposes what usage metrics miss.

Joey highlighted an Australian bank where a task costing around $0.08 per successful human outcome rose to approximately $1.40 when completed by an agent.

Across 200,000 tasks, the cost moved from roughly $16,000 to $280,000, a 17-fold increase.

A deployment metric could still record that initiative as successful. The economics show the opposite.

Organisations need to measure token, task, and outcome costs from the start, then connect them directly to revenue, cost, risk, and the financial measures used to allocate capital.

Workflow redesign creates structural value

AI delivers limited enterprise impact when existing processes, hand-offs, decisions, and role structures stay intact.

Redesigning the workflow clarifies where agents should execute, where people should apply judgement, and where accountability should remain with a person or team.

71% of CIO Edge respondents said their organisations were not prepared to manage an agentic workforce, while more than half lacked formal AI controls.

Joey frames organisational maturity across three stages. AI-enabled organisations use the technology to augment existing tasks.

AI-first organisations rebuild workflows and decisions around it. AI-native organisations design value propositions with it at their core.

Start with three to five high-volume, high-friction workflows where the economics justify deeper change.

Redesign the full flow of work, shift suitable execution to agents, and direct people towards judgement, critical thinking, and higher-value decisions.

Leadership and workforce capability separate stronger performers

Wider access to the same models, platforms, and vendors reduces the advantage of technology access alone.

Stronger performers make better decisions about where to invest, redesign work across functions, assign clear accountability, and build the skills required to operate with AI.

The 71% agentic workforce readiness gap shows how far organisational capability trails investment.

Capable agents create little value when leaders cannot judge the economics, teams cannot redesign workflows, or responsibility remains unclear.

Joey links stronger performance to judgement, adaptability, accountability, and commercial acumen.

These capabilities help leaders choose the right use cases, challenge weak business cases, redesign work, and maintain clear decision rights as agents take on more execution.

As the tools commoditise, the quality of the organisation around them will determine the value they create.