Published on the 18/09/2026 | Written by Heather Wright

Stop chasing AI volume, start creating value…

Local organisations are racing to deploy AI, but Gartner says most are still struggling to answer some fundamental questions: How do you govern it, pay for it and prove it’s creating value?

That challenge has been a recurring theme at Gartner’s IT Symposium/Xpo on the Gold Coast this week, with analysts saying that enterprises are becoming increasingly proficient at deploying AI, while remaining far less effective at managing its risks, costs and outcomes.

“Start by limiting careless consumption before it limits your critical use cases.”

In the opening keynote, Gartner analysts Kristen Moyer and Darrell Plummer painted a picture of organisations building momentum around AI without necessarily heading in the right direction.

“AI is the thing. Everybody wants it. Everybody’s gotta have it. Everybody’s building momentum with it. Or so they say,” Moyer told attendees. Yet while 60 percent of Australian and New Zealand executives say they’re getting positive value from AI (ahead of the 58 percent globally), only one in five AI projects generates positive ROI, a figure Plummer noted is unchanged from last year.

“The problem is AI risk is compounding much faster than value,” Moyer noted. AI use cases have increased more than 400 percent in a single year, but organisations are often ‘reinventing the wheel with every AI project’, rather than building on earlier successes.

At the same time, Gartner says executives are increasingly worried about the downside. Eighty-six percent of CIOs report risk growing faster than value, while only 27 percent proactively integrate technology risk and business risk.

Gartner’s answer is a concept it calls an ‘AI central bank’.

Drawing parallels with financial regulation, Plummer argued that enterprises need a central function that determines acceptable levels of risk, governs spending, coordinates decisions and establishes accountability across all AI initiatives, because AI is no longer just a tech problem.

The concept is a central governing body, with a ‘rotating chair’ between the CIO and CFO with permanent consultation from the CISO and others, that decides what AI is worth funding, what level of risk is acceptable and who is accountable for outcomes. Rather than allowing individual business units to pursue disconnected AI projects, the central bank would act as a control plane overseeing governance, economics, technology choices and shared intelligence across the enterprise.

“The AI central bank doesn’t regulate one flywheel in your organisation. It regulates them all,” Moyer said.

The concept emerges as Gartner sees a growing mismatch between AI deployment and AI governance, and the emergence of ‘an era of finger pointing’. Gartner figures show 65 percent of organisations expect to deploy agentic AI within the next 12 months, but only 27 percent currently have agentic AI governance capabilities in place.

“If you’re called to account and your answer is AI did it, boy, are you in trouble,” Plummer warned.

The pair pointed to examples including that of PocketOS, where an AI agent deleted the company’s entire production database and primary backups in just nine seconds in April, arguing that organisations’ need clearer ownership structures before AI becomes more deeply embedded in critical business functions.

“Regardless of who built the agent or who approved its use, when something goes wrong, who are they going to call?,” asked Moyer. “It isn’t Ghostbusters,” quipped Plummer.

Outcome, not consumption

The pair repeatedly returned to the idea that organisations are obsessed with AI consumption, rather than outcomes.

Moyer warned “AI doesn’t just create value, it also creates waste”, pointing to low-value AI-generated content and what Plummer called ‘careless consumption’.

“Like those AI generated dancing videos,” she noted. Every token, model call and GPU cycle consumed by frivolous workloads, comes at the expense of more valuable use cases.

“Start by limiting careless consumption before it limits your critical use cases,” she said.

The concern extends beyond infrastructure costs. Gartner cited research showing that 40 percent of workers received AI-generated work containing errors in a single month. Each occurrence took almost two hours to clean up, which the analysts calculated could equate to a US$9 million annual loss in a 10,000-person organisation.

“Start by limiting careless consumption before it limits your critical use cases,” Moyer said, arguing organisations need stronger control over AI spending and usage patterns.

“Vendors are desperate to sell and monetise AI,” Plummer noted. “They want you to build momentum for their products or just to buy more tokens. For you, that is bad momentum.”

He cited figures showing technology vendors are pitching productivity surges of 50 percent or more, while Gartner data shows just 16 percent in actual productivity gains.

The keynote also challenged assumptions about AI agents.

While vendors are encouraging organisations to deploy agents everywhere, Gartner warned that organisations are often solving simple automation problems with expensive AI tools.”AI agents are an inefficient way to do a title search in a relational database. Just use a function call,” Plummer told attendees.

A financial playbook 

In a later session, analyst Rober Naegle put the focus further on the economics.

Opening with a question about whether attendees would describe their AI budgets as ‘chaos’ or ‘control’ (a solitary hand for that one – ‘Quick, grab their business card’, responded Naegle), he argued that most enterprises are using cloud-era thinking to manage something fundamentally different.

“Cloud and AI are almost apples and oranges in comparison,” he said.

Cloud spending is largely driven by infrastructure consumption, capacity and utilisation. AI spending is shaped by prompts, interactions, outcomes, user behaviour and model selection. Organisations looking for the same tools, processes and funding models used for cloud, Naegle warned, are likely contributing to the chaos rather than reducing it.

One of the biggest mistakes, he suggested, is budgeting around tokens.

“If you’re trying to budget now for next year’s or two years’ utilisation, you’ve got to change the thinking,” he said, arguing that predicting token consumption and pricing years into the future is largely impractical.

Instead, he said, organisations should budget around value.

He proposed dividing AI expenditure into three primary categories: Personal productivity, functional priorities and business-critical capability.

Personal productivity includes individual use of copilots and assistants and currently accounts for roughly 60 percent of AI usage, according to Gartner’s observations. Functional priorities account for about 30 percent, while only around six percent of AI spending today is directly tied to business-critical outcomes such as revenue growth, cost reduction or risk mitigation.

Those figures reveal why many AI funding conversations continue to struggle.

For individual productivity tools, Naegle said organisations should focus on spend controls rather than extensive ROI analysis.

“I would tell you don’t waste your time,” he said of attempts to precisely calculate individual productivity gains. Instead, organisations should determine a spend appetite and manage within it.

Business-critical AI, by contrast, should be evaluated against measurable business outcomes.

“When it becomes business critical, I’m actually able to monetise and put that value on the balance sheet or the income statement,” he said.

The challenge is that many executives still struggle to articulate that value in financial terms.

Naegle cited Gartner research showing that when CFOs are asked about AI value, 74 percent point to time savings. Only five percent cite cost savings and six percent point to increased profits.

At the same time, CEOs continue pushing aggressive AI adoption agendas, often bypassing traditional business case processes.

“We’ve got to be AI ready. We want to be an AI-first company,” Naegle said, characterising the pressure many CIOs face. The result is organisations purchasing AI capabilities today without necessarily knowing how they will justify or fund them 18 months from now.

While Gartner’s keynote warned organisations to stop ‘compounding AI volume’ and start ‘compounding AI value’, Naegle’s session provided the financial playbook, arguing AI should be funded according to business value rather than model consumption.