Data and IT leaders are under pressure to deliver measurable business outcomes from artificial intelligence initiatives amid ongoing industry hype and fears that the AI bubble may be about to burst. Yet according to analysts speaking at a major data and analytics summit in Sydney, achieving true business value from AI goes far beyond return on investment.
AI is not simply another technology upgrade. One vice-president analyst noted that it represents a change as profound as the arrival of the internet. That comparison helps explain why many organisations are struggling to decide whether to lead, follow, or wait. It also raises the stakes for those responsible for data governance, risk management, and workforce planning.
A director-analyst at the same event said that, while nearly three in five organisations had put an AI service into production in 2025 and four in five are now doubling down on AI, investment alone will not solve the underlying challenge. “You can’t just continue to increase your investments in AI without getting clarity on the goals and ambition of your organisation,” she warned.
Define AI ambition and risk tolerance
According to the vice-president analyst, data and analytics professionals should redefine their AI ambitions with input from stakeholders, particularly around their tolerance for disruption. Organisations with a low tolerance can take a cautious route, carefully assessing risk and following the safest course. Those with a greater appetite for disruption can adopt a more opportunistic approach. Meanwhile, organisations with a high tolerance may decide to be pioneers, even if that means accepting the largest risks.
These choices should be made deliberately rather than through reaction to hype. Without a clear ambition, companies can easily spend heavily on proof-of-concepts that never move into production or fail to align with business strategy.
Manage unpredictable AI costs
One of the first questions stakeholders ask about any AI initiative is, “What is this going to cost?” This is difficult to answer because AI costs are highly unpredictable and often hidden, the director-analyst pointed out. The problem is compounded by vendors using pricing models based on metrics that are difficult to forecast, such as graphics processing unit hours and token consumption.
Those pricing structures can quickly turn a small pilot into a large bill. Research shared at the summit found that six out of ten IT leaders are worried about AI agents running up unexpected costs, yet only two out of ten data and AI leaders are concerned that unpredictable pricing might limit the value they get from the technology. The analysts described this disconnect as a wake-up call for data and AI teams, who need to work more closely with CIOs and finance departments.
“AI can be an expensive lesson,” warned the director-analyst. She noted that less than half of organisations manage and optimise their AI-related spending. To avoid overspending, organisations should track expenses from the outset, especially during prototyping, and adopt cost-driven design. This means understanding the financial impact of various components before going into production. For example, teams should explore the cost implications of using different large language models, or even small language models, to power an AI agent. These choices affect not only the initial pilot but also ongoing operational costs.
Look beyond the dollar value
When communicating with stakeholders, however, the focus should remain on value rather than cost alone. Value in AI is not purely financial. The vice-president analyst highlighted a local government example to make this point. The council created a digital citizen named Dotty and mapped her journey through public services to make the impact of data relatable for all employees.
In that example, transposing two digits in a home address might result in a tradesperson being sent to the wrong house to install a handrail for an elderly person. That wasted journey carries a direct financial cost, but there are ripple effects too. What if the lack of a handrail results in the resident falling and suffering a serious injury? Such examples show how data quality and governance directly affect real-world outcomes, making a case for investment that goes beyond simple ROI calculations.
Invest in AI foundations
Whatever an organisation’s ambition, foundational investments are key. A survey on modern data realisation found that respondents who were most satisfied with the outcomes of their AI use cases spent 30% more on foundational activities, such as data management, governance and talent, compared with those who were unsatisfied. This suggests that cutting corners on data foundations will likely lead to disappointing AI results.
Other research highlighted the pressure many leaders feel. Fifty-nine per cent of IT leaders said they were being pushed into adopting generative AI tools before they were ready, while 61% felt pressure from senior leaders, directors or stakeholders to move forward with AI. These statistics point to a risky dynamic in which speed is prioritised over readiness.
Secure data and build context
One of the biggest issues is whether an organisation’s data is secure and well-governed enough to be opened up to further AI applications, including autonomous agents. “We need to prevent the exposure of the wrong data to the wrong people, applications or LLMs with AI governance,” said the director-analyst. “And avoid inaccuracies, misunderstandings and hallucinations with a well-designed context layer.”
This will help to ensure that data is AI-ready, trusted and aligned to the use case. In that sense, governance should be repositioned as a business value accelerator rather than a function focused purely on compliance. Too often, governance is seen as an obstacle, but the analysts argued it enables safer and more effective AI deployment.
Three steps to stronger AI governance
To improve AI governance, the analysts suggested three key steps. The first is to connect existing governance groups, such as risk, data and cyber security, into a unified AI governance team. They predicted that organisations connecting governance bodies in this way will experience a 10% greater business impact than those that do not.
The second step is to rationalise governance. A unified team should review and consolidate various policies into a clear, consistent framework that reflects the organisation’s risk tolerance and cultural values around responsible AI use. Rather than having a patchwork of conflicting rules, organisations need a single, coherent approach.
Finally, governance must be embedded across both the culture and the technology of the business. This requires shifting the organisational mindset from compliance to one where everyone understands how to use data responsibly and ethically. On the technology side, leaders should adopt policy-as-code so that rules are automatically enforced throughout the technology stack. The analysts predicted that by 2028, organisations using specialised governance tools will decrease the cost of regulatory compliance by up to 20%.
Build an integrated context layer
Even when data is well-governed, context remains critical. If an employee asks how many active customers the business has, the answer depends on the definition of “active”. Does it mean someone who made a recent purchase, holds an ongoing subscription, or recently visited the website? Without context, an LLM can easily misunderstand the prompt and rapidly amplify that error.
The analyst argued that it is time to build an integrated context realisation layer – a layer that connects every piece of information so everyone and everything, people and agents alike, can see the bigger picture and make more informed decisions. While semantic layers are becoming commonplace, they are no longer sufficient on their own. Organisations are now experimenting with ontologies, knowledge graphs and other methods to attach deeper meaning to data. Combining these approaches yields far more accurate results and helps AI systems reason about information rather than simply retrieve it.
Invest in people and skills
Another challenge is that technology is evolving faster than the workforce can adopt it. “If you’re investing in AI without investing in your people, you are throwing money away,” warned the director-analyst. “The change management and training effort for AI tools takes nearly twice as long as implementing the AI solution itself, which means planning for longer timelines and higher costs than for any other technology implementation you have managed before.”
To counter this, a “mindset, skillset, toolset” approach is highly effective. IT leaders must ask: What mindset obstacles exist in the organisation, and how can they be overcome? What skills gaps are present, and how can they be remedied? Only after addressing mindset and skillset should leaders ask what tooling changes are needed.
This sequence matters because tools alone will not deliver value. Employees need to understand why AI is being introduced, how it changes their roles, and what responsible use looks like. Organisations that skip the people dimension may find that even the most advanced AI systems are ignored, mistrusted, or used inappropriately.
Workforce impact and fusion teams
Finally, there is the ongoing concern about AI-driven job losses. Research shared at the summit found that 34% of CIOs expect to reduce the size of their workforce over the next three years. Conversely, only 4% of chief data officers have decreased their team size in the past year, while 44% have expanded their teams. This suggests that data and analytics teams are currently growing rather than shrinking, even as automation spreads.
“Currently, we’re not seeing much reduction in data and analytics team size, but this is happening in other areas,” said the director-analyst. She also noted that some organisations may be using the introduction of AI as a convenient excuse for layoffs that would have occurred regardless.
At the same time, AI is reshaping how teams work. “The value of human skills and talent will still sit at the core of delivery teams, but these teams will now combine human expertise with AI agents to make more productive, AI-powered fusion teams,” she said.
Source: ComputerWeekly.com News