Data and IT leaders are under pressure to deliver business outcomes from artificial intelligence initiatives amid ongoing industry hype and fears of a bursting bubble. But achieving true business value goes beyond return on investment, according to analysts speaking at Gartner's Data and Analytics Summit keynote in Sydney.

AI represents more than just a technology shift, said Jorg Heizenberg, vice-president analyst at Gartner. It marks a change that could be as profound as the arrival of the internet. Heizenberg and fellow analyst Georgia O'Callaghan argued that organisations need to prioritise governance and take a disciplined approach to AI investments if they want to avoid costly failures and disappointment.

Defining AI ambition

Nearly three in five organisations had put an AI service into production in 2025, and four in five are now doubling down on AI, according to Gartner research. Yet O'Callaghan cautioned that enterprises can't just continue to increase their investments in AI without getting clarity on the goals and ambition of their organisation.

Heizenberg advised data and analytics professionals to redefine their AI ambitions with input from stakeholders, especially regarding tolerance for AI disruption. Organisations with a low tolerance for disruption can adopt a cautious approach, carefully assessing risk and following the safest course. Those with a greater appetite for disruption might take a more opportunistic approach. And organisations with a high tolerance could become pioneers, even if that means accepting substantial risks.

The message is that there is no single right way to adopt AI. Leaders must make deliberate choices about how aggressively they pursue AI-enabled change, and those choices should align with the organisation's broader strategy, risk appetite and stakeholder expectations.

The hidden costs of AI

One of the first questions stakeholders ask, however, is: What is this going to cost? This is complicated by the fact that AI costs are highly unpredictable and often hidden, O'Callaghan noted. Vendors often use pricing models based on metrics that are difficult to forecast, such as graphics processing unit (GPU) hours and token consumption.

Gartner research shows that six in ten IT leaders worry about AI agents running up unexpected costs, but only two in ten data and AI leaders are concerned that unpredictable pricing could limit the value they get from AI. Heizenberg described this disconnect as a wake-up call.

AI can be an expensive lesson, warned O'Callaghan. Fewer than half of organisations manage and optimise their AI-related spending. Teams should track expenses from the outset, especially during prototyping, and adopt cost-driven design to understand the financial impact of different components before moving into production. For example, they should compare the cost of using different large language models (LLMs) or small language models (SLMs) to power an AI agent.

Cost visibility should also extend beyond the technology itself. Organisations need to consider the cost of data preparation, integration, ongoing monitoring, retraining and the change management required to embed AI into daily workflows. Without a complete financial picture, AI projects can easily exceed budget and fail to demonstrate value.

Value beyond money

When communicating with stakeholders, however, the focus should remain on value rather than just cost. Heizenberg highlighted North Yorkshire Council, which created a digital citizen named Dotty and mapped her journey through public services to make the impact of data relatable for all employees. Transposing two digits in a home address could 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: what if the lack of a handrail results in the resident falling and suffering a serious injury?

Such examples show that AI success cannot be measured solely in monetary terms. Data quality, governance and user trust all contribute to business value, even when they are hard to quantify. A narrow focus on ROI can lead organisations to underinvest in the foundational capabilities that make AI useful and safe in the first place.

Foundational investments matter

Whatever an organisation's ambition, foundational investments are key. A 2025 Gartner 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.

Other Gartner surveys found that 59% of IT leaders felt they were being pushed into adopting generative AI tools before they were ready, and 61% felt pressure from senior leaders, directors or stakeholders to move forward with AI. These findings suggest that the hype surrounding AI is leading to rushed decisions and inadequate preparation.

Organisations that succumb to external pressure may create a patchwork of pilots and proof-of-concepts that never scale. By contrast, those that invest in data foundations and clearly define their AI ambition are better positioned to deliver reliable, sustainable outcomes.

Governance as a business accelerator

One of the biggest concerns is whether an organisation's data is secure and well-governed enough to be exposed 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, and avoid inaccuracies, misunderstandings and hallucinations with a well-designed context layer, said O'Callaghan. This will help to ensure that your data is AI-ready, trusted and aligned to the use case.

Governance should be repositioned as a business value accelerator, rather than a function focused purely on compliance. To improve AI governance, the analysts recommended three steps.

• First, organisations should connect existing governance groups, such as risk, data and cyber security, into a unified AI governance team. Gartner predicts that organisations connecting governance bodies in this way will experience a 10% greater business impact than those that do not.
• Second, the 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.
• Third, 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.

Technologically, leaders should adopt policy-as-code so that rules are automatically enforced throughout the technology stack. Gartner predicts that by 2028, organisations using specialised governance tools will decrease the cost of regulatory compliance by up to 20%. Policy-as-code can also help organisations respond quickly to changing regulations and new AI risks.

Context is critical

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? In the absence of context, an LLM can easily misunderstand the prompt and rapidly amplify that error.

It's time to build an integrated context realisation layer, said Heizenberg. This layer 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 operate reliably across different use cases.

Investing in people

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 O'Callaghan. 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.

The human dimension also extends to worries about AI-driven job losses. Gartner 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.

Currently, we're not seeing much reduction in data and analytics team size, but this is happening in other areas, said O'Callaghan. She noted, however, that some organisations may be using the introduction of AI as a convenient excuse for layoffs that would have occurred regardless.

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, said O'Callaghan. In this environment, data and analytics leaders must balance technological innovation with strong governance, clear strategy and sustained investment in their people.

Source: ComputerWeekly.com News