Chaosamran_Studio - stock.adobe.
Gartner: Build trust in data before betting the business on AI
At its Data & Analytics Summit in Sydney, Gartner analysts advised businesses to prioritise data trust over artificial intelligence hype and outlined the coming era of autonomous business processes guided by AI agents
“‘Garbage in, garbage out’ isn’t the worst thing in the world, but putting trust in that garbage is,” warned Gartner vice-president analyst Carlie Idoine during the keynote at the Gartner Data & Analytics Summit in Sydney this month.
She was joined by fellow vice-president analyst Gareth Herschel, who highlighted a 2024 Gartner survey which found that data availability and quality were the top obstacles to implementing artificial intelligence (AI), adding that “if you cannot trust the data, you cannot trust the AI that uses it.”
While governance is key to trustworthy data, Herschel said it is not practical to achieve fully governed data before delivering AI-powered capabilities. The answer, he suggested, is to implement trust models that rate the trustworthiness of data based on its lineage and curation, which can significantly reduce the risk of people using incorrect data.
A key theme of the summit was the distinction between different forms of AI. Erick Brethenoux, chief of AI research and distinguished vice-president analyst, pointed out that generative AI and AI agents “have nothing to do with each other”.
AI agents, he said, have been around for at least 30 years and used for tasks like predictive machinery maintenance, whereas agentic AI is primarily a marketing term. Vendors tend to conflate the two concepts because of the significant revenue potential in generative AI, but Brethenoux said “it’s important to name things the right way.”
“AI agents can use models, like large language models [LLMs], or not,” he said, but combining the two can yield interesting results. However, because generative AI is non-deterministic, meaning the same prompt can yield different responses, it is impossible to rely on traditional testing. Instead, organisations need to place guardrails around the model and run simulations to ensure it behaves as intended.
One of the main advantages of agents is that they only consume resources when active, making them a faster and more cost-effective way of implementing multi-step processes in parallel. For example, if a loan application involves multiple checks, each can be allocated to a separate agent. The process can then be terminated as soon as any one check fails, rather than waiting for all of them to complete sequentially.
On AI working in the background without necessarily having a human to make the final decision, Brethenoux said where reaction time is important or delaying a decision increases risk, it might be appropriate to allow a system to respond automatically. This also applies where risks are low – for example, an agent could automatically make travel arrangements based on a user’s past preferences. In other situations, it is better to have a human check a proposed action before it is implemented.
“Autonomy is one of the most sticky problems we have with software agents,” Brethenoux observed, noting that while people are comfortable receiving advice from software, they are still adjusting to autonomous action.
Return on investment
The economics of AI, however, are not as simple as they might seem, according to Gartner vice-president analyst Luke Ellery. Citing Microsoft’s figures, he noted that Copilot saves the average employee only 14 minutes a day. Once productivity leakage is factored in, this is worth around $800 a year, while the all-up cost of Copilot is around $1,150.
The benefit, Ellery explained, lies elsewhere. People using Copilot daily perceive themselves as 2.5 times more productive, and their employee net promoter score (NPS) is 59, compared with an average of 21. “NPS is a hard value, but it is not a financial value,” he noted.
Ellery said that while returns on investment is appropriate for evaluating generative AI use cases that extend current capabilities, it is not the whole story. Using generative AI to fundamentally change a business model is a more complex situation that often involves several simultaneous, multi-million-dollar investments over a long-term horizon, which should be viewed as a bet on the future.
Consequently, he recommended that use cases should be categorised by the type of value they create, expectations should be set carefully with stakeholders, and organisations should build a portfolio of projects that collectively match their desired outcomes.
Perceptive analytics
Gartner’s director-analyst Georgia O’Callaghan spoke on the future of analytics, explaining that Gartner uses the term ‘perceptive’ to describe always-on systems that can understand an environment and either raise alerts or take action.
She stressed that the amount of automation should be appropriate, based on the specific task at hand. For example, a system might suggest a course of action in a medical situation for a human to approve, but be trusted to autonomously generate maintenance tickets for factory machinery.
O’Callaghan predicted that by 2027, augmented analytics capabilities will evolve into autonomous analytics platforms that fully manage and execute 20% of business processes. This shift will have several implications, as analytics work moves from specialists to non-specialist users guided by AI agents, and analysis itself becomes proactive rather than reactive. This will also see standalone tools being replaced by integrated systems and interactive dashboards give way to dynamic, embedded insights. “Perceptive analytics is going to be everywhere,” she said.
Gartner also presented its top data and analytics predictions to guide client strategy. It predicted that by 2027, 50% of business decisions will be augmented or automated by AI agents for decision intelligence. In addition, organisations that emphasise AI literacy for executives will achieve 20% higher financial performance than those that do not. Gartner also expects organisations to implement small, task-specific AI models with a usage volume at least three times greater than that of general-purpose LLMs.
Looking further ahead, Idoine declared that “AI agents are the new UI [user interface],” predicting they will replace 30% of software-as-a-service UIs by 2030, relegating the underlying application to a semantically enriched data source.
Idoine concluded with a specific warning for chief data and analytics officers, noting that 75% of those who fail to make organisation-wide influence and measurable impact their top priority will be assimilated into technology functions.
“Your role may be relatively new, but as it becomes more established, tension can arise over your authority and responsibilities,” she said. “AI has become a juicy prize that other senior leaders also have their eyes on.”
“Trust yourself, push the frontier, and show how you and your team are essential to the success of AI for your organisation,” she added.
Read more about AI in APAC
- Microsoft co-founder Bill Gates underscores AI’s transformative potential in healthcare, education and agriculture while highlighting the need to ensure its benefits reach lower-income nations.
- The AI Cloud Takeoff initiative, part of the Singapore government’s Enterprise Compute Initiative, will offer local firms up to S$500,000 in incentives to establish in-house AI centres of excellence and build AI capabilities.
- OCBC Bank rolls out a GenAI chatbot powered by Azure OpenAI to assist employees with writing, research and idea generation.
- Mistral AI’s CEO details how the French startup is balancing its open source ethos with a strong enterprise focus and delivering efficient, customisable AI to solve business problems and expand its global reach.
