AI has made it much easier for employees across an organisation to explore, analyse and interpret enterprise data. Instead of serving a relatively small group of analysts, data teams are now supporting a much broader community of users, each expecting fast, reliable insights. As a result, the role of the data team is shifting away from producing reports and towards creating trusted data foundations, consistent business definitions and governance that enable confident decision making at scale.
For years, organisations have invested in making data more accessible. Self-service reporting, modern analytics platforms and intuitive dashboards have steadily expanded the number of people able to explore and use enterprise data.
AI has accelerated that shift dramatically.
Today, almost anyone can ask an AI tool to compare trends, identify anomalies, summarise performance or generate recommendations. In seconds, they're presented with polished charts, confident observations and seemingly well-supported conclusions.
That's an extraordinary step forward and puts analytical capability into the hands of far more people than ever before. But accessibility shouldn't be confused with trust. AI has made sophisticated analysis accessible but it hasn't guaranteed trustworthy inputs or trustworthy conclusions.
One of the unintended consequences of AI is that it creates the appearance of expertise. People who have never considered themselves data analysts can now produce sophisticated-looking analysis in minutes. Sometimes those insights are excellent and sometimes they're misleading. The challenge is that they often look equally convincing.
As more people rely on AI to explore, interpret and present information, CIOs are beginning to wrestle with a different question.
Can we trust the foundations those conclusions are built on?
That's where the role of the data and analytics team begins to change. Their role isn't to be the analyst anymore. It's to create the foundations that allow everyone else to analyse with confidence.
AI has changed who gets to analyse data
Until recently, sophisticated data analysis was still largely the domain of specialists. While self-service reporting and analytics tools had expanded access over the past decade, they still required a degree of technical capability. Someone needed to understand the data, know how to interrogate it and interpret the results.
That created a natural layer of expertise between enterprise data and business decisions. AI has dramatically lowered that barrier.
Today, a sales manager can ask an AI assistant to compare regional performance, an HR leader can explore workforce trends, and an operations manager can investigate the causes of production delays. Questions that once required specialist skills can now be answered in natural language, often within seconds.
This is exactly what modern organisations need. Markets move faster, customer expectations change more quickly and teams are expected to make better decisions with less delay. Delays caused by reporting backlogs or slow data refresh cycles are increasingly at odds with the pace at which organisations need to operate.
In an increasingly competitive environment, organisations don't simply compete on products or services. They compete on the speed and quality of the decisions they make.
The challenge is that much of the friction we've removed wasn't just delay. It was also expertise. That expertise didn't just improve the quality of analysis. It improved confidence in the decisions made from it.
In the past, experienced analysts weren't simply producing reports. They were applying context. They understood how the data had been collected, where inconsistencies existed, which business definitions had changed over time and when a result simply didn't look right. Much of that judgement happened almost invisibly as part of the analytical process.
AI can dramatically accelerate analysis, but it doesn't automatically replace experience. It works with the data it's given and the context it can infer. If the underlying information is incomplete, inconsistent or poorly governed, AI can still produce answers that appear entirely credible.
That's why this isn't simply the next evolution of self-service reporting. Analytical capability has spread far beyond the traditional reporting community, but the expertise that underpins reliable analysis hasn't disappeared. It's moved upstream.
Increasingly, the role of the data and analytics team isn't to sit between the business and the answers. It's to ensure everyone starts from reliable foundations.
The team isn't broken. The operating model is.
This shift can't simply be absorbed by existing data and analytics teams. In practice, we're seeing many organisations discover that the challenge isn't capability. It's capacity, focus and the way that capability is organised.
Most data and analytics teams were designed for a very different pattern of demand. They supported a relatively small community of analysts, finance teams and business users. Work arrived through projects, reporting requests and planned initiatives. Priorities could be managed, workloads planned and expertise applied where it was needed most.
Today, people across the organisation increasingly expect to explore, analyse and use enterprise data as part of their everyday work. AI has made that expectation a reality. The result is a dramatic increase in the number of people relying on enterprise data every day.
The same team, using the same operating model, can't simply support ten, fifty or a hundred times the consumers while maintaining the same levels of quality, consistency and confidence.
For many organisations, this doesn't necessarily mean building a permanently larger data team. It does mean recognising that the next few years are likely to require a concentrated investment in capability. Building reliable data foundations, establishing consistent business definitions, creating reusable data products and embedding governance into the way data is prepared and consumed requires significant work. As those foundations mature, the capability mix may evolve again. Today's priority is building the platform that allows AI adoption to scale with confidence.
Every organisation will approach that differently. Some will grow internal capability while others will combine specialist partners, managed services, modern platforms and automation to accelerate their maturity. Most will do some combination of all four.
The organisations that adapt most successfully won't all build the same operating model. But they will all make deliberate decisions about the capability they need, where it comes from and how it evolves as AI adoption grows.
Build capability one trusted foundation at a time
The shift is already underway. AI tools are changing how people analyse information across the organisation. Building the capability to support that change won't happen overnight, but it does need to start now.
Few organisations have the appetite, resources or need to transform every data domain at once. In practice, we're seeing organisations make the strongest progress by starting where better foundations will create the greatest value. That might be finance, customer, operations or workforce data. Starting somewhere and building capability is more important than trying to identify the perfect first domain.
That's a pattern we're seeing more broadly as organisations move AI from experimentation into day-to-day operations. The teams gaining momentum are building capability, learning through delivery and applying those lessons as they expand into the next area.
Each domain delivers value in its own right, but it also strengthens the foundations for everything that follows. Data models, business definitions, governance, and reusable data products become assets that can be applied across the organisation. Each step makes the next one faster, easier and more valuable.
The organisations making the greatest progress are the ones making deliberate investments in capability, building reliable foundations one domain at a time and carrying that experience into the next.
As analytical capability becomes available to more people and more AI systems, the quality of those decisions increasingly depends on the foundations the data team has built.
The data team is no longer just supporting a reporting function. It's becoming the capability that underpins how the organisation makes decisions.
Ian Vanstone | Chief Technology Officer - Data & Integration NZ
Ian works with business and technology leaders to turn data, AI, and digital transformation ambitions into practical, achievable outcomes. He shares real-world insights that help organisations make better decisions, unlock value from data, and navigate change with confidence.
Craig Gerken | General Manager Solution Strategy - Data & Integration NZ
Craig partners with organisations to simplify complexity and unlock value from data, integration, and emerging technologies. He offers real-world guidance that helps leaders make confident decisions and prepare their businesses for what's next.
FAQ
Why is AI changing the role of data and analytics teams?
Why are trusted data foundations becoming more important with AI?
AI can generate sophisticated analysis in seconds, but the quality of that analysis depends entirely on the quality of the data it receives. Inaccurate, inconsistent or poorly governed data can lead to misleading conclusions that appear credible. Trusted data foundations help ensure employees and AI systems are working from consistent, reliable information, improving confidence in the decisions they make.
How should CIOs prepare their organisation for AI-enabled decision making?
Rather than trying to transform every part of the organisation at once, many CIOs are taking an incremental approach. They begin by strengthening trusted data foundations in a priority business area, such as finance, customer or workforce data, before expanding into other domains. This allows organisations to build capability, learn through delivery and create reusable assets that support future AI initiatives.
Does AI reduce the need for data and analytics teams?
No. While AI reduces the technical barriers to analysing data, it increases the importance of the data and analytics function. As more employees rely on AI to explore enterprise data, organisations become increasingly dependent on reliable data models, governance, business definitions and high-quality information. The focus of the data team shifts from answering questions directly to enabling trusted analysis across the organisation.
What is the biggest challenge organisations face when scaling AI?
For many organisations, the challenge isn't access to AI tools - it's ensuring those tools are working from trusted, well-governed data. As analytical capability spreads across the business, existing operating models often struggle to support the growing number of users and AI systems. Building strong data foundations and investing in the right capability helps organisations scale AI with greater confidence and achieve more consistent business outcomes.
