I’m the Head of Professional Services at Montage, a specialist BI consultancy based in New Zealand. We spend our days helping organisations build data foundations that actually work, not ones that look good in a slide pack.
As we head into 2026, I want to share my thoughts on the dominant trends and predictions for the year ahead in the BI and AI space.
1. Platforms are going to be, well, more platformy than before
Platform dominance will continue to increase, driven by further vendor consolidation. Over the past few years, the large cloud data platforms have been acquiring and integrating technologies at a rapid pace. That's not slowing down. In fact, it's accelerating.
Most data platforms already cover core analytic workloads, and we're seeing increased expansion into the broader data management and governance landscape. Over the next 12 to 18 months, expect acquisitions in areas like data movement, quality, governance, security, targeted AI services, engineering tools, integration engines, and Master Data Management.
Translation: if you're building a data stack, expect vendor consolidation to simplify your life and force you to think about platform integration. Whichever way you look at it, the platforms are getting more platformy.
Higher risks of supercharging garbage
Organisations are realising that if they want the benefits of AI, the fundamentals of data management matter. AI has exploded into the mainstream, promising automation, cost reduction, and revenue growth. But AI doesn't magically fix poor data foundations. It relies on good data. Otherwise, you are simply supercharging garbage.
AI also relies on context. Without it, any responses you get are shallow and linear. This is where BI and analytics teams are experienced in dealing with this reality. BI projects incrementally improve data quality, codify business rules, and build semantic understandings. And this happens steadily, sustainably, sometimes painfully. But it works.
So if organisations genuinely want AI to deliver value, they've got to do several fundamental things. They must build data capability in their people. Training and enablement are not optional. They are mandatory. They must build sustainable processes for data management, cleaning, validation, and categorisation. They must capture and codify the meaning of their data in relation to real business processes. Context is king.
This prediction is far less about technology and far more about people and processes. Which is exactly why most organisations will struggle with it.
3. Public AI tools are trained on the swamp of the internet
Self-service is shifting from building reports to conversational and guided experiences. Self-service BI has always meant different things to different people. Executives want fast access to key metrics. Managers want to explore performance trends. Analysts want more freedom to interrogate more data.
Historically, BI teams have responded by creating more reports or workbooks, and over time, this has led to bloated environments that are difficult to maintain. Public-facing AI tools like ChatGPT have fundamentally changed user expectations. People now expect to ask questions in plain language and receive immediate conversational answers.
The challenge is that public AI tools are trained on the swamp of the internet and are designed to give answers even when confidence or accuracy is low. This level of uncertainty and inaccuracy is frankly unacceptable for business decision-making.
BI and analytics teams must meet users where they are, but in a governed and secure way. The immediate opportunity is to replace the long tail of one-off or infrequently used reports with a conversational experience that sits alongside a curated set of trusted, repeatable reports and dashboards that represent gold-standard outputs.
There's also an education responsibility here. Teams have got to explain why organisational data must stay within secure environments and why business-grade AI experiences are fundamentally different from public-facing tools.
What this means for you in 2026
If you're leading data, BI, or analytics teams, these predictions point to three immediate priorities:
- Get comfortable with vendor consolidation. Your stack is going to shift, whether you like it or not.
- Stop treating data quality as a side project. If you want AI to deliver, you need to invest in the fundamentals. Boring work moves needles!
- Start experimenting with conversational BI, but do it inside your governed environment. Don't let ChatGPT become your organisation's default data analyst.
The organisations that get this right in 2026 won't be the ones with the fanciest tools. They'll be the ones who've invested in the boring stuff: data quality, semantic layers, and user education.
That's where the real work happens. And frankly, that's where the real value is.