Business Intelligence 2026

A market analysis with commentary from M2 — July 2026

In most organizations, reporting runs smoothly. Dashboards are built, licenses renewed, budgets planned. Look more closely at the numbers behind that surface, though, and the picture gets less calm. The market is growing, but not where the money is actually flowing today. Prices are rising faster than the value being delivered for them. At the same time, AI expectations are running years ahead of actual production readiness. None of these three findings is surprising on its own. Together, they add up to a picture worth a closer look — and the obvious question that follows: would it surprise you how large the gap currently is between your own AI planning and actual deployment maturity in the market?

A market that's growing, but not measured consistently

According to Market Research Future, the global BI and analytics market was valued at roughly USD 36.9 billion in 2025 and is projected to grow to USD 108.3 billion by 2035. That corresponds to an annual growth rate of 11.4 percent.

Here's a caveat that serious market reporting shouldn't leave out: other market researchers arrive at noticeably different figures for the same market. Depending on the study, 2025 estimates range from just under USD 30 billion to over USD 40 billion, with 2035 targets ranging from USD 51 billion to USD 115 billion. The reason is mundane but important: there's no single, agreed definition of what counts as the "BI market." Platform licenses, consulting services, and adjacent analytics software are scoped differently by different research firms. The direction of growth is consistent across every study — clearly double-digit for the coming decade. The exact decimal point, though, isn't something we'd take at face value either — and that goes for every figure in this article.

What does that mean for you as a decision-maker? How growth is distributed within the market matters more than the headline billion-dollar figure itself. Classic reporting and visualization tools are growing solidly, but linearly. The real competition for budget is happening where AI capabilities are being embedded into existing platforms — and that part of the market is also what's currently reshaping vendors' pricing logic.

The pricing dynamic is real, and it's hitting everyone the same way

This is where it's worth looking at a source that gets updated regularly and therefore has to hold up over time: Vertice's SaaS Inflation Index 2026. The report currently puts the SaaS inflation rate at 12.2 percent. According to Vertice, that's nearly five times the general market inflation rate across the G7 economies. In concrete terms: SaaS spend per employee rose from USD 7,900 (2023) to USD 8,700 (2024) to USD 9,100 (2025) — an increase of nearly 15 percent within two years. Vertice also points out that 60 percent of software vendors are actively obscuring price increases, for example through restructured packaging rather than open price hikes.

The pattern we recognize from our own projects: vendors are increasingly bundling AI features into higher-priced tiers, regardless of whether a customer actually asked for them. Anyone who isn't regularly checking their enterprise contract against actual feature needs today will pay noticeably more over a three-year cycle — usually without any added value. What gets paid for, in that case, is whatever's included in the package, used or not.

Agentic AI: the expectation is real, the production readiness isn't — yet

This is where it gets interesting, and where extra care is warranted: a lot of numbers are circulating in this space right now that don't hold up to close scrutiny.

What Gartner actually says: by 2027, 75 percent of all newly created analytics content is expected to be contextualized by generative AI. Today's augmented analytics platforms are expected to evolve into autonomous systems that, by then, independently manage and execute 20 percent of business processes. An older, frequently cited Gartner forecast from 2024 also projected that by 2025, ninety percent of today's analytics consumers would themselves become content creators, enabled by AI. We're not aware of any solid follow-up research confirming whether that threshold has actually been reached since. We're therefore citing it as a forecast, not as a result that has already materialized.

At the same time, Gartner's first standalone Hype Cycle for Agentic AI (April 2026) paints a considerably more sober picture of the present: only 17 percent of organizations currently have AI agents actually in production, a further 42 percent plan to within the next twelve months, and another 22 percent the year after that. Gartner itself also warns of "agent washing" — relabeling existing automation and RPA tools as "agent-based" without any genuine autonomous capability behind them. Gartner further forecasts that more than 40 percent of all agentic AI projects will be canceled by the end of 2027, due to rising costs, unclear business value, or insufficient risk controls.

A concrete sense of the scale involved in mature use cases comes from Gartner's current forecast for supply chain software: the market for software with agentic AI capabilities is expected to grow from under USD 2 billion (2025) to USD 53 billion by 2030. That figure applies to one specific market segment — supply chain management — not to the BI and analytics market as a whole.

How we read these two figures together: the high expectations and the low actual adoption rate reinforce each other rather than contradicting one another. They describe exactly the phase the market is in right now — high willingness to invest, but still little production-ready implementation. For organizations, that means less "get in right now" and more "check your foundation now, before deployment arrives."

What the underlying research shows: governance beats hype

BARC's Data, BI and Analytics Trend Monitor 2026 — the latest edition of the world's largest user survey on this topic, with 1,579 participants — shows a remarkably stable picture: data quality management again ranks as the number one trend for 2026, closely followed by data security and data privacy (both rated 7.9 out of 10 in importance). Only after that come a data-driven corporate culture, data and AI governance, and data and AI literacy. Topics like generative AI, machine learning, or decision intelligence rank lower in overall importance. According to BARC, vendors emphasize these topics considerably more than the user organizations surveyed do themselves.

This is confirmed in practice: AI initiatives rarely fail because of model quality. The real problem lies in the data foundation underneath — inconsistent term definitions, missing semantic layers, and data models built for reports rather than for open-ended queries. Where that foundation is missing, AI systems either deliver inconsistent results or stay stuck at the pilot stage.

What follows from this: three consequences for decision-makers

Three things can be drawn from the solid data available here, without going beyond what the studies themselves show.

First: the gap between expectation and production readiness in agentic AI — 60 percent adoption intent within two years versus 17 percent actual deployment today, against a projected cancellation rate of over 40 percent — isn't a reason to wait. It's a window of time. Organizations that assess their own data architecture now make that decision while there's still time to act on it — not only once a contract expires or a competitor moves first.

Second: BARC's finding confirms a simple sequence: organizations that invest in data quality and governance before investing in tools are building on a foundation that can actually put new AI capabilities to use once they're production-ready. Reverse that order, and you may end up with some very impressive pilots that don't scale.

Third: the pricing dynamic in the SaaS market means that "doing nothing" via contract renewal is itself a cost decision — just an unconscious one. Regularly checking actual feature needs against the tier you're paying for pays off regardless of any AI question.

Our takeaway: what we keep seeing in our projects is this — the organizations that take these three points seriously aren't the ones with the biggest budget. They're the ones that understand first that they actually have a choice. Doing nothing is one of those choices — it just comes with its own price tag. The question that remains isn't one for the next Gartner report — it's one for your next internal meeting: where does your organization currently stand in this gap — and who on your team actually knows that?

This article is a market analysis with commentary from M2. technology & project consulting GmbH, based on publicly available studies and forecasts. Berlin, 2026.

Sources

1. Market Research Future – Business Intelligence Market Research Report 2025–2035, as of April 2026. Source (accessed: 07/03/2026)
2. Vertice – SaaS Inflation Index 2026. Source (accessed: 07/03/2026)
3. Gartner – Gartner Predicts 75% of Analytics Content to Use GenAI for Enhanced Contextual Intelligence by 2027, June 2025. Source (accessed: 07/03/2026)
4. Gartner – Gartner Predicts Defensive Spend to Derisk IP Loss and Copyright Infringement Will Slow GenAI Adoption and Diminish Returns (includes the 90% forecast for content creators, target year 2025), March 2024. Source (accessed: 07/03/2026)
5. Gartner – 2026 Hype Cycle for Agentic AI, April 2026. Source (accessed: 07/03/2026)
6. Gartner – Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027, June 2025. Source (accessed: 07/03/2026)
7. Gartner – Gartner Forecasts Supply Chain Management Software with Agentic AI Will Grow to $53 Billion in Spend by 2030, April 2026. Source (accessed: 07/03/2026)
8. BARC – Data, BI and Analytics Trend Monitor 2026 (n=1,579, surveyed summer 2025). Source (accessed: 07/03/2026)