The short answer.
BI buyers now build a ranked shortlist of three to five tools before they contact anyone, using AI chatbots, comparison searches, review sites and analyst lists. A vendor gets on that list, and is described accurately, by publishing the facts buyers compare: pricing model, connectors, governance, security, deployment options and honest limits.
The large platforms are named by default. Everyone else has to give the engines and the buyers specific reasons to include them for a particular use case. That is the work we describe on our AI search visibility service page, and this guide applies it to analytics software.
- 01 · CATEGORY“Best BI software for a mid-size company”
- 02 · COMPARE“X vs Y”, “X alternative”, review grids
- 03 · CHECKPricing, connectors, security, reviews
- 04 · RANKThree to five tools, in order
- 05 · CONTACTThe top-ranked vendor first
Steps 1 to 4 happen mostly without a salesperson.
The shortlist is set before first contact.
Buyers contact vendors late, and usually contact the one they have already ranked first. 6sense’s 2025 buyer study, as summarised by CustomerThink, found that the average point of first contact came when buyers were 61% of the way through their buying process, down from 69% in earlier 6sense studies.
6sense buyer experience research, as summarised by CustomerThink, 30 November 2025.
The same summary reports that buying groups filled four of the five spots on their vendor shortlist on day one, that 94% ranked their shortlist before contacting any vendor, and that 79% initiated contact themselves. They contacted their top-ranked vendor first about 80% of the time, and bought from it 77% of the time.
Buyers also prefer to do this alone. Gartner research reported in 2025 found that 61% of B2B buyers prefer a completely rep-free buying experience. For a BI vendor, the implication is blunt: if your product is not on the list, and described well, on the day the project starts, a good demo later rarely saves it.
AI chatbots now shape the shortlist.
AI chatbots have overtaken review sites as the single biggest influence on which software vendors make a buyer’s shortlist. G2 surveyed 1,076 B2B software buyers in March 2026; 51% now start research with an AI chatbot more often than with Google, up from 29% in G2’s 2025 report, and 71% use chatbots at some point.
G2, 2026 AI search insight report (survey of 1,076 B2B software buyers, March 2026). Buyers could choose several sources.
The effect on vendors is direct. In G2’s data, 69% of buyers said a chatbot led them to choose a different vendor than they first planned, and one in three bought from a vendor they had never heard of before. 41% use Deep Research tools for structured software evaluations, which means long, multi-source reports that compare products feature by feature.
Forrester’s 2025 Buyers’ Journey Survey, reported by Digital Commerce 360 in January 2026, points the same way: generative AI tools were the single most cited meaningful interaction type for researching purchases. Forrester also found that 13 internal stakeholders and nine external participants influence a typical B2B purchase. In BI, that means finance, IT, data engineering and the business teams may each be asking an AI engine a different question about you. Our explainer on how ChatGPT decides which companies to recommend covers what the engines read.
Comparison and “alternative” questions.
Almost a third of buyers open their AI research by naming a competitor, so comparison and “alternative” questions are where challengers win or lose. G2’s 2026 AI search report classified buyers’ first prompts when researching software:
G2, 2026 AI search insight report.
Competitor-based prompts (“what are the alternatives to Tableau for a team on Google Cloud?”) made up 31%, close behind category prompts at 33%. Requirements and process prompts were 22%. Ordinary search shows the same appetite: Google search data via DataForSEO (November 2026) gives about 5,400 monthly US searches for “business intelligence software” and 390 for “best BI tools” (50 in the UK).
For the market leaders, competitor prompts are a defence problem. For everyone else, they are the main way in: a buyer who asks for alternatives to a leader has already decided the leader is not quite right, and is looking for a reason to choose something else.
ILLUSTRATIVE EXAMPLE · WHAT BUYERS ASK AI
Buyer: “We’re a 400-person distributor on Snowflake and NetSuite. Power BI licensing is getting complicated and our finance team wants governed self-service. Compare three alternatives on connectors, row-level security, semantic layer, embedded dashboards and pricing model.”
What a good answer needs from a vendor’s site: a connector list naming both systems, a security page covering row-level security and SSO, a plain description of the semantic layer, an embedding page, and a pricing page that explains the model even if it gives no figures.
This is a made-up prompt to show the kind of detail buyers now type. It is not a recorded conversation.
What Google and AI showed us.
On 10 November 2026 we asked ChatGPT, Gemini and Google one category question: “best business intelligence software for mid-size companies”, from the US, once each. The answers were built from vendor pages, but Google’s results were dominated by third-party lists.
- ChatGPT named five products: Power BI first, then Tableau, Qlik, Looker and Sigma. Each got the same template: best for, pricing, strengths, a weakness, and a one-line verdict. It cited each vendor’s own site, then asked four questions back: company size, current systems, primary objective and monthly software budget.
- Gemini used a similar template, with a “starting price” and “the catch” for each of five entries. It took per-user prices for the two best-known products from third-party articles, not the vendors’ own pages, and for one pair of products it simply wrote “Varies” with an estimated range. It cited one third-party roundup nine times and one BI vendor’s own content five times.
- Google’s AI Overview cited three sources, all third-party software roundups and directories. None of the top nine ordinary results came from one of the six vendors Gartner named as Leaders in 2025; they were roundups, directories, a video and one smaller vendor’s blog.
Three lessons follow. Every product gets a “weakness” or “catch” line whether or not the vendor supplies one, so it is better to publish an honest one yourself (“not the best fit for teams that need X”) than to leave it to an old review. And roundups and directories still decide much of what Google’s AI Overview says, so their facts about you matter as much as your own pages. And if your pricing is not clearly published, an engine may estimate it for you.
Analysts, reviews and peers.
Analyst reports still define the category’s default list, but buyers now lean more on reviews and AI than on reading the analyst reports themselves. Gartner’s 2025 Magic Quadrant for Analytics and Business Intelligence Platforms, as reported by CX Today in June 2025, named Microsoft, Salesforce (Tableau), Google, Qlik, Oracle and ThoughtSpot as Leaders, and commended Power BI’s “dominant” market presence. A 2026 edition was published on 29 June 2026.
Those names are what AI engines reach for first. But direct use of analyst reports is falling. TrustRadius’s 2026 research, as reported by MarketScale, found only 13% of buyers used analyst reports in their most recent purchase decision, a 63% decrease since 2022, while 74% consulted customer reviews. 63% used AI during the purchase, and 94% of those fact-checked its output at least some of the time.
Reviews matter twice: once when a buyer reads them, and again when an AI engine cites them. G2’s 2026 report found that 45% of B2B software buyers say review-site citations are the most confidence-inspiring signal in an AI answer. Its 2025 report found enterprise buyers (1,000 to 5,000 employees) named review sites (56%) and AI search (55%) as their top two research sources. Our guide to the off-site sources AI uses to recommend firms explains how to prioritise review platforms and lists.
What buyers actually rank on.
BI buyers rank on data quality, security and governance first; AI features come well behind in what they say matters. BARC’s Data, BI and Analytics Trend Monitor 2026, based on 1,579 responses, put data quality management and data security and privacy joint first, both rated 7.9 out of 10.
BARC Data, BI and Analytics Trend Monitor 2026 (1,579 responses). Top two scores from BARC; others from a BI vendor’s summary of the study.
A summary of the same study by one BI vendor lists the rest of the top five as data-driven culture (7.3), data and AI governance (6.9) and data and AI literacy (6.5), with generative AI for analytics 13th and agentic AI 19th. The gap between what vendors lead with and what buyers rank highest is large. 6sense’s 2025 study adds a nuance: 58% of buyers said the need to evaluate how vendors implement AI made them engage sellers earlier. Buyers want to understand your AI, but they choose on trust in the data.
So the pages that win shortlists are not the AI launch pages. They are the pages that explain how your product keeps data accurate, secure and governed, in language a finance director and a data engineer can both check.
What a BI vendor must publish.
Publish every fact a buyer would put in a comparison table, on a crawlable page, in plain text. If an engine cannot find it, it either leaves you out or fills the gap from someone else’s page.
| Fact | What to publish | Why it matters |
|---|---|---|
| Pricing model | Per user, per capacity or per usage; editions; what costs extra | Engines quote published prices and describe the rest as unknown or “contact sales” |
| Connectors | A full, searchable list of sources, with native vs partner connectors | Buyers name their systems in prompts; the engine needs both names on your page |
| Semantic layer and modelling | How metrics are defined once and reused, and who maintains them | Central to governed self-service, the top requirement in many evaluations |
| Governance and security | Row-level security, SSO, audit logs, certifications with dates | Security and governance rank at the top of buyer priorities |
| Deployment | Cloud regions, on-premises or private cloud options, data residency | A frequent reason for elimination in regulated sectors |
| AI features and limits | What the AI does, on which data, with what controls and accuracy caveats | Buyers check how AI is implemented before engaging |
| Embedding | Embedded analytics options, licensing for external users | A separate buying use case with its own comparisons |
| Who it is not for | The use cases where another tool fits better | Engines write a weakness line anyway; yours is more accurate |
| Migration | Guides from the common incumbents, with timelines | Answers the competitor prompts that start a third of AI research |
Pricing is the clearest example. Microsoft publishes Power BI Pro at $14.00 per user per month, paid yearly, and Premium Per User at $24.00. Google’s Looker pricing page lists its editions on annual commitment with “Call sales”. In our test, Gemini gave prices for the best-known products from third-party articles and estimated a range for products without clear public pricing. You do not have to publish list prices, but you should publish the model and the drivers, so an engine can describe your pricing rather than skip it. Our article on SEO for complex B2B and SaaS products covers how to structure feature, use-case and integration pages. Our guide to GEO for B2B SaaS looks at pricing, integrations and documentation pages as sources for AI answers.
Fair comparison and alternative pages.
Publish comparison pages that a buyer would trust if a competitor wrote them about you: specific, dated, sourced and honest about where the other product is better. Engines and buyers can both tell a fair comparison from an attack page.
- One page per real comparison. Pick the products you are actually compared with in sales calls and AI answers, not every name in the Magic Quadrant.
- Compare on the buyer’s criteria: connectors, governance, pricing model, deployment, AI controls, embedding, support.
- Date every claim about the other product, and link to its own pricing or documentation page.
- Say who should choose the other tool. It is the line that makes the rest believable.
- Add a migration section with steps and a realistic timeline.
- Review quarterly. Competitors change prices and features; a stale claim becomes the error an engine repeats.
The comparison pages play in our AI Shortlist Plays sets out the format in more detail.
Being described correctly.
Getting named is half the job; the other half is making sure the description is right. BI products change fast, and AI engines often describe a product as it was two years ago: an old pricing model, a retired edition, a connector that now exists listed as missing.
Buyers notice. In the TrustRadius research, 94% of buyers who used AI fact-checked its output at least some of the time. A wrong description costs you twice: you lose the buyer who believed it, and you lose credibility with the buyer who checked. Patients check AI answers in the same way, as our guide for physio, chiropractic and wellness clinics notes.
- Run the category, comparison and requirements questions your buyers ask, several times in each engine, and record every claim made about your product.
- Trace each wrong claim to its likely source: an old page of yours, a review, a roundup, a directory listing.
- Fix your own pages first, then ask third-party sites to update their facts, with evidence.
- Keep one dated product facts page (editions, pricing model, deployment, certifications) and link to it from everywhere.
- Re-test monthly, and after every pricing or packaging change.
Our guide on fixing wrong information AI gives about your company covers the correction process step by step. To see what the engines say now, start with an AI search visibility check. Reviewing how the engines describe you is part of our GEO and AEO services.
The BI vendor shortlist scorer.
Tick each item your public website clearly shows today, without a login or a form. You get a score out of 100 and a list of gaps. Nothing you tick is sent anywhere.
Shortlist readiness: 0 / 100
Tick only what a buyer could find without booking a demo.
The heaviest weights sit on pricing, connectors, security and reviews: the facts buyers put in their comparison tables and the sources engines cite most. We have written similar guides for corporate team building companies and construction and engineering consultancies.
Questions BI vendors ask.
How do buyers choose business intelligence software in 2026?
They form a ranked shortlist of three to five tools before contacting any vendor, using AI chatbots, comparison searches, review sites and analyst lists, then contact their top-ranked vendor first. Most rank on data quality, security, governance, connectors and pricing model.
Do AI chatbots really influence BI software shortlists?
Yes. G2’s March 2026 survey of 1,076 software buyers found chatbots are now the top source influencing shortlists, and 69% chose a different vendor than first planned because of a chatbot’s recommendation.
Should BI vendors publish pricing?
Publish at least the pricing model and what drives cost. In our test, Gemini took prices from third-party articles and estimated a range where pricing was unclear. If you do not explain your model, someone else’s page, or a guess, will.
Are “alternative to” pages worth building?
Yes, if they are fair. G2 found 31% of buyers’ first AI prompts name a competitor. Build one page per real comparison, date every claim, link to the competitor’s own documentation and say who should choose the other tool.
Does the Gartner Magic Quadrant still matter?
It shapes the default list engines start from, but fewer buyers read analyst reports directly: TrustRadius found 13% used them in their latest purchase, down 63% since 2022. Reviews and AI answers now carry more weight.
Sources: Prolinkage test of one buyer question in ChatGPT, Gemini and Google (US), 10 November 2026; Google search data via DataForSEO, US/UK, November 2026. External: G2, The Answer Economy, April 2026; G2, 2026 AI search insight report; G2, 2025 Buyer Behavior Report; CustomerThink on the 6sense 2025 study; Walnut on Gartner’s rep-free research; Digital Commerce 360 on Forrester, Jan 2026; CX Today, Gartner ABI Magic Quadrant 2025; Qlik, Magic Quadrant 2026 citation; MarketScale on TrustRadius 2026; BARC, Trend Monitor 2026; Zoho summary of BARC 2026; Microsoft, Power BI pricing; Google Cloud, Looker pricing. All accessed 10 November 2026.