Augmented Business Intelligence Platforms

This category name is retired. Forrester evaluated this market as Augmented BI Platforms in 2021 and Augmented Business Intelligence Platforms in 2023, then dropped the adjective for The Forrester Wave: Business Intelligence Platforms, Q2 2025. Current buyers should work from that evaluation. What follows explains why the word disappeared.

The word augmented entered this category name because it described something only some vendors could do. It left because every vendor could.

That is a more interesting death than most category renames get. Augmented BI did not fail, get absorbed, or turn out to be a bad idea. It won so completely that describing a platform as augmented stopped conveying information, which is the specific way successful technology becomes invisible.

What augmentation meant

Traditional business intelligence answered questions you already knew to ask. Somebody built a dashboard, defined the measures, and the tool displayed them. Any question outside the dashboard required a request to a BI team and a wait.

Augmented BI added machine learning to that model. Forrester's framing described it as adding vital capabilities to core enterprise BI, letting users become citizen data scientists through machine learning, and broadening the reach of data and analytics to every decision-maker through conversational interfaces.

In practice that meant a handful of specific capabilities. Natural language query, so a person could type a question rather than build a chart. Automated insight generation, where the system surfaces anomalies, trends, and drivers without being asked. Forecasting built into the product rather than exported to a data scientist. Natural language narratives, turning a visualisation into a sentence explaining what it shows. And the ability to ask why a number moved rather than only what it was.

The criteria Forrester scored in 2023 reflect that scope: core enterprise BI, augmented BI and advanced analytics, conversational user interface, enterprise functionality, deployment options, application development, and integration with business applications.

Notice that the augmented capability is one criterion among seven. Even in the evaluation named after it, augmentation was never the whole product.

How a differentiator becomes a baseline

One number from Forrester's own research explains the rename better than any commentary.

In its 2022 data and analytics survey, ninety one percent of decision-makers at firms with advanced insights-driven maturity reported adopting augmented BI.

A capability at ninety one percent adoption among sophisticated buyers is not a differentiator. It is a table stake with an adjective still attached, and analyst firms eventually notice when a modifier stops modifying anything.

The pattern is familiar once you look for it. Nobody sells a mobile-responsive website any more, because a website that is not responsive is broken rather than differently specified. Nobody markets a cloud-native SaaS product as cloud-native to a buyer who has not run a data centre in a decade. The adjective survives exactly as long as its absence is plausible.

Inside The Forrester Wave: Augmented Business Intelligence Platforms, Q2 2023

Authored by Boris Evelson, the evaluation scored fourteen providers against twenty seven criteria, addressed to technology and data professionals.

Oracle placed as a Leader with what Forrester assessed as the strongest current offering in the market, and was the only cloud hyperscaler to take the maximum score in both core enterprise BI and augmented BI and advanced analytics. That combination is the interesting part. Scoring top marks on the traditional capability and the new one simultaneously was rare, because most vendors were strong at one or the other.

Amazon Web Services placed as a Strong Performer with QuickSight, evaluated on core BI features, machine learning integration, conversational interface, ease of use, and flexibility.

The predecessor, The Forrester Wave: Augmented BI Platforms, Q3 2021, scored fifteen providers against twenty five criteria: AWS, Domo, Google, IBM, Microsoft, MicroStrategy, Oracle, Qlik, Salesforce, SAP, SAS, Sisense, ThoughtSpot, TIBCO Software, and Yellowfin.

Worth noting the audience shift between editions. The 2021 Wave was addressed to business insights professionals. The 2023 Wave was addressed to technology and data professionals. The buyer moved from the business side toward IT over two years, which usually indicates a market where implementation complexity has grown faster than self-service capability.

What the 2025 rename revealed

The Forrester Wave: Business Intelligence Platforms, Q2 2025 dropped the adjective and, in the same move, made generative AI functionality a scored differentiator in its own right. Microsoft placed as a Leader with the highest score of any vendor in that criterion.

That is the whole arc in one sentence. Machine-learning augmentation became assumed, and a newer capability took its place as the thing worth measuring.

There is a caution buried in that for anyone reading vendor marketing. The generative AI capability in a BI platform today occupies the position augmented BI occupied in 2021: genuinely differentiating, unevenly implemented, and about three years from being a baseline nobody mentions. Weighting an eight-figure platform decision heavily on a capability with that trajectory is a defensible choice only if you will extract value from it in the window before everyone has it.

The problem augmentation did not solve

Forrester's definition promised to turn users into citizen data scientists. That promise is roughly as old as BI itself and it keeps underdelivering for a reason that has nothing to do with the tooling.

The bottleneck was never the interface. It was semantics.

Ask a natural language BI tool what revenue was last quarter and it has to know which of the seven revenue-shaped columns in your warehouse is the one finance recognises, whether that figure is booked or recognised, whether it includes the subsidiary acquired in March, and what your organisation means by last quarter given a fiscal year that starts in February.

None of that is in the question. All of it is in the semantic layer, and building a semantic layer is unglamorous, political, and slow, because it requires the organisation to agree on definitions it has historically been able to leave ambiguous.

A natural language interface over an undefined semantic layer produces confident answers that are wrong in ways nobody catches, which is considerably worse than the dashboard it replaced. The dashboard was at least wrong consistently and someone knew why.

This is the question to press hardest in any evaluation, and it is the one vendors most want to skip past, because the honest answer involves work on your side rather than capability on theirs.

Where this goes next

The interesting development in this market is not better answers for humans. It is that the questioner is changing.

If agents are going to act on business data, they need to query it, and they will do so at volume and without the intuition a human analyst brings. A person who receives an implausible number usually notices. An agent that receives an implausible number acts on it.

That raises the stakes on exactly the semantic problem described above. A BI platform serving humans can tolerate ambiguity because the humans resolve it informally, by knowing which report to trust and which analyst to ask. A BI platform serving agents cannot, because there is nobody in the loop to apply that judgement.

Forrester's broader agentic coverage has established categories for platforms, orchestration, and oversight. Nothing in that framework works if the data layer underneath returns different answers to the same question depending on how it was phrased.

Which means the semantic layer, which the industry has treated as a data engineering chore for twenty years, is becoming a governance requirement. That is a much better argument for investing in it than any BI vendor has managed to make.

What to test

Ask three phrasings of the same question. Take a metric your business genuinely cares about, ask for it three ways in the natural language interface, and check whether the numbers match. If they do not, you have found the semantic gap before you paid for it rather than after.

Test with someone who does not know the data model. The entire premise of augmentation is reach beyond the analytics team. Watching a specialist drive a demo tells you nothing about that.

Interrogate the automated insights. Every platform will surface anomalies and drivers. Ask how many of those insights a working analyst would consider actionable rather than statistically real but operationally meaningless, and ask reference customers the same question.

Establish who owns definitions. Not who owns the tool. Who decides what revenue means, how that decision gets recorded, and what happens when finance and sales disagree. If the answer is nobody, the platform will not fix it.

And separate the platform decision from the semantic layer project. They are different pieces of work with different timelines and different owners, and organisations that treat the second as a subtask of the first consistently discover otherwise about eight months in.

Forrester does not endorse any vendor named here, and tier placement should not be read as a recommendation to buy.