Futurum’s acquisition of ETR has been described as a combination of analyst expertise and institutional-grade spending data. Futurum CEO Daniel Newman believes that’s only part of the story. In this conversation, Newman argues that AI is forcing a fundamental rethink of the research and advisory industry itself, why reports are no longer the product, and why the future belongs to what he calls AI Decision Intelligence.
There is no point pretending I’m a neutral observer here.
Techstrong is part of Futurum. I’ve had a front-row seat to the company’s evolution from analyst firm to intelligence platform, media company, advisory business and increasingly something that doesn’t fit neatly into any traditional category.
So when Futurum announced its acquisition of ETR, I obviously paid attention.
What surprised me wasn’t the acquisition itself. Acquisitions happen every day.
What caught my attention was the framework Daniel Newman was using to explain it.
Most acquisition announcements focus on scale. More customers. More products. More capabilities.
Daniel’s argument is different.
He believes the research and advisory industry itself is approaching an inflection point. The traditional publication-driven model that has dominated enterprise technology research for decades was built for a different era, and according to Newman, AI is exposing structural limitations that can no longer be ignored.
Whether he’s right is a bet I am personally invested in.
Either way though the argument is provocative enough that it deserves examination.
So I sat down with Daniel to discuss AI Decision Intelligence, the role ETR plays in that vision, the rise of what he calls the Forward Analyst, and why he believes the next generation of technology intelligence will look very different from the one most of us grew up with.
Shimmy:
Daniel, let’s start with the statement that jumped off the page at me. You write that the research and advisory industry is facing a category extinction event. That’s a strong claim. What exactly is becoming extinct?
Daniel Newman:
The easiest way to understand the argument is to separate the quality of the firms from the architecture of the industry.
Whenever people hear a statement like that, they assume I’m criticizing the major analyst firms or suggesting that traditional research no longer has value. That’s not what I’m saying. Many of these firms created enormous value for enterprise technology buyers and continue to employ exceptional analysts. The issue isn’t the people. The issue is the operating model.
The modern analyst industry was built around a publication architecture. Research was gathered, synthesized, edited and distributed according to a publication schedule. Buyers consumed reports, used them to support decisions and returned when the next cycle was published. That architecture made perfect sense when technology markets moved at a pace that aligned with publication calendars.
The challenge is that today’s markets no longer move that way. AI infrastructure, software platforms and enterprise adoption patterns evolve on timelines that would have seemed impossible a decade ago. Organizations increasingly need intelligence that reflects current conditions, not conditions that existed when a research cycle began. The value is moving away from the publication itself and toward the ability to support decisions in real time.
That’s the shift I’m describing. Not the extinction of expertise. The extinction of an architecture that was designed for a slower operating environment.
Shimmy:
One of the things you repeat throughout the manifesto is that people don’t actually buy research. They buy decisions. Explain that.
Daniel Newman:
I think the industry has spent so long focusing on the artifact that it sometimes mistakes the artifact for the outcome.
If you ask a CIO why they subscribe to analyst research, they rarely tell you they enjoy reading reports. If you ask an investor why they consume research, they aren’t looking for another PDF. If you ask a technology vendor why it invests heavily in analyst relations, it isn’t because the process itself is enjoyable.
In every case, the underlying objective is the same. They’re trying to reduce uncertainty around a decision. The CIO is making a platform choice. The investor is evaluating an investment thesis. The vendor is deciding where to invest resources. The board is assessing risk. Research has historically been one of the mechanisms through which confidence was delivered.
What AI changes is the delivery mechanism.
For decades the report was the product because the report was the most efficient way to package expertise. Today intelligence can be dynamic. It can be conversational. It can be updated continuously. It can combine proprietary data, market signals, analyst judgment and contextual reasoning in real time.
Once you begin looking at the market through that lens, the center of gravity shifts. The report becomes one possible output rather than the primary product. The decision itself becomes the product.
Shimmy:
Let’s talk about ETR because that’s obviously a major part of this story. Most people see the acquisition as Futurum buying a great dataset. Is that too simplistic?
Daniel Newman:
It’s not wrong, but it is incomplete.
The value of ETR certainly includes the data. You have a community of more than 9,000 enterprise technology decision-makers, more than $2 trillion in tracked spending power and a longitudinal dataset that institutional investors have relied on for years. That’s an extraordinary asset.
But if this were simply about acquiring data, there would be easier ways to accomplish that. We could license data. We could partner. We could integrate third-party sources.
What made ETR strategically important is that it becomes a foundational layer within a broader intelligence architecture. AI-native intelligence platforms ultimately depend on the quality and uniqueness of their underlying data. Many organizations today are building AI experiences on top of publicly available information. Those experiences can be useful, but they are inherently limited because competitors have access to the same underlying inputs.
ETR changes that equation. It provides a proprietary signal layer that cannot easily be replicated. That signal layer becomes significantly more valuable when combined with analyst expertise, AI-native workflows and distribution channels that reach decision-makers where decisions are actually being shaped.
That’s why I see ETR as much more than an acquisition. It’s a foundational component of the architecture we’re building.
Shimmy:
You compare your vision to both Bloomberg and Palantir. That’s not exactly a modest comparison.
Daniel Newman:
The comparison isn’t about scale or valuation. It’s about pattern recognition.
Bloomberg built a proprietary data spine and then created the environment where financial decisions were made. The data, the workflows and the distribution all reinforced one another. Over time the Bloomberg Terminal became less of a product and more of an operating environment.
Palantir approached a different problem. They identified a specialized operator role that organizations needed and then built a platform capable of scaling that operator’s output. The software became a mechanism for productizing expertise.
When I look at the future of research and advisory, I think both patterns matter.
A proprietary data spine without expertise is ultimately a database. Expertise without a differentiated data foundation becomes consulting. The interesting opportunity emerges when proprietary data, AI-native workflows, analyst expertise and distribution operate as an integrated system.
The Bloomberg analogy helps explain the data architecture. The Palantir analogy helps explain the operator model. Together they help describe what we’re trying to build.
Shimmy:
That brings us to your concept of the Forward Analyst. What exactly is a Forward Analyst?
Daniel Newman:
The simplest answer is that a Forward Analyst operates inside the decision loop rather than around the publication cycle.
Traditional analysts were organized around research outputs. Reports, rankings, evaluations and periodic publications formed the primary interface between the analyst and the market. The Forward Analyst is organized around the flow of decisions occurring across buyers, vendors, investors and operators.
Those constituencies increasingly operate simultaneously. A CIO evaluating infrastructure, an investor evaluating a company and a vendor evaluating its market position are often responding to the same underlying signals. The traditional industry tends to address those audiences through separate products and separate workflows.
The Forward Analyst operates across those vectors.
What makes that possible is a combination of proprietary data, AI-native tooling and continuous engagement with the market. The analyst is no longer producing periodic snapshots. They are participating in a continuously evolving intelligence environment.
The output is fundamentally different. It isn’t simply a faster report. It is a different category of work.
Shimmy:
You’ve spent years building media assets, analyst practices, advisory capabilities and now you’ve added ETR. Were you consciously building toward this?
Daniel Newman:
In hindsight, yes.
At the time, each step was driven by a recognition that customers were trying to solve interconnected problems. Enterprises didn’t want research disconnected from advisory. Vendors didn’t want analyst insight disconnected from market visibility. Investors didn’t want data disconnected from context.
Over time we found ourselves building capabilities that increasingly reinforced one another. Media improved distribution. Analysts improved interpretation. Intelligence platforms improved access. Advisory improved application. ETR strengthens the underlying signal layer.
What emerged was a realization that these weren’t separate businesses. They were components of a broader intelligence system.
That’s really what Futurum Forward represents. Not a product. Not a report. Not a subscription. A platform that connects those capabilities into a single operating environment.
Shimmy:
What’s the hardest thing for people to understand about this vision?
Daniel Newman:
Probably that this isn’t really a discussion about reports.
People naturally anchor to familiar categories. They think about quadrants, waves, rankings and subscriptions because that’s how the industry has operated for decades.
The larger shift is that enterprises increasingly need continuous intelligence rather than periodic intelligence.
Once you accept that premise, many assumptions start to change. You begin thinking differently about analyst roles. You think differently about data. You think differently about AI. You think differently about distribution.
The conversation moves away from research publishing and toward decision support.
I believe that’s where the industry is heading, whether people use the term AI Decision Intelligence or not.
Shimmy’s Take
What I find most interesting about Daniel’s argument is that it isn’t really an argument about ETR.
ETR is part of the story. An important part. But it isn’t the story.
The story is whether the architecture of the analyst industry still matches the architecture of the markets it serves.
That’s a much bigger question.
Technology has always gone through periods where the old operating model suddenly stops fitting the environment. Infrastructure management changed. Software delivery changed. Marketing changed. Media changed.
Research and advisory may be entering a similar period.
That doesn’t mean traditional analyst firms disappear. History rarely works that way. Incumbents often adapt. New models emerge alongside old ones. Markets evolve gradually and suddenly at the same time.
But Daniel’s core observation is difficult to dismiss.
Technology cycles are moving faster.
Decision windows are shrinking.
The volume of information continues to explode.
If those trends continue, organizations will increasingly need something more dynamic than periodic research artifacts.
Whether AI Decision Intelligence becomes the category name remains to be seen.
Whether the underlying shift is real feels like a much harder question to ignore.
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