Why ~95% of AI efforts fell short in 2025 — and the two questions every CEO racing to go agentic should bring to their next board meeting. Spoiler: one of them is "Do we actually have product marketing fundamentals?"
Every company says it’s customer-obsessed. But follow the money — product, positioning, go-to-market — and a lot of those calls still get made from opinion. The loudest voice in the room. A gut feel. A message someone was sure would land.
That worked as long as humans made those decisions one at a time. Slowly, with a chance to catch a bad one before it spread. And the people making them held an edge no model has: real context. Someone who knows your product, your customers, and your market reads between the lines. They feel when a message is off and fix it before it ships. An LLM carries none of that on its own. It reasons only from what you put in front of it, and fills whatever you leave out with a guess.
That window is closing. Gartner expects at least 15% of day-to-day business decisions to be made autonomously by agents by 2028, up from essentially zero in 2024. The agents are coming for exactly the calls your best people make today — and they’ll make them faster, and in far greater numbers. And an agent doesn’t pause at the seam between what it knows and what it’s guessing. Hand it a gap and it fills it, confidently, fluently, with a plausible assumption instead of a fact.
Which is why “customer truth, not opinion” isn’t a tagline. In the agentic era, it’s the whole ballgame. Ground your agents in what customers actually said and the shift scales your best thinking. Leave them to guess and it scales your worst. Just as fast.
That’s why now.
Grow to ten million euros in ARR. Ten people. Maximum. That’s what our business plan says.
😱
Every time I say that out loud, a little voice in my head does the math and looks at me like I've forgotten how companies work. And that little voice is right. Ten humans do not run a €10M tech business. The headcount doesn't add up — and it's not supposed to. The rest of the company is an agentic workforce. Agents doing the work, making decisions, moving things forward, at a scale and speed ten people never could.
So my co-founders and I spent the last year staking our company on a single bet: that agents can carry the load.
And somewhere in that year we realised the bet isn't really about the agents at all.
Let's start with what's actually happening, because it's bigger than my company or yours.
Every board on earth has "become agentic" somewhere on the 2026 agenda. This isn't a fad you can wait out. Gartner expects that by 2028, at least 15% of day-to-day work decisions will be made autonomously by AI agents — up from essentially zero in 2024 — and that a third of enterprise software will have agentic capabilities baked in. The shift is happening independently of what any one of us decides. Agents are going to be making decisions inside your business. Many of them. Faster than your people, and in far greater numbers.
That's the change. And it comes with a stake attached, the way real changes always do: the agents are already deciding. The only open question is what they're deciding on.
Start from something every strong company already knows: growth begins with a deep understanding of your customers. The best operators make almost every decision a customer-centric one — most of all in product and go-to-market, which happens to be exactly where most companies spend most of their money. So when we talk about making good decisions, whether it's decisions made by humans or agents, these need to be grounded in deep customer insights and truth.
Now the uncomfortable part.
In 2025, MIT's Project NANDA published The GenAI Divide: State of AI in Business — 52 structured interviews, 153 survey responses from senior leaders, and a review of 300+ real AI initiatives. The headline number went everywhere: roughly 95% of organizations got zero measurable return on their generative-AI investment. Billions spent, a rounding error back. (The authors themselves call it a directionally-accurate six-month snapshot, not a final verdict, and it's drawn its share of pushback — so take the exact figure with a grain of salt. The direction is harder to argue with.)
But don't stop at the number, because the instinct it triggers — the technology just isn't ready yet — is the wrong lesson. Read the why and a different story shows up. MIT found the failures weren't about model quality at all. They were about what the authors call the learning gap: tools that couldn't retain context, couldn't plug into real workflows, couldn't improve over time. Buyers who partnered with vendors and integrated deeply succeeded far more often than teams building disconnected tools of their own — internal builds succeeded only about a third as often.
And don't write this off as a 2025 story that newer models have since solved. Through 2026 — a year of fast model progress — the numbers barely moved. S&P Global found firms abandoning most of their AI projects at more than double the prior year's rate; separate 2026 surveys from BCG, PwC, and Foundry all landed in the same place, with most enterprises still unable to show a return. Capability had stopped being the bottleneck: the frontier models converged, and the gap moved into the workflow, not the model. That's the tell. If a year of dramatically better models doesn't move the number, the number was never about the models.
Gartner tells a compatible story from the agentic side: it predicts over 40% of agentic AI projects will be cancelled by the end of 2027 — naming escalating cost, unclear business value, and inadequate risk controls. Different words from MIT's learning gap, but they rhyme: projects die on integration, ownership, and value, not on how smart the model is.
Here's the through-line I read across both — and I'll own it as my read, not their finding. When a system isn't grounded — no reliable source to reach, no one owning the truth it acts on — it fills the gap with a confident guess. MIT names the mechanism a learning gap; Gartner counts the wreckage in cancelled projects. I call the thing underneath both the confident guess.
That's the enemy. Not AI hype. Not slow models.
Left to its own devices, an agent fills every gap it hits with a training-data prior, a plausible-sounding assumption, or a deep search across whatever the open web happens to serve up that day. It doesn't hesitate. It doesn't flag the seam between what it knows and what it invented. It hands you fluent, self-assured, beautifully-formatted output — and some unknowable fraction of it is made up.
I want to be honest here: we nearly walked into this ourselves. Early on it's intoxicating to watch an agent produce a polished answer in seconds. You have to keep reminding yourself that polished and true are not the same word. Everyone chasing agentic is fighting the same villain, and most don't know it yet.
Let me make that concrete, because I've watched it cost real money. A team we work with built an entire campaign around a message they were sure would land — a positioning call made from gut and a few confident voices in a room, not from anything their customers had actually said. The creative made by agents looked sharp at first sight. The launch was clean. And it produced almost zero pipeline, because the message answered a problem the buyers didn't really have. The copy turned out to be generic. The buyers saw right through it. That one ungrounded decision cost them north of €500,000 — a confident guess, delivered beautifully.
Here's why the confident guess is so much more dangerous now than it was a year ago. That was one team, one campaign, one bill. When an agent makes the same kind of call from the same kind of assumption, that flawed logic runs across thousands of decisions, automatically, downstream, at machine speed. The agentic era doesn't just scale your good execution. It scales your bad execution just as fast — and hands you the bill quarters later, when someone in a budget review asks what the project actually returned and the room goes quiet.
That silence is what a cancellation sounds like. It's what 95% sounds like.
So picture the other side of that divide.
Picture an agent that, when it hits a gap, doesn't guess. It reaches into a repository of verified customer truth — real findings, anchored to real quotes from real customers, each carrying a confidence label that says how much weight it can bear. It answers. And every claim traces back to something a customer actually said.
Make it concrete. A product marketer hands an agent the next launch. Instead of inventing benefit statements no buyer has ever uttered, it drafts positioning built from the exact phrases churned customers used on their way out — each line traceable to the interview it came from, each carrying a confidence label that says how many customers actually said it. Or an SDR agent personalizing outreach: not a plausible-sounding pain scraped from a job title, but the real objection three lost deals raised last quarter, quoted. Win/loss, churn interviews, ICP refinement, messaging, battlecards, willingness-to-pay — these are the surfaces where go-to-market teams already spend, and every one of them is a place an agent either grounds in what customers said or makes something up.
That's not a fantasy feature. It's how the technology was designed to work. Retrieval-augmented generation — grounding a model in an external, trusted source instead of its own priors — was shown years ago to produce measurably more factual, grounded output, with citable provenance for every answer. The mechanism has existed the whole time.
If you've built with RAG, you're already objecting: we wired up retrieval and it still hallucinated. You're right, and it's the most important thing to be clear about. RAG the technique is commoditized — a retrieval step anyone can bolt on. Point it at an unvalidated dump of documents and it will still lie confidently, because it's grounding in noise. The differentiator was never the retrieval. It's the corpus: whether what the agent reaches is curated, quote-anchored, confidence-labeled, and traceable to a real source — or a pile of PDFs nobody validated. Naive RAG over a messy corpus is a confident guess with a citation stapled to it. The moat is the quality and structure of what you ground in, not the act of grounding.
In the promised land, your fundamentals stop being academic hygiene and become the thing that lets the business move fast without making things up. Confidence labels and quote-level traceability turn into a governance layer — everyone can see how much an insight can bear and trace it back to the customer's own words. That's what makes it safe to let an agent act. For those of us building in Europe, that traceability isn't a nice-to-have either. When an agent makes a claim about a person, or acts on customer-interview data, provenance is what lets you show where a decision came from and honor an access or erasure request — the difference, under GDPR, between an agent you can account for and one you can't.
And here's the flip — the counterintuitive heart of the whole thing:
Model limitations make your fundamentals more valuable, not less.
The more decisions you hand to agents, the more leverage a single well-grounded, validated insight carries — because it now shapes hundreds of automated decisions, not one person's Tuesday. The researcher who used to "produce reports people skimmed" becomes the owner of the customer-truth layer the whole business runs on, humans and agents alike. Rigor was never the boring part. In the agentic era, rigor is the moat.
This is the part everyone gets backwards. They spend the budget on the agent — the model, the orchestration, the demo that dazzles the board. They spend almost nothing on what the agent stands on.
The general fix is grounding: give the agent a trusted external source to reach instead of its own priors, and make someone own it. That principle is well established, and it isn't mine. The bet I'm making is narrower — that for the decisions product and go-to-market teams hand to agents, the source worth grounding in is a customer-truth layer: insights stored atomically, each one a finding plus its evidence quote, its tags, its confidence label — connected, queryable, and wired into the tools where decisions actually happen. Grounding is the principle. Customer truth is where I'm putting my chips.
There's a sharper edge here for anyone who builds software. In mid-2026 Gartner put a number on it: up to $234 billion of enterprise-application software spending — about a fifth of that SaaS category by 2030 — is exposed to what it calls agentic arbitrage, as agents complete work across systems and reduce the need for the interface. Once agents become the primary users of your product, Gartner argues, the interface — the thing SaaS has competed on for two decades — stops being a differentiator and the software goes invisible. For a software company that should be a cold-water moment: the moat you've spent years defending is the one about to erode.
And this isn't only a forecast. The market already repriced it in real time — a February 2026 selloff erased roughly $285 billion in SaaS value in weeks, quickly dubbed the "SaaSpocalypse," with analysts downgrading seat-heavy names like Workday. The per-seat model itself is being dismantled live: Salesforce, ServiceNow, and GitHub have all shifted from charging per human user toward charging for what agents actually do. When the seat becomes fiction, so does the moat built on it.
So where does the moat go? Gartner points in a related direction — its survivors are the vendors who capture and keep their customers' knowledge instead of letting it accrue to some shared model. Gartner means operational memory, the context a system builds up as it's used; I'd extend that one layer down, and I'll own the extension as my read, not theirs. The same logic makes a proprietary voice-of-customer layer a moat — because when the interface stops being defensible, the one thing a competitor's agent still can't reach is what your customers actually said. A customer-truth layer isn't just a faster way to run go-to-market. It's what keeps a software company relevant after agents eat the interface.
Connect that layer to the applications running your agents, and every claim they make anchors to specific evidence instead of a plausible hallucination. The agent stops being a liability and becomes a conduit for verified customer truth. That's the system we built at Bubble — and it's the reason I can put that absurd number in our business plan.
Here's the most honest thing I can offer, and I want to be precise about what it is and isn't: we run on this ourselves. Not proof — the €10M number is a target, not a result, and I'd be doing the exact thing this article warns against if I dressed a projection up as evidence. What it is, is conviction with skin in the game.
The €10M-with-10-people bet only works if our agents aren't guessing — so we grounded them in the same customer-truth layer we sell. When an agent makes a call about a segment, a pain, a churn risk, it's reaching a real quote with a confidence label, not improvising from priors. If I'm wrong about grounding, I don't get to watch it fail in a slide deck. I watch it fail in my own company, at machine speed. That's the strongest version of the claim I'm entitled to make: not this works, but I've bet the company that it does.
That's also why I think this is the moment, and not a year from now. Three things had to line up: a genuine board-level shift (agentic is real, not hype), a market that's been underserved (customer research built for everyone except the product marketers who need it most), and a defensible, EU-native system built specifically for them. Remove any one of those and the fire goes out — it's a clever tool with no urgency, or urgency with nothing underneath. Right now all three are lit at once.
If you take one thing from this, don't take a product. Take a diagnostic. Walk into your next leadership meeting and put two questions on the table:
1. Do we have the fundamentals — decisions rooted in verified customer truth, with the evidence and confidence to back them? Not slides. Not a wiki nobody reads. A living, validated, traceable layer of what your customers actually said.
2. Do our agents have access to them? Because a truth layer your agents can't reach is a truth layer that doesn't exist as far as the decisions are concerned.
Sit with what each "no" costs you.
If the answer to the first question is no, the agentic shift will take your weak fundamentals and scale them — bad decisions and poor execution, automated, downstream, at machine speed, straight into your growth and your margins.
And if you decide to sit the transition out entirely to avoid that risk? That's the other cliff. Failing to move agentic isn't a safe default — it's a slower, quieter loss that shows up in your valuation and your next raise, while competitors who made both moves pull away.
There's no version of this where the fundamentals don't matter. The agentic era just raised the stakes on getting them right — and shortened the time you have to do it.
The agents are already deciding. The only question left is whether you've given them the truth to decide on.
Sources referenced: MIT NANDA, "The GenAI Divide: State of AI in Business 2025"; Gartner press release, "Over 40% of Agentic AI Projects Will Be Canceled by End of 2027" (June 2025); Gartner press release, "$234 Billion in Enterprise Application Software Spend Is at Risk from Agentic AI" (July 2026); Lewis et al., "Retrieval-Augmented Generation" (2020). 2026 return-on-AI figures draw on S&P Global Market Intelligence (Voice of the Enterprise), BCG's AI at Scale survey, PwC's Global CEO Survey, and Foundry's State of the CIO. Market corroboration (February 2026 SaaS selloff and the shift away from per-seat pricing) draws on contemporary reporting including CIO Dive and PYMNTS.