Everyone I talk to has had the same experience with AI. You ask for an analysis. What comes back is fast, confident and not quite right. So you sharpen the prompt. Closer, but still off. You try again. You're always one prompt away.
Here's the uncomfortable part. The model isn't missing because it lacks intelligence. It's missing because it has access to trillions of pieces of information that have nothing to do with your business. Nothing to do with your planning logic, your approval flows or the exception you granted last quarter and the reason behind it. It assembles patterns that look like answers. Without your context, that's what even the smartest model produces: a confident guess.
That's the gap we set out to close. This week we announced a definitive agreement to acquire Morpheo AI and introduced Vena Omega, the cumulative context engine powering Vena AI. I want to explain the thinking behind both decisions: why context is the problem worth solving, and why cumulative is the word that matters most.
Dharmesh Shah, a well-regarded expert on enterprise AI and the co-founder and CTO of HubSpot, recently put math behind that gap. He argues that agent success comes down to IQ times EQ times CQ, where CQ is the context quotient: how much the AI knows about your business, your goals, your constraints and your history. The important word is times. The relationship is multiplicative, not additive. If context is zero, it doesn't matter how brilliant the model is. The value is zero.
He's right. And nowhere is that more true than in finance. Real financial intelligence means tying every dataset back to its impact on the numbers. Headcount, operations, sales, whatever the source. Getting the data is rarely the hard part. Understanding how those relationships work, the way an FP&A team does, takes years of specialized financial planning knowledge that no generic model has and can't infer from surface level data.
Our industry has spent years chasing model capability. Smarter models, better benchmarks, longer context windows. All of that matters, and all of it is becoming table stakes. Every serious vendor will soon have access to capable models, and no model arrives knowing your business: the drivers behind your forecast, the assumptions inside your plan, the definitions that give your metrics meaning. Models are converging. Understanding is not.
Most of what our industry calls context is really retrieval. The AI reaches for what it can find in the moment, uses it and forgets it. However capable the model, it meets your business as a stranger, and it's still a stranger the hundredth time you ask, because nothing carries forward. Working with it is like onboarding a brilliant new analyst every single morning. Every prompt is day one.
Cumulative context is a different design goal entirely. The system keeps what it learns. Every variance investigated, every scenario run, every correction made, every plan approved adds to a governed body of context about how the business actually operates. The tenth cycle stands on the other nine.
Context in finance is also layered: how each business defines its numbers and runs its processes, how each user works and what they're responsible for, and underneath it all, the financial domain expertise the system brings on day one, so it understands the work before it ever learns your business.
Finance people understand compounding better than anyone. You know what happens when small gains accumulate cycle after cycle, and you know the difference between an asset that compounds and one that resets to zero every period. Apply that instinct to AI and the conclusion writes itself. Context that compounds beats context that resets.
And this kind of context can't be bought or scraped. The reasons behind an approval, the correction that taught the system what a metric really means, the definition that only makes sense inside your business: none of it exists in any dataset a model can train on. It only surfaces inside the governed work itself, one cycle at a time, in the place where planning actually happens. Which is also why it stays yours. It compounds inside Vena, on your data, under your governance. It never trains someone else's model and it never pools across companies.
Building that takes a team that has already spent years on the hardest part of the problem. That's why we went to Morpheo AI, an enterprise agentic data platform company that has done exactly that: preparing fragmented enterprise data so it can be structured, enriched and used by advanced AI. Their technology becomes the foundation of Vena Omega, and their team joins ours to build what comes next.
The name was deliberate. Omega is where accumulated context becomes a complete picture of a business. Every plan, close and decision adds to that picture, so it only gets more complete the longer a business runs in Vena.
Understanding is half of trust. The other half is precision. Large language models are probabilistic, and finance runs on deterministic outcomes. Much of the industry is trying to resolve that tension by making models more careful. But a more careful guess is still a guess, and finance doesn't need better odds. It needs certainty where certainty is possible. You resolve the tension with architecture.
Generative reasoning is what makes AI flexible enough to build a calculation, a model or a plan around your specific logic. That's generative work, and it should be. But once reviewed and approved, execution runs on deterministic, governed logic. The same calculation produces the same answer every time, with the auditability finance requires. Intelligence to analyze, recommend and act. Determinism where it matters most: in the data, definitions and calculations every decision depends on.
For the business, the stakes come down to one gap, and it's no longer the gap between data and insight. AI has largely closed that one. It's the gap between insight and action: knowing what to do and actually doing it. We call it decision latency, and by the time a slow organization moves, it's often no longer the same decision.
Context that compounds attacks that gap directly. When AI already understands your drivers, your definitions and your history, it doesn't just answer faster. It prepares the work: the analysis drafted, the variance explained, the recommendation ready for review. When production gets cheaper, judgment gets more valuable. Your team spends less time assembling the answer and more time deciding what to do about it.
This isn't a new direction for us. When we built Vena Copilot, we made a bet that AI belongs inside governed planning workflows, not bolted on beside them. That bet shaped this acquisition. Vena Omega deepens the context underneath the AI our customers already work with, which means nobody starts over. If your team uses Copilot today, this is the road you're already on.
What changes over time is how much of the work AI can take on. Preparing data. Detecting variances. Running scenarios. Drafting recommendations. The repeatable production that consumes so much of a finance team's week shifts to AI, which brings the work back ready for review. Finance moves from doing the grunt work to orchestrating decisions.
I said this on stage at Excelerate in May and I believe it more today: the next era won't belong to the companies with the most insight. It will belong to the ones that turn it into action across the business. Context that compounds is how AI earns a real role in that, one cycle at a time.
Finance directs the outcome; AI does more of the work. The work shifts; the accountability never does.