¶By now, every company you know is using AI. Not in the press-release sense, in the actual sense. Slack rolled AI features into every paid plan. Notion bundled AI into Business and Enterprise. Microsoft sells Copilot inside Word, Excel, and Outlook. Roughly three in four knowledge workers report using a generative AI tool at work, with frequent use concentrated among leaders and a few high-leverage roles.
¶The official version of this story is that AI is transforming the enterprise. The version your operators tell you on Tuesday is that the meetings still go badly, the deals still slip, and the Thursday board prep still happens at 11 p.m. on Wednesday by a chief of staff manually pulling threads from four systems. Every company is using AI. Almost no company is running on it.
Every company is using AI. Almost no company is running on it.
¶The instinct, when you notice that gap, is to blame the model. Maybe Claude is wrong for this. Maybe we should try Gemini. Maybe the next version will be better, the next version is always going to be better. That instinct is the whole reason the gap stays open. The model is not the problem. The problem is that none of these AI tools, deployed across every system in your company, has any idea what your company is.
¶Each of the major vendors has spent the last year wiring connectors into the others. Slack AI now reaches Gmail and Drive. Notion AI reads Slack and Drive. Microsoft Copilot indexes Salesforce, ServiceNow, and a hundred other sources. The connectors are real. The front door is still the vendor's. Each one is, in its own narrow lane, capable. Stack them together across a company and the result is an intelligence layer that knows everything except what you actually do for a living. The customer call from Tuesday. The deal that slipped because of a budget cycle nobody flagged. The Slack thread where two account executives argued about pulling forward an enterprise contract. The decision the CEO made on a flight that nobody wrote down. None of those events lives in a single system. Most of them do not live, in any retrievable form, in any system at all.
¶This is the gap that matters. Your company already generates institutional intelligence every day. It is in meetings, threads, decisions, customer calls, the comments on a doc nobody reopens, the Slack DM where a deal actually got worked out. The volume is enormous and growing. It also has, with vanishing exceptions, nowhere to go. It accumulates in the seams between systems and in people's heads, and it stays there until somebody leaves and takes it with them. Your company knows things it does not know it knows, because the knowing is distributed across forty channels and three hundred people and no system above any of them is looking.
¶McKinsey's AI Transformation Manifesto, published in April, names this in the language of data infrastructure. "Scaling AI starts by productizing data, making it easy to discover, access, and consume across many AI-powered applications. That requires investments in building data products. Over time, the game shifts to data enrichment, deepening its quality, context, and uniqueness for sustained performance gains with AI" (McKinsey QuantumBlack, April 2026). Translate the consultant prose. The companies that win at AI are the ones that turn what they already know into something an AI can use. The ones that lose are the ones that bolt models onto a stack that has no memory above the application layer.
¶MIT's Project NANDA reported last year that 95% of enterprise AI pilots fail to deliver measurable P&L impact. The instinct is to read that as a model failure. It is not. Pilots fail because the model arrives at the table without the context the work actually requires. Sales pilots fail because the agent does not know which accounts matter and why. Customer-success pilots fail because the agent has read every help doc and none of the eighteen Slack threads where the actual policy got decided. The model is doing exactly what a model can do with no institutional context. Which is, on the things that matter most, very little.
¶The phrase to hold here is the inverse of the one every AI vendor is selling. They are selling general intelligence. What companies actually need is institutional intelligence. The two are different categories of asset. General intelligence is rented from a foundation model and resets every time the model resets. Institutional intelligence is what your company has accumulated, decision by decision, customer by customer, quarter by quarter. It compounds. It belongs to you. It is also, today, almost entirely unretrievable by any system that could act on it.
What companies actually need is institutional intelligence.
¶Closing that gap is not a model problem and not a prompt problem. It is an infrastructure problem. The piece of infrastructure missing from almost every company is a layer that reads across the systems work actually happens in, holds the institutional context in a form that survives the next model release, and surfaces it back into the work at the moment it matters. That layer has a name worth giving it. Memory as a service.
¶Maasv is that substrate. It is dedicated per organization, deployed inside your boundary, on your infrastructure, under your keys. It connects to the systems your team already runs on, ingests at write-time, classifies what matters, and surfaces briefings, alerts, and patterns to the people responsible for noticing. It is not a chatbot. It is not an integration platform. It is not another search box bolted onto your stack. It is the layer underneath the layer, doing the work no single-system AI can do because the shape of the problem is between the systems, not inside any of them.
¶Your company knows things it does not know it knows. The work of the next decade in enterprise software is building the substrate that lets your company act on what it knows. Memory is table stakes. Cognition is the moat. The companies that get this right will look, five years from now, like they are running on a different operating system than everyone else. Because they will be.