AI gives answers. Your company needs the right ones.

In the mid-nineties, Lew Platt, then CEO of Hewlett-Packard, compressed the problem into one sentence: “If HP knew what HP knows, we would be three times as profitable” (as quoted in Davenport/Prusak, Working Knowledge, 1998, p. xii). Three decades later the sentence has not aged—it has changed direction. Answers have become cheap, now that any company can rent a capable language model. What has become scarce is exactly what Platt meant: the knowledge only your own house has.

Hence my thesis. Knowledge management, long a topic for corporate staff functions with long horizons, is becoming an infrastructure question for every company that earns its money with contracts. Not although AI exists, but because it does.

What searching costs

The finding has been stable for twenty years. The McKinsey Global Institute put it at roughly a fifth of the workweek in 2012: time knowledge workers spend searching for internal information or tracking down the colleagues who have it. A YouGov survey commissioned by Panopto (2018, n = 1,001) arrived at 5.3 hours per employee per week lost to waiting for knowledge or reconstructing it—extrapolated to 47 million dollars of lost productivity per year for a large US business. And the most recent large survey by a KM vendor (Bloomfire, 2025, over 10,000 respondents across 115 companies) measures 8.5 search hours per week without structured knowledge management against 4.6 with it: almost half the searching gone, arithmetically the capacity of 98 full-time employees per thousand staff.

For the record, because source hygiene is part of the craft: two of the three studies come from vendors selling software for precisely this problem. I cite them as what they are—vendor studies. What remains remarkable is that independent and interested sources have pointed in the same direction for two decades, and that nobody measures the opposite.

For contract work, the finding has a particular sting. The legal department answers the same question about a penalty clause three times a quarter, because the answer sits in three inboxes. Procurement renegotiates a liability clause for which an agreed position existed long ago—nobody can find it. And when the contract manager leaves, her playbook leaves with her, because it was never written down anywhere. How wide the gap is at the level of fundamentals was recently shown by an empirical study among employees of commercial enterprises (Gran, IHR 3/2025, 85): on core questions from the duty to give notice of defects to Incoterms®, the majority answered incorrectly. I discussed the study here; training closes the gap in people’s heads. This piece is about the second half of the task: the knowledge that has to stay in the company when the heads change.

Answers become a commodity, context does not

What changed in 2025 and 2026 is not that companies suddenly document better. What changed is who reads the documents, and what happens without them.

Microsoft’s CEO Satya Nadella—of all people, the largest landlord of AI models—classified the models themselves as interchangeable on his India tour in December 2025: there are “lots and lots of capable models”, models alone are “not sufficient”, and the strategic value sits in the data and experience layer above them (report). When someone who sells models tells you the model is not the point, you may believe him on this one. The capital side puts it even more directly. Insight Partners, one of the large growth investors, calls access to specific, often messy company data “one of the strongest moats in AI”. And from the operator’s seat it sounds like this:

In parallel, the audience is changing. GitBook measures on its own platform an increase of over 500 percent in AI read access to documentation in 2025; in the accompanying State of Docs Report, a quarter of the documentation professionals surveyed expect documentation to be written primarily for machines in the future. Both figures come from a platform vendor; the direction is unambiguous all the same: machines are the fastest-growing readership.

For contract knowledge, that means something concrete: the playbook that helps your procurement team today is tomorrow’s context that keeps your internal AI assistant from inventing a liability clause. Technically the pattern is called retrieval augmented generation—the model answers not from memory but from your documents. A language model without that foundation answers anyway: fluently, politely, and wrongly. With it, the same question returns a correct answer with a source.

That inverts the calculation. As long as answers were expensive, you could afford unstructured knowledge; the bottleneck was the expert anyway. Now the bottleneck is the material. Whoever does not have their contract knowledge in structured form cannot use the cheapest resource of the decade.

Why the knowledge graveyard is no accident

The obvious objection: we have an intranet for that. You do, and that is exactly why you know how this story ends. After two years the repository is an archive nobody trusts. That is not your team’s fault. Generic repositories fail at two points, always the same ones.

First, structure. A page that wants to explain, instruct, and serve as a lookup at the same time does none of the three. The software industry has had an ordering principle for this problem for years, Diátaxis: every page answers exactly one kind of question. Transferred to contract knowledge, it looks like this:

This separation is not a matter of taste. It decides whether the sales manager under time pressure finds the how-to instead of working through a treatise, and whether the machine finds an unambiguous, citable source instead of a wall of mixed prose.

Second, ownership. Content without a professionally responsible curator goes stale, and stale contract knowledge is more dangerous than none, because somebody trusts it. A platform loses its capital the moment the first wrong page surfaces. Maintenance is therefore not an ancillary service. Maintenance is the product: case law, reforms, and new clauses have to be worked in, quarterly and event-driven, by someone who commands the field and stands behind the content.

What “machine-readable” means in practice

To keep the magic honest: machine-readable is not a property you tick afterwards. It emerges from the same decisions that make knowledge usable for people. One page per question, so every answer has an address. Structured text instead of a PowerPoint archive and a PDF graveyard, so search and language model find the same source. Citations on every statement, so the answer stays verifiable—for the human and for the machine. Versioning, so it is traceable what applied when; for contracts not a formality but the question of which version governed at signing. And access rights that think along, because not every playbook belongs in every chat.

That is why I build knowledge platforms as versioned, searchable text systems and not as slide collections on a shared drive. Structured text is the only format that serves both readers equally well: the buyer on Monday morning and the AI assistant he asks next year.

From answer-giver to architect

For my profession this means, frankly: a change of role. When the individual answer becomes a commodity, the value of legal work shifts to where the answers come from—into the maintained knowledge base from which your team draws correct answers today and your systems draw them tomorrow. The lawyer who only sells answers will be competing with a flat rate. The lawyer who structures, curates, and keeps current his client’s contract knowledge builds infrastructure that works between mandates.

What belongs inside is no secret: playbooks for the most frequent negotiation situations, a clause library with agreed positions, how-to guides for processes with deadlines and form requirements, background pages for everyone who wants to understand. Plus operations that fit the subject matter: European infrastructure instead of a shared US cloud, data processing under Article 28 GDPR, German and English, readable for people and machines.

The platform does not stand alone. It is the middle of three stages: advice generates the knowledge, the platform preserves it, training puts it into people’s heads. Every mandate sharpens the playbooks; every training session shows which page is still missing. Knowledge that is only produced is an expense. Knowledge that is preserved and trained is an asset.

When a platform pays off, and when it does not

Plain speaking is part of the job: not every company needs this. If you conclude a handful of uniform contracts a year, a good template package and a short line to your lawyer serve you better. A knowledge platform pays off when several factors come together: multiple teams working with contracts without being lawyers. Recurring questions to the legal department or outside counsel. Noticeable turnover in key roles. International contracts where the knowledge gap gets expensive. And the plan to deploy internal AI assistants seriously, instead of letting them hallucinate on an empty intranet.

If three of these apply, the question is no longer whether but where to start. The answer is almost always the same: with the three topics where not knowing cost the most last year.

Platt presumably meant the tripled profit rhetorically. The arithmetic behind it holds all the same: the distance between what a company knows and what it uses is one of the most expensive gaps in the organization. What is new is that this gap can be closed with less effort than ever, and that for the first time machines stand on the other side, translating structured knowledge directly into productivity. The companies that start now gain a quiet head start: their knowledge works while everyone else searches.

Häufige Fragen

What is legal knowledge management?

A maintained, searchable system that makes a company’s contract knowledge available in structured form: playbooks, clause libraries, how-to guides, and background pages, written for the teams that work with contracts every day, not for lawyers.

Does an AI assistant replace the knowledge platform?

No, it presupposes one. A language model answers every question, including wrongly. It only becomes reliable when it draws on curated, current company knowledge. The platform is exactly that foundation, readable for people and machines.

Where do you start?

With discovery: the contract landscape, the recurring questions, the three topics where not knowing caused the most damage. The result is a prioritized plan. A focused start with one area of law and one team is live within a few months.

Reference: Poleacov, P. (2026). AI gives answers. Your company needs the right ones.. INN.LAW. https://inn.law/en/perspectives/knowledge-management-in-the-age-of-ai/

---