Every investment committee is now asking about AI
Almost every investment committee now asks some version of the same question about a target: how exposed is this business to AI? Two years ago it came up mostly on software deals. Today it comes up on franchise platforms, consumer brands, wealth managers and compliance vendors alike.
The concern is well founded. AI has cut the cost of work that used to be billable, from first-draft design to document review, and it is changing how buyers find and choose suppliers. Private credit managers spent much of this year worrying about their software loan books, enough that the worry picked up its own nickname, the SaaS-pocalypse. Sponsors holding assets bought on pre-AI multiples want to know whether those multiples will hold at exit.
What hasn't kept pace is the way the question gets answered.
Why the one-slide answer doesn't hold up
In most diligence packs the AI section is a single slide. It carries a rating of Low, Moderate or High, a few lines on the market and a list of the company's AI initiatives. The rating may well be right. The trouble is that nobody can tell how it was reached, and it becomes hard to defend the moment a partner starts asking questions.
The biggest weakness is blending. One rating is asked to carry two different things: how much pressure the market is putting on the business, and how well the company can respond to it. A company under heavy pressure that is adapting quickly and a company under light pressure that is doing nothing can both come out Moderate. They are very different investments.
Ambition also gets counted as capability. Roadmaps, board decks and strategy papers describe what management intends to do with AI, and they often end up read as evidence of readiness. For valuation, what counts is what is live in production today.
Then there is vagueness about the threat itself. Saying AI could disrupt the category gives an investment committee nothing to test. A useful assessment names the tool, competitor or platform doing the disrupting and shows where that evidence came from.
Three ways AI takes value from a business
Across the companies we analyse, AI threats tend to come from one of three places, and each one needs a different kind of evidence.
The most visible is AI that does what the company sells. Canva's AI lets anyone design a poster in seconds, work that used to be paid for by the hour from a designer or an agency. When the output a customer pays for can be generated, the business either gets cheaper to replace or stops being needed at all, and the analysis has to tell which of the two is happening.
The second is competitors building AI into their own products and workflows. Nothing about the category disappears here. A rival software provider automates a task the target still does manually, delivers it faster at the same price, and wins customers one renewal at a time. This threat rarely shows up in a market sizing exercise, because the market is still there. The share is what moves.
The third is AI positioning itself between the company and its customers, and it is often the biggest risk we find. When a shopping assistant decides which brands a buyer sees, or an AI agent picks the supplier, the company can lose the customer relationship without losing a single sale to a direct competitor. Discovery moves to a platform the company doesn't control, and so does the data about what customers want. A business can sell something AI cannot replicate and still be in real trouble here.
How Binocs AI Disruption Analysis works
We built AI Disruption Analysis to give deal teams an answer to the AI question that they can stand behind in an IC. It runs on top of a commercial due diligence report generated in Binocs, adds structured public research on the company's market and competitors, and hands that same evidence to three specialist agents working in parallel.
The External Disruption agent analyses whether AI can disrupt the company's current market position and business model, across all three of the threats above. The Internal Readiness agent looks inward. It checks whether the company is adopting AI in its own workflows and tasks, and whether that adoption is properly managed and monitored once it goes live. Each of these two agents scores seven parameters from 1 to 5, so each total comes out of 35. A higher external score means more disruption, while a higher internal score means more readiness.
The third agent runs an AI and technology review, grading ten tracks from D to A+, including information security, vendor risk, data privacy, architecture and AI governance. We added it because some of the most serious risks we find turn out to be basic technology gaps. One wealth management target we reviewed graded C overall, with D grades on information security and vendor risk, and those findings mattered far more to the deal than anything in its AI plans.
Three evidence rules sit underneath all of it:
- Only capability confirmed as live gets scored. Roadmaps and strategy documents are recorded as ambition and kept out of the score.
- Every disruption score points to a named AI tool, product or competitor, with a source behind it, either a page reference in the CDD report or a public link.
- Anything that can't be confirmed is flagged as unconfirmed and turned into a specific question for management, rather than treated as a finding.
The output is a set of scorecards with a written rationale for every parameter, plus a management questionnaire built from the gaps.
Reading the two scores together
The real value appears when the external and internal scores are read side by side. The report crosses them and places the company in one of four positions, with 21 out of 35 marking a high score on either axis.
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Each axis splits at 21 out of 35, and the quadrants are drawn equal rather than to scale. The consumer wellness brand sat one point short of Exposed, where the compliance software target described below already was.
A company with high disruption and low readiness is Exposed. High disruption with strong readiness means it needs to Defend and adapt. Low disruption with strong readiness leaves it Positioned to capture value from AI, and low pressure on both sides puts it in Insulated,
low urgency. Each position comes with a one-line headline and a short explanation of what the two scores mean together, which is usually the first thing an investment committee wants to read.
The position matters most near the lines. In a recent analysis of a consumer wellness brand, the company landed in Insulated, low urgency. AI couldn't replace the product itself, but almost all of the brand's exposure sat in marketplace and retail channels, where AI shopping assistants were starting to stand between it and its buyers. It had no data or team of its own to respond. Its disruption score was one point below the high band, so the report flagged that a small rise in marketplace AI adoption would move it into Exposed, and put the related management questions at the top of the list.
Keeping the axes apart also changes which question a deal team ends up asking. On a multi-brand franchise platform, the holding company and its three operating brands all landed in the same moderate disruption band, at 19 or 20 out of 35. Their readiness ranged from 15 to 24. A blended rating would have called all four Moderate. Read together, the scores showed that the real question was which of the brands could respond in time.
What it looked like on a live deal
On a recent compliance software deal, a single 274-page Binocs CDD report and public research produced all three scorecards. The company had a real AI product, which on a one-slide view would have read as a strength.
External disruption still came out High, at 23 out of 35. AI-native competitors had agents in production that could monitor controls and collect evidence automatically. Workflow platforms were adding AI agents that could make the company's product less central to its customers' processes. And its own platform vendor was offering a free tool that covered part of what customers were paying for.
Internal readiness came out at 18 out of 35. The AI product was live, but ML operations, the AI team and internal use of AI across functions were all early. The technology review gave the company a B overall, with data privacy and security as its weakest areas. Read together, that put the business in Exposed, a position the AI product would have masked in a blended rating.
The analysis also generated 130 management questions, each linked to a specific scored gap. The deal team went into management meetings knowing exactly what they needed to test.
What this changes for deal teams
For a PE deal team, the output is an AI section in the IC memo that is backed by evidence, a focused agenda for management meetings, and a consistent way to compare AI exposure across deals and across the portfolio. CDD advisors get a structured AI module that sits alongside the commercial work they already deliver. Corporate development teams can apply the same view to acquisition targets, and to their own business when the board starts asking.
It's worth being clear about what this is. AI Disruption Analysis is a first-pass view built from the documents a deal team already has and from public research, and it is designed to sharpen judgement rather than replace it. Some scores will move once management answers the questions. That is how it is meant to work: send the questionnaire, collect the answers and re-run the analysis on what comes back.
AI will keep reshaping which businesses are worth owning and at what price. The firms that price that risk well will be the ones that can show their working when the committee pushes back.
If AI risk is coming up on a deal you're working on, we'd be glad to show you what the full output looks like.
Frequently asked questions
What is AI disruption risk in due diligence?
AI disruption risk is the likelihood that AI erodes a target company's market position, pricing or customer relationships during the investment hold period. In due diligence, it is assessed alongside the company's own AI readiness, so an investment committee can see both how much pressure the business faces and whether management can respond to it.
How do you assess a company's exposure to AI disruption?
A robust assessment scores two things separately. The first is external disruption: whether AI tools, AI-enabled competitors or AI platforms can replace, undercut or bypass what the company sells. The second is internal readiness: whether the company is using AI in live products and workflows, with the data, team and monitoring to support it. Every score should cite named competitors and verifiable sources.
What are the main ways AI disrupts a business model?
AI disrupts a business in three main ways. AI tools can do what the company sells, as Canva's AI does for basic design work. Competitors can build AI into their products and win share at the same price. And AI assistants or agents can sit between the company and its customers, deciding which brands or suppliers a buyer sees.
What is AI disintermediation and why does it matter for valuation?
AI disintermediation happens when an AI shopping assistant, search engine or buying agent takes over how customers discover and choose suppliers. The company can lose the customer relationship, and the data that comes with it, without losing a sale to a direct competitor. It matters for valuation because it weakens pricing power and customer retention even when the product itself cannot be replicated by AI.
What is the difference between AI disruption risk and AI readiness?
AI disruption risk measures pressure from outside the company, from AI substitutes, AI-enabled rivals and AI platforms. AI readiness measures what the company can do with AI today, based on live tools, data quality, ML operations, architecture and the team that owns AI. Scoring them separately avoids a blended rating that makes a well-prepared company under pressure look the same as an unprepared one under little pressure.
What are the four AI disruption positions?
Binocs AI Disruption Analysis crosses the external disruption score and the internal readiness score, each out of 35, to place a company in one of four positions. High disruption and low readiness is Exposed. High disruption and strong readiness is Defend and adapt. Low disruption and strong readiness is Positioned to capture value. Low scores on both axes is Insulated, low urgency. A score of 21 or more counts as high.
How do you verify a target company's AI capabilities during diligence?
Only AI capability that is confirmed as live in production should count toward readiness. Roadmaps, board decks and strategy documents describe intent, so they are recorded as ambition rather than scored. Anything that cannot be confirmed from the CDD report or public evidence becomes a specific question for management, which gives the deal team a focused agenda for management meetings.
What is Binocs AI Disruption Analysis?
Binocs AI Disruption Analysis is a scored, fully cited assessment of a company's AI risk, built by Binocs, the agentic AI platform for commercial due diligence. It runs on a CDD report generated in Binocs plus structured public research, and uses three specialist agents: External Disruption, Internal Readiness, and an AI and technology review that grades ten tracks from D to A+.
What does an AI disruption analysis deliver to a deal team?
The output is a set of scorecards with a written rationale and a source for every parameter, a headline position combining the two main scores, a technology review, and a management questionnaire tied to each scored gap. On one compliance software deal, a 274-page Binocs CDD report produced all three scorecards and 130 management questions.
Which companies need an AI disruption assessment?
Any company whose value depends on work AI can now do, on pricing that AI-native rivals can undercut, or on customers who find it through search, marketplaces or AI assistants. That now includes far more than software. Binocs has run the analysis on compliance software, a multi-brand franchise platform, a consumer wellness brand and a wealth management firm.
Who uses AI disruption analysis?
Private equity deal teams use it for the AI section of an IC memo and to compare AI exposure across deals and portfolio companies. Commercial due diligence advisors use it as a structured module alongside their commercial work. Corporate development teams and investment banks apply it to acquisition targets, and private credit teams can use the same view when weighing how AI affects a borrower's business.
Is an AI disruption score final?
No. It is a first-pass view built from diligence documents and public research, designed to sharpen a deal team's judgement. Scores can move once management answers the questionnaire, so the recommended process is to send the questions, collect the answers and re-run the analysis on what comes back.
