There is a conversation happening in manufacturing M&A right now that most sellers aren’t part of, yet.
It doesn’t start in the data room. It starts before the letter of intent, in the quiet diligence work sophisticated buyers do before they ever pick up the phone. The question they’re asking isn’t just “Is this business profitable?” It’s “Is this business legible?”
“Is this business legible?” – the question sophisticated acquirers are asking before the LOI is signed.
By legible, I mean something specific: Can the business articulate its own operations in a way a new owner can understand, validate, and build on? Or is the institutional knowledge locked in three people’s heads? Are the sales processes documentable, or are they personality-dependent rituals that collapse the moment a key rep walks out? Does leadership have real-time market intelligence, or are they making decisions on data that’s 60 days old?
These are not technology questions. They are operational maturity questions.
But here’s what’s changing: every one of them maps to a category of AI capability. And as AI tools have become accessible to mid-market manufacturers, the absence of them is increasingly read as a signal — not of technology lag, but of management depth and future margin potential.
AI readiness is not a technology upgrade. In manufacturing M&A, it is an operational maturity indicator, and it is beginning to move deal economics.
This article is for owners preparing for an exit, for PE partners evaluating manufacturing targets, and for corporate development teams building a framework for AI readiness in operational due diligence. What follows isn’t a technology checklist. It’s a practitioner’s translation of four questions sophisticated buyers are beginning to ask, and what the answers reveal about the companies behind them.
Why AI Readiness Is Now an M&A Signal
The numbers have moved fast.
According to McKinsey’s 2025 State of AI report, 88% of organizations now use AI in at least one core business function, up from 78% just one year earlier. BCG’s 2025 Widening AI Value Gap report finds that 70% of AI’s potential value is concentrated in core business functions: sales, manufacturing, supply chain, and pricing.
These aren’t edge functions. They’re the operational core of every manufacturing business that will be bought or sold in the next five years.
88% of organizations now use AI in at least one core function – McKinsey 2025
Against that backdrop, the manufacturing company that has done nothing is no longer simply behind the technology curve. It’s beginning to look like a company with an operations problem.
What sophisticated buyers, both strategic acquirers and PE firms with manufacturing portfolios, have discovered is that AI readiness serves as a reliable proxy for three things that directly affect deal economics:
- How transferable the business is
- How scalable is it under new ownership
- How much post-close integration risk does it carry
FTI Consulting’s AI Radar for Private Equity 2024 survey found that 59% of PE funds now cite AI as a key driver of portfolio value creation — a figure that has overtaken traditional factors like historical growth rates and customer retention.
The implication for sellers is not that they need a cutting-edge AI stack. They need to demonstrate operational infrastructure that is documented, repeatable, and system-supported. The AI tools are the mechanism. The outcome they signal, a business that can run and grow without being held together by three critical individuals, is the actual valuation driver.
In one documented example cited by EisnerAmper advisors, a regional distribution company that implemented AI-driven demand forecasting improved inventory turnover by 15% and watched its valuation multiple move from ~7x to ~9x EBITDA. The technology was incidental. The operational improvement was real. (Source: EisnerAmper: How AI Is Shaping the Valuation of Private Companies)
The inverse also holds. Key-person dependency remains one of the most common risk factors uncovered during M&A diligence in manufacturing,and it carries a valuation discount of 10–25% depending on severity, according to analysis from William Buck. The discovery of a critical dependency during diligence can derail a funding round, force an earnout where a clean close was expected, or simply hand the buyer a negotiating lever that shouldn’t exist.
Documentation and system support, whether AI-enabled or not, are the mitigation. AI just happens to be the most efficient way to get there.
Four Questions Buyers Are Beginning to Ask
What follows is not speculative. These are the operational diligence questions that sophisticated acquirers — and their operating partners, advisors, and lenders — are incorporating into manufacturing due diligence right now.
Each question is paired with the AI capability it maps to, and the risk signal it sends when the answer is “we’ve been meaning to get to that.”
DUE DILIGENCE QUESTION 1: Is your institutional knowledge in systems or in people?
AI Capability: Knowledge Capture & Documentation
When your experienced sales director, plant manager, or senior estimator walks out the door, what do they take with them?
In most mid-market manufacturers, the answer is: everything that matters. Pricing logic, customer relationship history, vendor exception terms, engineering workarounds, specification knowledge — this is the operational DNA of the business. In companies with high key-person dependency, it lives exclusively in individual memory. Buyers understand they cannot acquire a key person. They can only try to retain them. And retention is never guaranteed.
AI-powered knowledge capture systems change this. Conversation intelligence tools, structured CRM protocols, and AI-assisted documentation workflows convert tacit knowledge into organizational assets. A company that has made this transition can walk a buyer through a searchable history of every significant customer interaction, every specification won or lost, every pricing decision and its rationale.
That is a materially different business than one where the answer to “how do you price custom orders?” is “call Mike.”
During diligence, buyers probe this directly. They ask for sales process documentation, ask who owns key accounts, and run a mental simulation of what happens if the top two or three people leave within 90 days of close. Companies with knowledge infrastructure answer these questions with systems. Companies without them answer with earnout negotiations.
DUE DILIGENCE QUESTION 2: Are your sales processes documented and repeatable or relationship-dependent?
AI Capability: Process Automation & Sales Intelligence
Specification-grade manufacturing businesses live and die on relationships. That isn’t going to change. But there’s a meaningful difference between a business where relationships are the strategy and one where relationships are the only strategy. The former is a competitive advantage. The latter is a succession problem.
What buyers want to see is that the process behind the relationship is documented. How are prospects identified? What does a qualified opportunity look like? What’s the follow-up cadence from specification to order? How are rep agencies being managed against defined performance metrics?
These questions are answerable, but only if someone has thought to capture the answers in a system rather than a habit.
In the specification-grade lighting sector, where rep relationships and project pipeline visibility are among the most valuable assets a manufacturer holds, this maps directly to how acquirers assess channel infrastructure. A business that can produce a documented rep management protocol, a CRM-tracked specification pipeline, and a defined follow-up cadence answers with evidence. A business that can’t is asking the buyer to take the founder’s word for it.
Sophisticated buyers no longer accept that as sufficient.
DUE DILIGENCE QUESTION 3: Does leadership have real-time market intelligence — or are they working on lagging indicators?
AI Capability: Signal Filtering & Market Intelligence
One of the most consistent findings in manufacturing operational due diligence is the gap between the market data companies believe they have and what they actually have.
Monthly reports compiled from disconnected sources. Annual rep reviews reflecting the prior year. Competitive intelligence gathered through conference conversations. This is how most mid-market manufacturers operate. It’s not irrational; it reflects the historical cost of building real-time intelligence systems.
AI has eliminated most of those barriers. Specification tracking tools, competitive positioning alerts, distributor sell-through analytics, and pricing signal monitoring are accessible to companies at every scale. What was a competitive advantage for large manufacturers with dedicated intelligence teams is now accessible infrastructure for any business positioning for a premium exit.
From a buyer’s perspective, this matters for two reasons. First, a seller with real-time market intelligence can articulate their competitive position with data rather than instinct, a materially more credible management presentation. Second, a business making decisions on current signals is structurally better positioned for the critical first 90 to 180 days post-close.
When buyers ask, “How do you track competitive pricing?” and the answer is ‘We watch what comes through our reps,’ they hear risk. When the answer includes a system and a dashboard, they hear infrastructure. Those two answers affect both the offer and the structure.
DUE DILIGENCE QUESTION 4: Can you demonstrate margin performance at the product and channel level — in real time?
AI Capability: Operational Analytics & Margin Visibility
Gross margin by channel, product line, customer segment, and geography. This is the financial granularity acquirers need to model post-close value creation.
In many mid-market manufacturers, this data exists. It just lives in a spreadsheet that one person updates quarterly, pulling from three systems that don’t talk to each other. The data is real. But it’s not a business intelligence capability. It’s a reporting habit.
AI-enabled operational analytics change what a company can show during diligence. Real-time margin visibility by product, automated variance flagging, and integrated P&L modeling are no longer the province of enterprise manufacturers with seven-figure ERP implementations. They’re available at every scale, and their presence throughout the sales process tells a clear story: this management team knows where the money is.
When a company can’t produce margin by channel without a three-week project, buyers reasonably ask what other operational visibility gaps exist. And that question carries a cost; it shows up in the offer.
The Pre-Transaction Window
The companies positioned best for premium exits in the next 24 to 36 months are not the ones that will scramble to deploy AI tools in the 90 days before going to market. They are the ones deploying now, not because they have an exit on the horizon, but because running a more legible, more operationally documented, more intelligence-driven business is how you build durable competitive advantage in a consolidating industry.
The practical reality is that AI readiness is not binary. It does not require a complete technology overhaul. It requires a deliberate decision to move institutional knowledge into systems, document what has historically been tribal, and connect existing business data to dashboards that leadership can actually use.
The 90-day window before a transaction is too late to build that infrastructure. The 24 months before going to market is exactly the right time to build it.
At Marlow Advisory Group, we work with manufacturing companies, both those preparing for a transaction and those not yet thinking about one, to assess and build operational infrastructure that positions them for premium outcomes. That work includes evaluating AI readiness not as a technology audit but as an operational maturity assessment:
- Where is institutional knowledge living?
- What sales process documentation exists, and what is personality-dependent?
- How current and granular is leadership’s view of margin and market position?
We also work with acquirers, conducting operational due diligence on manufacturing targets and applying the same framework used on the buy side. The companies that hold up best in that process are not the ones with the newest technology. They’re the ones where the answers to these four questions are in a system, not in an individual.
CLOSING PERSPECTIVE
What This Means Right Now
For manufacturing owners who are five years or fewer from a potential exit, the due diligence environment is shifting faster than most advisors are telling you.
The financial review will be rigorous. The legal review will be thorough. But the operational review, the assessment of how your business actually runs, whether it can run without you, and whether it can grow under new ownership, is increasingly informed by the AI readiness signals that sophisticated buyers know how to read.
You don’t need to be an AI-forward company. You need to be a company that can demonstrate operational legibility: documented processes, captured institutional knowledge, real-time market and margin intelligence, and a sales infrastructure that is system-supported rather than personality-dependent.
AI readiness is not a technology audit. It is an operational maturity assessment, and in the current M&A environment, it is one of the clearest leading indicators of whether a manufacturing business is built to be transferred or built to depend on its founder.
For private equity partners and corporate development leaders: the framework here is not speculative. It’s the translation of established M&A risk factors, key-person dependency, information asymmetry, and operational opacity into the current technology environment. The due diligence questions are the same ones that have always mattered. AI readiness just gives you a faster, more reliable signal on the answers.
The blind spot in manufacturing M&A right now is not that buyers don’t understand AI. It’s that sellers don’t yet understand what their AI readiness,or the absence of it, is communicating to the people across the table.
The best time to fix that is before anyone is sitting at that table.
