Why boring Industrial Assets Are PE's Best AI Opportunity
Mar 24, 2026
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Key Takeaways ▶ Highest AI ROI isn’t in tech-forward companies - it’s in industrial businesses with the most operational headroom ▶ Industrial businesses can see 20-40% improvements vs 5% in already-optimised tech companies ▶ 40%+ of team time in industrial businesses consumed by manual processes - massive automation potential ▶ Firms like OpenGate Capital and American Industrial Partners are already building dedicated industrial AI programmes |
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20-40% improvement potential in industrial vs 5% in tech |
40%+ of team time consumed by manual processes in industrial businesses |
€45m at-risk revenue identified in 6 weeks at one industrial distributor |
There’s a bias in how PE firms think about AI deployment. The assumption is that AI works best in data-rich, tech-forward portfolio companies - the ones with modern systems, structured data, and teams that already speak the language of analytics.
The assumption is wrong.
The highest AI returns in private equity come from the businesses that look least ready for it. Manufacturing. Distribution. Logistics. Facilities management. The “boring” industrial assets that most AI vendors ignore - and most operating partners deprioritise.
This matters because Bain’s 2026 GP Outlook found that 39% of PE firms report no material AI impact on portfolio performance, and 79% face flat or declining multiples at exit (McKinsey Global Private Markets 2026, 11.8x average). The firms failing at AI are deploying it in the wrong places. The firms succeeding are deploying it where the operational headroom is widest.
The headroom argument
AI ROI is a function of headroom, not sophistication. A tech company that already has clean data pipelines, modern analytics, and optimised workflows might gain a 5% improvement from AI. The marginal gain is real but incremental. The systems are already reasonably efficient.
Now consider a mid-market industrial business. Data trapped across ERPs, spreadsheets, and the heads of experienced operators. Manual processes that consume 40%+ of team time. Pricing logic that exists only in tribal knowledge. Customer intelligence scattered across individual relationships rather than centralised systems.
That business can see 20-40% improvements from targeted AI deployment. Not because the AI is more sophisticated. Because the headroom is wider.
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“The gap between current performance and achievable performance is where AI creates value. In industrial businesses, that gap is enormous.” |
BCG’s 2026 research reinforces this: PE-backed companies with systematic AI in operational roles generate 30-35% ROI when the AI layer targets specific business outcomes. The key word is “operational.” Generic AI initiatives produce generic results. Outcome-led AI deployment in high-headroom businesses produces measurable EBITDA impact.
The contrarian thesis in practice
Some of the most sophisticated PE firms have already figured this out. They’re not deploying AI in their tech portfolio companies first. They’re deploying it in the industrial ones.
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OpenGate Capital - OGx Platform Built a proprietary operating platform specifically designed to deploy “big tech” tools in “small industrial” turnarounds. The thesis is explicit: the highest returns come from applying modern technology to businesses that have never had access to it. |
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American Industrial Partners - Industry 4.0 Programme Runs a dedicated programme across its manufacturing and industrial portfolio. Systematic deployment of operational intelligence, automation, and data infrastructure in businesses where these capabilities create step-change improvements rather than marginal gains. |
Both firms reached the same conclusion independently: the “boring” industrial portfolio company is where AI creates the most value. Not because these businesses are simple, but because the gap between current operations and what’s achievable is so wide.
What operational AI looks like in industrial businesses
This isn’t theoretical. Operational AI in industrial businesses takes concrete forms that directly impact the P&L:
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1. Order book intelligence Industrial distributors and manufacturers sit on years of transactional data that nobody analyses systematically. AI can process thousands of customer accounts simultaneously - identifying declining purchase patterns, at-risk relationships, and untapped cross-sell opportunities that no human team could surface at that scale. |
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2. Tribal knowledge capture In every industrial business, critical knowledge lives in the heads of experienced operators - pricing rules, customer quirks, process workarounds, supplier relationships built over decades. When those people leave the business, the knowledge goes with them. AI systems can capture, structure, and make this tribal knowledge available across the entire organisation, turning individual expertise into institutional capability. |
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3. Predictive maintenance Equipment data in manufacturing environments is typically abundant but underused. AI analyses vibration patterns, temperature readings, and operational logs to predict failures before they happen - reducing unplanned downtime, extending asset life, and protecting production capacity. For PE-owned manufacturers, this directly impacts both revenue and margin. |
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4. Quote automation In industrial manufacturing, producing a customer quote can take 3-5 days - involving technical configuration, pricing calculation, and multiple handoffs between engineering and sales. AI-driven quote automation achieves 80% automation of this process, reducing turnaround from days to minutes. A global manufacturer deployed this capability and saw immediate impact on win rates and sales team capacity. |
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Case in Point We deployed Order Book Intelligence for a European industrial distributor. Six weeks from start to production system. EUR 45m in at-risk revenue identified. The sales team uses it daily. Not a pilot. Not a proof of concept. A working system that moved straight into daily operations - in a business that most AI vendors would have called “not ready” for AI. |
Why this matters for operating partners
The implication for PE operating teams is straightforward: stop prioritising AI deployment in the portfolio companies that seem most ready for it, and start prioritising the ones with the most operational headroom.
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39% of PE firms report no material AI impact on portfolio performance. The majority are deploying AI in the wrong places - starting with tech-forward businesses where headroom is smallest. Source: Bain GP Outlook 2026 |
The “boring” industrial portfolio company - the distributor running SAP on-premise, the manufacturer with pricing in spreadsheets, the logistics business where route planning happens in someone’s head - that’s where AI delivers the biggest returns. Not because the technology is different. Because the headroom is enormous.
McKinsey’s 2026 Global Private Markets Review shows average PE multiples at 11.8x - which means value creation at exit depends almost entirely on operational improvement, not multiple expansion. The firms that direct their AI investment toward high-headroom industrial assets will generate the measurable EBITDA uplift that drives DPI. The firms that chase AI deployments in already-optimised businesses will join the 39% reporting no material impact.
The bottom line
The biggest AI returns in PE come from the businesses with the most operational headroom - not the most technical sophistication. Industrial assets have legacy systems, manual processes consuming 40%+ of team time, and critical knowledge trapped in the heads of experienced operators. That’s not a weakness. It’s the opportunity.
OpenGate Capital and American Industrial Partners have built explicit strategies around this thesis. BCG’s research shows 30-35% ROI when AI targets specific operational outcomes. And the maths is simple: a 20-40% improvement in a “boring” industrial business produces more EBITDA impact than a 5% improvement in a tech portfolio company.
The question for every operating partner: are you deploying AI where the headroom is widest, or where the business looks most “ready”? The answer determines whether AI becomes an EBITDA driver or an expense line.
Sources & References
| McKinsey & Company: Global Private Markets Review 2026 - 11.8x average PE multiples, operational improvement as primary value driver |
| BCG: PE’s Future: The AI-First Firm 2026 - 30-35% ROI with AI layer targeting operational outcomes |
| Bain & Company: GP Outlook 2026 - 39% of PE firms report no material AI impact, 79% face flat multiples |
| OpenGate Capital: OGx Platform - “Big tech” tools for “small industrial” turnarounds |
| American Industrial Partners: Industry 4.0 Programme - Dedicated industrial AI deployment across manufacturing portfolio |
Frequently Asked Questions
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Why are industrial assets the best AI opportunity in PE? Industrial assets - manufacturing, distribution, logistics, facilities management - have the most operational headroom of any portfolio company type. They run on legacy systems, data trapped in ERPs and spreadsheets, and manual processes that consume 40%+ of team time. This means AI deployments can deliver 20-40% improvements, compared to 5% in already-optimised tech businesses. The biggest AI returns come from the widest gap between current and achievable performance. |
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What is operational headroom? Operational headroom is the gap between how a business currently operates and how it could operate with better data, faster decisions, and automated processes. Industrial businesses typically have enormous headroom because they rely on legacy ERP systems, spreadsheets, manual workflows, and the institutional knowledge of long-tenured employees. This headroom is precisely what makes them high-ROI targets for AI deployment. |
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Why doesn’t AI work as well in tech portfolio companies? Tech companies have already optimised most of their data workflows and processes. Their systems are modern, their data is structured, and their teams already use analytics tools. AI in these environments delivers incremental improvements - typically around 5%. Industrial businesses, by contrast, have so much manual process and trapped data that AI can deliver step-change improvements of 20-40%. The ROI is a function of headroom, not sophistication. |
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What does AI look like in an industrial business? In industrial businesses, AI takes four main forms: (1) Order book intelligence - analysing thousands of customer accounts to identify at-risk revenue and growth patterns. (2) Tribal knowledge capture - encoding the pricing logic, customer relationships, and process knowledge that experienced operators carry in their heads. (3) Predictive maintenance - using equipment data to prevent downtime before it happens. (4) Quote automation - turning multi-day manual quoting processes into minutes by automating technical configuration and pricing. |
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What is tribal knowledge capture? Tribal knowledge capture uses AI to encode the critical operational knowledge that exists only in the heads of experienced employees - pricing rules, customer relationship history, process workarounds, supplier quirks. In industrial businesses, this knowledge often represents decades of accumulated expertise. When people leave the business for any reason, that knowledge disappears. AI systems can capture, structure, and make this knowledge available to the entire organisation. |
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What is quote automation? Quote automation uses AI to handle the technical configuration, pricing calculation, and document generation involved in creating customer quotes. In industrial businesses, quoting is often a 3-5 day manual process involving multiple specialists. AI can automate up to 80% of this process, reducing turnaround from days to minutes. This directly impacts win rates, customer satisfaction, and the capacity of sales teams to handle more opportunities. |
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Which PE firms are deploying AI in industrial assets? Several PE firms have built explicit industrial AI strategies. OpenGate Capital built its OGx platform specifically to deploy what it calls “big tech” tools in “small industrial” turnarounds. American Industrial Partners runs a dedicated Industry 4.0 programme across its manufacturing and industrial portfolio. These firms recognise that industrial assets offer the highest AI ROI precisely because of their operational headroom and the scale of manual processes that can be automated. |
This is Part 6 of an 8-part series on the structural shift from financial engineering to operational value creation in private equity. New articles publish weekly through April 2026.
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