Key Takeaways

  • Brookfield targets legacy industrial companies valued at 8 to 9 times EBITDA that offer immediate cash yields rather than paying tech multiples for pure-play AI vendors.
  • Many acquisition targets generating $1 billion in revenue and $250 million in EBITDA still operate without modern ERP systems, CRMs, or properly structured spreadsheets.
  • Enterprise implementation, not algorithmic capability, remains the primary barrier to realizing productivity gains from machine intelligence.
  • Value creation focuses on three practical applications across physical operations: predictive maintenance, dynamic pricing, and robotics.

The Missing Digital Layer in Nine-Figure Businesses

Ranjan points out a persistent blind spot in modern private equity. Silicon Valley assumes American industry already digitized its core operations a decade ago. The reality inside industrial balance sheets is primitive.

“You would be shocked how many times we buy a business that is a billion dollars of revenue, two hundred and fifty million dollars of EBITDA, and they're barely using Excel properly,” Ranjan says. These companies generate massive cash flow without enterprise resource planning software, structured customer databases, or cloud infrastructure. They run on fragmented paperwork, tribal knowledge, and legacy desktop files.

For private equity sponsors, this gap is an operational margin gift. Venture capital firms pay 30 to 50 times forward revenue for frontier models that struggle to capture enterprise budgets. Meanwhile, buyout shops acquire cash-flowing physical monopolies at single-digit EBITDA multiples. The operational playbook does not require reinventing neural architectures. It requires cleaning dirty operational data, setting up basic digital plumbing, and wiring existing predictive models into plant floors.

Why Model Power Does Not Equal Enterprise Production

The chasm between running a prompt in a consumer interface and rewiring an industrial supply chain explains why software vendors struggle to monetize enterprise accounts.

“It's one thing for all of us to use ChatGPT or Claude and think it's interesting,” Ranjan explains. “It's another thing for a company to know how to actually use this technology in its business and improve its productivity.” The friction sits squarely in change management and physical deployment. Model weights can predict when a pump will fail, but the plant still needs telemetry, sensors, reliable networks, and a maintenance crew willing to trust an algorithmic ticket.

Because of that friction, Ranjan separates model capability from enterprise absorption. “The real bottleneck to AI actually isn't the technology, it's the deployment of it in enterprise at scale.” Brookfield concentrates its efforts on three tangible operational levers: predictive maintenance to reduce factory downtime, dynamic pricing engines to capture margin leakage, and robotic automation on warehouse floors. These tools do not rely on future artificial general intelligence. They rely on basic operational discipline applied to physical assets. As Ranjan puts it: “If we're in the fourth industrial revolution, which I truly believe we are, that keyword is industrial.”

Buying Cash Flow Instead of Chasing Frontier Tech

The arithmetic behind this strategy runs counter to the prevailing tech narrative. Instead of underwriting software risk, Brookfield underwrites operational catch-up.

“You know, the technology opportunity is very obvious,” Ranjan notes, “but I think one of the best ways to invest in AI is to buy a boring industrial company, buy it at eight, nine times EBITDA with a real cash yield.” When a sponsor acquires an un-digitized manufacturer at 8 times EBITDA, downside protection comes from the existing cash flow and tangible asset base. Every operational efficiency wrung out through automation directly expands the bottom line, rather than subsidizing massive cloud compute bills.

Why It Matters

This dynamic signals a shift in where value accrues during major computing transitions. While public markets price massive capital expenditure into chipmakers and frontier cloud providers, private market alpha is migrating to asset-heavy businesses with under-digitized operations. Sponsors who master mundane implementation on physical plant floors capture productivity gains without taking technology obsolescence risk.