Key Takeaways

  • Brookfield PE CEO Anuj Ranjan oversees roughly 1,200 active AI deployments across the firm's global portfolio, generating about $300 million in recurring EBITDA.
  • Cross-portfolio scale provides the primary training advantage, pooling operational data from 300 businesses into a shared data lake to train machine learning algorithms.
  • In manufacturing facilities, IoT sensors capture real-time mechanical and vibration telemetry to identify equipment stress prior to catastrophic factory breakdowns.
  • In corporate carve-outs, Brookfield replaces manual, static legacy customer pricing agreements with dynamic models calibrated to changing client needs.

Predictive Maintenance at Industrial Scale

Most private equity sponsors talk about software companies as the primary beneficiaries of artificial intelligence. Anuj Ranjan looks in the opposite direction. The chief executive of Brookfield Private Equity is capturing cash flow inside physical assets, including chemical plants, manufacturing lines, and transport networks.

Brookfield tracks roughly 1,200 active deployments across its portfolio today. Ranjan pins the tangible return at $300 million of recurring EBITDA. Brookfield achieved that run rate through automated telemetry and mathematical models applied to standard plant operations rather than experimental chat tools.

“Today, you can install IoT devices across all of that machinery,” Ranjan told host Hugh MacArthur on Dry Powder. “You can collect real-time data.” In heavy industrial businesses, Brookfield outfits legacy production assets with IoT vibration sensors and mechanical monitors. These sensors detect small micro-strains inside bearings and turbines weeks before an operator notices heat or sound. Catching an anomaly early prevents unscheduled factory downtime, where a single broken gear can stall production lines for days and destroy quarterly plant margins.

The operational data advantage is hard to build from scratch. A mid-market sponsor owning four manufacturing assets cannot train predictive maintenance algorithms with high accuracy because catastrophic equipment failures happen infrequently.

Brookfield sidesteps this problem through raw volume. “A group like us, which owns three hundred businesses around the world, has this kind of data now from so many different places that we've got quite a nice data lake that we can actually run these models and train them on,” Ranjan noted. Pooling data across hundreds of operational sites allows machine learning models to see edge cases repeatedly. Failure signatures become visible to Brookfield before an isolated plant manager ever encounters them. Scale turns rare operating hazards into predictable, scheduled maintenance.

Fixing Commercial Drift in Carve-Outs

The revenue side of Ranjan's program centers on corporate carve-outs. When Brookfield buys an industrial division separated from a large parent conglomerate, it routinely inherits broken commercial architecture. Divisional sales teams often rely on static spreadsheets, tribal knowledge, and gut-feel discounts negotiated years earlier under corporate owners focused elsewhere.

“We take that, we carve that company out, and we put in proper pricing models,” Ranjan explained. “Dynamic pricing models that price properly for that customer and can even price sometimes based on the need that that customer has, which is also changing.”

Instead of static annual contracts, algorithmic pricing engines ingest spot market data, input material volatility, and buyer order frequency. A customer buying engineered components with tight delivery deadlines pays for that urgency. Another buyer with flexible delivery windows receives a rate that fills slack production capacity. The system adjusts to capture margin on every order without losing transaction volume to regional competitors.

Why It Matters

Operational AI value creation is shifting away from tech platforms and toward balance-sheet-heavy physical infrastructure. Scale operators with diversified asset pools can aggregate operational data across industrial subsidiaries, creating operating efficiencies smaller buyout firms cannot easily match. For private equity sponsors, carve-out value creation increasingly requires real-time algorithmic pricing and automated asset monitoring on day one, raising the baseline standard for operational underwriting.