How AI speeds up carbon data collection across private equity portfolios

Private equity firms managing dozens of portfolio companies hit the same bottleneck every reporting cycle: collecting comparable carbon and energy data fast enough to act on it. Spreadsheets and manual data entry don't scale across a portfolio, and consultants can't turn data around in the weeks a deal team has. AI changes that maths.

Why portfolio-wide carbon data is a bottleneck for PE firms

Every portfolio company has its own invoicing systems, spreadsheet formats, sites and languages. Consolidating all of that into a single, auditable emissions baseline usually means weeks of manual work per company, multiplied across the fund. The webinar Investing with impact: carbon measurement for private equity, with Sebastian Hanna, ESG Manager at Columna Capital, addresses exactly this challenge: measurement only becomes useful for investment decisions when it can be repeated, compared and updated across every company in the portfolio, not just piloted on one.

How AI removes the manual work: invoices, site assignment and data imports

Manglai's AI layer targets the tasks that eat up most of a data collection cycle:

• Mass invoice reading: the AI reads electricity, water, fuel and waste invoices in PDF, extracts consumption data and works with invoices from different countries, languages and currencies.
• Automatic classification and assignment: each consumption line is classified by scope (1, 2 or 3) and assigned to the right site and company, which matters when a fund adds a new acquisition every quarter and needs it on the same structure as the rest.
• Smart data import: spreadsheets and supplier or provider exports are converted into the platform's format without manual mapping for each company.
• A copilot for the results: deal and ESG teams can ask questions about emissions in plain language, such as which site has the highest Scope 2 emissions, instead of exporting data to analyse it.

What this means for due diligence and reporting timelines

Faster, standardised data collection shortens the two moments that matter most to a PE firm: pre-deal ESG due diligence and annual portfolio reporting to LPs. Instead of waiting for each portfolio company to hire a consultant, GPs can run a comparable carbon assessment on a target in days and keep it running post-acquisition without adding headcount to the ESG team. Our article on AI agents and copilots for ESG due diligence goes into the deal-side use case.

From data to action across the whole portfolio

Once the data is centralised, Manglai's AI proposes reduction initiatives for each company, ranked by environmental impact and implementation cost, with estimated CO2 savings. A GP can then compare which portfolio company has the highest-impact, lowest-cost measures available and prioritise capital accordingly, the kind of comparison our article on how private equity firms turn ESG compliance into profit explores in more detail.

For funds measuring carbon across many companies at once, the constraint was never ambition; it was the manual work standing between data and decisions. Discover how Manglai's platform for investment funds automates that step end to end.