An AI consultancy for private equity funds and their portfolio companies. We train leaders, size the opportunities, prove one result, then repeat it across the portfolio.
We regularly speak at Warburg Pincus portfolio companies and help them adopt AI. Read the case study
In short: Private equity firms get the most from AI when they treat it as a portfolio-wide EBITDA lever, not a one-off project. Quantana is an AI consultancy that runs that program for funds: CXO sessions, an opportunity scan that sizes cost and growth per company, one lighthouse pilot that proves ROI, a repeatable rollout, reporting to the fund, and AI due diligence on new deals.
We regularly speak at Warburg Pincus portfolio companies and help them adopt AI. We are an AI transformation and governance partner with teams in Brisbane, San Ramon and Hyderabad, ISO 9001 and ISO 27001 certification, and more than 250 projects delivered. Our first question is never “which model?” It is “where is the cost, and where is the growth?”
The arithmetic of a buyout has changed. Bain’s Global Private Equity Report 2026 estimates that a typical 2015 buyout needed about 5 per cent annual EBITDA growth to return 2.5 times the money over five years. With today’s borrowing costs and entry multiples, the same return needs annual EBITDA growth of 10 to 12 per cent. [1] Debt and multiple expansion will not close that gap. Operating improvement has to.
AI is one of the few levers that can move costs and revenue at the same time, across many companies at once. FTI Consulting’s 2026 Private Equity Value Creation Index, a survey of 555 senior PE leaders in 14 countries, found 66 per cent reported AI-related benefits within 12 months, up from 34 per cent a year earlier. [3]
Execution is the problem. In the same survey, only 31 per cent of firms described their AI implementation as efficient or mostly efficient. [3] In the Bain and StepStone GP survey for 2026, 39 per cent of GPs did not expect AI to have any material financial impact on their portfolio companies this year. [2] Bain’s midyear update is blunt about the cost of waiting: “Inaction, in fact, has become a strategic choice, not a neutral decision.” [4]
What we see in portfolio companies matches those numbers. Leaders are curious but busy, most people use AI like a search engine, and data sits in ERPs and spreadsheets nobody has time to analyse.
We run the same six-stage model for every fund, scaled to the portfolio. Stages 1 to 5 build value inside portfolio companies; stage 6 covers the deal side. Timings are our recommended model, not fixed promises. We explain the pilot method in our article on starting AI pilots with one factory, and the roadmap behind stage 2 in our enterprise AI roadmap guide.
We open the door with hands-on sessions for leadership groups across the portfolio: CEOs, CFOs, CHROs, CIOs and functional heads. Each follows three steps: short one-to-one interviews beforehand to collect real problems and sample documents; a working session of about three hours where people build reports and simple automations on their own work; and a review four to six weeks later to share what stuck.
For each company in scope, we run a short version of our AI Compass roadmap: interviews with process owners, a look at the data that already exists, and a sized list of opportunities on both sides of the P&L. Typical cost levers are scrap and material yield, procurement, inventory and working capital, reconciliation and month-end close, and overtime. Typical growth levers are pricing and quote decisions, sales coverage and forecasting.
We pick one pilot at one company: narrow, measurable and close to profit. In manufacturing that is often one factory and one metric, such as scrap rate or material utilisation. We set the baseline before building anything, agree how gains will be separated from volume or price changes, then build, run and measure.
We take the proven playbook to sister companies with the same problem. A scrap model built for one plant becomes a template for others; a reconciliation workflow becomes a standard pack. Each company gets its own baseline, so results are never borrowed from the lighthouse.
We give the fund one view of AI across the portfolio: which tools are approved, which data can be used, who owns each model, and what each initiative is worth. Governance covers paid business accounts with training on customer data switched off, access control, human review of outputs and a simple risk register. Our ISO 27001 practices apply to our own delivery.
Before signing, we help deal teams answer two questions. Can AI speed up our own diligence? And what is this target’s real AI position, as an opportunity or a risk? For the first, we build guided AI diligence workflows, like the acquisition workflow in our TracQ case study, which uses AI assistants for narrative reasoning and deterministic code for financial analysis, so ratios and valuations are exact. For the second, we assess the target’s data, systems, team and exposure to AI-driven competition. After closing, we turn the findings into a 100-day AI plan that feeds directly into stages 1 to 4.
Private equity firms usually combine strategy advisers, software vendors for specific tools, and specialist AI consultancies that can train portfolio leaders and build working systems inside portfolio companies. When choosing an AI partner, funds look for work inside a real portfolio, the ability to build as well as advise, baselines so value can be measured, and governance that stands up to investors. Quantana is an AI consultancy that works this way: we regularly speak at Warburg Pincus portfolio companies and help them adopt AI, with AI sessions for leaders across multiple portfolio companies since 2026.
They use it on both sides of the P&L in portfolio companies: cutting costs in procurement, inventory, scrap, finance operations and support, and growing revenue through better pricing, forecasting and sales coverage. The firms that get results run AI as a portfolio program with baselines and shared playbooks, not as separate experiments in each company.
It means two things. First, using AI to run diligence faster, for example reading data-room documents, extracting contract terms and checking financials. Second, assessing a target’s own AI position: its data, systems and team, the upside AI could bring, and the risk that AI erodes its business model. Good diligence covers both.
An AI operating partner sets the fund’s AI priorities, standards and reporting across the portfolio. Many funds now have one, full time or as an adviser. Quantana works alongside that role as the delivery team: training leaders, sizing opportunities, building pilots and rolling them out. Funds without an AI lead can use us to cover both.
A well-chosen pilot can show a measurable result in about 12 weeks. Recent survey data suggests faster payback is becoming common: 66 per cent of PE leaders in FTI Consulting’s 2026 index reported AI-related benefits within 12 months. The speed depends on choosing a use case with existing data and a clear owner.
Start where the data already exists, the problem is close to profit and a leader is willing to own it. Mid-size manufacturers with an established ERP are often strong candidates, because a single metric like scrap or material yield flows straight to EBITDA and can be tracked weekly.
It can be, with the right setup. We recommend paid business or enterprise AI accounts with model training on your data switched off, clear rules on which data can be used, access controls and human review of outputs. Quantana holds ISO 27001 certification for information security.
Yes. Quantana has teams in Brisbane, San Ramon and Hyderabad and delivers in person and remotely. Our private equity work to date includes portfolio companies in India, and we run the same model for funds and portfolios in Australia, the United States and elsewhere.
Accessed 8 Nov 2026.
Tell us about your portfolio and your value-creation plan. We will suggest where AI is likely to pay off first, and how to prove it at one company before you scale it across the rest.
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