The rush to partner with Private Equity firms by the big labs is obfuscating the larger point of AI adoption. Private equity firms have begun using AI to improve portfolio company productivity, but most deployments still address only one part of the value creation equation.
Today, the dominant use case of AI in PE is operational efficiency: automating manual work, reducing cost, and thus expanding EBITDA margins. Improving operational efficiency directly drives PE returns through increased earnings, but two other critical levers are usually left untouched: the exit multiple, and the holding period over which value is realized.
Deployed correctly, AI does not just automate work; it standardizes how work gets done. That standardization process is what produces a portfolio company that is easier to scale, manage, and underwrite - one that can be exited sooner, at a higher multiple. Best-in-class PE firms therefore view AI transformation as a lever on all three drivers of return, not as a workflow efficiency play alone. With this approach, AI begins to affect the broader drivers of PE returns beyond margin.
Deploying AI into a portfolio
PE AI transformations are constrained by a finite 5–7 year window to create value, which means slow transformations leave a meaningful share of the AI opportunity unrealized. This time-pressure for transformations is exacerbated by the fact that firms are not deploying into clean operating environments; they are deploying into operational sprawl. The same workflow runs differently across e.g., regions, teams, products/SKUs, service lines, and systems inside one company - which creates 10s of workflows in need of transformation for each department within a given portfolio company.
Aggregated across a PE firm’s portfolio companies, this compounds into 100s of processes in need of AI transformation. Figure 1 illustrates how a simple PE portfolio with only 4 portcos results in >100 necessary AI deployments.
When workflow fragmentation creates hundreds of distinct variants across a portfolio, PE firms are left with a deployment challenge - each variant must be separately scoped, built, governed, and validated for value.
Faced with this level of fragmentation, firms can either transform workflows sequentially or in parallel. This is the exact challenge PE firms face today.
Running 100s of AI transformations sequentially is too slow for a finite, 5–7 year PE holding period.
Running them in parallel is faster in theory, but leaves the fund exposed to coordination risk and broader bandwidth challenges, as it attempts to manage multiple vendors, deployments, and system migrations.
In both cases, the portfolio is being transformed variant by-variant, rather than through a smaller set of repeatable deployment patterns.
Varick’s PE AI Transformation Strategy
Before attempting to deploy AI across each variant, the more effective approach is to apply a two-step consolidation method: 1) standardizing workflows within each company, and 2) identifying companies across the PE portfolio that share similar workflows. This collapses 100s of bespoke transformation efforts into a smaller set of repeatable deployment patterns that can scale across the portfolio.
We think of the first step as an operational consolidation. This standardization reduces operational complexity inside the company by turning multiple internal workflow variants into one standardized operating model. Figure 2 illustrates this through the lens of a portfolio company with 5 distinct AP processes.
Once each portfolio company has a normalized version of its processes, PE firms can group companies that run that same workflow logic and transform them together - compressing what would have been dozens of separate deployments into a much smaller number of repeatable transformations. Figure 3 shows an example of such a transformational consolidation.
Once a reusable transformation pattern has been built for companies that share the same operating logic, AI agents can be deployed across that cluster simultaneously rather than company by company. This 2-step approach drops the number of transformations in a portfolio from over a hundred to a handful. Figure 4 illustrates the optimized transformation approach requiring only 3 reusable AP Agents.
How consolidation affects PE returns
The value of AI consolidation in PE does not come from automation alone. It comes from changing both the economics of how work gets done inside a company and the speed at which that transformation can be repeated across a portfolio.
The result shows up in three places: EBITDA expansion at the company level, higher exit multiples when selling to a growth-oriented buyer, and shorter holding periods at the fund level.
Taken together, these 3 effects increase enterprise value, allow it to be realized sooner, and thus, drive IRR increases:
1. EBITDA expands when workflows become faster, cheaper, and simpler
Operational consolidation expands EBITDA by removing the cost, friction, and coordination burden created by fragmented workflows. When the same process is no longer run three or five different ways across regions, teams, or systems, companies can eliminate duplicate effort, reduce handoffs, and simplify the software stack supporting the work.
The impact goes beyond FTE savings: standardized workflows shorten cycle times, reduce SaaS spend, improve control quality, and unlock working-capital benefits as collections, approvals, and reconciliations move faster with fewer exceptions.
2. The multiple expands when growth becomes easier to underwrite
At first glance, an operational cleanup can look like it removes the buyer’s upside: if simplification creates value, a business with less left to fix should be worth less to the next owner. But that is not how complex is priced.
A buyer inheriting that operation prices in the cost, time, and risk of fixing it, and keeps whatever value the fix creates. This is why PE firms pull the cost lever themselves before exit: leaving the complexity in place does not raise the sale price, it lowers it.
Consolidation converts that exhaustible cost lever into a standardized operating model that scales. In doing so, it changes the nature of the buyer. For a cost-focused buyer, the improvement is priced entirely through EBITDA: the same multiple, applied to a higher earnings number. The price at exit goes up, but the multiple does not.
A revenue-oriented buyer pays a higher multiple, because cleaner operations make growth easier to underwrite - customers onboard faster, approvals and handoffs hold as volume rises, and each new dollar of revenue drops through at higher incremental margin because the operation no longer breaks under an increase in demand.
In that case, the buyer is not paying for a cheaper business. They are paying for a business where growth is more repeatable, more scalable, and less likely to get trapped inside operational complexity. That is what supports a higher multiple.
3. Holding periods shorten when transformations become repeatable
Transformation consolidation creates value differently. Its main effect is on the speed at which transformation can reach the portfolio. Once companies are grouped by shared operating logic, the fund no longer has to manage dozens or hundreds of one-off AI deployments.
A much smaller set of reusable transformation patterns can be deployed across multiple companies at once, reducing the time to value of AI gains. That acceleration in time to value supports a shorter holding window, because the firm can create operating improvements earlier in the hold period, prove the gains faster, and bring the asset to market with a cleaner, more scalable operating model. When value is created sooner, the asset can be exited sooner, and the same dollar of value realized over a shorter period produces a higher IRR.
Together, these three effects change the PE AI thesis from workflow automation to enterprise value creation. The firms that consolidate operations before deploying AI will realize value faster, while creating the foundations to exit with cleaner, more scalable assets.
Transform Your Portfolio Companies
Varick agents run in production today inside portfolio companies of leading private equity firms. We are now taking on sponsor engagements for August and September.
If you are a private equity firm with portfolio companies above $1B in revenue ready for AI transformation, whether you are preparing an asset for exit, folding in add-ons, or standardizing operations across the portfolio without forcing a software migration, book a discovery call at varickagents.com. We’ll map what a transformation would look like for your portfolio companies.
