Finance automation is the defining lever for PE firms who scale their portfolio companies efficiently, versus those that bleed value through manual processes.
Investor-backed CFOs have received the message. In the past two years, finance leaders actively testing or deploying AI in the finance function jumped from 74% to 94%, according to Consero’s most recent CFO survey.
Adoption is accelerating for good reason. Installing the right integrated technology layer — APIs, AI agents, and cloud-based ERP systems — early in the hold period directly accelerates close timelines, reduces headcount risk, and increases enterprise value at exit.
The challenge is knowing where to start, what to prioritize, and how to avoid the costly missteps that slow everything down.
Why Does Finance Automation Matter for PE-Backed Companies?
Manual finance processes are the silent drag on portfolio company performance. When analysts and controllers carry data between systems by hand, errors compound, data integrity breaks down, and sponsors don’t get the insights needed to make decisions.
“Anything algorithmic should ideally be buttoned up right away, and that doesn’t even require AI. It just needs algorithmic processes — taking data and converting it into either journal entries or reporting insights for stakeholders and decision-makers.” – Mitt Mehta, EVP of Client Services
The month-end close is the clearest proof point. It touches every part of the finance function and it’s where manual bottlenecks show up first. When those steps are automated and connected, close timelines compress fast.
As more finance leaders deploy AI at scale, Consero found that the share of organizations closing the books within nine days improved by 675% — from just 8% in 2024 to 62% in 2025.
Why (and Where) Most Portfolio Companies Have an Automation Gap
CFOs at PE-backed companies almost always inherit a tech foundation that wasn’t built for where the business needs to go. Data teams, reporting structures, and systems were configured by prior leadership under different priorities. The result is a patchwork that requires constant manual intervention to produce basic financials.
The most common gaps fall into three categories.
Disconnected systems
Analysts, controllers, and accountants manually carry data from one platform to another because the systems don’t integrate. Every manual handoff introduces the risk of duplication, omission, or mistranslation. Finance teams end up juggling data across Excel files until insights get lost or distorted before reaching a decision-maker.
Closed-loop platforms
Systems that aren’t API-friendly or open to connectivity make finance automation nearly impossible. When the tech stack won’t talk to itself, workarounds multiply and so do human touchpoints.
Data accuracy problems
Finance leaders at portfolio companies frequently inherit data infrastructure that doesn’t accurately reflect what the business is actually doing.
Reports exist, but the underlying data is miscategorized, duplicated, or manually adjusted. Decisions get made on top of that uncertainty and the errors compound until an audit or a buyer’s due diligence process surfaces them.
“They’re playing gymnastics with data, and in the process they miss a beat where that data doesn’t translate into insights or the right insights. That’s typically the risk we’re always going to face until automation exists.” – Mitt Mehta, EVP of Client Services
What Finance Automation Looks Like for Portfolio Companies
Successful finance automation connects systems so data moves without human intervention — from source transactions through journal entries, reporting, and insights — with human judgment reserved for exceptions, approvals, and strategy.
For PE-backed companies, this starts with the basics: ensuring the ERP, AP/AR platforms, payroll, and reporting tools can communicate through API integrations.
“If I can leverage technology to create integrative processes, anything that has API connectivity between systems, that’s the approach. You’re not carrying data from one system to another. It converts into journal entries or reporting insights automatically.” — Mitt Mehta, EVP of Client Services
What Should PE Firms Prioritize First?
The first priority is data accuracy and system connectivity, not buying new software. Before layering in automation tools, the finance function needs a foundation where systems talk to each other through API connections.
“The first step is to validate data accuracy, where the data is coming from, and how it’s being used.”
For a typical portco turnaround, the first two to three months focus on confirming:
- Data accuracy, where it is coming from and how it’s being used
- Understanding what systems and processes are in place
- Identifying what needs to change
The basic automation work then takes days or weeks, not quarters.
The more advanced layer is AI-native automation: agents that can read and code bills, reconcile bank transactions, clean up charts of accounts, and generate financial reports with minimal human input.
AI Agents: The Next Frontier
Beyond adopting AI-native applications, the real opportunity lies in building custom AI agents tailored to each portfolio company’s specific processes.
Agents that can pick up data from source systems, transform it, and push it into the ERP at scheduled intervals without manual orchestration.
The human role shifts from doing the work to reviewing it: approving outputs, managing exceptions, and guiding the agent when judgment calls are needed.
With agentic coding now possible in plain English, the people best positioned to build these agents aren’t technologists. They’re the subject-matter experts who understand accounting processes, operational workflows, and the specific needs of the business.
“I feel like we have superpowers. Before, we had to rely on a technology infrastructure and a technology team. But now I have the ability to build the API connectivity, collect the data from sources, connect it and push it through the right platforms at any given time.”
Finance Automation Doesn’t Require a Heavy Investment
One of the first questions PE sponsors ask when a CFO proposes technology changes: is this going to be expensive? Especially in late-stage situations where the goal is exit readiness, the appetite for a multi-month ERP implementation is low.
The reality is that the cost and complexity of finance automation have dropped dramatically. Solutions that once required dedicated tech teams are now plug-and-play, thanks to tools like Zapier that let finance teams connect systems almost instantly.
“The efforts are much easier to implement than three or five years ago. You have an instruction manual, you go in and do the API yourself rather than having to do it through a team of consultants.”
What are the Benefits of Finance Automation for Portfolio Companies?
The ROI of finance automation doesn’t need a spreadsheet to prove, it shows up in faster closes, leaner teams, and higher multiples at exit.
Just watch what technology and automation can do today. It pays for itself within weeks.”
Here’s where PE firms see the most immediate impact.
Scale Acquisitions Faster
For PE firms running buy-and-build strategies, scalability is the real test of a finance function. Every add-on acquisition that needs to be folded into a consolidated reporting structure stresses existing processes. Manual ones break under pressure.
Automation changes the math. New entities, new locations, and opening balance sheet entries can be handled by AI agents. The modular integration work that used to require weeks of manual configuration now compresses into days.
“Scaling has become so easy with AI. I (used) to build a modular framework of API connections. Now, you can button up all the different data points. It’s easy to scale now in ways most people haven’t caught up to yet.”
Given that 30% of investor-backed CFOs cite integrating post-M&A financials as their top challenge, automating the acquisition integration process is one of the highest-value investments a PE firm can make in its growth thesis.
Transform the CFO’s Role
When the manual work disappears, the CFO’s job shifts from managing an operating structure to analyzing insights for business decision-making, which is where a CFO should be spending their time.
“The role shifts to actually using the insights that the team was built to generate. That’s what the CFO position was always meant to be, and technology finally makes it achievable.”
For PE firms, this means a portfolio company CFO with the right tech infrastructure spends less time policing data and more time identifying which revenue levers to pull, where to cut costs, and how to position for exit.
Higher Exit Multiples
PE buyers see value in a company with a tech-enabled finance infrastructure — it signals operational maturity and reduces the integration burden for the next owner.
The impact shows up in two ways.
- Direct EBITDA improvement: automation reduces headcount needs, compresses close timelines, and eliminates costly manual errors. In multi-site healthcare, for example, AI-driven call center automation reduces operating costs from $500,000 to under $50,000 — savings that flow straight to the bottom line and get multiplied at exit.
- The strategic premium: faster insights mean sponsors and leadership have real time intelligence embedded in the finance stack rather than within individual team members. Knowing which revenue levers to pull, which to shut down, and where new opportunities exist separates a clean exit from a premium one.
“Today I can walk into a business and say I can increase the enterprise value right away. If there are gaps in the tech infrastructure, there’s an immediate EBITDA improvement you can see and multiply. The headcount savings are clear and direct. The strategic value comes through the tech stack.”
Where PE Firms Get Finance Automation Wrong
Not every approach to finance automation creates value. Some of the most common mistakes are structural.
Buying tools without a process foundation
PE firms are forming AI committees, signing up for AI-native software, and jumping on the bandwagon without asking whether the underlying processes are sound enough to automate.
If the chart of accounts is miscategorized and data flows are fragmented, an AI layer on top just automates the mess. AI’s real value is delivering custom solutions tailored to each company’s specific operations.
“PE groups are scrambling to leverage AI technology…but there’s a bigger picture. It’s actually having a capable team that understands true business processes and can build agents within their framework.”
Hiring technologists instead of operators
The people who drive the most value in finance automation aren’t software engineers, they’re controllers, accountants, and finance leaders who understand the processes deeply enough to train AI agents and design automated workflows.
“The right people in this space are operators, subject matter experts in their functional roles. They’re the true value add. It’s not the tech team. The process can now be built by the person who actually has the knowledge.”
Cutting the talent that should be training the AI
Laying off experienced finance professionals to fund AI tools is counterproductive. Those professionals are the ones who know which processes to automate, where exceptions occur, and how to evaluate whether AI outputs are accurate.
The right approach is to identify team members who have both domain expertise and a willingness to embrace technology and invest in them.
Building unvalidated processes from scratch
Even firms that avoid the first three mistakes often lose time reinventing the wheel. Without a proven process foundation to build on, each portfolio company implementation is an experiment.
PE hold periods don’t leave room for experimentation across ten or twenty companies. Firms that want rapid, validated automation deployment need a partner who’s already pressure-tested these processes at scale.
How to Evaluate a Finance Automation Partner
When PE firms assess finance partners for their portfolio companies, the evaluation should center on operational depth.
“You don’t really need a tech partner. You need a partner that’s talented in building processes and playbooks — one that understands accounting operations really well, customized to that company.”
Look for:
Proven, end-to-end coverage
A strong partner arrives with documented playbooks for the end-to-end accounting and finance journey. This is the foundation that makes AI agents effective, because those agents need structured operating procedures to follow.
A hybrid delivery model
The best approach combines AI agents with experienced finance professionals. AI handles the algorithmic, repeatable tasks. Humans handle judgment calls, exception management, and strategic review.
“In the next 12 months, the future is a hybrid of AI agents and human capabilities to deliver best-in-class financials and insights. Our internal team can train agents the best because they have the knowledge. They can customize for clients because they understand how those clients operate.”
Speed to value
The right partner can onboard a portfolio company, clean up its financials, and deploy automation within weeks.
“It’s being able to create a solution that’s light enough to quickly plug in and get moving within weeks or a month.”
The Window to Act Is Now
Finance automation has reached a tipping point. An average tool improvement costs a fraction of what it did five years ago. AI agents can produce in minutes what used to take weeks. The ROI is obvious.
PE firms that act now can walk into a company and identify enterprise value improvements before stepping through the door. The firms that delay finance automation will watch their competitors scale faster, exit cleaner, and generate stronger returns.
The winners in this space will be the ones who leverage the right partner—one with the process documentation, the operational expertise, and the finance automation capabilities to turn the back office into a strategic force for growth.
The only remaining variable is whether your firm moves now or later.
