If you’re leading finance inside a private equity-backed company, then you’re working against the clock. The new owners are seeking to improve the business and exit within a certain timeframe. You are being asked to deliver better information for operational and cash visibility, a repeatable reporting package and progress against the value creation plan. And somewhere in the background, someone is asking when AI can start solving it all.

But here’s the thing: You can’t start with the shiny tool. Technology transformation works best when it follows the way the business creates value. For most companies, that means moving through five stages: define the business needs, create data visibility, standardize the finance language, modernize the technology stack, and automate and augment with AI. The key theme is: get visibility first, then make technology decisions.

1. Define business needs

The first step in choosing a system is to get clear about what the business needs to know.

• What numbers does the sponsor expect to see every month?
• What business decisions are being delayed because no one trusts the data?
• Which reports take employees hours to assemble manually?
• Which performance measures matter most?

A newly acquired company often has multiple systems, inconsistent processes and a lot of Excel work holding it all together. The immediate need isn’t ERP consolidation; it’s understanding the pain points, reporting requirements and the minimum level of visibility required to run the business.

Diving into an ERP consolidation before fully understanding the reporting requirements can result in a system that technically works but doesn't answer the questions the business actually needs answered, forcing costly rework, shadow spreadsheets and workarounds down the line.

Start by defining the reporting package, determining the KPIs, agreeing on the level of detail, and deciding what must be consistent across entities and what can remain local. Teams that skip this step end up retrofitting their processes to the software instead of the other way around.

2. Create data visibility before committing to systems replacement

Portfolio companies commonly inherit multiple accounting systems, billing platforms, CRMs and operational tools — each containing different pieces of the truth. Rather than immediately replacing every system, the company can create a central data warehouse or purpose-built data mart, i.e., one central storage area that collects and organizes information from all the existing systems. It may sound less exciting than an ERP implementation, but it’s the fastest path to visibility.

A well-designed data layer lets the company pull from existing systems, standardize definitions and create a single source for management reporting. Finance should work with IT to assign ownership. In a mid-market company, one owner is usually better than a committee with no decision rights. In larger companies, a governance group may make sense, but someone still needs to own the data quality, definitions and refresh cadence.

The goal isn’t perfect data. The goal is decision-grade data: clean enough, consistent enough and timely enough to support the next operating decision.

3. Standardize the finance language

Once the data has been collected, people may disagree about what the numbers mean. Different departments might calculate revenue in different ways; business units may assign different account names or define customers, margins, bookings or churn differently.

This is when finance needs to do the hard but necessary work of standardizing the analytical framework. The company needs one shared financial language. That means aligning the chart of accounts; normalizing entity structures; and defining revenue, margin, bookings, backlog, churn, working capital and any other metrics that drive the investment thesis.

This isn’t clerical work — it’s the infrastructure of value creation. Without common definitions, every board package becomes a negotiation, every forecast becomes a translation exercise and every add-on acquisition adds more complexity.

It is at this point that the portfolio company should also separate close reporting from forecasting where possible. The close team is focused on accuracy, controls and historical performance. FP&A is focused on drivers, scenarios and what comes next. In smaller companies, the same people may wear both hats, but the activities they perform should be distinct.

4. Modernize the tech stack with the hold period in mind

Now that the company understands what it needs and has established sufficient visibility into its data and reporting requirements, it can make a sensible technology decision.

• Upgrade and unify accounting with one ERP.
• Build data warehouses.
• Scale operations with integrated workforce, payroll and HR systems.

The critical question is: What technology architecture gives us the visibility, control and scalability needed before exit? There is no universal answer; the right choice depends in part on how long the PE firm expects to own the company and how much disruption the business can handle.

ERP implementation timelines and complexity vary widely, and the right approach depends on the situation. When a longer hold period is expected, consolidating onto a single common ERP typically makes sense. It delivers the operational standardization and clean reporting that support long-term value creation. For shorter time horizons, some ERPs can be implemented in short order, depending on the requirements, while others offer the ability to consolidate multiple existing general ledgers for a faster win without the full lift of a unified platform. In either case, actual implementation time will depend on the organization's complexity and which modules are in scope for the business.

5. Automate and augment after the foundation is credible

AI can’t magically fix bad data, inconsistent definitions or broken business processes. It performs best when it sits on top of a trusted foundation. If the chart of accounts is misaligned, reporting definitions keep changing and the source data is unreliable, AI will simply produce faster answers based on bad information.

That said, the company can still use AI for smaller tasks such as drafting commentary, identifying anomalies, summarizing variance drivers or speeding up manual analysis. But the more advanced uses, such as predictive forecasting and automated decision support, require clean, consistent and well-managed data.

A practical sequence for tech implementation at portfolio companies

To boil it down, the five-step sequence looks like this:

1. Diagnose and define: Identify the reporting pain points, sponsor requirements and decisions the business needs to improve. Agree on KPIs, reporting packages, ownership and decision rights.
2. Connect: Build a data layer that consolidates information from existing systems into a trusted source for management reporting.
3. Standardize: Align charts of accounts, metric definitions, entity structures and reporting cadence.
4. Modernize: Decide whether to consolidate ERP systems, integrate around current systems or operate a hybrid model.
5. Automate and augment: Apply BI, workflow automation and AI where the foundation is strong enough to support them.

Technology isn’t an IT project; it’s part of the value creation plan. The tech stack should help finance move faster, see performance more clearly and make better decisions with less manual effort. With the right technology implementation sequence, finance advances from a reporting function to an operational advantage.

About the Author

Beth Thomas leads QueBIT Consulting's Financial Technology Practice, where she oversees a team of FP&A, ERP and Treasury experts delivering strategic and technical solutions to finance organizations across industries. Under her leadership, the practice combines deep platform expertise — spanning tools like Pigment, IBM Planning Analytics and NetSuite — with hands-on experience in financial planning and systems implementation to help clients modernize their finance technology stack.