There is a familiar distinction between technology businesses and traditional businesses. Fintech, proptech and healthtech companies sell technology, while everyone else simply uses it. That may describe what a business sells, but not how it operates or creates value.

Every business now depends on technology. In traditional businesses, it sits underneath the service, managing customers, supply chains, payments and information. AI makes that dependence, and its urgency, impossible to ignore.

That is why technology and EBITDA now belong in the same conversation. For investors and portfolio leadership teams, technology is not merely a support cost to manage after the deal. It is part of the commercial infrastructure that determines whether the value creation plan can be delivered.

Traditional businesses are already technology businesses

In a technology company, product roadmaps, engineering capability and technical debt are visible because they sit close to the commercial proposition. The business may lack mature governance, but nobody questions whether technology matters.

Traditional businesses can be more exposed. Technology accumulates through acquisitions, urgent fixes and local decisions. The combined estate becomes fragmented and expensive, with data across separate systems and reporting dependent on manual reconciliation.

These businesses often depend on systems never designed for their current scale or ambition. The risk is easy to miss because the service still reaches the customer. Underneath, complexity consumes capacity, increases unit cost and slows change.

AI is also changing customer behaviour. People can research, compare and challenge more effectively before buying. In some markets, they may use AI to bypass parts of an established service. Businesses must understand how AI may alter demand, differentiation and the value customers expect.

Technology affects the quality of EBITDA

Technology does not create EBITDA by itself. A platform cannot repair a weak proposition or create demand. However, technology can determine how much revenue growth reaches the bottom line and whether it can be sustained.

The connection appears across the business. Integrated platforms can support higher volumes without equivalent headcount growth. Better data can improve pricing, forecasting and commercial decisions. Automation and AI can process work faster and increase capacity, while resilient operations protect revenue and customer confidence.

Poor systems create hidden labour, delayed decisions, errors and customer friction. Those costs rarely appear neatly under “technology”. They are distributed across operations, finance, customer service and management time.

AI does not make this capability free. Models, computing, specialist platforms, integration, data preparation, governance and skilled people all carry cost. This is why reviewing only the IT budget misses the point. The commercial question is whether technology, data and AI investment will grow revenue, drive margin or improve resilience.

Buy-side technology due diligence defines the starting point

The first opportunity comes before investment. Financial and commercial diligence can support the investment case, but technology due diligence tests whether the organisation can deliver what happens next.

The assessment must go beyond infrastructure and cyber controls. It should examine architecture, data and AI readiness, operating model, leadership, suppliers, cost and technical debt. Most importantly, it must connect findings to the investment thesis.

If growth depends on new locations, markets or acquisitions, can the platforms support it? If margin improvement depends on automation or AI, is the data reliable and are the processes understood? If rapid change is required, can the organisation execute it?

Good diligence establishes the starting point, identifies constraints and exposes likely investment. The buyer enters the hold period informed, rather than discovering later that the value creation plan rests on foundations that cannot carry it.

The first 100 days must turn insight into a plan

A diligence report has limited value if nobody uses it. After close, its findings should become part of the value creation plan, with clear priorities, owners, investment decisions and commercial outcomes.

The first 100 days are not an invitation to replace everything. Leaders must stabilise risks, validate the findings and decide what changes first. Some investments protect the business. Others remove constraints or enable growth. Attempting too much can consume the capacity needed to deliver.

This requires honest leadership assessment. A capable team may still need strategic direction or transformation experience. Fractional or interim CIO and CTO leadership can provide it without fixing a permanent structure too early.

Strong strategy and delivery plans connect technology, data and AI priorities to the business plan. They define the expected value, operating change and measures. Funding a platform or AI initiative is not the outcome. Adoption, cost removal and commercial performance are.

Platforms, data and AI must enable non-linear growth

Value creation is not achieved when sales, cost and complexity rise together. The operating model must support greater volume without like-for-like growth in the cost base.

Core platforms must support the value chain, not departmental preferences. Data needs clear ownership, processes should be simplified before automation and governance must maintain control without slowing every decision.

AI is not a substitute for those foundations. Applied to fragmented processes and poor data, it may increase output without improving EBITDA. Used against a clear commercial problem, it can accelerate decisions, reduce manual work and create capacity.

This is not a mature, settled market. Models, capabilities and costs keep changing, while the eventual competitive impact remains uncertain. Today’s AI is probably the least capable organisations will ever use, but not every current investment will age well. Moving without direction can create another layer of tools, suppliers, data risk and cost.

Most immediate disruption is concentrated in digital and knowledge work. The physical impact will deepen as AI combines with robotics, but businesses do not need to predict the final destination. They need an AI strategy connected to technology, data, automation and commercial priorities, followed by disciplined execution. What outcome changes, what will it cost and how will value be proven?

This is the operating phase of private equity enablement, where strategy, leadership and delivery must remain connected. Value does not appear because a roadmap exists. It appears when operating change reaches revenue, margin or resilience.

Exit readiness starts 12 to 24 months before exit

Exit preparation should not begin when the data room opens. Starting 12 to 24 months before a transaction gives the business time to address weaknesses and prove its performance is sustainable.

A buyer will ask whether platforms support growth, risks are controlled and knowledge sits with one person or supplier. They will examine data, reporting, technology cost, AI readiness and required investment. Technical debt or an unclear roadmap can reduce confidence even when trading is strong.

The business needs a defensible technology narrative. It should explain how investment improved performance, how the operating model supports scale and what the next owner can build on. Claims need evidence through adoption, cost, data quality and delivery results.

Earlier work now compounds. Diligence created the baseline, the value creation plan connected investment to outcomes and delivery produced evidence. Exit readiness brings the story together so the buyer sees both proven value and remaining headroom.

Technology belongs in the value creation plan

Every business is technology-enabled. In traditional organisations, technology, data and AI can be the difference between profitable scale and growth that adds cost, complexity and risk. Private equity cannot leave that question to an annual IT budget review.

Technology must be understood at buy-side, built into the value creation plan, governed through the hold period and prepared for scrutiny before exit.

It belongs in the EBITDA conversation because it shapes how value is created, evidenced and transferred to the next owner.

Relentica helps investors and portfolio leadership teams connect technology due diligence, strategy, leadership and delivery across the investment lifecycle. Start the conversation.