Where Should a PE-Backed Company Start with AI? Copilots, Automation, or Agents

The hard part is not adopting AI, it is placing the first bet well

Across private equity portfolios, AI has moved past the awareness stage. Leadership teams are now focused on a more practical question: where AI actually fits within the value creation plan.

A lot of the time, that comes down to how companies choose where to begin. The decision is driven more by visibility than by a clear view of where AI can have the strongest operational impact. That is how you end up with companies rolling out copilots because they are easy to test, while others jump into more ambitious initiatives before the business is ready for them.

What we tend to see next is not failure, exactly. It is something less dramatic and more common: AI gets layered into the organization, but the way work actually moves does not change enough to produce a clear business result. Teams may adopt the tools. What stays harder to prove is whether the business is performing differently because of them. 

For PE-backed companies, that is the wrong kind of progress. The first use case should earn its place through business impact. The question is whether it can improve execution, reduce friction, or expand capacity in a measurable way.

Why the wrong AI starting point creates so much drag

The problem is not that companies start with copilots, automation, or agents. It is that the first AI decision is often misaligned with how value actually gets created inside a portfolio company.

When that happens, AI gets introduced around the business instead of inside the way the business runs. That is when we start to see familiar patterns: companies roll out copilots across teams without a clear view of where the productivity gains should show up in the P&L; others launch narrow proofs of concept that never reach the systems where real work happens; others move toward agents before the underlying process is stable enough to support automation.

In practice, AI starts compounding when it becomes part of a live operating flow, not when it sits next to one. That is why the first decision matters so much. It shapes not only early results, but also whether the organization builds trust in the technology or starts treating it as another pilot that never changed much.

The three most common starting points

Before comparing them, it helps to define these options in practical terms.

Copilots

Copilots are usually the easiest entry point because they fit naturally into existing habits, helping individuals perform specific tasks faster or with more support inside the tools they already use.

Typical examples include:

• code assistance
• knowledge retrieval
• drafting responses
• summarizing information

Workflow automation

Workflow automation tends to create value faster when a company is dealing with repetitive tasks inside a structured process, especially when delays, manual handoffs, or inconsistencies are already affecting speed, cost, or capacity.

Examples include:

• document intake and routing
• data extraction and validation
• approval flows
• recurring back-office steps

Agents

Agents become relevant when work spans multiple steps, systems, and decisions, requiring a higher degree of coordination and contextual judgment than a fixed workflow can usually handle.

Examples include:

• handling an end-to-end internal workflow
• coordinating actions across multiple systems
• resolving exceptions inside a defined operating environment

What each AI approach solves, and where it falls short

This is the key distinction: these are not three versions of the same thing. They solve different problems, at different layers of the business.

When a copilot is the right place to begin

A copilot makes sense when the company wants a relatively low-friction way to improve how teams work day to day.

That is often the case when:

• the work is knowledge-heavy
• the task sits mostly within one person’s flow
• the risk of a mistake is manageable
• the business wants fast adoption with minimal systems change

Engineering is the clearest example. AI copilots can help teams move faster on drafting code, reviewing code, documenting changes, or navigating large codebases. In those cases, the benefit is real, but the gain tends to stay local to the person or team using the tool. It does not automatically improve the handoff, the approval cycle, or the business process around the work.

That distinction matters. If the objective is to create immediate operational leverage across the company, copilots are usually too narrow on their own. They can be useful, but they are not always the first move that changes performance at the business level.

When workflow automation creates value faster

For many PE-backed companies, workflow automation is the strongest starting point because it addresses operational friction directly.

It tends to work best when:

• the process is repetitive
• delays are costing time or revenue
• people spend too much energy on coordination, follow-up, or exception handling
• the company needs more capacity without proportional headcount growth

This is where AI starts doing more than assisting. It starts removing drag from how the business runs.

A good example is Esquire Depositions, a U.S.-based legal deposition services company and part of Gridiron Capital’s portfolio. Esquire needed to support both organic and acquisition-driven growth while improving workforce efficiency and integrating operations more effectively. Making Sense helped build a scalable, centralized environment that improved decision-making, unified acquisitions, and increased workforce efficiency by 40%. The work also contributed to a reported increase in enterprise value.

What makes this example useful here is not just the result. It is the type of result. This was not a lightweight productivity gain inside one team. It changed how work moved through the organization.

That is why automation is often the best first bet. It usually sits at the intersection of three things private equity teams care about most:

• faster execution
• lower operating friction
• clearer path to measurable ROI

Why agents usually make sense later, not first

Agents become relevant when the business is dealing with a process that cannot be reduced to a simple sequence of fixed rules.

That often means:

• multiple systems are involved
• the work unfolds over several steps
• decisions depend on changing context
• exceptions matter as much as the standard path

This is where the promise of agents is real, but it is also where teams can get ahead of themselves.

An agent is rarely the right first move in a company where process ownership is still fuzzy, data is scattered, or integrations are fragile. In that environment, the business is not ready for autonomy. It is still trying to establish control.

For most portfolio companies, agents make more sense after the company has already improved process clarity and removed some of the manual friction around core workflows. In other words, they are often a second-stage investment, not the opening move.

What should be true before choosing any of them

Before deciding between copilots, automation, or agents, leadership should pressure-test the operating context.

A few questions matter more than the tools themselves:

This is one reason AI readiness matters so much in private equity environments. It is not just a technology question. It is an indicator of whether the company can turn a promising use case into a repeatable business outcome. 

A simple way to choose the first AI use case

A useful approach to prioritize is to assess each candidate use case across four variables:

• expected business impact
• implementation complexity
• operational risk
• speed to value

That produces a much clearer decision than asking which AI approach sounds more advanced.

Here is the practical rule of thumb:

This is where many leadership teams get clarity. They realize the real question is not “Which AI model should we adopt?” It is “Which use case will improve the business fastest without creating unnecessary execution risk?”

How this usually plays out across a portfolio company

In most portfolio companies, the path is not especially glamorous. Teams usually start by looking at where time, cost, or inconsistency is building up inside the business. From there, the work is more operational than experimental: tightening the process, improving access to the right data, and introducing automation where the burden is already visible. Once that foundation is in place, more adaptive, agent-like behavior becomes much easier to justify.

That sequence may not be the most ambitious-looking one, but it is usually the one that holds up better in practice. In a PE-backed company, the first AI initiative has to do more than signal innovation. It has to make the business run better under real operating pressure.

The best first move is usually the one with the clearest business case

Copilots, automation, and agents all have a role to play, but they do not solve the same kind of problem. Copilots usually improve how individuals work. Automation tends to improve how work moves across the business. Agents can become valuable later, once the company is ready to support more adaptive execution across systems and decisions.

So the right place to begin depends less on which option sounds more advanced and more on where the business is actually feeling the strain. That includes the nature of the problem, the maturity of the operating environment, and how much execution risk the company can realistically absorb.

For many PE-backed companies, that still leads back to automation. It is often the most credible way to reduce friction, improve execution, and produce an outcome the business can measure without overcomplicating the first move.

Not sure where to place the first AI bet across a portfolio company?

Schedule a quick meeting to identify the highest-leverage use case and move from prioritization to implementation with a clearer path to business impact.