The Value Creation Playbook: Building the Rhythm of Business in Private Equity

The first in a series on how private equity firms and portfolio companies are rethinking value creation in the era of AI.

The first is the environment. Debt has become extremely expensive, multiple expansion is less reliable since interest rates have risen, and operational alpha is now the main lever firms are relying on to hit their return targets. The pressure has increased along with it.

The second is technology. The old days of technology being owned by the CIO and considered a cost to be managed are over, and transformation is now being driven by technology. That has changed what technology leaders are expected to own. Their focus was traditionally infrastructure, and the refrain used to be that the company was not infrastructure ready, that its data was not ready for AI. Technology leaders are now accountable for how AI influences growth, customer experience, product innovation, EBITDA, and MOIC, whether or not the infrastructure and the data are ready. That is an enormous shift, and it has moved the focus to quick wins in the AI era.

Every portfolio company is talking about transformation. Delivery is where they struggle.

• Time. Bain is now saying 12 is the new five; in the private equity world, that is unacceptable. Hold times still run between three and seven years, and firms expect results within the first 18 to 24 months. True operational transformation asks for new systems, restructured teams, and culture change. None of these happen quickly, so companies live inside a constant push-pull between quick wins and long-term change.
• Management capability. The team that brought a company to acquisition may not be the team that drives the next chapter of growth. Private equity firms and management teams need to accept that early on and make changes where appropriate.
• Bandwidth. Portfolio companies do not carry extra people to take on all this new value creation.
• Too many priorities at once. A 100-day plan can translate into 15 to 20 workstreams, and that is a lot of change. Someone has to drive the data-based decision making that surfaces the highest and greatest initiatives to tackle first.
• External shocks. The assumptions that go into a value creation plan do not hold true indefinitely, and companies have to be nimble and agile enough to absorb them.

Taken together, these have moved the difficulty away from strategy and into execution. The importance of AI, modernization, and technical debt is not in dispute. Firms are struggling to execute it all in an agile and nimble manner, and to carry the change management and alignment that go along with transformation.

Those pressures perpetuate the need for a transformational leader: someone who owns the value creation plan, manages it daily, and sets priorities so the company can execute. Sponsors frame their search around capacity, looking for a person who wakes up every day with the plan as their job.

They often want, and need, more than that.

A true transformational leader gives the executive team a second set of eyes: someone involved in the day-to-day thinking who can challenge decisions, stress test them, and look at them cross-functionally. The CEO is the only other person holding that same view of the business, and more often than not, they cannot be involved in all the details. This is also the internal voice that will challenge the CEO and bring execution discipline to an agenda that is complex and fragmented.

The role carries the plan’s traffic as well. Data has to move back and forth and stay current, as do the KPIs, and inquiries arrive from the operating partner. Lifting that load off the CEO and the executive team is valuable in itself.

The job calls for someone who holds the big picture while making sure the details in the execution happen in an expedient way. A leader who dives into the weeds will be consumed by the nitty-gritty, with no bigger picture or strategy in mind.

Organizations expect one executive to solve what requires an entire operating model change.
The assumption is that this individual, the chief transformation officer or the chief of staff who owns execution of the value creation plan, will have visibility over the entire operating model. The reality that surfaces is that no operating model exists, or that the one in place is very informal. The job is to build that structure. The value creation plan needs visibility to the dependencies from one function to the next, to the decisions that need to be made, and to the priorities that are shifting, changing, and need challenging. That structure has to be established well enough to last beyond the leader’s own tenure.

Setting up that structure is one part of the job. Succeeding with it is another.

In some organizations that leader is already in place. In others, the leadership required to drive an operating model change is missing entirely. The best transformation leaders create rhythm and momentum in the organization, and they give their teams the power to make that change. To reach point B from point A, the team has to be told what point B looks like and how it is going to get there.

Too often companies jump into execution without getting the organization excited about where it is going, without the why behind the what, and without each individual understanding their role in it. Momentum requires execution, capacity, and visibility, which is where organizations struggle. Building that momentum is the rhythm of business: everyone rowing in the same direction, and excited about it. It is a team sport.

The absence of that rhythm shows up most clearly in AI. Almost every board meeting now begins with AI. Every CEO has an AI initiative, every portfolio company is experimenting, and many are not seeing the enterprise value.

Excitement and hype have produced a proliferation of projects, and those pilots often do not scale. Much of it comes down to the data, which is either missing or of poor quality, leaving the assumptions AI is built upon invalid. Measurement is unclear, and the vibe coding and productivity plays AI produces are hard to quantify.

Technology is rarely the constraint. AI escapes the discipline applied to every other value creation or transformational objective. It needs the same disciplined execution: clear objectives about what the business is looking to do, workflow redesign, investment in data clean enough to bet on, and change management, where the underlying effort required is often underestimated.
Technology without business ownership, AI for AI’s sake, is the most overrated transformation initiative there is.

AI is a means to what a company wants to achieve. Companies keep asking where they should use AI. They should be asking how AI can change the way the company operates so that it impacts growth, EBITDA, and MOIC at velocity, because no one can wait until kingdom come for AI to move those parameters. Start with engineering and how AI should change the way software is built, then run the same exercise through products, customer service, sales, finance, and the back office, attaching a tangible value to each.

The answer depends on the operating model. Without a rhythm of business that makes the dependencies visible, each function asks how AI might help its own organization when the answer sits across all of them. With the operating model and the rhythm established, a company can look at how AI complements and transforms that entire way of working.

Which returns to management capability, the second of those five factors. Accomplishing all of this comes down to the leadership team, and the biggest hiring mistake is addressing today’s business when the hire should address where the company wants to go tomorrow. Consider how the leadership team of a software company would be designed on the day it is acquired.

The single most important decision is the CEO, and the profile depends on the thesis. A growth thesis calls for someone commercially smart and experienced. A turnaround or a heavy integration calls for someone with operational discipline and a track record of change management behind them.

Whichever profile fits, that CEO needs learning agility, because the learning curve is very steep now and he or she has to become the chief transformation officer and the chief AI officer as well.

Six seats follow:

• Chief financial officer: The obvious second seat, given the reporting and financial focus of private equity.
• Chief technology officer: The architecture and delivery discipline required of today’s companies is paramount, which makes this seat non-negotiable. It also calls for business fluency, the ability to translate what he or she and the organization are doing into positive business impact, because sitting in a technology cocoon without realizing whether business value is coming out of it does not work.
• Chief product officer: The most underrated executive of the group, and the one who holds the connection between sales, customer feedback, and where the roadmap is going. Without that tie, it is hard to know what the company is building.
• Chief revenue officer: One individual responsible for marketing, sales, and customer success together, because inefficiencies get ingrained when those three are separated.
• Chief of staff or chief transformation officer: The executive who owns execution of the value creation plan.
• AI-native engineering leader: Transformation is not only about ideas. Somebody has to transform those ideas into an AI-native software engineering playbook. AI changes the economics of software development, and it should help talented people deliver exponentially more value.

With the right team in place, the reviews are what keep the rhythm going. Weekly and monthly reviews have to look at the right parameters, and too often companies and firms look at lagging indicators; revenue, EBITDA, and cash all tell yesterday’s story. The pivot has to be toward leading indicators.

Engineering output is one of them that is not looked at enough: a leading indicator of future revenue and margin in the same way sales pipeline is. Tech debt is another one that is often ignored, and it can undermine an exit and derail that exit process.

Companies are beginning to track AI on adoption rate, which is a reasonable start. Adoption measures activity. What needs tracking is the business impact underneath it, so that value creation is exposed.

When engineering moves faster, product development is faster and revenue recognition is faster. Customer retention comes next, along with whether the company is making enough changes to the product to remove tech debt and experience debt. Experience debt is what allows a company to gain market share, stay innovative, and increase margins through the value delivered to customers. Product velocity, product changes made according to what the customer wants, and optimized customer service are how a valuation improves.

It is also what a buyer is paying for: how much AI has gone into increasing customer retention and market share, and how repeatable and industrialized it can get. They pay for durability.