Due Diligence Finds the Deal. But there’s more to it.
TL;DR
As PE and VC firms hold assets longer, “fine performance” is no longer enough. Increasingly, value creation is being driven by operational redesign, workflow optimisation, technology alignment and AI readiness.
The challenge is that most organisations were never intentionally designed as cohesive operating systems. They evolved over time through fragmented workflows, disconnected systems, tribal knowledge and process accumulation.
This article explores:
- why “full potential” diligence is becoming critical;
- why technology is still commonly viewed through the wrong lens;
- how AI amplifies operational maturity (or lack thereof);
- and why operational discovery must happen before meaningful transformation can occur.
The real leverage is rarely found in replacing software.
It’s found in uncovering and redesigning the operational behaviours the software currently reinforces.
Recently, I came across a quote in an article discussing longer private equity hold periods and the growing importance of post-deal value creation.
One line in particular stood out:
“There is always a comprehensive diligence conducted pre-acquisition, but that diligence doesn’t always include a view of what we’d call the ‘full potential’ of the company across all commercial and operational levers.”
That observation is more important than it first appears.
Because in my experience, most due diligence processes are still heavily geared toward validating risk, not uncovering potential – especially in the realm of technology and tech-enabled organisations.
That being said, there’s absolutely nothing wrong with that. Risk matters. Financial quality matters. Legal exposure matters. Cybersecurity matters. [over] Analysis Paralysis matters. But those activities largely answer one question:
“Is this business safe enough to buy?”
They do not necessarily answer the more important question:
“What could this business become?”
And that gap is where enormous amounts of unrealised value continue to sit dormant inside organisations and is perhaps getting a new light shined on it in the wake of AI emergence and delayed exits.
The Challenge With Traditional Due Diligence
Traditional diligence is usually constrained by time, access and incentives.
Pre-acquisition, you rarely have unrestricted access to operational staff, internal politics, undocumented workflows, fragmented systems or the ugly reality of how decisions actually get made inside the organisation – In my experience, there is a slight veil of “honeymoon behaviour” when conducting pre-acquisition due diligence – completely normal mind you.
This polished operating narrative often looks like:
- The org chart.
- The approved process.
- The architecture diagram.
- The reporting pack.
- The “single source of truth”.
Then post-acquisition reality arrives.
- You discover five disconnected systems doing the same job.
- You discover the business runs on spreadsheets nobody wants to admit exist.
- You discover customer data is fragmented across departments, tools, vendors and legacy systems.
- You discover approvals are bottlenecked through tribal knowledge.
- You discover operational reporting is manually assembled by exhausted middle management every Friday night.
- You discover technology teams maintaining workflows the business no longer even understands.
- Lots of sticky-tape and bubble-gum.
And perhaps most importantly:
You discover the business has never actually been designed intentionally as an operating system.
It has simply accumulated over time…and to be quite honest, that’s actually completely normal. Systems and processes are built around the value-proposition, not the other way around.
Technology Is Still Commonly Viewed Through the Wrong Lens
One of the biggest mistakes I continue to see in both traditional due diligence and post-deal operations is that technology is treated primarily as infrastructure.
- A cost centre.
- A cyber risk.
- An integration challenge.
- A platform rationalisation exercise.
- A migration roadmap.
But technology is none of those things in isolation.
Technology is the enabler through which an organisation executes decisions.
It is the engine room between commercial ambition and day-to-day operations.
The real issue is not whether a company is running outdated legacy systems.
The real issue is whether the organisation’s workflows, decision-making structures, data flows and operational behaviours are aligned to the outcomes the business claims it wants to achieve.
That’s a very different conversation.
Because often the greatest value opportunities are not found in replacing software.
It’s found in redesigning the operating model the software currently reinforces.
“Full Potential” Is Operational, Not Abstract
The phrase “full potential” sounds abstract until you put it to work.
To me, full potential is:
The measurable value left on the table because the organisation’s workflows, systems, data structures and decision-making processes are not aligned to its commercial ambition.
That value leakage appears everywhere:
- Revenue leakage.
- Approval latency.
- Manual handling overhead.
- Poor customer visibility.
- Duplicated effort.
- Delayed reporting.
- Broken feedback loops.
- Slow decision cycles.
- Inability to scale.
Oftentimes regressing back into the “Business vs IT” tug-of-war.
Most organisations don’t lack ambition.
They lack operational coherence.
And the longer a business is held, the more those inefficiencies compound, impacting uplift, exits and ultimately value.
Which is why sustained “full potential” diligence makes complete sense in a modern PE environment. Its part strategy and operational opportunity combined.
This Is Where AI Changes the Equation
The article also referenced the increasing use of generative and agentic AI across portfolio companies.
That trend is real.
But most organisations are still approaching AI in a very traditional sense.
They are asking:
“How do we apply AI to the business?”
Instead of asking:
“What operational friction exists inside this business that AI could help eliminate, accelerate or augment?”
Those are radically different starting points.
AI is not magic.
AI amplifies operational maturity, brutally punishes those that are not (a tale for another day).
If the organisation’s workflows are fragmented, undocumented or politically inconsistent, AI often magnifies the chaos rather than resolving it, creating some bizarre frankenstein monster commonly referred to as “AI Slop.”
Which is why workflow discovery and operational mapping are becoming increasingly important before large-scale AI adoption.
You cannot coherently automate what you fundamentally do not understand.
The 4D Gap
This is one of the reasons I’ve increasingly framed transformation work through the lens of Neon Light’s 4D framework:
Discover → Diagnose → Design → Deliver
Not as a consulting slogan.*
As an operational discipline.
Because most organisations skip directly to delivery.
They implement platforms before understanding workflows.
Attempt to deploy AI before understanding the impacts.
Buy tooling and or engage MSPs before identifying operational constraints.
Scale process before validating whether the process is even correct.
The result is predictable:
More technology.
More complexity.
More operational drag and cost.
Not more value.
The “Discover” and “Diagnose” phases are where full potential actually becomes visible.
Not just through interviews and workshops, but through understanding how the organisation truly operates beneath the surface – usually where the value has either been operationalised or quietly trapped.
That process is rarely linear.
It involves operational friction, competing priorities, legacy behaviours, technical debt, organisational fatigue and often a fair amount of uncertainty. But there’s also a certain level of shared understanding that emerges when teams begin unpacking those challenges together.
In many respects, that’s where meaningful transformation actually starts.
Not from a vendor pitch deck or a migration roadmap, but from aligning technology enablement with the operational realities and ambitions of the business itself.
That is where the hidden leverage exists.
(* kind of a consulting slogan 😹)
The Shift That’s Coming
Longer hold periods are forcing a shift in mindset.
“Fine performance” is no longer enough.
And increasingly, PE and VC firms are realising that value creation is not simply financial engineering or aggressive cost reduction.
- It is operational redesign.
- It is workflow optimisation.
- It is information clarity.
- It is decision acceleration.
- It is technology alignment.
And increasingly so, it is AI readiness – not jumping into bed with AI on day one.
The firms that understand this will likely outperform over the next decade, keep exit schedules tight and enjoy the benefits of increased organisational value.
Moving forward, the leverage will not come from simply buying good businesses.
It will come from systematically uncovering the unrealised potential already trapped inside them.