The 272-Day Problem*: Why Your B2B Sales Cycle Keeps Getting Longer, and What to Do About It

Image above, source: Demandbase

I’ve spent almost my entire career in b2b sales and marketing, most of it in software. And I am seeing changes in the business that are unprecedented, and are hitting companies like a perfect storm.

- Plummeting conversion rates and ROAS on advertising, compounded by the already existing issue of often very small target audiences (as compared to B2C)

- Difficulty in attributing revenue, since a sale involves such a flywheel of both marketing and sales collateral and channels, that sometimes it would take a marketing analytics genius to unpick what caused that sale.

- The continual march of new technologies, like ABM platforms, Intent, and list building tools, AI – the game changer, and now, often hard to fathom AI hybrids like Clay (try explaining that to someone unfamiliar with it – ‘its like a cross between zoominfo and seamless AI, except it also ingests data from lots of other intent and list building tools like bombara’).

- The unrelenting pressure, which I’ve never seen before in my entire career, to prove ROI on every single action. In the 'roaring' 1990’s you could throw money around to get sales, these days CFO’s want military precision in the way marketing (and sales) budgets are allocated. 

- Falling lead to sale conversion rates, which sometimes can accompany declining average order values at the same time – in countries or sectors with low or negative growth, for example. 

Yes, B2B sales has certainly changed dramatically in the last thirty years. Some of those changes have been slow and incremental, for example the changes in business intelligence, from using magazines and trade publications to research prospects (yes, that did happen in the ‘olden days’ as my 17 year old son Jack calls any time pre-2000), to basic computer research (but google was very basic back then), to the adoption of tools like zoominfo and discoverorg, that provided you with both list building and business intelligence features – this would take us from the 1990’s, up until say 2018.

Others have been dramatic and disruptive – with the adoption of AI, which hit us all hard at the start of the 2020’s. Continuing that business intelligence example,  where now where you can get a full breakdown on a prospect in seconds using a variety of AI tools. 

But possibly the most alarming change, and one that is baffling teams across the world, from small startups, to huge behemouth companies, is the lengthening of the sales cycle 

Dreamdata** just dropped their 2026 LinkedIn Ads Benchmarks Report, and the data confirms what we already felt:

• The B2B sales cycle is getting longer, more complex, and more demanding.
• The average B2B customer journey now takes 272 days — up from 211 last year*
• Each deal involves 10 stakeholders and 88 touchpoints across 4 channels.
• And 81% of that journey happens BEFORE a prospect ever enters the sales pipeline.

That means buyers are spending roughly 7 months doing their own research before they ever talk to you. This aligns with other research I’ve read that show that 80%+ of the sales evaluation is completed before a b2b prospect even talks to your sales person. With AI that number will no doubt increase. 

So what is going on, and why?.... and I’m sure an even more important question you are asking: How can I fight this trend, and start to shorten our sales cycles?

Firstly, Looking at the data across B2B (Saleshive) this is the pattern

What’s particularly interesting about this data to me is that it matches what I’ve seen in reality working at a variety of mainly b2b saas companies, from Zscaler, Visual IQ (Now part of Nielsen), and Hansen, working on average annual deal sizes of $1M -$5M, to working at companies like Mention Me, or Mintago, where it was at the other end of the spectrum, $20,000-$75,000. But across the board I’ve seen slower sales cycles, especially in low growth regions.

Why B2B Sales Cycles Are Stretching

Expanding Buying Committees: A typical complex B2B purchase now requires consensus from 6 to 10 distinct decision-makers (ranging from IT and security to finance and procurement). Every stakeholder added introduces another calendar, new objections, and internal misalignment

Intense Budget Scrutiny: Due to broader economic slowdowns, purchases that previously required a single manager's signature now need multiple sign-offs, often terminating in a strict review by CFOs or procurement teams

The "No Decision" Paradox: The fear of making a bad software or vendor decision has increased. Research shows that 40% to 60% of B2B deals end in "no decision" because champions cannot justify the business case internally, or because the team is overwhelmed by information.

What can we do about it? 

Gen AI is restructuring the entire B2B buying journey. AI is not just adding a new channel, but displacing the controlled channels (sales reps, distribution networks, owned media) that B2B go-to-market has always relied on. 

Buyers now use AI tools to discover vendors, compare options, and evaluate fit long before they ever talk to a salesperson, which means much of the influence happens before sellers even know a buyer exists. 

This means that the current ‘80% of buying done before a prospect even talks to a sales person may move up to 90 or even 95% over the next five years’. 

Obviously, the sales person who can make the most of that 20%/10%/5% of influence they have at the end of the buying cycle is critical. 

If the sales person is not armed with all the buying information they need, they will loose out to better informed, and supplied sales people (even if those sales people are not as technically proficient or experienced).

How the funnel changes

The old funnel ran on controlled channels and a slow, resource-intensive evaluation phase. Buyers worked through offerings, use cases, pricing, and internal alignment over months. 

Gen AI is inverting the shape: the top widens because buyers can access a far broader set of vendors via AI synthesis, but options get eliminated much earlier and faster. 

75% of US B2B technology buyers now finish their purchase journey in 12 weeks or less, versus 11 months in 2024. That is a fast and dramatic compression. IDC's predicts that 62% of traditional B2B demand generation will be AI-led by 2028.

Buck the increasing Sales cycle trend with smart search strategies

You'll have no doubt spotted the contradiction. Everything above says B2B sales cycles are getting longer, yet here's IDC forecasting an AI-led buying surge that makes them dramatically shorter. Both are true. They're just describing different parts of the funnel.

The lengthening happens at the back end: more stakeholders, tighter procurement, CFO sign-off. That's structural and it isn't going away. But the front end, discovery, research, shortlisting, is where AI-led buyers move at a completely different speed. 

A buyer who once spent months researching now lets an AI assistant synthesise the options in an afternoon. The committee still takes its time; getting onto the committee's shortlist now happens in days.

That's the opening. The average cycle is lengthening, but a fast-growing pocket of AI-native buyers, concentrated in the US, where these shifts usually begin, is compressing the early stages hard. 

Get your content built for how those buyers actually search, and you don't just keep pace with the trend. You can pull your prospects through the slowest, most expensive part of the funnel before your competitors even surface as an option.

Two opposing truths in one market is a lot to hold at once. It feels like cognitive dissonance. But sit with it, and the contradiction dissolves: the cycle is lengthening and compressing at the same time, in different places, for different reasons.

From an SEO perspective, using a famous analogy, you don’t want to be the organisation producing the absolute top flight best buggy whips, as the age of the motor car begins. Yet so many organisations are stuck in the old models. 

McKinsey’s 2025 B2B Pulse Survey finds that only 19% of respondents are implementing use cases involving gen AI tools for B2B buying and selling.

A pilot study conducted by Digitas UK, a subsidiary of Publicis Groupe, examined B2B fintech payment solutions in the UK and U.S. markets. The findings revealed that more than 80% of the sources leveraged by LLMs originated directly from the brands themselves, such as Stripe, Adyen, PayPal, and Visa. 

Caitriona Gallagher, strategy partner at Digitas, said: “This makes sense because these brands have significant amounts of content on their sites to help support B2B buying journeys, including product comparison content and sector/audience led content".

So even if your industry is in a category that you don’t think warrants much of your own in-house quality technical content, AI will still be pulling search data for your prospects from the internet.

And if you don’t have anything out there, content wise, your company will fall at the first discovery phase (Awareness/Consideration/evaluation) of your prospects AI driven buying journey. And as I already explained, the trend is moving to faster choice of vendor at the outset using AI research capabilities. 

So if your content is not adapting to the new world, you could be left out in the cold, with your company not even being considered as an option at outset, let alone making it to the final decision stage, and eventual sale. 

When I first worked in sales over 25 years ago, there was complete information asymmetry on the part of the seller. The buyer had very little information to go on – trade magazines, perhaps some scraps of information on the then nascent internet. 

As time has passed, that balance has swung the other way completely. In the past, the sales person controlled perhaps 80% or more of the information that the buyer went on, but now, as discussed those numbers are reversed and may even move to 10% or less. 

We live in a world where the sales people have fast diminishing opportunities to make their mark on the prospect. So the sales person who is best informed will win most of the time. 

The Dark Funnel (AKA 'Iceberg'): Why 80% of Your Buyer's Journey Is Now Invisible to You

That is where tools like Demandbase, 6sense, clay, hubspot, alongside bespoke AI solutions, can make a decisive difference. These tools enable you to get into the heads of your buyers: find out what problems they have (maybe even before they themselves can articulate them), what motivates them to buy, where they are in the buying cycle, and when the 95% of buyers finally jump to that sales nirvana of being ‘in market’. 

Some companies are sprinting ahead with these tools and methodologies; For example, in the Enterprise B2B SaaS markets I've worked, we started to transition from the MQL-SQL-Sales model a long time ago, to the 'Buying groups' model. 

Whilst others are even struggling to get the most basic data and insights, even from their own prospects and customers! You feel like saying 'hey, 2005 called and they want the martech and crm stack back' (this joke comes from the team at Akamai, who used to laugh at my wife, Catherine, still using a blackberry when everyone else was on Iphones already - I'm glad to say I instrumental in that 'transition' process). 

Peer to peer content, research, and review sites

When I purchase Hubspot CRM, and Marketing Automation for the UK and Italy arms of an international company just prior to its IPO, I was pretty nervous about making the right decision. There was an array of options available to us; from building in-house, keeping our existing pipedrive/mailchimp infrastructure, or using combinations of Salesforce/Hubspot/Marketo/Dynamics and other providers out there. 

What helped me enormously in that critical final purchase stage, was the array of high quality peer-to-peer review sites, like trustpilot, google, and most importantly for me, G2 Crowd - the most professional and reliable of them all. That was in addition to reading the best research from companies like Forrester, Gartner and IDC. 

I guarantee that your prospects will be doing the same. And the biggest trend of them all is the peer-to-peer content, which has skyrocketed - G2 crowd, Quora (I'm pretty active on that one), Linkedin, and to a lesser extent, reddit. These are also sites that AI use to drive their search answers. Win the reviews, win the revenue, and faster than your competitors. 

Cracking the UK Productivity Puzzle

My family and I just got back from a week in Greece. It’s a beautiful country with wonderful people. But more importantly, almost all that we have today sprang from that vibrant, innovative, intellectually brilliant culture – Democracy, Mathematics, the Sciences, Philosophy, the arts. 

Sure there are other cultures that have influenced the word a great deal from the Romans (though they stole a lot of their ideas from Greece too!), to ancient India, China, then later the French, German, Spanish, Portugese, Dutch and the British.

But the Greek culture has had a disproportionate impact on the world we live in today. My American wife, Catherine, often has quite a ‘new world' take on issues – On Greece, she also had a very thought-provoking insight; "it’s amazing that this country was the birthplace of so much that is exceptional in the modern world. Yet look at it today, mired in unemployment, poverty, and with a failing political and economic system".

And I got thinking – what caused that decline? I came to the conclusion that one powerful factor was declining productivity growth.

"Making the safe decision is the fastest way to become irrelevant."

Teresa Barreira, Global Chief Marketing and Communications Officer at Publicis Sapient, argues that growth comes from challenging the status quo: 

"Fortune favors the brave who are willing to disrupt, challenge, and change things. It applies to everyone and everything, from individuals to companies and brands." Teresa Barreira on LinkedIn

Countries need to keep innovating, taking risks (as in the quote above), challening the status quo, and improving, to maintain their economic strength. And without economic strength, no other can really persist. 

No country can afford to rest on its laurels, otherwise they risk going from being the Greece of the ancient world to the Greece of today (still beautiful but certainly no one would claim it is a global powerhouse).

Then I started to think about three countries that I am most intimately acquainted with – I have lived and worked extensively in all three countries. The Netherlands, where I was born, and the home of my mother. 

The UK where I was raised for much of my life, and where I went to school, and university. And finally, the USA, the country I moved to by choice – first, to study for an MBA, and then raising a family and working there, for ten years.

But my experiences are one part of the story only. I will rely on data to inform me as to my thesis on falling productivity. But there are other, rather wonderful reasons why comparing the UK to the Netherlands, and the USA can provide such powerful insights.

The USA has the most free market Economy of the three – the lowest taxes, the highest inequality (though the UK is getting closer), and the least protective legislation.

It varies from state to state though – Massachusetts, where I lived, is closer to the UK in workers protection. Whilst States like North Carolina, Florida, or Texas are truly the ‘wild west’ with very little in the way of workers rights, paid vacation, or maternity/paternity leave).

The Netherlands is at the other end of the spectrum politically, and socially. It is very hard to fire someone (as in France, or Germany). Workers are far more protected than in the UK. 

Dutch employees also work some of the shortest hours in the world: 27 hours a week in the Netherlands versus 36 in the USA and 31 in the UK.  https://worldpopulationreview.com/country-rankings/average-work-week-by-country  - as you can see one way thay achieve that is by being super-productive.

It’s hard to talk about large macro economic issues like productivity, or innovation, without veering into politics. When you start to talk about solutions to issues like low productivity growth, it’s almost impossible to do so without talking politics.

However, I am aiming to, as far as possible, avoid taking a political position. That is one of the reasons why I chose two countries with much differing political outlooks, to compare to the UK. I am far more interested in finding out what your views are on how to solve this problem than promoting my political outlook.

 

The Problem – low productivity growth in the UK over the last twenty years, which has been exacerbated by each crisis – from the 2008 financial crash, to Brexit, to the Covid pandemic. 

UK productivity, already struggling, has been hit by each crisis, and not recovered from them (in the way that the USA, has, for example). Why is that?

So I have two theories backed with extensive evidence, why the UK is underperforming both a more free market economy like the USA, as well as a more planned economy like the Netherlands. And one that is more of a conjecture, based on my personal experience and observations. However, I have provided some good data to support that theory as well. 

Lack of investment

My experience of working in the US and the Netherlands bears out the hard evidence, that business investment in the UK is very low and falling, and that this is a major factor in our poor productivity growth. 

I’m not just talking about investment in hardware, software or other work tools. There is very little on the job training in the UK, versus in the US, or the Netherlands.

The numbers behind that observation are stark:

Whole-economy investment in the UK was just 18.9% of GDP in 2025 — the lowest in the G7. The US sits at 21.6%. The Netherlands at 19.7%. 

A three-percentage-point gap against the US doesn’t sound like much. But sustained year after year for two decades, it compounds into a yawning chasm.

The Institute for Public Policy Research went further in their April 2026 analysis. They calculated a “capital gap” — how much less capital British workers have to work with compared to peer countries. 

The answer: British workers have 38% less capital per hour worked than the average of comparable economies. In manufacturing specifically, the gap rises to 47% — versus a peer set of the USA, Germany, France and the Netherlands. 

British factory workers are working with roughly half the machinery, equipment, robots and IT systems that their American, German, French and Dutch counterparts have. Of course they produce less per hour. They’re working with one hand tied behind their back. (IPPR press release, April 2026)

Somehow this fact made me come back to Greece again - They were using the state of the art equipment, three thousand years ago. But today, they sit significantly lower than any of the countries mentioned, including the UK (Greece is sub 18%, and they're more than half less productive than the Dutch or the Americans).

And it gets worse on intangibles. 

The US invests 6.7% of GDP per year in software, IP, data and organisational know-how. The UK invests just 4.2%. 

That gap is hugely significant because intangibles — software, design, brand value, training, R&D — are increasingly what drives productivity in a modern economy. 

The US has been quietly out-investing us in the very things that compound the fastest, and the gap is widening, not closing.

Then there’s the on-the-job training I mentioned. The hard numbers: British businesses spend roughly half the EU average per worker on training and development. 

Anecdotally, when I attended a US business school, half of the MBA part-time class were funded by their employer, and around a ten percent of the full time. 80% provide financial support to employees pursuing MBA programs specifically (Georgetown CEW / GMAC). In the UK, that would be unthinkable. 

And that, ultimately, is the point: if we invest less in our people, our tools, our skills and our future, we shouldn’t be surprised when productivity flatlines. One way to look at this problem, is that the countries that out-innovate us are simply out-investing us.

The Chartered Management Institute reports that around 82% of UK training spend is funded by employers themselves 

— and naturally, employers prioritise senior and professional roles, not the middle managers and frontline workers who actually run day-to-day operations. 

And that is born out anecdotally by myself. I got a tremendous amount of on-the-job training when I worked in the Netherlands, and in the USA. In the UK, I've had virtually zero.

The result is a workforce that doesn’t get developed beyond what each individual chooses to invest in themselves.

In the States, every employer I worked for had a real training budget — actual money set aside to develop me. In the UK, not so much.

Poor management

If I’m honest, this is the one that surprised me most when I started digging. I’d always vaguely assumed British management was a bit stuffy but basically fine. It turns out, it isn’t.

There’s a serious field of academic work measuring management quality across countries, called the World Management Survey. It’s been running since 2002, led by Nick Bloom (Stanford), John Van Reenen (LSE/MIT), and Raffaella Sadun (Harvard). 

They’ve trained interviewers to score firms across 18 management practices — things like target-setting, monitoring, talent development, and how firms deal with poor performers — using structured interviews with middle managers. They’ve now covered around 13,000 firms in 35 countries.

The results aren’t kind to us.

The US comes in first. Then Japan, Germany, Sweden and Canada. The Netherlands ranks sixth, at 3.04 out of 5. The UK ranks seventh, at 2.95. The gap to the Netherlands is small. 

The gap to the US is roughly half a standard deviation — which sounds technical, but Andy Haldane (then chief economist at the Bank of England) translated it in a memorable 2018 speech, “The UK’s Productivity Problem: Hub No Spokes.” 

UK management practices, he said, are about half a standard deviation lower than comparator countries, and these management skills are statistically significant determinants of productivity.

But here’s what really matters: the headline average understates the problem.

Haldane showed that the UK has TWICE the share of firms with low management scores compared to the US and Germany. 

We have plenty of world-class firms at the frontier — but a long, fat tail of badly-managed companies that other countries simply don’t have to anything like the same extent. 

And that tail has been growing. Between 1997 and 2023, the number of UK firms below the 25th productivity percentile nearly doubled, from around 444,500 to 873,000.

Why? My theory — and this is where it gets uncomfortable — is that we’ve created a class of “accidental managers.” The Chartered Management Institute estimated in 2023 that 82% of UK managers had no formal training before being put in charge of people. In 2019 the figure was 68%. So it’s getting worse, not better. 

The downstream economic effects are brutal. A UK SME generates roughly £147,000 of output per worker per year. A German SME generates £335,000. That’s more than double. 

A UK business with ten employees could theoretically add £1.9 million in annual turnover if it operated at German productivity levels. Now think about how many small British firms are scraping by, when with better management they’d be thriving.

How much of the international productivity gap does management actually explain? 

Bloom and colleagues estimated in 2016 that management practices account for roughly a third of the productivity gap between the US and other countries.

Management isn’t a soft factor. It’s a major component of why the UK is falling behind.

When I worked in the US, most of my managers had been trained — through MBAs, or rotational programmes, or formal leadership development.The Netherlands has also has built-in formalised training for all employee levels.

Lack of cognitive diversity

Scott Page (Michigan, complex systems) published the foundational paper with Lu Hong in PNAS in 2004 showing, with proofs, that a randomly-selected group of diverse problem-solvers outperforms a group composed of the highest-ability individual solvers, on sufficiently complex tasks. 

Page shows that various types of cognitive diversity — differences in how people perceive, encode, analyze, and organize the same information and experiences — are linked to better outcomes. https://www.pnas.org/doi/10.1073/pnas.0403723101

My experience working in both the USA and the Netherlands is that neither country treats conventional wisdom as sacred.

In America especially, the belief that one person with a better idea can overturn the status quo sits at the heart of the culture. 

From the revolution against King George III to Moneyball, where a young economist transformed baseball by ignoring decades of accepted thinking, Americans have long admired people who challenge established assumptions.

That culture doesn't just produce entrepreneurs and innovators. It creates an environment where different viewpoints are encouraged, disagreement is accepted, and new ideas are more likely to emerge.

Interestingly, the Netherlands, despite its many differences, resembles the USA in one important way: everyone speaks up. In a meeting, every Dutch colleague will offer their view, and no one assumes their opinion matters less because of rank or title.

The Dutch and Americans also share a striking directness, especially compared with the famously oblique British style.

Their meetings can feel blunt, even bruising, but they’re rarely short on ideas. Admittedly of the two, Americans get things done the fastest. The Dutch rely heavily on decision by committee, which can slow decisions down a lot. 

            

By contrast, the UK’s hesitancy sometimes feels cultural, a mix of deep-rooted elitism, and an over-reliance on conventional wisdom. It’s a tendency that thinkers from Napoleon to, more recently, Elon Musk have warned against.

Why CRM Systems Drift Into Disorder

Understanding CRM Data Entropy in Complex Systems

“These systems were working fine a few months ago. Why are they going wrong?”

This question is common in any organisation running large, interconnected data systems. At first glance, it seems reasonable: if a platform functioned well before, why not now?

But CRM and marketing systems are not like cars—you can’t simply “service, refuel, and go.” Their behaviour is closer to that of ecosystems than that of machines.

Why Systems Decay

A key distinction with CRM platforms is that they are not purely technical. They are socio-technical systems: a fusion of software, integrations, workflows, incentives, and—most unpredictably—human behaviour.

The Gremlins Metaphor

Engineers in WWII jokingly blamed mysterious aircraft malfunctions on “gremlins.”
It was a way to acknowledge that complex systems fail for reasons that aren’t immediately obvious and often emerge from subtle interactions. Crucially, it also bolstered morale by avoiding blaming any one individual for the failure.

CRM systems behave similarly. It’s rarely “one big issue.” It’s the quiet accumulation of tiny mismatches, workarounds, and human shortcuts.

Was It Ever Truly “Working Fine”?

A system that appears stable may in fact be held together by ad-hoc patches, legacy logic, or assumptions that only worked under lighter data loads.

Like a bridge that seems sturdy—but once traffic increases, stress fractures emerge.

        

We have this exact same issue around the corner from my house, at Hammersmith Bridge, London - closed to all traffic except pedestrians and cyclists for the last seven years for these very reasons.

Initially, the bridge was strong. But as traffic increased, small structural weaknesses began to show — bolts loosened, joints flexed, stress fractures appeared. This important connecting bridge in the UK has not functioned for seven years now. 

The Role of Entropy

The second law of thermodynamics states that systems tend toward disorder unless energy is continually applied to maintain structure.

CRM ecosystems follow the same principle. Even if perfectly configured on day one (they never are), entropy creeps in through:

• Human behaviour
• System complexity
• Time

These forces push the system toward disorder unless actively countered.

The Beehive Model: When Systems Work

A healthy beehive functions because:

• Roles are clear
• Communication is consistent
• Inputs are reliable
• Activity flows are coordinated

In CRM terms:

• Leads flow correctly
• Fields are completed consistently
• Deals follow standard paths
• Dashboards reflect reality

Information moves cleanly through the “colony.”

When the Hive Breaks Down

Entropy emerges through small, seemingly harmless actions:

• Required fields skipped
• Inconsistent tracking parameters
• Manual deal creation
• Logic edited without documentation
• Data duplicated
• Attribution overwritten

Individually trivial.
Collectively destabilising.

This mirrors research across socio-technical systems: micro-errors compound in non-linear ways, producing instability that no single actor intended.

Complexity Magnifies Entropy

Modern revenue stacks include:

• CRM platforms
• Marketing automation
• Intent data systems
• BI tools
• Sales engagement platforms

Every integration introduces risk:

• ID mismatches
• Sync failures
• Schema drift
• Conflicting definitions

The more integrated the ecosystem, the faster entropy accelerates.

Time as a Force of Disorder

Even without major changes:

• Definitions evolve
• Teams rotate
• New fields accumulate
• Legacy data lingers
• Integrations layer on top of integrations

The result is gradual “data model drift”—the system you have is no longer the one originally designed.

Below: Dashboard of a Lockheed Martin F-35 Lightning II fighter jet 

Dashboards Are Instruments, Not Reality

A dashboard is to a business what cockpit instruments are to a pilot: essential, but only a representation of reality—not reality itself.

The dashboard provides important signals such as altitude, speed, heading, but it is not the sky, the weather, or the terrain itself. 

Instruments must be monitored, calibrated, questioned, and cross-checked. No pilot assumes the sky looks exactly like the dial.

Dashboards can provide powerful insights, but they should never be treated as the entire picture of what is happening in the business. 

And just as in aviation, the most effective organisations combine instrument readings with context, culture, and human insight to understand what is really happening.

The same principle applies in organisations. Leaders evaluating CRM outputs should adopt a similar mindset.

The Core Insight

CRM systems are not static assets.
They are living organisms that require constant:

• Maintenance
• Alignment
• Definition clarity
• Human behavioural guidance
• Technical calibration

Without active energy input, the system naturally drifts toward disorder—data entropy is not a failure of people or platforms, but an expected property of complex socio-technical systems.

The Reality for Leaders

Managing these systems requires constant vigilance, thought, testing, imagination, planning, and long-term strategy, just as the rest of the business does.

Below: Solving Data Disorder, Managing Organisational Culture

Because left unattended, complex systems drift toward disorder. 

How you manage this complex system will depend a lot on your 'problem-solving culture'. If you look at the matrix above, and according to Jim Collins, author of 'Good to Great', only about 5% of organisations sit in the ideal top right-hand quadrant. 

But essentially, CRM/Marketing Automation/Analytics systems are no exception to the second law of thermodynamics - they drift into disorder, naturally. 

Like any complex system built on human inputs, software integrations, and evolving processes, they naturally accumulate entropy over time.

Which means the question is never whether disorder will appear. That is a given.

The real question is who is paying attention when it does. A Strong culture with psychological safety, populated by teams with diverse cognitive styles, will solve these issues faster and more effectively than others. 

The Real Edge of Private Equity: Active Ownership

I’m a big fan of Scandinavian thrillers, especially the original The Girl with the Dragon Tattoo. So when I walked into the auditorium at the London School of Economics, I had the strange feeling I was looking down at three lead actors from a Nordic noir drama.

The speakers were Ulf Axelson, Professor of Finance and Private Equity at LSE; Per Strömberg, Professor of Finance at Stockholm School of Economics and LSE; and Kurt Björklund, Founder and Executive Chairman of Permira, with roughly $50bn under management.

What followed was one of the clearest, data-driven explanations I’ve heard of why private equity (PE) ownership so often outperforms public equity, and where its limits lie.

Why Private Equity Outperforms: Start with the Data

The first half of the lecture was led by Per Strömberg and focused squarely on the evidence. Rather than starting with anecdotes or ideology, he began with productivity data across countries and firms.

In Germany, for example, fewer than 1% of firms accounted for roughly 65% of positive productivity growth over the period studied. Most firms contribute little. Some actively destroy value.

This matters because private equity does not rely on averages. Its entire model is built around identifying, creating, and scaling outliers.

       

The Mechanism: How PE Actually Creates Value

Strömberg argued that the performance gap between PE-owned and publicly listed companies is not primarily due to regulatory arbitrage or tax advantages, though those exist at the margin.

The core driver is active ownership.

Drawing on both academic literature and operating evidence, PE value creation can be grouped into three broad mechanisms:

1. Governance engineering

PE owners are not distant shareholders. They:

• Sit on boards
• Hire and fire management
• Set incentives tightly linked to value creation
• Intervene early when performance slips

This sharply reduces classic agency problems between owners and executives.

During my MBA at Northeastern, one of my finance professors specialised in corporate governance, and I conducted research on shareholder activism. One theme emerged repeatedly: public-company executives often optimise for personal incentives that diverge from shareholder value.

Below: PE-owned companies are rigorous in selecting customers that add value

PE ownership compresses that gap. In the same way that active shareholders hold senior leadership to account, Private Equity owners can step in to ensure the company is run as efficiently as possible. 

Per explained that the productivity and efficiency gains of Private Equity ownership (according to him, 2-3% higher than Public Equity, according to Kurt, head of a PE firm, it is closer to 6-7% higher), can be divided into three key categories:

Three types of engineering/tools

1. Governance engineering – being an active owner of the company

2 . Financial engineering – reduce cost of capital 

3. Become sector experts – can leverage networks to assist management

Well, that begs the question – why don’t other companies copy the behaviour of PE companies, to improve their performance?

According to Strömberg, this opens a “can of worms”.

First, PE performance may not be indefinitely sustainable. Funds have finite holding periods, typically six to seven years. Active ownership delivers diminishing returns once the biggest inefficiencies are removed.

However, within that limited time frame, PE seems to be doing better than ever. Exit value experienced a rebound in 2025, increasing 41 per cent to $1.3 trillion, the second-highest year on record. 

Second, PE capital is more expensive. While leverage can be cheaper than equity, the cost of financial distress rises sharply as leverage increases.

PE is not a universal solvent. It is a precision tool, effective under specific conditions.

An Operator’s Perspective: Kurt Björklund of Permira

The second half of the session (unrecorded) shifted from data to practice. Kurt Björklund described himself not as a financier, but as a “financial entrepreneur” and "Sector disrupter".

His framing was revealing.

Public equity investors, he argued, suffer from information asymmetry. Even large shareholders rely on periodic disclosures and carefully curated narratives.

PE ownership, by contrast, is built on information abundance:

• Proprietary KPIs
• Weekly operational interaction
• Direct access to management and systems

Björklund was blunt: unlike asset managers such as BlackRock, he cannot afford to be wrong. Every investment must succeed. That forces extraordinary diligence and relentless focus post-acquisition.

He also warned about classic PE pitfalls:

• Buyer’s curse in auction processes
• Cyclicality of capital markets
• The temptation to “take your eye off the ball” during exit processes

“In my business,” he said, “only the paranoid survive.”

Disruption, People, and the Role of AI

One of the most charged parts of the discussion came during the Q&A, where students (from the LSE, Imperial, Oxford, and Berkeley, USA) repeatedly asked about AI and job security. There were also several questions from analysts at various Private Equity firms.

Björklund acknowledged the anxiety, but did little to soothe it.

He described investments in complex B2B businesses where agentic AI, and improved automation have reduced headcount by orders of magnitude, particularly in areas such as KYC and compliance.

In one example, automation reduced a team from 5,000 people to 500, while increasing profitability. Many in the organisation were conducting relatively complex tasks, which could nevertheless be performed more effectively with AI and Automation.

Above - Top Target Universities (non-US) for Goldman Sachs. Source: Krugman Insights

His view was unsentimental: there will always be jobs for the very best, and the traditional path: An elite education, a top investment bank such as Goldman Sachs, and then a good Private Equity firm, remains viable. But the middle is being hollowed out.

Interestingly, he noted that older employees often adopt AI more effectively than younger ones, attributing this to psychological barriers to AI in younger workers. 

Perhaps it's also because you need deep experience in solving the problems, to ask AI the right questions? It's very easy to generate 'AI workslop' that drives no insight, and diminishes your credibility in the organisation. And that is no doubt from whence that fear emanates.

The recording was switched off halfway through the lecture, at which point the atmosphere in the room changed perceptibly. Kurt (The Chairman of Permira) smiled and said he would assume there were no journalists present, which meant he could now speak a little more freely than usual.

The professors, clearly enjoying the moment, joked that in Sweden, Kurt is known as “Superkurt”: the complete package: handsome, physically fit, wildly successful, and extremely wealthy.

Kurt laughed, didn’t deny it, and carried on.

Which confirmed something I’ve learned from working with private equity firms in the past: there is often remarkably little self-deprecation in the room, even when the person in question is a typically reserved and humble Swede.

Joking aside, this was one of the best lectures I've seen, unique in that it presented top-level insights from both the academic and 'real-world' perspectives. 

Further Reading