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 (My father, and my daughter both studied classics). Sure there are other cultures that have influenced the word a great deal from the romans, to ancient india, china, then later the 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? And I came to the conclusion that one powerful factor was declining innovation, and productivity growth.

Countries need to keep innovating, 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.

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 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 – 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, is almost akin to the UK in workers protection, whilst States like 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. 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

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. I am far more interested in finding out what your views are on how to solve this problem.

 

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 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. 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. 

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. The Netherlands was the same. 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 — and up to half for countries like Italy and Portugal. 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 formalised training.

One colleague of mine, an Oxford graduate, and content strategist, says that he thought Dutch professionals command of english was more adept than british professionals (despite it being the Dutch pros second language)! But of course, that is merely anecdotal. 

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 of working in both the USA and the Netherlands, is that both countries actively encourage looking at problems from different angles. The USA has a culture of radical individualism. 

The idea of one person with a brilliant new idea, breaking out of the failing established status quo is at the heart of this culture – from the American people rebelling against King George’s taxes without representation, which lead to the war of independence, to the movie Money Ball (below), where a gifted economist shakes up the then tired game of Baseball.

Funnily enough, the Netherlands, though very different in other ways, is quite similar to the USA in that regard. When you have a work meeting, everyone puts in an opinion – no one feels that their view is somehow less important due to their status, or position in the company. 

Both the Dutch and the americans are also much more direct in their communications than the famously oblique Brits. It can make some Dutch or American meetings quite hard to handle! But you will never lack for good ideas.

I feel that this malaise is deeply rooted in our culture of elitism (much greater in the UK than the Netherlands, and still more so than in the USA) in the UK, compounded by the British over reliance on conventional wisdom – which great minds, from Napoleon, to most recently, Elon Musk, have commented on. 

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 remarkably little self-deprecation in the room, even when the person in question is a typically reserved 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

What You all want to Know About Marketing in 2025: Growth Hacks, Customer Psychology, and ROI.

   

Did you know that the average time a LinkedIn ad takes, from first impression to B2B revenue, is 320 days? And 235 from ad engagement. Are you giving your campaigns enough time to work?

- Most companies in my sphere do not have enough, surprise, surprise! - patience. However, I do achieve success with retargeting ads, which, as you might expect, work faster.

But even with retargeting, you have some serious drags on your success – firstly, the economy, which is slowing, flat, or even in reverse, depending on where you are. Secondly, your brand – the biggest brands in the world only have to put out mediocre ads, and they will still get good results. 

But a small startup with an unknown brand, needs to hit it out of the park every time to connect with the customer. Plus, it’s fighting against the adage ‘no one ever got fired for buying IBM’

I guess you’d change that to some variation of Microsoft today. But the point is that it's very hard to convert a lead for a small company with little or no brand awareness.

However, that’s not to mean it’s not impossible! Companies like Zscaler, where I worked, did this – but it was not due to marketing alone. They had an outstanding product, which can make up for a multitude of sins. We also have a dynamite sales and marketing team.

On the contrary, if you are dealing with a poor or not well-established product market fit, it's easy for a company to become sucked into a viciously accelerating churn cycle 'whirlpool': Company loses customers at an alarming rate, so it needs to ramp up sales and marketing efforts (cue unrealistic promises), which speeds up churn, and so on.

How do you avoid such a situation? Make sure that you have virtuous feedback loops from sales and marketing to product and back. And that you have a strong culture of psychological safety. If you have a culture of ‘shoot the messenger’, then no one in senior management will be aware of these problems until it's too late.

How did I get so interested in this topic, I hear you ask? Well, actually, I’ve been very busy on various platforms trying to crack the code of how SEO is changing with AI, in the b2b space.

I feel like I’m getting there! Since my organic blog and web traffic have increased more than tenfold in the last year!

If you look at this chart, you can see what’s driving AI search – I would say for B2B it skews a little heavier towards LinkedIn and Quora. So I’ve been super active on these channels, and I’m getting over 50 b2b questions a day on just Quora, for instance. 

I recently did an entire audit of all my social media engagements across all my key channels (Quora/LinkedIn/Reddit) to see what the trends are

When I fed it all into AI, these were the top topics:

AI-driven GTM & the new search stack
How to win with AI Overviews/AI Max + LinkedIn/Quora/Reddit; practical playbooks, not theory.

Demand gen that actually drives pipeline (esp. in tight markets)
What to cut/defend/double-down on, sequencing experiments, and real metrics (demo-rate, win-rate, payback) — not vanity MQLs.

Sales psychology & decision-enablement content
Buying signals, persuasion without pressure, and case studies that sell (HBS-style narratives over “advertorials”). Quora hits on buying signals, mass outreach, and monetising lists point here.

MarTech/ABM stack & accountability
HubSpot/SFDC + Demandbase/6sense choices, clean ops, SLAs with sales, and agency performance models (pay-for-performance vs. retainers).

Trust engines: community, authenticity & micro-influencers
Turning brand communities and creator voices into a qualified pipeline; founder POVs and no-BS thought leadership resonate with the LinkedIn crowd.

Over the next few months, I will dig into these topics in much more detail with a focus on each one every month. 

It’s great to see that what you are asking me very much aligns with what I am good at, and what I have a strong understanding of – so the algorithms are working!

What are the patterns? – Once again, thanks to my AI agent for compiling this, based on all the research I fed into it from all my channels.

• Uncertainty + urgency → People want growth now, but budgets are tight.
• Confusion about tools & tactics → Which platform, which channel, which metric to trust?
• Fear of wasting time → Nobody wants to spend months on SEO, or money on ads, only to fail.
• Craving clarity & confidence → They want straight answers, practical hacks, and a sense of control in a messy marketing world.

In short, my audience is looking for shortcuts to certainty in an increasingly erratic and noisy world. 

Truth is, there are no true shortcuts—only sharper strategies, faster feedback loops, and the courage to test and adapt quicker than the next marketer. 

And the winners won’t necessarily be the ones with the biggest budgets, but the ones who move sharpest, fastest, and smartest.

AI Max for B2B: Unlock Maximum Growth with AI Search

AI is radically changing the search landscape. For B2B marketers, this shift presents both a significant challenge and a massive opportunity. Historically, B2B search has been a challenging arena, characterised by low keyword volume, high CPCs in competitive verticals such as SaaS, and the constant struggle to generate a steady stream of high-quality, converting leads through non-branded SEO (branded b2b search has typically fared much better, though).

Now, AI has completely altered the playbook. The entire SEO strategies of yesteryear are thrown out the window. It’s platforms like Reddit, and Quora which are driving AI search to a large degree, and even Google search has altered radically.

Where Search is being conducted in 2025 and beyond


The introduction of Google’s AI Max for Search campaigns, for example, a one-click, AI-powered suite of targeting and creative innovations, along with the Search Generative Experience (SGE), means the old keyword-first strategy is quickly becoming obsolete. B2B marketers must adapt to survive and thrive in this transformed environment.

This post will provide a comprehensive framework for founders, CMOs, and demand generation leaders to leverage AI-powered search for maximum growth. We will explore how to integrate tools like AI Max and Performance Max, into your B2B AI marketing strategy to drive demand, enhance personalization, and ultimately, increase conversions.

The New Reality of AI in B2B Search

The way customers find information is undergoing a fundamental transformation. AI search assistants, such as Google AI Overviews, are increasingly delivering summarized insights directly on the results page, often bypassing traditional organic listings. This new reality requires a new approach to both paid and organic search.

Google’s AI Max for Search campaigns, currently in beta, is at the forefront of this change. It enhances standard search campaigns with broad-match expansion, AI-generated headlines and descriptions, and automatic final URL selection.

According to Google, advertisers who activate AI Max typically see a 14% increase in conversions or conversion value at a similar CPA or ROAS. For campaigns that have heavily relied on phrase and exact match keywords, the uplift is even more substantial, up to 27%.

These AI-driven enhancements allow B2B marketers to discover new, relevant queries they might have otherwise missed. Recent updates also provide greater transparency, surfacing metrics for AI-driven expansions, including traffic from AI-generated keyword matches and landing pages. This data gives marketers the insights needed to optimize their campaigns effectively.

The AI Max Framework for B2B Success

To succeed in this new era, B2B leaders need a fully integrated strategy that integrates AI across the entire marketing and sales funnel. This framework breaks down how to apply AI-powered tools, including AI Max, to achieve maximum growth.

Search & Market Visibility

The first step is ensuring your brand is visible where your future customers are searching. This means optimizing for how AI search engines work.

Optimize SEO for AI Search: Your focus must shift from pure keyword targeting to establishing topic authority. Create comprehensive pillar pages and structured content, like FAQ schema, that AI summarizers can easily parse and surface in AI Overviews. Your content needs a clear, brand-aware narrative that positions your company as a thought leader.

Leverage AI Max for Search Campaigns: This is where you can truly amplify your reach. To get the most out of it, you should:

Enable Search Term Matching: This feature uses broad match and keywordless technology to discover new, relevant search queries, expanding your reach to high-intent audiences you weren't accessing before.

Use Text Customization: Allow Google AI to automatically generate compelling ad copy, including headlines and descriptions, based on your landing pages, existing ads, and keywords.

Deploy Final URL Expansion: Let the system send users to the most relevant destination page on your website based on their search context, improving user experience and conversion potential.

Maintain Precision with Controls: AI Max isn't a ‘set it and forget it’ tool. Use the built-in brand and location controls to maintain precision and actively monitor reporting to refine your B2B AI marketing strategy.

AI Demand Generation

Once you have established visibility, the next step is to capture and convert intent into qualified leads. AI accelerates this process.

Use Predictive Platforms: Tools like 6sense, Demandbase, and Apollo leverage AI to identify and target high-intent accounts that are actively researching solutions like yours. This allows you to engage prospects early in their buying journey.

Activate AI-Powered Bidding: Whether on LinkedIn Ads or through AI Max campaigns, AI-driven bidding strategies optimize for cost-per-lead (CPL) efficiency and help you reach your most valuable audiences at scale.

Deploy Conversational AI: Implement chatbots and virtual assistants from providers like Drift or Intercom on your website. These tools can instantly qualify visitors, answer questions, and book meetings, seamlessly aligning with your CRM to ensure no lead falls through the cracks.

Where your prospects and customers are searching with AI

Content & ABM Personalization

In B2B, personalization is paramount. AI enables you to scale your account-based marketing (ABM) efforts and deliver tailored content to every key stakeholder.

Accelerate Content Creation: Use AI writing assistants like Jasper or Writer to create first drafts of blog posts, whitepapers, and ad copy. This frees up your marketing team to focus on strategic editing, ensuring the final content reflects your brand’s unique tone, nuance, and credibility.

Customize Content for ABM: AI allows you to tailor messaging for different personas within the same target account. For example, you can create distinct content variations that speak to the specific pain points of a CIO versus a CMO, increasing relevance and engagement.

Sales & Marketing Alignment

A successful B2B AI marketing strategy requires tight alignment between marketing and sales. AI can bridge the gap and create a seamless feedback loop. But this part also relies heavily on human interaction. You, as a marketer, must talk to your sales team, to your CRO, to the SDRs and SDR manager – to ensure that you explain the new AI-centric approach, and that the entire sales and marketing team are in concert.

Automate Lead Scoring: Ensure that only the most qualified, high-fit marketing qualified leads (MQLs) are passed to your sales team. AI-powered lead scoring analyzes behavioral and firmographic data to prioritize leads accurately.

Leverage Conversation Intelligence: Tools like Gong, Clay, and Chorus analyze sales calls to provide real-time insights. This data is a goldmine for marketers, offering direct feedback on messaging, customer objections, and competitor mentions that can be used to refine your strategy.

Analytics & Growth Operations

Finally, you need to measure what matters. AI enhances your ability to track performance and tie marketing activities directly to revenue.

Apply GA4 Anomaly Detection: Use the built-in AI features in Google Analytics 4 to uncover real-time patterns and performance shifts. This helps you identify opportunities and address issues before they impact your pipeline.

Build AI-Powered Dashboards: Create dashboards that connect ad spend and engagement metrics directly to pipeline generation and revenue outcomes. This provides a clear view of your marketing ROI and helps justify future investments.

Harnessing AI While Maintaining Human Intelligence (empathy + creativity)

As mentioned, adopting AI doesn't mean removing the human element. The most successful organizations will be those that blend AI-driven execution with human-led strategy.

Strategy Comes from Humans, AI Scales Execution: Your team’s expertise is irreplaceable. Use AI to automate tasks and scale your efforts, but let human insight guide the overall direction.

Clean Data is Foundational: AI is only as good as the data it’s fed. Poor CRM hygiene, inconsistent tracking, and inaccurate data will undermine the effectiveness of any AI tool.

Test Before You Expand: AI Max offers built-in experiments that allow you to run a 50/50 split test to validate its impact without duplicating your campaigns. Always test and validate before rolling out changes broadly.

'Social' is the new 'Search'

Blend Organic and Paid Strategies: AI-driven ads and topic-based B2B SEO are not mutually exclusive; they reinforce each other. A strong organic presence builds trust and authority, which can improve the performance of your paid campaigns.

Ideas that take off and go viral organically, are the perfect ones to run, and boost with advertising. As Gary Vaynerchuk explains, 

It’s better to put out one hundred good but not perfect posts, and let the audience decide what they like, than have five ‘Hollywood production level’ ads that could all easily misfire, with near zero ROI 

- as well as providing you with little or no useful optimization data. Once you see which posts have performed well organically, then 'boost' them with paid, or even adapt them in ads, which you can then run.

AI can help you create thousands of new creatives in minutes. This facility was not available to me as a marketer only a few years ago. This is a huge positive change.

Focus on Meaningful KPIs

Track the KPIs that truly matter to your business: pipeline growth, conversion rates, customer acquisition cost, ROAS, CLTV (to avoid churn further down the road) and marketing ROI.



How is AI transforming B2B search?

AI is transforming B2B search by shifting the focus from keywords to user intent. Generative results like AI Overviews provide direct answers, while campaign tools like AI Max for Search use automation to find and convert high-intent users. The modern search journey now centres on deep content authority, intent understanding, and AI-powered optimization.

What makes AI Max for Search campaigns unique for B2B?

AI Max for Search bundles broad-match expansion, dynamic asset generation, and intelligent landing page selection into a single, one-click solution. For B2B marketers, this means discovering new pockets of demand and delivering highly relevant ads, all while retaining control over brand and targeting parameters. Based on Google's data, this can lead to significant boosts in conversions at a similar cost.

Should B2B companies still invest in B2B SEO?

Absolutely. In fact, SEO is more important than ever, but the strategy must evolve. The focus should be on building thought leadership and creating structured, authoritative content that is easily readable by AI. This ensures your brand is surfaced as a credible source in AI-generated answers and maintains a strong foundation of organic traffic.

Your Path to a New Era of B2B Growth

The traditional B2B search playbook is being rewritten in real time. AI Max for Search, AI-powered B2B SEO, and full-funnel automation are no longer optional add-ons; they are essential components of a modern B2B AI marketing strategy.

Founders, CMOs, and demand generation leaders who embrace this change will gain a significant competitive advantage. By anchoring your strategy in human creativity and insight while leveraging AI to scale execution, you can navigate the post-keyword era and unlock unprecedented growth. 

The journey starts with understanding these new tools and integrating them in an empathetic, human way into a cohesive, revenue-focused plan.