B2B marketing strategy is the process of deciding which customer you want to win and why, before planning a single channel. When all competitors have access to the same AI model trained on the same data, executing better no longer produces real differentiation. The advantage shifts to whoever holds a first-party understanding of demand that only exists inside the company and does not appear in any public dataset.
- 96% of AI strategic recommendations converge on identical ‘differentiation’. The AI produces the problem it promises to solve.
- The advantage moves from execution to judgement: which customer you want to win, and why.
- That judgement comes from conversations with the people who hold the technical, commercial and financial knowledge of the market.
- Strategic clarity raises AI’s business impact by 25 percentage points. Better tools without it: only 5.
- B2B vs B2C and SaaS vs industrial: market insight lives in different places depending on the business model.
The irony of the experiment that changed how I read my sector’s search results
In March 2026, a team of HBR researchers ran 15,000 simulations asking the six most widely used LLMs for strategic advice.
The result was predictable in the worst possible way: 96% recommended ‘differentiation’ as the primary strategy. When industry-specific context was added, the recommendations varied by only 11%. The researchers called it ‘trendslop’: advice that sounds strategic, comes wrapped in recent data, and is identical for everyone.
You do not need to see the image accompanying that study to picture it: competitor websites with the same tone of voice, carbon-copy value messages, and unique selling propositions with nothing unique left in them. Cover the logo, and you cannot tell whose website you are looking at.
I know because I run an agent system that handles a large part of my marketing: research, drafts, SEO analysis, distribution. AI proposes and executes with an efficiency that previously required an entire team. And when I look at the search results in my own sector, I recognise the pattern: we all delegate the ‘what to say’ to similar models, trained on the same data, and the output converges.
That is what happens when competing teams deploy AI on the same market data. The systems do not copy each other, they converge on their own, with no one deciding it or noticing. Strategy does not leak. It homogenises. An HBR article from May 2026 calls this the agentic convergence trap: differentiation disappears without anyone copying it.
The AI that promised to differentiate produces the very problem it was meant to solve.
What B2B marketing strategy actually is
To know whether convergence is happening, you first have to define what it is that supposedly gets differentiated.
The search results for ‘B2B marketing strategy’ in 2026 answer this unanimously and incorrectly: it is the channel plan, the tactics map, the ABM programme, the 95/5 rule. That is the execution plan. It has value, but it is the second layer, not the first.
B2B marketing strategy
B2B marketing strategy is the decision system that determines which customer you want to win and with what argument, such that the customer chooses to act in your favour rather than the competitor’s. The channel plan and tactics carry that argument to the market, but they are not the argument.
The operational difference is this: if you change agency or technology stack and your strategy does not change, you had a strategy. If everything changes with it, you had an execution plan with no strategy behind it.
This distinction matters particularly now. Previously, executing well was a real barrier because it was costly and slow. Today any company with access to an LLM produces more in a month than it produced in a year. The barrier is gone. So the advantage moves to the quality of the decision about what to produce, for whom, and why they should choose you.
B2B marketing strategy is the channel plan, the target segments and the demand tactics.
It is the decision about which customer to win and with what argument, taken before any channel.
When that decision is delegated to the same model the competitor uses, the channel plan changes but the argument converges.
Where market insight comes from (and why AI cannot replace it)
The advantage that does not converge comes from knowledge not present in any public dataset.
The decisions that have made a real difference in my work I changed after conversations with people who know the market from the inside: the sales team that hears the real objections, the technical team that knows why the product solves or fails to solve a problem, the finance team that understands which margins make a segment viable. That is what lets me redirect the system and give it instructions no other user of the same AI can give.
The salesperson who has lost the same tender to the same competitor for ten years knows something no LLM knows. The distributor who calls on a Friday to warn that a technical argument has stopped working in their market holds information that exists in no dataset. That knowledge is never trained on, because nobody transcribes it and nobody publishes it.
Over time I have learnt to draw a clear line: I delegate production and the search for evidence. The call on which customer to win and why, I do not.
And here is the most dangerous temptation: the better the system becomes, the easier it is to delegate that judgement too. That temptation is precisely the trap.
There is a figure that confirms it. BCG, in its AI at Work report of June 2026, measured that strategic clarity raises the business impact of AI by 25 percentage points, against only 5 points from better tools without that clarity. The difference is no longer in the tools.
The same study adds the flip side: 42% of employees save more than a day of work per week thanks to AI, yet 66% receive no guidance on what to do with that time. Execution capacity grows. Direction does not grow by itself.
When the decision about which customer to win gets delegated to the same model the competitor uses, the channel plan changes but the underlying argument converges. — Reyes Brusola, CMO
Why this works differently in B2B vs B2C, and in SaaS vs industrial
Market insight lives in different places depending on the business model.
In B2C, demand is read through behavioural data at scale, and AI is good at that. The terrain for differentiation moves to brand and community, which take years to build.
In B2B the useful knowledge is not on a dashboard. It is spread across sales, technical, distributors and the customers who renew, and it does not aggregate itself. Within B2B, the business model decides where it concentrates.
In SaaS the signal is structured and close at hand: who adopts, who churns, which feature predicts a renewal. The judgement lies in reading that data before the competitor and framing the right hypothesis. AI handles the analysis, but someone has to decide which question is worth asking.
In industrial, that signal is far less structured and rarely centralised. The differentiating knowledge sits with the channel: the distributor who knows which argument convinces the procurement engineer, which lead time is real and which is wishful thinking, which competitor fails on after-sales. That tends not to sit in any CRM. It is in conversations marketing rarely has first-hand.
- Product signals: adoption, churn, expansion
- Channel and field knowledge: distributor, sales engineer, technical contacts
The error is the same in both cases: believing the system builds that judgement on its own. It does not build it. It amplifies it. The advantage belongs to whoever feeds it what nobody else has.
The objection worth taking seriously
The strongest argument against this is that AI improves every month, and that this judgement will eventually be replicable too.
AI improves so fast that market judgement will eventually be replicable, just as the edge of executing well disappeared.
The judgement that does not converge lives in what is never published. The gap may widen, not close.
The companies that build that knowledge systematically also do it with better tools than those that do not, so the advantage compounds. And it correlates with results: Gartner asked 125 CEOs and CFOs about the effectiveness of their CMO in shaping the market, and only 14% consider their CMO highly effective. Companies in that 14% are 2.6 times more likely to exceed their revenue and profit targets. It is also why the CMO role is fragmenting into separate archetypes, only one of which keeps that strategic authority.
of B2B marketers say their function has taken on a more strategic role in the past year.
of CEOs and CFOs consider their CMO highly effective at shaping the market.
The same pattern appears in the economy: investment in intangibles (brand, relationships, data, knowledge) is now 1.7 times tangible investment in the US, and accounting still treats it as expenditure. The market values judgement. The measurement systems have not yet caught up.
What changes in practice for the CMO who leads this way
The operational consequence is not adopting more tools.
The CMO who builds advantage with first-party knowledge does three things the one who delegates judgement to AI does not. For a leader stepping into the role, this is the work that defines the first 90 days, long before any campaign launches.
First, they talk regularly with sales, technical and finance to extract what sits in no report. These are not alignment meetings, they are market intelligence interviews with internal sources.
Second, when an output from the system arrives, the first question is what it could not answer because the data does not exist in public sources. That gap is where the advantage lives.
Third, they direct the time AI frees up to building that knowledge (conversations, customer observation, working with sales on real deals), not to producing more of the same faster.
Because producing more of the same, faster, with the same AI the competitor uses, is the convergence trap on fast forward. And if 66% of teams receive no guidance on what to do with the time they gain, that is the default answer.
Frequently asked
How is B2B marketing strategy defined when everyone has the same AI?
B2B marketing strategy is the decision system that determines which customer you want to win and with what argument, before selecting channels or tactics. When all teams use the same AI model trained on the same market data, the execution plan converges. What differentiates is the quality of the market judgement that directs that system.
What separates a B2B marketing team that produces real advantage from one that does not?
The difference is not in the tools or the volume of production. It is whether the CMO has access to knowledge about customers that exists in no public dataset: the real objections the sales team hears, the product limitations the technical team knows about, the patterns the channel team has observed over years. That knowledge feeds the AI system with instructions the competitor cannot replicate with the same subscription.
Why does B2B marketing strategy work differently in SaaS and industrial?
In SaaS B2B, the signal is structured in product data: adoption, churn patterns, correlations between features and renewal. In industrial B2B, that signal is less structured and spread across the channel: the distributor, the sales engineer, the customer’s technical contacts. The error in both cases is delegating the construction of that knowledge to the AI instead of feeding it with what the system cannot see.
What does the CMO who leads B2B marketing strategically in 2026 do?
They extract systematically the knowledge that does not sit in public data through conversations with the commercial, technical and financial teams. They use that knowledge to redirect the AI system, not to replace it. And when allocating the time AI frees up, they direct it to building more first-party knowledge, not to producing more of the same output with the same instructions the competitor is using.
What is the risk of strategic convergence when marketing teams use the same AI?
When competing teams train AI systems on the same public market data, their strategies converge without anyone copying anyone else. It is structural convergence, not imitation. 96% of major language models recommended ‘differentiation’ as the primary strategy in 15,000 simulations, with only 11% variation when industry context was added. The paradox is that AI recommends differentiating in exactly the same way to everyone.
Sources: [1] Romasanta, Thomas & Levina, ‘Researchers Asked LLMs for Strategic Advice. They Got Trendslop in Return’, Harvard Business Review, March 2026. [2] van Esch, Cui & Black, ‘Beware the Agentic Convergence Trap’, Harvard Business Review, May 2026. [3] BCG, ‘AI at Work: Why Strategy Matters More Than Tools’, June 2026. [4] Gartner, ‘Market-Shaping CMOs Survey’ (n=125 CEOs/CFOs, 2024), published May 2025. [5] Mauboussin, ‘Intangibles and Modern Value Investing’, Ben Graham Centre, Ivey Business School, April 2025. [6] Martin, ‘Revisiting My Definition of Strategy’, Playing to Win, 2025. [7] Evans, ‘AI Eats the World’, B2BMX 2026. [8] MarketingWeek, ‘State of B2B Marketing 2025’ (n=450).
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