GTM strategy for AI products: convincing sceptical B2B buyers
Marc GasserSerial Founder · GTM & MarketingConnects AI with revenue operations and builds autonomous GTM systems for predictable growth.TL;DR
- The KPMG study with the University of Melbourne (48,000+ respondents, 47 countries, 2025) shows the GTM problem in one number: 66 per cent use AI regularly, but only 46 per cent trust it – just 39 per cent in high-income countries. Your buyer uses AI and distrusts it at once.
- Position the outcome, not the technology: “halves response time” beats “GPT-powered”. And answer the distrust questions proactively – hallucinations, data flow, hosting – before procurement asks them.
- Pricing AI features needs a double calculation: value for the price, token costs for the margin. Combine usage-based costs with flat-rate prices and you subsidise your power users into your own loss zone.
Key findings
- The DACH market is catching up but stays sceptical: according to Bitkom, 36 per cent of German companies already used AI in 2025 (up from 20 per cent), with another 47 per cent planning or discussing it. Demand is there – it just buys differently.
- “AI-powered” is no longer differentiation, it's background noise. Differentiation comes from context (knows your business), trust architecture (hosting, logging, oversight) and proven results.
- In AI GTM the pilot customer matters more than the launch event: one reference number (“minus 38 per cent handling time at customer X”) defuses more objections than any demo.
The trust paradox: your buyer uses AI – and distrusts it
KPMG's global “Trust in AI” study (with the University of Melbourne, 48,000+ respondents in 47 countries, published 2025) delivers the year's most GTM-relevant number: 66 per cent already use AI regularly, but only 46 per cent are willing to trust it. In high-income countries – precisely DACH – trust drops to 39 per cent. Your B2B buyer is not an AI refuser; they are an AI user with open trust questions.
At the same time the market is growing fast: Bitkom counted 36 per cent of German companies using AI in 2025 – nearly double the previous year – with another 47 per cent planning or discussing. The consequence for your GTM: you are not selling against rejection, you are selling against scrutiny. Every unanswered question about hallucinations, data flows or hosting adds weeks to the sales cycle.
How to build positioning and launch on trust
Outcome before technology. Buyers pay for solved problems, not model names. “Cuts ticket handling time by a third” is positioning; “GPT-5-powered” is an implementation detail that is outdated tomorrow.
Pre-empt the objections. The three questions always come: what happens on errors? Where does our data go? Do you train on it? Answer them in the pitch deck, not in the security review – proactive honesty is a deal accelerator in DACH.
Pilot before breadth. Start with two or three design partners and one measurable success metric. A single proven reference number replaces ten slides of market promises – and gives you the error-tolerance data for the rollout as a bonus.
GTM readiness check for your AI feature
The feature may impress, but sales will fail on the trust questions. Build the answers first, then launch.
Tick what is already in place for your launch – the result shows whether your GTM is built on trust or on hope.
Pricing: sell value, control token costs
AI features break an old SaaS certainty: marginal cost per use is no longer zero. Every prompt costs inference, and power users can consume a multiple of a normal user. Dump AI functions flat into existing plans and you build a margin that shrinks as the feature succeeds.
The practice winning out is hybrid: base AI in the plan (drive adoption), intensive use via quotas or add-ons (protect margin). Anchor prices to value – what does the hour the feature saves cost? – and keep the token calculation as an internal guardrail. And for DACH: the premium for EU hosting and data residency is not an obstacle, it is a premium segment.
Recommendations
- Cut “AI” from the headline. Position the measurable outcome and mention the technology in the second sentence. Test both variants in the pitch – the outcome variant almost always wins.
- Build the trust package before launch. One document for procurement: data flows, hosting, training, error behaviour, AI Act status. In DACH that is part of the product, not the appendix.
- Run the pricing maths twice. Once from customer value, once from inference cost. If the intersection is uncomfortable, quotas are the more honest solution than cross-subsidy.
- Sell with reference numbers. Before the broad launch, win two pilot customers with publishable results. In a distrust market, one proof beats every demo.
Scope & caveats
- The KPMG/Melbourne figures (46 per cent trust, 66 per cent usage, 39 per cent in high-income countries) come from a survey run November 2024 to January 2025 and measure attitudes, not buying behaviour. Bitkom surveyed 604 German companies with 20+ employees – both are reference points, not forecasts for your segment.
- Pricing recommendations depend heavily on market position and cost structure. The hybrid logic (base included, intensive use metered) is a pattern from practice, not a law – validate it against your own token economics.
The takeaway
In AI GTM the loudest promise doesn't win – the most credible answer to distrust does. Position the outcome, pre-empt the objections, scale with references instead of superlatives, and DACH scepticism becomes your competitive advantage.
Matching use cases from the library
From the article straight into practice: these use cases put the concepts to work with Teklens.
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