The GTM mistake early-stage tech startups can’t afford to make

Speaking recently at a digital transformation conference where senior leaders from across the technology industry gathered to discuss how AI is reshaping the sector, I made a simple argument: the biggest shift isn’t in how startups market themselves today – it’s actually in how they make decisions. The industry is moving from gut feel and long-lasting assumption validations to real signals in a few weeks or even days, and that changes everything about how digital technology companies get built.

The most valuable go-to-market decision I made recently for a travel startup wasn’t a campaign, or a channel, or a hire. It was moving away from a market that was supposed to be the priority target before an actual app went live. It was a call that saved months of misallocated resource and came from just a week of deep research.

According to the Founders Group research, 42% of startups fail due to no market need. Not bad execution but a wrong commercial thesis that unfortunately has been discovered too late. Getting from working product to product market fit (PMF) typically takes 9-18 months. And these 9-18 months are risking to be burned on a foundation that hasn’t been properly tested.

Working on a solo travel startup from zero but also other startups before, I came up with what I now call the Pre-Build Validation Loop – a three-step process that can be replicated across any early-stage digital technology company and go to the market smarter.

Step 1: think of the market selection as a capital decision, not a marketing one

Getting the market wrong doesn’t just waste marketing budget, it actually impacts the whole product roadmap, and also hiring, fundraising narrative and many other things. Although, many teams in most cases treat these capital allocation decisions as marketing ones.

Before any product decisions were locked, I used AI to research and size three target markets – running competitive analysis, demand signals, and unit economics simulations at a speed previously inaccessible to a lean team. One market came off the list entirely although it was included in top three at the beginning. The solo travel segment there was too small to support the model. That conclusion used to require weeks or months of manual research or a local agency. We got it in days.

Step 2: let users write your value proposition before you do

The most expensive assumption a founding team makes is building a product way before understanding what users actually want and what they actually need to change their behaviour. By the time most startups run user research – post-MVP, post-launch – the team has already committed architecturally. I ran a few user interviews before the product existed and most importantly – before the value proposition was written. And the main objective was not to pitch the product – it was asking where the problem hurt, what people had tried, and what a solution would need to do differently.

AI synthesised those conversations – identifying recurring language patterns across interviews, clustering pain points by frequency, and surfacing the exact phrases users repeated unprompted. It helped me to turn raw qualitative data into a ranked map of the commercial opportunity in users’ own words. That output drove product decisions and market entry sequencing. Earlier, I would spend months on what was done in just a few weeks.

Step 3: know your message works before you pay to scale it

I tested positioning variants before committing to any channel. These were not campaigns but targeted message tests, each traceable to direct user language, measured against a pre-defined behavioural signal.

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One variant held consistently across geographies and user types. And that variant became the foundation for onboarding, retention, and paid acquisition – and held because it was validated before scaling, not reverse-engineered from campaign performance.

What the Pre-Build Validation Loop changes

So, the reason this matters beyond one startup is that AI has collapsed the resource requirement for the pre-build validation. What previously needed a research function, external support, time and budget, local market presence, can now be run by a small team or in my case, by one person, before committing capital.

Strategic decisions such as which markets to enter, what problem to solve, how to position against incumbents – no longer need to be made on intuition. A few days of AI-powered research and validation now replaces what used to take months. That’s not a small efficiency gain, it actually fundamentally changes how digital technology companies get built. I’m genuinely excited about where this takes the industry.