Intro
It’s the golden age of AI startups – or so it seems. Every week, another founder takes the stage to unveil a “revolutionary” SaaS platform, armed with sleek decks and borrowed Steve Jobs quotes. But while reports keep showing that many AI projects fail to deliver real ROI, the number of these companies has already surpassed 90,000 and keeps rising.
The industry is now heavily driven by investors’ optimism about AI and their chase for the next big thing. As a result, capital often flows faster than product-market fit. But while whispers about an AI bubble grow louder, vertical AI companies have fewer reasons for concern.
Built around deep industry expertise and real-world use cases, they are less dazzled by hype and more grounded in real-world complexity that’ll help them endure market correction.
We’re now seeing a clear shift as highly regulated and complex sectors like construction, logistics, agriculture, and healthcare become magnets for AI innovation. The question is: how can founders build effective, scalable vertical AI solutions from behind their screens, without losing touch with the field realities they’re trying to change?
Why Vertical Industries?
Every technology, sooner or later, enters the Trough of Disillusionment – a natural selection process that sifts the real value from the hype “husk”. If it gets demanded without external infusions, it’ll survive. If not, it waits until the market is ready.
Often, stakeholders try to delay this process, but it can’t be avoided. And for many AI companies, vertical industries have become that sieve. Built on entrenched workflows and rigid systems, sectors like healthcare, logistics, and construction are really slow to change. But once you crack them, they reward you with long-term clients and exceptional LTV.
For these industries, rapid disruption can be pretty daunting. And often, the issue is not in resistance, but their readiness: they lack the infrastructure or need longer validation cycles to trust new technology.
That’s a problematic aspect for AI companies, 95% of which get stuck at the pilot stage, and few make it to production. Citing McKinsey, “most organizations are still navigating the transition from experimentation to scaled deployment”. And I believe that this process will take much longer than expected.
Yet, with the right approach, AI can radically improve efficiency, accuracy, and outcomes in vertical industries. Companies, producing industry-specific solutions, are growing 4X faster than general-purpose AI, and this speaks better than words.
In Pre-Seed to Succeed, we stand to the principle that true economic value comes from solving grounded, operational problems – not chasing general intelligence dreams.
Understanding and Entering Vertical AI Markets
The products of vertical industries define the quality of our daily life – from food we eat and clothes we wear to the houses we live in and medical support we receive. All the tiny aspects of our material lives have a complex, heavily nuanced production process behind. A mistake at any stage of this process doesn’t just affect one person – thousands, involved in production and final consumers.
That’s why embedding AI for verticals requires a fundamentally different mindset. It starts with founders putting on a hard hat and rubber boots – visiting construction sites, warehouses, farms, and hospitals – to understand the real pain points, decision-making processes, and nuances of day-to-day operations.
A founder with firsthand industry experience, or a strong advisor on board who “speaks the language”, have a significant advantage in negotiations with decision makers. They must be able to discuss not flashy perspectives, but the nuances of production workflows, regulatory constraints, and, most importantly, how value is actually created.
For example, AI solutions like Luminance (legal document review), Abridge (medical transcription and note-taking), and IBM’s manufacturing AI (for supply chain optimization and maintenance) all thrive because they are tailored to their industries’ unique challenges and regulations.
Innovating in Disruption Tolerant Environments
When AI meets vertical industries, many founders discover an uncomfortable truth: their cutting-edge solutions are incompatible with outdated digital infrastructure – legacy systems, obsolete software, and fragmented data pipelines. Success won’t come from a plug-and-play approach; it demands purpose-built solutions tailored to specific, high-impact use cases. For example, defect detection in construction, crop monitoring in agriculture, or automated reporting in insurance.
In the enterprise world, integration is slow and complex. But once achieved, it forms a durable foundation for scaling. The move fast and scale faster mantra may work in consumer tech, but it doesn’t translate well to industry verticals. Here, it’s wiser to start small: focus on niche players and build tailored solutions. Many of these companies lack the internal IT capabilities to adopt broad, horizontal platforms, and that’s precisely where the opportunity lies.
The strength of vertical AI is in its stickiness. Once embedded, these solutions become integral to daily operations, so instead of sourcing new vendors or trying to stitch together incompatible tools, clients will prefer to expand with new modules from the same trusted provider.
The Founder’s Mindset
The next wave of successful founders will think beyond chatbots and LLM wrappers. True vertical AI combines LLMs, machine learning, computer vision, and data parsing, seamlessly embedded into the tools people already use. It augments, rather than replaces, how real work gets done.
Founders must ask tough questions early:
- Does this AI solve a mission-critical problem, or is it just adding noise?
- Can the technology integrate with – not override – legacy systems?
- Is the target customer technologically mature enough to adopt it?
The startups that answer “yes” to these questions will shape the next chapter of enterprise AI – one rooted not in hype, but in hard-earned, measurable value.
Beyond the Hype
Vertical industries may be hard to break into, but that’s exactly why they matter. They are slow to change, but they do. And once you’ve managed to integrate your solution, they’ll be equally slow to let go, because their business depends on it.
While the hype is peaking and the AI Bubble remains a topic of whispers, it’s the right time to convert your pilots into production, for sooner or later, inflated expectations will collide with reality.
If you’re a founder, take it as a warning and an opportunity. The only path forward is to focus on building AI that delivers tangible, measurable value for real industries. If, or when the bubble pops, survival will belong not to those who chased hype, but to those who built something that actually works.
