The AI startup landscape is booming. New companies pop up every day, all claiming to have game-changing technology that’s going to transform entire industries. But the truth is, very few of them actually make it past the flashy demo phase.
As a founder who’s been building an AI company for the past eight years, I’ve learned that scaling isn’t just about having a great model — it’s about making a series of smart, sometimes tough decisions, and ensuring those decisions are proven in the battlefield of real-world deployments.
So, what separates the AI startups that scale from the ones that stall? Here’s what I’ve learned along the way.
1. Solve Specific Business Problems
Your AI is only as strong as the problems it solves. It’s not about having the flashiest model or the most advanced algorithms, it’s about solving a specific business problem and delivering real, measurable value to your customers.
The best way to do that? Start with a strong data strategy. Your product is only as good as the data behind it — and if you’re relying solely on publicly available or generic datasets, you’re already at a disadvantage.
That said, I’ve seen some innovative startups use public datasets to augment their historical data in creative ways. For example, 90% of Americans live within 10 miles of a Walmart store. Mapping out 10-mile radius polygons around each store could help define service territories based on population density and access.
But in most cases, the AI startups that successfully scale gain their competitive edge by using high-quality, domain-specific data that’s tightly connected to the real-world problems they’re solving. It sounds simple. Many do think it’s simple. McKinsey published that only 3% of companies who adopted AI projects could do it at scale — meaning only 3% achieved at least 20% of their EBIT from AI. The other 97%? They either tried and failed, or found only local success.
How we did it? We powered our product with data that no one else has, whether that’s through exclusive partnerships, unique data collection methods, or customer-generated data that compounded in value over time. This proprietary data makes our product better. You can do it too.
What can you do to create a top-notch data-strategy:
- Reduce Noise. Curated, well-labeled, and domain-specific data beats massive but noisy datasets.
- Continuously improve data pipelines. Scalable companies treat data collection, cleansing, and enrichment as a core capability, not an afterthought.
- Incorporate real-world feedback loops. Successful AI companies ensure their models learn and adapt from actual customer interactions and evolving use cases.
- Create defensibility. Whether through unique data partnerships, customer-generated data, or first-mover advantage in a niche domain, scalable startups make their data assets difficult for competitors to replicate.
We first applied AI to help a medical device customer reduce equipment downtime. Their service data was a mess — unstructured notes, shorthand, and inconsistent terms. But by building AI that understood their specific service language, we not only solved their problem but created the foundation for a scalable product that adapts to any service organization’s unique data.
Takeaway: The best AI startups don’t just build great models, they solve meaningful problems with data no one else can access.
2. Keep Pace with AI Innovation Without Chasing Every Trend
In AI, innovation moves fast, and scalable startups know how to strike the right balance between staying cutting-edge and staying focused.
You need to keep an eye on emerging models, new techniques, and advances like small language models (SLMs), multi-modal AI, agents, or retrieval-augmented generation (RAG). But you also need the discipline to avoid jumping on every new trend just because it’s popular. The most successful AI startups build a clear framework for evaluating new technology — asking questions like:
- Does this advance actually improve our core product or customer outcomes?
- Is it mature enough for production, or still experimental?
- Will it introduce unnecessary complexity into our stack?
I’ve been at this for a while and I know how hard it can be to not chase every trend – especially when it feels like everyone else is and you’re at risk of falling behind. But over time, I’ve learned the importance of evaluating new models and techniques without blindly adopting them. At Aquant, we only bring in what actually improves the precision, adaptability, or efficiency of our service intelligence platform. That mindset – focused innovation – helps us stay ahead without getting distracted.
Takeaway: Scalable AI companies don’t just adopt the latest technology – they adopt the right technology for their product, customers, and long-term vision.
3. Infrastructure Built for Scale, Not Just Speed
What I see a lot is people who can build the first 60% of a product very fast. But the remaining 40% – that’s where the real art comes in, requiring a lot of work, resources, and patience. It’s like building a new house. Putting up the frame can happen relatively quickly, but turning it into a home takes far more time, care, and attention to detail.
It’s easy to build a flashy prototype or train a model with off-the-shelf tools. But scaling that model into a reliable, cost-effective product that supports thousands of customers across geographies, industries, and regulatory environments? That’s an entirely different challenge.
What scalable startups do differently:
- Design for deployment flexibility. They plan for multi-cloud, hybrid, and on-prem deployments – because customers’ infrastructure preferences vary widely.
- Optimize for cost-efficiency early. Running AI models can get expensive fast. The best startups build in cost optimizations around inference, data storage, and compute from the start.
- Embed monitoring and governance. Scalable AI companies know that AI products aren’t static – they continuously monitor performance, detect drift, and ensure compliance with evolving regulations.
- Plan for extensibility. Whether it’s adding new data sources, expanding to adjacent use cases, or adapting to new regulations, scalable infrastructure makes it easy to evolve without rebuilding from scratch.
Takeaway: Scalable AI companies design infrastructure with the assumption that every part of the system — from model training to inference to data governance — will need to grow and adapt over time.
It’s not about how fast you launch – it’s about how well you scale
The next generation of AI leaders won’t be defined by how fast they launch – but by how well they scale.
This is something I’ve seen firsthand throughout my career, from my time in the intelligence community where we had to turn massive amounts of data into clear, actionable insights, to my years working with service organizations trying to make sense of their service data. Shahar and I started Aquant because we knew AI could solve real business problems, if it was powered by the right data, tailored to the right domain, and built to evolve as customer needs changed.
Scaling an AI company is never just about the technology – it’s about staying focused on solving the right problems, making smart bets on innovation, and building a foundation that can adapt and grow for years to come.
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