Building an Enterprise AI platform in 2025 is like constructing a skyscraper in a hurricane on top of an actively erupting volcano. Creating such a platform requires nimble reflexes in both technology and business.
Launching and running any business is hard! In Committed: Startup Survival Tips and Uncommon Sense for First-Time Tech Founders, Eldon Sprickerhoff explains the extraordinary level of commitment needed to start and grow a business, a dedication many might consider extreme. Even if you’re not planning to start a company, the book provides a fascinating account of Eldon’s journey in building a billion-dollar cybersecurity company, offering valuable insights and inspiration for entrepreneurs and readers alike.
AI for business is hot, and demand continues to surge. Even so, less than 10% of AI startups will survive their first year. Let’s put that in perspective: roughly 20% of all startups fail in their first year.
Here are 4 reasons more than 90% AI Startups fail to survive their first year:
- Poor Product-Market Fit Too many startups develop innovative tech without solving real business problems. Enterprises need practical, scalable solutions—not flashy tools with no clear use case.
- Financial Instability Building AI is expensive. High development costs, coupled with long sales cycles and funding gaps, make it hard to stay afloat.
- Operational Challenges Scaling AI solutions is hard. Many startups stumble over inefficient models, team issues, or a lack of operational maturity.
- Breakneck Tech Evolution AI tech moves at lightning speed. Startups struggle to keep up as the underlying tech stack changes, making it feel like they’re chasing a moving target.
Anisha Mathews explores five startups that failed in 2024:
- Artifact: An AI-powered news app that struggled with limited user demand and intense competition from established news platforms.
- Shyp: An on-demand shipping service that faced challenges due to an unsustainable business model and inability to compete with major shipping companies.
- Tally: A fintech company that aimed to help consumers manage credit card debt but encountered financial instability and difficulties adapting to market changes.
- Eaze: A cannabis delivery service that, despite leveraging AI for operations, was hindered by financial troubles, regulatory burdens, and competition from unlicensed operators.
- Ghost Autonomy: An AI startup focused on autonomous driving that struggled with industry skepticism regarding its technical approach and faced financial hurdles.
The California Gold Rush: A Parallel
The AI boom today mirrors the California Gold Rush of 1849. Less than 1% of prospctors struck it rich, while nearly 30% lost everything — even their lives. Most arrived too late to claim the richest gold deposits, spent more money than they earned, or lacked the expertise to scale their efforts. Ironically, the real winners weren’t the miners but the suppliers selling picks, shovels, and provisions.
The same dynamics apply to AI startups. Many enter a crowded market, burn through cash trying to keep up with breakneck innovation, and fail to deliver lasting value. Just like the Gold Rush, success in AI isn’t about chasing hype; it’s about providing something practical, scalable, and truly valuable to businesses.
Our Story is Data
Twenty years ago, I founded the Saberin Group. We’re a team of 50 based in Long Island, New York. Unlike most data companies, we don’t sell data—we help businesses do amazing things with their own.
Our clients, primarily alt-fin companies like Funds and Broker Dealers, operate in a space where data is abundant, but the market is fiercely competitive. With many companies striving to make their mark, it’s a challenging environment. We’ve succeeded by aligning ourselves with the success of our customers, leveraging our skills, experience, and relationships—and above all, by remaining steadfast in our commitment to integrity. You’d be surprised how much business you can win by consistently doing the right thing, even when it hurts.
When is a startup not a startup?
Over the last decade, we’ve invested tens of thousands of hours building with AI. But the last two years? They’ve been extraordinary.
Shortly after ChatGPT was unveiled in November 2022 (props to OpenAI for demolishing the status quo with that one), we built our own internal AI Chat App. The goal was simple: safely use generative AI securely without sending sensitive data to OpenAI. It wasn’t designed for customers—but when they saw it, they lost their minds.
Our product, “Audition AI”, is an enterprise AI app that lets you use AI in your own Azure Tenant so you can trust it with your most precious data. Also, it does stuff. For example, we use it to turn project plans into Jira tasks. People now call this “agentic”.
So, while Saberin Group is a 20-year-old company, we’ve become something unexpected: a thriving AI startup.
Product-Market Alignment
Our AI startup wasn’t built as a startup. It was built to solve an internal need. We saw immediate success because we delivered a product that was demanded by our customers. It continues to evolve in response to guidance from customers.
Even so, our journey has not been without challenges! It feels like every time we release a new feature that makes us unique in the market, someone else quickly announces a similar feature. We expect this to continue. While it’s reassuring to know we’re setting or keeping pace, there’s always some disappointment when others announce something we thought was unique. Still, our customers tell us they see this as proof that we’re staying ahead, and their trust motivates us to keep innovating.
At the heart of our success in this category is staying true to our core principles: Security by Design, Enterprise Data Protection, and Compliance at the Core. These aren’t just features—they’re the foundation of everything we build. By prioritizing security and compliance, we enable enterprises to adopt innovative AI solutions with confidence, knowing their data is protected. This commitment sets us apart and continues to earn the trust of our customers.
Financial and Operational Stability
Financial stability is where most startups falter, but our longevity has allowed us to build a strong foundation. Over the years, we’ve developed a diversified customer base and an amazing team. This stability ensures we can adapt and endure, even in a fast-changing market.
Operationally, our team’s commitment to excellence and process gives us the best of both worlds: the agility of a startup combined with the reliability of a well-established company. This reassures our customers that when they trust us with their data, they’re trusting a partner that will still be here tomorrow.
Product Stability
Building an enterprise AI platform requires striking a delicate balance: the product must evolve rapidly while remaining stable and user-friendly. Core elements like security, compliance, and logging are built to last, ensuring the platform is reliable even as underlying AI models and related innovative technology continue to evolve.
For example, our Salesforce connector allows users to ask natural-language questions about their CRM data. Even as the AI behind it improves, the connector itself remains robust, proving that strong foundations support long-term innovation.
One of our biggest challenges lies in updating the user experience to keep up with new features. While we know that consistency is key to allowing users to establish confidence and proficiency, we also must constantly introduce revolutionary ideas. As someone who frequently uses tools like Outlook and Jira, I understand how frustrating it is when things like button placement and shortcuts change unexpectedly. We’re committed to maintaining a seamless user experience, even as we push the boundaries of what’s possible.
By focusing on stable foundations, we’ve been able to embrace rapid change while empowering enterprises to adopt new features at their own pace.
Conclusion
High failure rates among enterprise AI startups might seem alarming, but they’re an expected byproduct of innovation in a rapidly evolving field. Despite these setbacks, the adoption of AI in enterprise is non-negotiable—companies that fail to embrace it risk falling irreversibly behind their competitors. The demand for AI solutions in enterprise is massive, diverse, and only continuing to grow. The logical conclusion? We don’t need fewer people in the AI space; we need more. Meeting this demand will require an even larger, more skilled workforce committed to delivering transformative AI applications for enterprise.