By Collin West
Every founder, venture capitalist, and Fortune 500 is talking about artificial intelligence (and machine learning) today. We can’t go more than a few days without another tweet by Elon Musk or Sam Altman about the promises (and dangers) of AI.
That said, we are still only at the tip of the iceberg of what AI could become for business. At this point, software development has gone through many major iterations (waterfall, agile, etc.) and best practices have emerged. To date, the same can’t be said about artificial intelligence.
So, how should startups think about building AI products? What is the right approach for a startup founder who wants data-driven feedback loops (ship product, collect data, improve product)?
We interviewed Ash Fontana, author of The AI-First Company, to explore this concept. The book gets its title because startups should be putting AI “at the start of every conversation in your business… It affects what products you’re building, the people you’re hiring, where you’re allocating capital, what initiatives you’re going to take, and so on.”
What is Lean AI Methodology?
Lean software development has taken off because it aligns engineering goals with business goals. The key is quick iterations, frequent releases, and talking to customers throughout the entire process.
Ash shares a similar framework for building AI products:
“The key is to narrow the constraints so you can get to the point where you can ask one question, solve it with one model, run it over one dataset, and get one result.”
The key is to not try and do everything at once. “Don't try to turn everything into an AI product. Don’t start with multiple datasets or complex algorithms,” shares Ash. “The key is to get something small over the finish line and assess from there.”
Pick the Right Problem and Process
It’s crucial to solve the right problem. It can be tempting to start throwing around “artificial intelligence” and “machine learning” without truly understanding why it’s needed.
You want to solve a specific, useful, measurable problem. The problem statement should be simple to write out and address all of these key points.
Moreover, you have to pay particular attention to the scope of the problem. You want a narrow problem that your team can build a great process around.
For instance, it’s not just building a more complex model that will increase the scope of your project. You have to think about the data infrastructure, data cleaning, data labeling, building pipelines, validating results, and so on.
You want to standardize parts of your workflow (like the tools being used) and give people the freedom to try different approaches to achieve the business outcome.
The best way to do so, according to Ash, is to get a single task completed. That way you can evaluate the results with customer data and get their feedback, instead of continually improving a process that may not be what solves their need.
Use the Resources You Have Today
There is also a big misconception today about what is needed to build an AI product. “Don’t start by hiring a CIO and 20 engineers,” said Ash. “That’s not the first step. The first step is giving your team the latitude to experiment and test.”
Your existing engineers have plenty of technical ability. There are some that are very comfortable working with data and statistics. Give them the freedom to try things out, to make these tasks part of stretch goals or continued education.
The key is to encourage training, online courses, and off-the-shelf solutions from vendors to develop your current team.
The wrong approach almost always hinges on investments of millions of dollars and months of time without a clear understanding of the returns.
Conclusion
There is hardly an industry or process that artificial intelligence won’t influence. From how packages are routed across the country to forecasting demand for a manufacturing plant and helping software developers make fewer errors, AI will shape our world.
The key is for startup founders to build a product and then think deeply about where AI could be used to create a sustained competitive advantage.
“You don’t need to be an AI company from day one. But any existing company could become an AI-first company,” Ash stated.
We encourage you to check out Ash’s book and think about how you can implement AI-first thinking at your company to solve a single problem this year.
IPO Alert: Groww goes public!
Ensemble-backed Groww goes public on NSE-India at $8.9B valuation Groww, one of Ensemble VC’s earliest data-driven investments, completed a successful IPO on the NSE, marking a major milestone for India’s retail investing market. Backed at the Series A pre-revenue, Groww exemplifies Ensemble’s strategy in action — identifying world-class teams early through its proprietary Unity platform.
Collin West to headline AlphaCore Wealth Summit 2025 in La Jolla
We’re heading to La Jolla for the 2025 AlphaCore Wealth Advisory Summit!In one week, our team will be on the ground at the AlphaCore Wealth Summit, one of the industry’s most forward-looking gatherings for wealth managers, allocators, and innovators. This year’s theme — Back to the Future — sets the stage for a conversation about how artificial intelligence and emerging technologies are reshaping wealth creation and capital deployment. If you’ll be there, we’d love to connect in person.
Why Ensemble is Backing Stablecore’s Mission to Bring Community Banks into the Stablecoin Future
Ensemble is thrilled to announce our investment in Stablecore, the infrastructure platform enabling banks and credit unions to offer stablecoins, tokenized deposits, and other digital asset services.
Gopi's Manifesto
In this excerpt from Ensemble’s Mid-Year Investor Letter 2025, Gopi Sundaramurthy, Founder and Head of Data Science, outlines a new model for investment firms in the age of AI. As one of the preeminent data scientists in venture capital, Gopi’s perspective is informed by over a decade of building data models for investors.
