Start-Up Boston Week (SBW) 2025, now in its ninth year, is an annual conference focused on facilitating “connection, learning, inspiration, and collaboration within the start-up community.” According to the Venture Capital Initiative out of Stanford University, Massachusetts produced the third most unicorn start-ups (52) from 2021-2024, only behind California (358) and New York (137). And with more than 100 sessions and over 300 speakers, both the attendance and range of topics were impressive for a regional event.

But as the crowds gathered each day at the Suffolk University venue, one thing was clear: founders, investors, technologists, and operators are all working frantically to get an early jump onto the AI train. How big is this train? Forrester forecasts US Tech spend will eclipse $2.6 trillion in 2025 (a robust 5.6% of growth) with much of it being attributed to AI-related opportunities and challenges. Three of the most insightful AI sessions and accompanying key takeaways that business leaders from any size organization should use included:

“Closing The Gap: Recruiting And Upskilling For AI Success”

The panelists felt that business leaders and talent recruiters will face a buffet of challenges in the next few years: from a scarcity of AI talent to knowing which skills to prioritize; from deciding between hiring vs. training to building an engineering culture that’s ready for AI integration.

Tommy Barth, Senior Manager of Talent Operations and Analytics at Apollo.io, leaned in on how much AI even impacts the hiring and talent assessment process – they now do AI-focused interviews for the tech-focused roles. These interviews “are meant to ensure that candidates are not just interested in AI but also have a certain level of AI fluency.” The company is also using AI-adoption as a performance review benchmark, where individuals must “articulate how they are using AI in their jobs to find efficiencies.”

“Standing Out In The AI Crowd: Strategies For Real Product Differentiation”

The abstract of this session stated it best: “With AI products flooding the market, building something technically impressive isn’t enough – you need to stand out strategically.” One of the main insights was the need for an AI development strategy to be about solving for a pain point that creates – or has the potential to create – a significant negative impact on a community of consumers or businesses.

Scott Weller, CTO & Co-Founder of AI startup EnFi, a data intelligence and automation solution for managing commercial credit, leaned in on this. He thinks about an ideal customer profile as really a community with similar pain points. He stated, “Just building a product doesn’t have the ability to build a community. You really have to be addressing pain points, and you have to be addressing pain points with consequences…communities are built around consequences.”

“Data Gold Rush: Mastering Acquisition And Annotation For AI Success”

Panelists in this session focused on how to acquire and use high-quality data for AI models. Key points were controlling data acquisition costs while maintaining quantity/quality, making considerations for annotating complex data (e.g. computer vision, natural language processing), and applying ethical data acquisition practices while avoiding bias in datasets. However, the common thread in the hour-long session was this: Build your data strategy first – consisting of data procurement, storage, lineage, genealogy, purpose, quality assurance, governance, and processes – before rushing to build LLMs and launch AI.

Nirav Shah, CEO of analytics solution provider OnPoint Insights and adjunct professor at Tufts University, summed it up nicely by stating, “People don’t spend a lot of time on [building data strategies]. Everyone wants to just acquire data and build LLM models, which is great for just a POC or MVP. But a data strategy is very important.”

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Interested in learning more? Schedule with me to learn how these three nuggets of wisdom can be applied to your business strategy.