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 that 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 organizations of any size should use, included those detailed below.
“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 and knowing which skills to prioritize to deciding between hiring vs. training and building an engineering culture that’s ready for AI integration.
Tommy Barth, senior manager of talent operations and analytics at Apollo.io, focused on how much AI impacts even the hiring and talent assessment process — as the company now does 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 in which 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 and cofounder of AI startup EnFi, a data intelligence and automation solution for managing commercial credit, keyed 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. But 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 large language models (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.”
