Episode 69

Seven Years of Applied AI, From NLP to Agents and the Push to Prove What Works

AI has changed dramatically over the past seven years. When the Applied AI Summit first launched, practitioners were focused on machine learning, NLP, data science, and the challenge of getting models into production. Today, the conversation has expanded to foundation models, generative AI, RAG, autonomous agents, healthcare AI, and increasingly important questions around evaluation, safety, reliability, and measurable impact.

In this episode of the AI Loves Data Podcast, Anna Anisin sits down with David Talby, CEO of John Snow Labs, to reflect on seven years of building the Applied AI Summit together and seven years of watching the AI industry evolve alongside it.

They explore which technologies genuinely changed the field, what organizations have learned about moving from prototypes to production, and why the industry’s focus is increasingly shifting from asking “What can AI do?” to “Can you prove that it works?”

Key Highlights:

• How the industry has evolved from traditional machine learning and NLP to foundation models, GenAI, RAG, and agentic systems.
• Why creating an impressive AI prototype has become easier while building dependable production systems remains difficult.
• What organizations have learned about data quality, infrastructure, evaluation, monitoring, workflow integration, and human oversight.
• Why proving that an AI system actually works has become a defining challenge for practitioners in 2026.
• Why traditional accuracy metrics aren’t enough for generative and agentic systems, particularly in high-stakes industries.
• What the next three years could bring and which fundamental AI challenges may prove much harder to solve.

🎧 Tune in to Episode 69 for a look back at seven years of applied AI and a conversation about what needs to happen next as the industry moves from experimentation toward proving that AI systems are reliable, measurable, and ready for the real world.

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