How IBM’s Tiny Models Deliver Big ROI: A Guide to Build Fit-for-Purpose AI Models

In this episode of the AI Monetisation Podcast, Nicholas Fuller, IBM's Vice President of AI and Automation, joins Binny Mathews, Co-founder of ProjectPro, to explore how IBM is achieving real-world business impact with tiny, fit-for-purpose AI models. Guest Background: A 23-year IBM veteran, Nicholas leads global efforts in LLM and agentic AI innovation at IBM Research. From mainframe code modernization to tiny time-series models, his team is redefining how enterprises build scalable, trustworthy AI products.What’s Inside?Why fit-for-purpose models outperform massive LLMs for real-world ROIInside IBM’s 5M parameter “tiny mixer” models for time-series forecastingHow Guardian models ensure enterprise-grade safety and data privacyThe future of agentic AI in IT operations and incident managementWhat it takes to build industrial-grade AI benchmarks like ITBench Key Takeaways Fit-for-purpose AI is more efficient and enterprise-ready than massive general-purpose models.Domain-specific models trained on smaller, high-quality datasets can outperform larger counterparts.Security, provenance, and explainability are critical for enterprise AI adoption.Internal co-creation and enterprise-specific benchmarks accelerate trust and deploymentHiring and retaining top AI talent requires purpose-driven work and deep academic partnerships. This episode is essential listening for enterprise leaders, product builders, and AI practitioners seeking practical strategies to scale AI without compromising on safety, trust, or impact!For full show notes, visit- https://bit.ly/43I4eE5ProjectPro helps you tackle your AI projects with ease. Upskilling the team for specific AI projects, collaborating with experts to co-build AI solutions, or opting for end-to-end project outsourcing—ProjectPro has you covered. With ongoing 1:1 support, enterprises can overcome roadblocks and seamlessly take their projects from PoC to production. Learn more: https://bit.ly/3RFrZoTResources Mentioned:Here are some links to dig deeper into the topics:IBM GraniteGranite Time Series ModelsGranite Guardian ModelsIBM SoftwareITBenchPrompt Declaration Language

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