Large Language Models (LLMs) in 2026: Trends, Use Cases & Business Impact | Richard Bownes скачать в хорошем качестве

Large Language Models (LLMs) in 2026: Trends, Use Cases & Business Impact | Richard Bownes

What’s actually happening with LLMs in 2026 and what does it mean for business leaders? Michael is joined by Richard Bownes, Head of AI Solutions at Vector8, for a discussion on how large language models are evolving and why the biggest shift isn’t capability, but consistency, reliability, and system design. Rather than chasing the next capability jump, Richard explains why we’re seeing a slowdown in headline performance gains alongside an improvement in consistency and reliability. In this conversation, Richard shares: ✔️What Richard says is really happening with LLM development right now in 2026 ✔️Why the enterprise production lens matters: the trade-off between brilliant but unstable outputs and reliable, predictable systems. ✔️Richard’s practical map of the stack: naked LLM calls → workflows → tool calling → MCP servers → agent skills (and what each step is designed to solve). ✔️“Stateful” AI explained clearly ✔️The organisational gap Richard flags as adoption scales. Guest information Richard Bownes is Head of AI Solutions at Vector8, where he helps organisations design and deploy practical AI systems that move beyond experimentation into real-world business impact. He’s worked across public and private sector environments, bridging strategy, delivery, and real-world implementation. His experience spans Voice AI systems, applied NLP, and the ethical considerations that come with deploying machine learning at scale. Connect with Richard Bownes on LinkedIn:   / rjbownes   About Your Host: Michael Young is the Managing Director of MBN Solutions and host of A Class Act: Conversations in Data & Leadership. With nearly two decades of experience helping organisations build high-performing data, analytics, and AI teams, he brings frontline insight into what makes exceptional talent and leadership. Please note: The views expressed by the guest in this episode are their own and do not reflect those of their current or former employers. Connect with MBN Solutions: If you’re hiring for data or AI leadership roles, scaling AI capability, or rethinking how talent supports your AI strategy, the team at NBN partners with organisations to build data and AI teams that actually deliver. MBN Solutions on LinkedIn – Explore industry updates, groundbreaking projects, and the latest in data and analytics talent. Join the conversation and discover how MBN Solutions is shaping the future of data leadership. For questions, guest recommendations, or to connect: Email: michael@mbnsolutions.com Visit MBN Solutions website at www.mbnsolutions.com Chapters 00:00:00 — Welcome to A Class Act: cutting through the AI hype for leaders 00:01:25 — “Most people focus on capability leaps…” what’s the real story with LLMs? 00:02:02 — “Plateauing performance” + “increase in consistency of use” (why that matters in systems) 00:02:55 — Is slower capability gain a feature for enterprise adoption? 00:08:22 — Smaller models: cost + accessibility (running models locally / at the edge) 00:10:36 — The evolution map: naked LLM calls → workflows → tool calling → MCP → skills 00:12:12 — Tool calling: deterministic tools, computation, business logic (why it unlocked the app layer) 00:13:27 — MCP: persistence + endpoints + bringing CRM context into the model 00:14:01 — “Skills” (skills.md): markdown instruction sets + barrier-to-entry dropping 00:16:08 — Stateful explained 00:18:13 — From calling a model → orchestrating an agent that interacts with its environment 00:20:56 — Why MCP matters at enterprise scale (endpoints, deterministic calls, systems integration) 00:23:35 — Advising leaders: “from the data up”, value drivers, and getting the business ready 00:28:00 — Skills in practice: reducing cognitive load, token use, context window constraints 00:31:41 — CEOs/Boards: if capability plateaus but abstraction advances, how should investment change? 00:37:47 — The repeated mistake: governance + evaluation layer + traceability LLMs 2026, enterprise AI, generative AI, AI strategy, AI investment, Head of AI Solutions, Vector8, Richard Bounds, application layer, agentic AI, AI agents, tool calling