Description

AI agents are arriving fast, but the winners will be the organizations that pair them with trusted data, clear processes and security — not those who chase scale alone. 

In this episode, Andreas Welsch talks with analyst and diginomica co-founder Jon Reed about what’s actually next for agentic AI: where real value is emerging, why many pilots stall, and how to move from hype to measurable outcomes. They cut through the noise around model releases and scary headlines, and focus on practical pathways for enterprise adoption. 

Highlights include:

• Why throwing scale at models isn’t a cure-all and how enterprises succeed by constraining problems and adding domain context
• The “last mile” problem: when out-of-the-box LLMs miss industry-specific language, and when to fine-tune or use smaller domain models
• Data readiness as a multi-year journey — and how to weave data cleanup into early AI use cases so you can get wins fast
• Real business examples (procurement long-tail, finance workflows, shop-floor QA) where agents and ML already drive measurable impact
• Security, trust and identity for agents: new threat vectors, authentication challenges and when agent-to-agent interactions create additional risk
• Practical tech signals to watch: RAG + tool-calling, MCP/A2A protocols, zero-copy data approaches, and when to favor internal wins over multi-vendor orchestration
• People and culture: empower middle managers and junior staff to experiment safely, avoid short-sighted headcount cuts, and build communities of practice
• Legal and fairness considerations, including why frameworks like the EU AI Act offer useful risk-drive guardrails

If you lead an AI initiative, build data platforms, or are responsible for secure automation, this conversation gives realistic next steps — from choosing the right model size and partners to hardening agent deployments and capturing the first ROI moments.

Listen to the episode to learn how to move past hype and design agent strategies that actually deliver business outcomes.

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Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers.

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