The biggest opportunities in AI will come not from larger models, but from building strong context layers that help AI understand business data, relationships, and organizational knowledge. Companies that invest in context engineering—through structured data, metadata, knowledge graphs, and governance—will achieve more accurate insights, better decisions, and greater trust in AI outcomes.

AI’s greatest business impact won’t come from adding chatbots to existing processes—it will come from redesigning workflows around AI agents that can gather information, analyze data, and execute tasks across multiple systems. Companies that organize their data, provide the right context, and rethink how work gets done will achieve significantly greater productivity and competitive advantage than those simply automating individual tasks.

Agentic AI represents the evolution from traditional dashboards and reporting to intelligent systems that can reason, analyze, recommend, and take action on behalf of users. Rather than replacing humans, AI agents will become digital coworkers that help employees make decisions, automate workflows, and transform business data into measurable outcomes.

The future of enterprise AI depends less on bigger models and more on better context engineering — the ability to organize, retrieve, govern, and deliver the right information to AI systems at the right time. Together, the two talks show that successful AI requires both organizational context strategy and technical infrastructure including RAG, memory, tools, and structured workflows.

Artificial intelligence (AI) is no longer a tool reserved for large enterprises with massive budgets. Today, small and medium-sized businesses (SMEs) are using AI to improve sales performance, streamline marketing, and create more personalized customer experiences—all without adding headcount.