The four strategic areas for data and AI teams remain consistent, but what is happening inside these areas is changing rapidly. This article examines the technical and organizational shifts within each value story and identifies the specific team capabilities required to navigate them.

Value Story #1: AI Innovation

GenAI is moving toward multi-agent systems—a major shift from conversational interfaces and POCs. The focus is now on business process re-engineering, not just chat-based experimentation.

The New Face of AI Innovation

AI is now embedded deeply into processes with bi-directional system access, enabling write-back actions into systems like SAP. The landscape has shifted from "OpenAI eats it all" to a best-of ecosystem with hundreds of specialized tools and strong open-source options. AI is moving from experimentation to robust operations, but these probabilistic systems are far harder to test and maintain than traditional code.

Open, Plug-and-Play Architecture

Instead of debating which LLM is "best," build an open architecture where new components can be plugged in as needed.

Team Capabilities Needed

• AI product managers who understand business change and outcome delivery
• Full-stack AI engineers who can tie together diverse tools
• Multi-agent system designers—reflecting a shift from mathematical modeling to prompt-crafting and system orchestration
• IT integration skills for deep system integration (SAP, Salesforce)
• AI Ops engineering to operate complex systems

Build these skills within existing teams, not through hiring.

Value Story #2: Analytics Self-Service

Self-service analytics frees up team capacity by enabling users to perform analytics themselves. This requires a mindset shift: provide tools, training, and guardrails instead of delivering reports.