AI Talent and Organization: Building the Capability That Lasts

A hybrid model works best: a central function provides platform, standards, governance, and expertise; business and product teams own use cases, adoption, and outcomes. Central teams set the “how” (tools, guardrails, architecture); business teams define the “what” and “why.”

Pure centralization creates bottlenecks and distance from user needs. Pure distribution fragments standards, duplicates effort, and slows learning. The balance—clear center of gravity for capability and governance, with ownership and accountability in the business—enables both speed and consistency.

Practical implications: invest in a strong center of excellence or platform team for AI/ML infrastructure, model standards, and risk governance. At the same time, assign clear “AI owners” in key business or product areas and tie their objectives to adoption and impact metrics.

Talent strategy must balance building internal capability, targeted hiring, and selective partnership. Building internal capability—through upskilling, rotations, and clear career paths—is the only way to sustain advantage over time. Over-reliance on external hiring or vendors creates dependency, turnover, and knowledge loss.

Upskilling existing talent is underused. Many roles—product managers, engineers, operations—can be equipped to work with AI through structured learning, hands-on projects, and access to platforms and experts. Organizations that invest in broad AI literacy and applied training reduce dependency on a small pool of specialists.

Targeted hiring still matters for specialized roles—applied ML, MLOps, AI product—but should be integrated into teams that own outcomes, not isolated in a central lab. Partnerships and vendors are best for discrete capabilities (e.g., specific models or vertical solutions) rather than core strategy and ownership.

The demand for “AI-native” product, engineering, and operations roles is rising. At the same time, the standalone “AI scientist” role is being absorbed into product and engineering teams that own end-to-end value. Organizations that define clear roles and development paths capture and retain talent.

Career paths should reflect this evolution: routes into AI-from-product, AI-from-engineering, and AI-from-data, as well as dedicated tracks for those who stay deep in ML/AI research or platform. Without visible paths, talent migrates to firms that offer clearer growth and impact.

Titles and levels should be aligned with market and internal equity. “AI” or “ML” in the title is increasingly expected for roles that own model design, deployment, or AI product; clarity on level and scope reduces confusion and supports retention.

Measurement of AI capability—adoption, impact, and maturity—should sit with business and product leaders, not only with IT or a central AI team. When accountability for outcomes is diffuse, investment and prioritization drift. When it is clearly assigned, roadmaps and resources follow.

Define metrics at the right level: adoption (usage, coverage), impact (revenue, cost, quality), and maturity (governance, reuse, iteration). Tie these to the same governance and planning cycles used for other strategic initiatives. Accountability for outcomes drives sustained investment and prioritization.

The central AI or platform team should be measured on enablement—speed to value, reuse, standards—while business and product owners are measured on adoption and impact. This split keeps the center focused on capability and the business focused on results.