September 12, 2025

AI as Talent: Navigating the New Landscape of Artificial Intelligence Management

Transforming AI from a tool to talent requires strategic management for long-term business value.

5 min read

Key Takeaways

Discover why—given its ability learn, adapt, and evolve—AI is analogous to skilled human talent.
Shift from treating AI as a project to managing it as an evolving asset.
Invest in people who can effectively guide, train, and extract strategic value from AI.

The conversation around artificial intelligence (AI) is rapidly evolving. For us as IT leaders, this evolution presents a fundamental question: How do we manage AI to ensure it generates maximum strategic benefit and ROI, while mitigating emerging risks? It's time to shift our perspective. We need to move beyond viewing AI as just another technology initiative and instead embrace it as a new form of organizational talent.

Resource management versus talent management: a critical distinction

Traditionally, resource management has focused on deploying existing assets (that is, people and infrastructure) to fulfill defined needs. It's about capacity planning and efficient allocation. Talent management, however, takes a more strategic, long-term view. It recognizes that people—and, by extension, sophisticated AI—are investments. The goal of talent management is to cultivate, develop, and grow these assets, enhancing their capabilities and, consequently, their value to the organization.

When we talk about managing AI, this distinction is crucial. AI systems, particularly generative AI and advanced analytics platforms, don't just execute tasks; they learn, adapt, and evolve. They can be trained, given new contexts, and their performance can improve over time. This learning capability makes them analogous to skilled human talent, or even specialized contractors, rather than static project deliverables.

The AI "contractor": risks and rewards

Treating AI as a talent asset means we must consider the implications of its integration and development. Like a valuable external contractor, an AI model brings specific skills. However, as we become more reliant on its capabilities, we also increase our dependency. This dependency can lead to significant supplier risk, potentially leading to increased costs as AI's value to our organization grows.

Conversely, if we limit AI's exposure to our data and processes due to fear of this dependency, we risk hamstringing its potential. The very essence of advanced AI lies in its ability to learn and adapt, evolving beyond its initial programming to serve new needs and unlock unforeseen value. This creates a strategic dilemma: How do we foster AI's growth and maximize our return on investment, without becoming overly reliant on a single vendor or system?

Strategic imperatives for AI talent management

This is where proactive talent management strategies for AI become vital. It’s not just about deploying AI; it’s about developing it.

We must ask ourselves:

Does our organization possess the human talent to effectively guide and leverage AI? We need individuals who can properly extract value from AI systems.
Do we have processes in place to develop our AI assets? This includes providing them with the necessary context, training data, and oversight.
How do we balance the risks of vendor dependency with the rewards of AI evolution? This requires careful consideration of data governance, in-house capabilities, and long-term strategic alignment.

By viewing AI through the lens of talent management, we can begin to approach its integration strategically. This means understanding that AI isn't an "initiative" to be completed and handed over. Instead, AI is an evolving capability that requires ongoing investment, sustained development, and careful stewardship to maximize its long-term contribution to business outcomes and our overall ROI.

Key takeaways for IT leaders

As they chart their AI strategies, here are some key aspects for IT leaders to consider:

AI is evolving talent. Treat advanced AI systems as talent assets requiring development and strategic integration.
Beyond resource allocation. Talent management for AI means fostering growth, learning, and adaptation for increased value.
Manage dependency, unlock potential. Balance the risks of reliance with the rewards of AI's evolving capabilities.
Human oversight is crucial. Invest in the people who can effectively guide, train, and extract strategic value from AI.
Long-term value creation. Shift from viewing AI as a project to managing it as an evolving asset that enhances business outcomes.

By adopting a strategic talent management approach to AI, we can ensure these powerful tools become integrated assets that generate sustainable business value, improve our competitive edge, and manage the inherent risks responsibly.

Tag(s): ValueOps , Clarity , Strategic Portfolio Management , AI , SPM , Infrastructure Portfolio Management , IPM , Talent Management

Brian Nathanson

Brian Nathanson is a recovering certified Project Management Professional now serving as the Head of Product Management Clarity at Broadcom. He is the host of several popular Clarity-related customer webcasts (Office Hours, Release Previews, and the End-to-End Modern UX Demos) and has conducted many hours of both...

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