Reframing Seniority: Shub Faujdar on Navigating AI-Adjacent Roles

In a recent LinkedIn post, Shub Faujdar offers guidance to senior leaders looking to transition into AI-adjacent roles, addressing the common fear of starting over. Faujdar emphasizes that the primary hurdle is not a lack of skill, but a failure to effectively translate existing experience into the language of AI transformation.

Faujdar begins by acknowledging the apprehension many senior professionals feel when faced with job descriptions filled with terms like “product ownership,” “automation strategy,” and “AI transformation.” This can lead to a feeling of being a “graduate again” despite years of experience. Faujdar states:

“Twenty years of seniority and suddenly feeling like a graduate again. That fear is understandable.”

The Positioning Problem: Translating Experience

According to Faujdar, the core issue is a “positioning problem” – the inability to articulate current capabilities in a way that resonates with the demands of AI-focused environments. Faujdar outlines a clear method for this translation, contrasting previous descriptions of experience with how they should be reframed for AI-related roles.

From Management to Transformation Leadership

Faujdar provides an example of how to reframe experience in managing change. Instead of stating,

“I managed cross-functional teams through complex change programmes.”

The reframed version, as suggested by Faujdar, becomes:

“I’ve led organisations through ambiguous, high-stakes transformation – exactly the environment where AI implementation breaks down without senior leadership aligned to the outcome.”

This shift, Faujdar argues, highlights the senior leader’s ability to navigate complex, uncertain environments – a crucial skill for successful AI deployment, which often introduces ambiguity and requires strategic alignment.

Decision-Making with Incomplete Data

Another key area Faujdar addresses is the experience of operating in emerging markets. The post suggests moving away from describing it as:

“I built and scaled operations in emerging markets.”

And instead, framing it as:

“I have made high-quality decisions with incomplete data, no playbook, and real consequences. That’s the operating environment AI still creates for every senior leader deploying it.”

This reframing, Faujdar points out, directly connects past decision-making under pressure and uncertainty to the challenges inherent in implementing AI solutions. It underscores a leader’s capacity to make sound judgments even when faced with the kind of incomplete information and lack of precedent that AI projects often entail.

AI Fluency Beyond Coding

Shub Faujdar emphasizes that “AI fluency for senior leaders is not about learning to code.” Instead, it is about understanding how one’s existing strengths align with the actual problems organizations are trying to solve with AI. The critical skill is the ability to communicate this alignment effectively to stakeholders.

In Faujdar’s view, senior leaders do not need to “start over” but rather “reframe what you already are.” This involves a conscious effort to translate established competencies into the context of AI-driven business challenges. Faujdar recently updated workshops to incorporate these principles, sharing insights from a session with the Skills & Workforce Development Agency (SWDA) on Gaining Career Clarity, which was well-received by participants.