India’s AI ambitions are gradually becoming a reality. Accelerated by government initiatives, enterprise investments, industry-specific adoption, and advancing foundational models, this shift marks India’s transition into an AI-driven economy.

While most discourse surrounding AI revolves around technology (the novel models, tools, and use cases), there is another change, albeit a quiet one, that is emerging: it changes what organisations expect from their leadership, with the executive mandate itself expanding. The question most of these teams encounter today is who owns the business decisions that are increasingly influenced by AI and whether the aforementioned teams are equipped for this responsibility.

Changing Scope of Leadership

For years, the roles for senior executives remained well-defined. For example, it was clear that CEOs drive growth, CFOs protect financial performance, CIOs lead technology, and CHROs build talent. Despite the occasional overlap in responsibilities, the boundaries between them remained relatively clear for organisations to operate efficiently.

However, the advent of AI increasingly compels executives to look beyond these traditional boundaries. For example, in a scenario where AI is being deployed within customer service, what might start as an IT initiative transcends that, entering conversations and contributing to decisions about customer experience, workforce planning, governance, and board-level ones about competitive advantage. This initiative then demands simultaneous contributions from all the C-suite executives. This is because an AI investment is not simply about the infrastructure, but it also requires consideration of capital allocation, operating models, and long-term competitiveness.

This shift is evident in how firms perceive leadership. Recent C-suite research conducted by Deloitte highlights the emergence of hybrid executive responsibilities and so-called “double-hatting” as decisions transcend the traditional business domains.

A CXO doesn’t need to build AI models. They need to ask better questions.

This does not mean that every executive needs to become an AI expert. The responsibility of leadership will involve asking the right questions before the implementation begins. When auditing projects across the four strategic dimensions of commercial alignment, data infrastructure, human capacity, and regulatory risk, they must ask the following questions:

• • What specific operational bottleneck are we trying to eliminate, and is AI the simplest way to do it?
  • How does this implementation change our competitive advantage?
  • What does success look like in business metrics, not technical metrics?
  • Do we own the data infrastructure required to train and sustain this model?
  • Where does human oversight sit within this automated workflow?
  • When individual productivity scales up, where does the organizational bottleneck shift?
  • Why should our frontline employees trust and use this tool?
  • How will we redistribute the human capacity this tool frees up?
  • What is our mechanism for capturing individual AI discoveries and turning them into institutional knowledge?
  • What are the legal liabilities regarding data privacy, IP infringement, and regulatory compliance?
  • What is our fallback protocol when-not if-the model fails or degrades?
  • How do we audit the model for hidden bias or unethical optimization?

In many organisations, AI projects stall only because of insufficient clarity regarding the business problem they are trying to solve, or due to improper alignment on who owns the outcome. These are the aspects wherein leadership intervenes.

Building leadership for an AI economy

As expectations of leadership evolve, the decision about how to build leadership capability comes to the forefront, as AI changes how organisations access said expert capabilities, which may only be needed for a specific phase of the journey. Therefore, not every challenge would require a permanent hire.

For example, a company exploring AI-enabled operations may benefit from an interim transformation leader, while another may require a fractional technology advisor to shape strategy before making long-term investments. Meanwhile, others may require specialist expertise in governance, change management or data strategy for a specific period of time.

As organisations gradually realise that building every capability in-house is not always the most feasible or effective solution, one can observe a notable shift in how they access expertise, by complementing their leadership with experienced independent consultants and fractional executives who bring to the table specialised knowledge right when it is needed the most. This reconfiguration of the corporate structure reflects greater flexibility, as they no longer have to wait for their existing structures to catch up before responding to change.

How IndusGuru can help

At IndusGuru, we’ve seen how access to the right expertise at the right time can help organisations move from uncertainty to execution. As organisations rethink leadership for the AI era, the question becomes more about knowing when to bring in the right expertise. In such a landscape, the organisations that succeed with AI will be those that combine strong internal leadership with specialised expertise whenever the challenge demands it.

IndusGuru connects firms with a curated network of independent consultants, subject-matter experts and experienced professionals across business and technology domains. Organisations can engage the expertise they need for specific projects, challenges and transformation requirements without having to permanently integrate the capability.

If your organisation is rethinking its leadership capabilities for the AI era, we’d be happy to start that conversation. Explore our curated independent expert network at https://www.IndusGuru.in/ or write to us at [email protected].