The Board's AI agenda: How prepared are you to guide the transformation?

An Indian music company, listed and with annual sales of ₹310 crore and a modest market capitalisation of ₹8,110 crore, has flagged artificial intelligence (AI) disruption as the most significant risk to its business. In its disclosure, the company noted:  “AI is transforming music production and significantly raising concerns about job displacement and income reduction. (On the one hand AI-generated music) opens new avenues for creativity and democratizes music production, while on the other, it brings forth challenges related to copyright, royalties, and the value of human-created recorded music.” 

When a business that relies heavily on artistic talent cites AI as a disruptive force, it signals more than just an industry-specific concern. It highlights a broader reality, namely that AI has the potential to profoundly reshape key aspects of business, the economy, and society itself. The implications extend beyond music, and serve as a timely reminder for (all) companies to prepare for the structural changes that AI will usher in. 

What should be the board’s focus? 

The first is strategic. Boards must understand AI’s potential to fundamentally alter how their industry operates. In financial services, it is transforming risk assessment and customer service. In health care, it is revolutionising diagnostics and treatment personalisation. In information-technology services, it marks a transition from a headcount-driven model to one that is technology-augmented through tools like GitHub Copilot, and ChatGPT. Boards must grasp these sector-specific implications to provide meaningful strategic guidance. AI’s impact will not be uniform — each industry will be affected differently across the short, medium, and long term. 

The second is operational. This, too, will vary by industry. In financial services, it means turning reams of unstructured data into meaningful and potentially predictive insights. Companies must evaluate various operations — client acquisition, “know your customer” (KYC) checks, custody, cash transfers and remittances, and trade settlement. Many processes and workflows will become more optimised and efficient. Finally, there’s the underwriting or investment decision itself: How do firms use AI to make better risk-return trade-offs? 

The Indian information-technology (IT) services sector, arguably the most impacted, is reskilling its workforce by preparing them for AI-augmented roles. 

Companies are repositioning themselves as “transformation” partners rather than service vendors and are embedding AI across cloud, data, and engineering practices. 

Manufacturing firms are also harnessing the power of AI. A steel manufacturer used AI tools for predictive maintenance, significantly reducing unplanned downtime. An auto company directed AI toward improving supply-chain efficiencies. A company making chemicals used it for demand prediction to better manage inventories. There are as many use cases as there are businesses. This underscores the importance of openness to experimentation — as AI apps become more pervasive, more employees, across levels, use them to improve efficiencies, cut costs, or even develop new revenue streams. It is imperative that boards focus not only on the dangers of AI but also on its transformational potential, which will determine a company’s competitive position in the foreseeable future. 

Third, the board plays a critical role in overseeing the risks posed by AI, particularly in areas such as data privacy, model accountability, and algorithmic bias. For example, many fintech and digital-lending platforms in India use alternative credit-scoring models based on behavioural and mobile data. These models can reflect urban, upper-class biases, making it harder for rural borrowers or those without digital footprints to access credit, even if they are creditworthy. Additionally, the use of location or phone metadata can result in discriminatory exclusions based on caste, gender, or socioeconomic status. Ethical principles must inform every stage of AI deployment, and the board should advocate transparency and fairness, and insist on human oversight in the use of AI systems. 

Finally — and ironically — it comes down to people. The board must assess whether the organisation’s leadership is prepared for this transition. This includes evaluating the depth of AI literacy at executive level and the preparedness of the workforce. It’s not just about hiring or training a few data scientists — it is about preparing the entire organisation to work alongside intelligent systems, make data-driven decisions, and adapt to new roles and workflows shaped by AI. Companies must think about skilling their entire workforce. 

The board also needs to remain current. Martin Lipton of Wachtell, Lipton, Rosen & Katz, a law firm, summed this up best: “Remaining informed is a must. While the charge of oversight remains the North Star for boards, and directors themselves need not necessarily develop individual expertise, boards should ensure clear visibility into the core technological tools in use by the company and its competitors, as well as critical workflows that could be materially affected by technology and other salient market developments. Directors are entitled to rely on appropriate repositories of such information in management and qualified experts.” 

The question isn’t whether AI will impact your organisation — it is how prepared are you to guide the company through this transformation. 

The author is with Institutional Investor Advisory Services India Ltd. The views are personal. X: @AmitTandon_IN