The Reskilling Imperative: Why Standing Still Is No Longer an Option

In an era defined by exponential advances in generative AI and automation, Finance functions face both, unprecedented opportunities and existential challenges. At a recent online session of the CFO NxT Forum, Madhavan Hariharan, who comes with three decades of experience spanning strategic leadership at CK Birla Group, senior roles at Philips and governance responsibilities at Galaxy Surfactants, framed AI not as a distant novelty but as a present-day force reshaping financial operations, reporting and decision-making. His call was clear: complacency poses a far greater risk than the technology itself.

AI’s Evolution and Finance’s Adaptability

Artificial Intelligence (AI), encompassing machine learning, deep learning, neural nets and generative models that produce text, code, images, audio or video, has matured over the past 25 years. Only recently has AI reached rapid adoption, thanks to abundant data and computing power. As Stanford University’s 2025 AI Index Report demonstrates, model performance benchmarks have surged: scores on complex tests like SWE-bench jumped from 4.4% to 71.7% in a single year while training compute and inference costs have plummeted, widening access beyond Big Tech. This elevates AI literacy into an essential foundation that every employee must master. Finance has long embraced digital tools: ERP, RPA and BI dashboards. Adapting to technologies is not new for financial leadership, but generative AI demands a new level of curiosity and agility.

Core Insights: Speed, Quality and Data Imperatives

AI’s most transformative benefit is speed: investor-relations summaries that once took days now arrive in seconds; variance commentaries are auto-generated at the push of a button. Yet speed alone is not enough. High-quality outcomes depend on clean, well-structured master data and robust data governance to protect UPSI (Unpublished Price Sensitive Information), through anonymisation and enterprise-grade AI deployments. Companies must ensure that their data is accurate, perhaps even venturing to use AI to help clean up their data. 

AI should be approached not as a mechanistic tool but with genuine curiosity: experiment with prompts, refine queries and treat every use case as a learning opportunity, nurturing both technical literacy and storytelling prowess to translate AI outputs into executive insights. Even the most ‘traditional’ manufacturers must embrace modern technology. While their products may not need to change, their production processes do need to be digitised, such as by upgrading factory floors, automating workflows and instrumenting every step to generate rich, actionable data. That data then becomes the fuel for AI-driven optimisation, from predictive maintenance and quality control to energy efficiency and supply-chain planning. No business, regardless of its heritage or sector, can opt out. There is always an opportunity to weave AI into existing operations and unlock new levels of productivity and competitiveness.

Disruption and Functional Impact

Generative AI’s reach extends far beyond Finance. In banking, AI-powered fraud detection and emotionally intelligent chatbots are now standard; customer service operations leverage agents that draft emails and respond to vendors; marketing copywriting has seen up to 80% displacement by AI. Manufacturing and operations are piloting AI-driven visual inspection, while legal and audit teams use AI to summarise contracts and screen records. As these functions evolve, Finance roles will shift from transactional processing to strategic orchestration. It is the Finance professionals who manage to bridge domain expertise with AI fluency that will emerge as the most valuable.

At the same time, emerging Finance leaders must build their influencing skills, such as through ‘storytelling’. They must cultivate storytelling and persuasive communication as core competencies, both for themselves and their teams. In an era where AI can generate sophisticated analyses and dynamic visualisations at the click of a button, true value creation lies in translating those outputs into compelling narratives that resonate in the Boardroom. Without the ability to humanise data, framing insights in terms of strategic implications, risks and opportunities, AI-generated charts and numbers risk gathering dust. Storytelling is the second ‘scissor blade’ that, when paired with technical insight, cuts through scepticism and drives adoption: it bridges the gap between machine-driven intelligence and the human judgment required to act on it. By upskilling in the art of narrative, Finance professionals ensure that AI’s revelations don’t just inform, but inspire decisive, value-creating decisions.

Many Finance leaders hesitate to embrace AI because they believe they must foresee its full long-term return today and master every nuance before even logging in. However, AI’s power lies in its boundless flexibility: you don’t need a perfect roadmap at the outset. Progress emerges through hands-on exploration, iterative experimentation and curiosity-driven questions. Clinging to ‘Is it worth it?’ or ‘Should we even start?’ mindsets only lock you in analysis paralysis. Instead, treating AI as a learning journey, you can begin anywhere, ask bold questions, refine your approach as you go, and let insights unfold organically. In doing so, you transform a fear of the unknown into momentum for discovery and innovation.

Getting Started: A Practical Path

• Carve out time: Block dedicated sessions, however brief, for hands-on AI exploration.

• Leadership by example: Functional heads must be seen experimenting, even if initial sessions yield only 30% comprehension.

• Start small, iterate: Ask three questions daily, pilot 3–5 micro-use cases (e.g., automated budgeting commentary, data-cleanup agents) and share successes to build momentum.

• Democratise AI resources: Ensure that every team member has seamless access to AI platforms, tutorials and sandbox environments. This will fuel individual curiosity, accelerating hands-on learning and building organisation-wide AI fluency.

Risks, and Risk Mitigation

Concerns around data confidentiality and model bias are real but surmountable. Start by masking sensitive identifiers and leveraging enterprise AI offerings with built-in compliance controls. Establish a cross-functional data governance council (e.g., Finance - IT - Legal) to define acceptable use policies, monitor output quality and audit AI-generated content. Frame security as an enabler, not a blocker, and adopt fact-checking processes. These could unfold as post-generation automated checks or selective human reviews, to catch hallucinations before they impact decisions.

Recommendations

• Lead with curiosity: CFOs and other Finance leaders should publicly champion AI pilots and share the lessons learned.

• Institutionalise learning: Embed short, recurring ‘AI learning sprints’ within existing L&D frameworks.

• Promote a culture of experimentation: Encourage all Finance professionals to ask questions, refine prompts and build confidence with AI tools.

By embracing AI as both a strategic partner and a catalyst for upskilling, the Finance community can accelerate decision-making, elevate the CFO role and secure a competitive edge in a rapidly evolving landscape. Continuous iteration, strong leadership sponsorship, and a mindset of curiosity will be the keys to turning today’s AI imperative into tomorrow’s financial advantage.