India’s leadership is navigating an unspoken truth: The applause for AI’s computing power masks a deeper strategic challenge. The real transformation and the source of disruption is the mandatory, imminent reassessment of work models, leadership authority, and the core competencies that truly make our people valuable.
Kingsley Gate hosted a panel discussion with leading executives which brought to the forefront three pressing concerns that keep them up at night: Reskilling initiatives that cannot keep pace with change, senior leaders who resist in practice the very transformations they publicly champion, and the looming question of what happens to middle management – a question so uncomfortable, it has become the organizational elephant in the room.
What started as a panel on technology soon became a much deeper conversation. Prominent leaders from India-based and global organizations across e-commerce, enterprise software, aviation infrastructure, fintech, and professional services moved past talk of algorithms and efficiency to openly share fears, confront failures, and question fundamental assumptions about leadership.
Who Really Calls the Shots When AI Joins the C-suite?
To summarize the first concern, leaders worry that reskilling initiatives are not keeping up with the pace of change. Companies channel millions into training programs, launch learning platforms, and mandate upskilling hours, yet employees feel perpetually ‘left behind’. By the time a training program is designed, approved, and deployed, the skills it teaches may already be outdated. Organizations are fighting an ever-changing, exponential learning curve, reliant on linear solutions.
Nalini George, the CHRO of Bangalore International Airport, lent her perspective, having the point of view of someone who has watched technology evolve from .net to full-stack to data analytics, with each phase lasting years. “With AI,” she said, “the escalation has been dramatically different. If you don’t get on the bandwagon, there will be a fall-off.” What used to be a marathon has become a sprint, where the finish line keeps moving. But even as her team has successfully leveraged AI to devise multi-year talent planning in three days instead of 45, she emphasizes that the process still needs human analysis, not blind trust in all machine-generated outcomes.
The second critical requirement for organizations is that senior leaders must adopt and avoid being resistant to AI transformation. Any resistance could create a challenging on-ground reality, with the very people tasked to drive change often fearing it most. This creates a challenging on-ground reality, with the very people tasked to drive change often fearing it most. It comes from a comprehensible concern: After they have spent decades mastering their domains, building networks, and developing instincts, they are being told algorithms can do more, better, and faster.
Rahul Hariharan, Partner with Deloitte India, crystallized this challenge with a hard-hitting question, asking, “Completely trusting AI, or completely doubting it – are either of those healthy positions?” Addressing the heart of this dilemma, he observed that those at the top often swing between extremes, dismissing AI as hype or fearing it will make them obsolete. Neither stance, per him, will serve their organizations well. He noted, “Reports suggest that approximately 11% roles may not change, but the bulk will evolve, and need to adapt. Those unwilling to transform risk redundancy.”
The final, and arguably most challenging concern is that leaders have yet to figure out what will happen to the middle management, and they are not talking about it. Middle managers have always been the connective tissue of organizations, translating strategy into execution, developing future leaders, and maintaining cultural continuity. When AI handles coordination, analysis, and routine decisions, what is left to be done?
This concern runs deep. Banking executives report that 40-50% of middle and back-office roles could be impacted, with much of that work already feasible via AI. With AI enabling such radical eciency, the entire middle layer that traditionally transmitted culture could disappear. The Chief Operating Officer of an enterprise technology company, who has witnessed serial disruptions in banking, summarized, “True disruption comes when you think you’re safe, and then AI leaps ahead unexpectedly.”
The challenge goes beyond numbers. The CEO for a leading online fashion and lifestyle marketplace in India, raised a fundamental question: “One can start a company with three people instead of 30, but how do you build culture?” This effectively summarized the dilemma: The middle management question isn’t just about roles, but about how organizations function and maintain their human core when traditional structures dissolve.
In addition to the impact on middle-management, she highlighted a critical long-term consequence on junior roles: If entry-level jobs vanish, so will the pipeline of future leaders. With that, another social challenge will emerge – keeping people engaged in leaner teams, with lesser redundancies / repetitive roles.
Identifying opportunities within this fast pace and ambiguity, she recommended leveraging how AI empowers analytics; for instance, developing automation initiatives for marketing responsibilities across budget allocation, efficiency gains and collateral. In order to ensure seamless integration, she recommended two layers of ‘protection’ for the workforce: Identifying new roles enabled by AI, such as prompt engineering or AI-design, restructuring existing role responsibilities and reskilling teams to monetize AI.
The Precision Game: Why AI’s Accuracy Makes Leaders Nervous
The most striking revelation from our discussions went beyond appreciation that AI works, addressing also how unsettlingly well it works, and where it fails spectacularly. When polled on how AI most challenges their leadership instincts, 33% leaders identified hiring decisions as the primary battlefield between AI recommendations and ‘gut-feeling’. The remaining responses were split between performance evaluations, strategic planning, innovation direction, and crisis management, revealing AI’s pervasive impact across every aspect of leadership.
Anirban Bhattacharya, the VP of HR & Global Head Talent Innovation at Fiserv, describes this disconnect perfectly, stating, “The precision in hiring is uncanny – it made me uncomfortable, but excited about recruiting better and faster. That said, decisions do sometimes fail when one needs to check for culture.”
His statement was demonstrative of a paradox: How AI has the ability to exponentially optimize for measurable outcomes, but misses the immeasurable essence of human dynamics, thus impacting the evaluation of culture fitment and motivators in the hiring process. This shortcoming represents the central challenge of our time: It is not a technology problem; it is a leadership problem.
A real example brought this into focus: A CHRO received a detailed AI analysis recommending a high-performer with flawless metrics for promotion, but chose someone the AI ranked fifth. Six months later, her team engagement levels hit record highs, and attrition dropped by 40%. She, however, faced tough questions: Why did she ignore the data? Why invest in AI if leaders might overrule it with intuition?
This tension now defines leadership. As Anirban observed, “You cannot unleash AI without a tether – you need to think about it in a framework.” Another panelist reinforced this, stating, “You absolutely cannot unleash AI without a proper framework and governance structure.” Her experience implementing AI across her company’s customer service and seller-onboarding operations brought with it tough lessons about balancing automation with human oversight.
The framework is not about constricting AI and its value; it is, instead, about preserving human agency and retaining human values within automated systems.
Beyond Technology: What Really Matters
Throughout our discussion, a theme that resurfaced numerous times was that successful AI transformation has little to do with technology and more with the people, culture, and courage to admit what they do not know.
‘We are all learning how to prompt’ has become a recurring acknowledgment. In addition to developing technical prompting skills, this entails a fundamental shift in how leaders think. They are tackling the more complex questions that accompany this upskilling, including: How do you frame questions for systems that know everything but understand nothing? How do you interpret recommendations that are technically perfect but contextually wrong?
The organizations succeeding aren’t those with the most advanced AI technology, but the ones where leaders model transparency and vulnerability about their own learning journey. In such organizations, ‘I don’t know’ becomes an acceptable statement or response, and the openness to experiment trumps perfection. BIAL exemplifies this approach, testing open Copilot and Gemini enterprise systems with careful guardrails, while acknowledging they are still in an ongoing learning process for what works, what works less, and what works best.
The evolution of executive roles echoes this learning, as CTOs who were once valued primarily for their technical expertise now create the most impact as strategic translators between technology and business. The discussion revealed that the most sought-after CTOs aren’t the deepest technologists, but the best interpreters who understand people. They bridge AI capabilities with human needs, ensuring technology serves, rather than dominating.
New roles emerging across organizations prove the effectiveness of this humancentric approach: AI-augmented business analysts translate complex business problems into AI-solvable questions, prompt engineers master the art of communicating with genAI models to elicit optimal results, and human-AI collaboration managers facilitate seamless interaction between employees and AI systems. These aren’t just new job roles – they represent the fundamental evolution of human value and nuanced responsibilities in organizations building for the future.
One panelist lent further perspectives from the serial disruptions in BFSI, reiterating that true disruption comes when organizations think they are safe, and AI leaps ahead unexpectedly. Each wave of change, from branch to net to mobile banking, promised efficiency and delivered complexity. AI, however, differs fundamentally, with previous technologies changing how we work, and AI changing the very meaning of work.
Nalini offered practical wisdom, sharing, “Low attrition is tied directly to continuous upskilling.” This upskilling is more than technical training; organizations that use AI to enhance rather than replace human capability see dramatically better outcomes. They create cultures where employees voluntarily embrace change because they see support and opportunity, not threat. She noted, “Enterprise AI systems that nudge employees to learn without coercion succeed where mandated training fails.”
Building Trust in an AI-augmented World
In a world where impactful cooperation and collaboration between humans and systems is fast becoming the cornerstone of gearing towards success, it is unsurprising that trust has emerged as the foundation of successful AI transformation. Success entails more than just trust in technology; it also includes trust between humans navigating unprecedented change together.
This plays out in unexpected ways across organizations.
Examining this dynamic from the BFSI lens again, driving the customer service function provided telling insights, particularly where both human agents and AI can frustrate customers equally. This insight cuts deep, involving not just choosing between human and AI, but also understanding when each serves best. As an instance, for airlines handling distressed passengers, AI could optimize gate assignments, but it will not be able to provide the empathy and understanding that is required to defuse the crisis and build a satisfactory customer experience. Success here can be achieved if technology handles logistics, and humans handle humanity.
The talent pipeline challenge threatens this balance. Entry-level positions traditionally offered multiple deliverables: Accomplishing routine work, training future leaders, and filtering for potential. With AI eliminating a number of these roles, organizations lose their primary mechanism for developing talent. Per some of the leaders in attendance, students use AI for learning far more extensively than most faculty members realize, and the gap between academic preparation and workplace reality widens daily.
Rahul said, “Much of the dissatisfaction here comes from the loss of critical thinking and problem-solving tasks.” The consequences, then, are not unemployment, but underemployment of human potential. When AI handles analysis, where do future leaders develop analytical instincts? When algorithms make routine decisions, how do managers learn judgment? We risk creating executives who can operate AI, but cannot think without it.
The organizations building genuine trust share specific practices, including acknowledging uncomfortable truths publicly rather than maintaining corporate facades. They create transparency – not simply through policy, but also through practice. This helps show employees exactly how AI influences decisions affecting their careers, and establishes clear principles for when human judgment overrides algorithmic recommendations, before controversy forces the issue.
“Ethics in the age of AI are critical,” as Anirban emphasized, but ethics without trust remain theoretical. Trust is built through consistency between words and actions, through leaders who admit when AI recommendations proved wrong, and through organizations that invest in human development even when ‘AI could do the work cheaper’.
The Path Forward: Five Imperatives for Leaders
Drawing from the experiences of organizations at the forefront of this transformation, several leadership imperatives emerged:
Balancing the Equation: The Human-Centric AI Future
The AI revolution isn’t really just about AI, but about rediscovering and enhancing human intelligence, as we seek to understand and upskill the values that make us irreplaceable when machines can do nearly all the measurable work.
The question “Who is really calling the shots?” must include a deeper, more complex truth: It is not about choosing between human and artificial intelligence, but about creating organizations where both amplify each other. The aim should be to create cultures where efficiency serves purpose, and technology enables humanity rather than replacing it.
The executives who gathered in Bangalore demonstrated something valuable: The courage to voice uncomfortable truths, and the wisdom to know that navigating transformation requires both precision and humanity. They observed that AI has already delivered speed, efficiency, accuracy, and quality improvements. They also acknowledged that if AI is unleashed without a plan or framework, people may lose jobs, or decision-making could be adversely affected.
The future belongs to leaders who understand this distinction.
This perspective piece is based on insights from “CEO, CTO, CHRO… and AI : Who’s really calling the shots?”, a Kingsley Gate executive dialogue.
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