Lalitha Indrakanti


In today’s fast-paced world, the rapid advancement of Artificial Intelligence (AI) is making waves across industries. It’s no longer just a buzzword; AI, particularly Generative AI, is now seen as a transformative force that offers unprecedented opportunities for organisations to boost productivity and enhance customer service. However, with its boundless potential comes the pressure of the “fear of missing out” (FOMO), which keeps many leaders awake at night. But, is rushing into AI the right decision? Or are there risks that need to be carefully considered?

The AI Opportunity: A Double-Edged Sword


The key to AI’s growing appeal lies in its promise to revolutionize business operations. From automating repetitive tasks to providing deep insights through data analysis, AI is opening new avenues for businesses to increase their efficiency and deliver better outcomes for customers. The World Economic Forum’s Jobs of the Future report points out that nearly 44% of the workforce’s skills will undergo significant changes in the next five years, with a marked rise in demand for roles such as data analysts, scientists, and big data specialists. However, the myth that AI will replace jobs is gradually being debunked. The reality is that AI is more likely to enhance human capabilities rather than replace them entirely.

AI’s influence is far beyond just automating processes. The conversation should shift to how we, as organizations, can adapt our talent strategies to leverage AI’s capabilities effectively. Instead of focusing on AI answering all the questions, it’s time to groom talent that asks the right questions. A fundamental shift is needed in how we approach education and leadership: developing critical thinking and problem-solving skills that work in tandem with AI.


AI Beyond Programming: Building the AI Workforce

The narrative is evolving: AI is not just a tool for programmers, but a strategic enabler across all facets of an organization. Globally, 67% of organizations are creating new roles specifically for generative AI, and 87% already have dedicated AI teams. According to recent studies, about 40% of the workforce will require retraining by 2027 to keep pace with the rapid technological shifts. Leaders must embrace AI not as a futuristic luxury but as a strategic necessity. With AI, businesses can accelerate product development, streamline supply chains, and boost decision-making processes. However, simply jumping on the AI bandwagon without a strategic plan could lead to costly failures.

The Risk of AI FOMO: A Cautionary Tale

In the rush to adopt AI, many companies may feel pressure to deploy the latest technology simply because competitors are doing so. However, this “fear of missing out” can be a dangerous motivator. AI adoption should not be about keeping up with the Joneses; it should be driven by a genuine business need and a clear value proposition.

Also Read | Transforming Retail through AI: How GCCs are Pioneering Industry and Societal Impact

AI’s power is best realized when applied to critical business problems. Leaders should start by identifying the most pressing challenges within their organization and select AI use cases that provide tangible business value. Whether it’s improving efficiency, reducing costs, or enhancing customer experience, AI should be seen as a tool to solve specific problems, not just a shiny object to chase.

Leaders should also consider the long-term scale of AI implementation. It’s not enough to simply adopt AI and expect immediate results. AI adoption must be backed by sustained investment, the right talent, and a culture of continuous improvement. AI initiatives require constant refinement, and a structured, agile approach is key to ensuring they deliver lasting value.

AI in the Automotive Industry: A Case Study in Transformation

One industry that has seen substantial AI-driven transformation is the automotive sector. The evolution of vehicles, combined with intelligent technologies like automation, AI, and data analytics, has significantly boosted productivity. From product design to manufacturing and supply chain management, AI is helping automotive companies streamline operations and make smarter decisions that ultimately enhance the customer journey.

In this context, AI’s impact can be seen in several key use cases:

  • Risk Scanning: During the semiconductor crisis, automotive companies introduced AI-based risk scanning solutions to identify risks across the supply chain early. This allowed companies to mitigate potential disruptions and improve operational efficiency, enabling teams to focus on long-term strategic goals.
  • Vehicle Label Quality Control: AI is being used to read data from labels and cross-check it against the Bill of Materials to ensure compliance with manufacturing standards. This ensures that any discrepancies are detected early, reducing the risk of costly mistakes.
  • Full Vehicle Inspection: AI-driven algorithms are being used to inspect vehicles for damage, such as scratches or dents, and link them back to specific VINs. This helps maintain quality control at the source and ensures that the vehicles match their ordered specifications.

These use cases demonstrate how AI can deliver strategic value, improving efficiency and customer satisfaction. The AI-driven solutions implemented during times of crisis, such as the semiconductor shortage, have helped organizations maintain competitiveness while improving customer experience.

Best Practices for AI Adoption: A Roadmap for Success

For organizations considering AI, several best practices can guide a successful journey:

  • Start with Business-Driven Problems: Focus on the most important business challenges first, and prioritize AI initiatives that drive tangible value. Building credibility and demonstrating results early on can create momentum for broader AI adoption.
  • Secure Leadership Support: Successful AI implementation requires investment, both in terms of resources and leadership commitment. Ensuring that senior leadership is on board and actively supports AI initiatives is crucial for smooth deployment.
  • Measure and Improve Continuously: Adopt a structured approach to measuring the performance of AI models. An agile methodology allows organizations to refine their AI models continuously, ensuring that they remain relevant and effective.
  • Partner with External Experts: Don’t feel the need to reinvent the wheel. By partnering with ecosystem players, organizations can tap into external expertise and accelerate the pace of AI adoption.
  • Create a Portfolio of AI Projects: A portfolio-based approach allows businesses to evaluate opportunities, prioritize them based on potential impact, and build a robust pipeline of AI initiatives that are aligned with strategic objectives.

Also Read | Ethical AI for a Better, Smarter Tomorrow

Choosing AI Wisely

The excitement surrounding AI is justified, but clear business needs and value propositions must drive the rush to adopt AI. Leaders must resist the “fear of missing out” and focus on carefully selecting AI applications that align with long-term strategic goals. In the end, it’s not about being first—it’s about being smart in how AI is deployed and integrated into the organization’s operations. With the right approach, AI can be a powerful ally, enhancing productivity, improving customer experiences, and driving business growth in the years to come.

Views expressed by Lalitha Indrakanti, CEO, Jaguar Land Rover, Technology and Business Services India

 

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