The Enterprise AI Horizon

The Enterprise AI Horizon

As we have seen through the chapters of this book, the reach of AI has spread wider and deeper in every organisation, and where it has been embraced by all echelons of an organisation, it has had far-reaching impacts in every domain and across all functions. Possibly the most impacted function has been sales and marketing, which has been most exploited in media, professional services, technology and new industries. However, the use of AI has also been significant in product or service development, knowledge management, operations planning and control, and strategy simulation and finalisation. Over the last ten years and more, customer journeys and employee journeys have also been the subject of much experimentation and solution building.

Customer journeys involve a deep analysis of the customer’s awareness of the product, their consideration criteria, evaluation and price, alternative discovery process and, finally, the buying decision, including dealing with buyer’s remorse after the order has been placed. For the digital touch points and AI designers, this will involve detailing each customer persona, setting process and outcome goals for each person and designing the points of brand interaction. Maximising benefit perceptions at each moment of truth and the willingness to revise the customer journey, evolve new touch points and adapt the process and analysis with each new moment of epiphany are essential to build vibrant customer journeys.

As we have discussed earlier, employee journeys designed with AI bots and agents are not just touch points in a learning journey.

They can be a game-changing rethinking and redesign of the entire career planning and management process. A new product called Skills Alpha has a bot popping up as soon as a person logs in. For a new person, it engages in a conversation to discover career objectives and presents alternative career options. Once the option is chosen, it suggests skills to be reinforced and new skills to be learnt and presents optional paths for learning. In addition, it assigns a coach from within the organisation to assist the learner and keeps sending prompts and checking to assess the progress on the learning and career path. AI-enabled career management is now a heady mix of contextual curated learning sources adapted to the individual learning style, teamwork, collaboration, internal social networks, coaching, expert inputs and mentoring and providing accountability to the sponsor, the HR function and the overall goals of the team and the enterprise!

The horizons of enterprise AI are fast changing, with falling costs enabling the barriers to AI adoption to diminish and disappear rapidly and the performance levels increasing as AI’s ability to respond to prompts and process tokens approaches near real-time. In addition, the hallucinations are coming down, and decision quality and accuracy are accelerating. Slowly but surely, AI is moving from a new toy or novelty to a business necessity for every industry, enterprise and every part of the organisation.

AI in Every Domain

Whichever industry an organisation is in, there are applications of AI that affect all stakeholders—employees, customers and supply and demand chain partners. A quick tour of key players in BFSI, manufacturing, retail and hospitality will give us a sense of what is the opportunity area in front of leaders today.

Leaders in BFSI in India like the State Bank of India and Bajaj FinServ have been able to create an AI-supported integrated financial view of their business. AI is now a layer that sits on top of their technology and comprehensive applications portfolio, predicts and prevents frauds in the system and enables all key activities of customer acquisition, credit planning and allocation, loan management, retail and corporate activities and wealth management. Robo advisers in wealth management have proliferated for years, but now GenAI and agentic AI solutions are providing precision on all predictive and prescriptive applications.

In manufacturing, leaders like Siemens and Bosch, which have already used digital twins to simulate future factories before actual construction and factory layouts are done, are experimenting with AI-managed automation that manages every part of the plant and maximises uptime, uses Edge AI solutions in manufacturing and uses the power of quantum computing, robotics and AI to optimise every aspect of production and materials planning, warehousing and plant movement and integration with enterprise resource planning systems.

In retail, global majors like eBay and Amazon have led the way, and companies like Flipkart in India have created AI-led seamless shopping experiences, implemented AR- and VR-based ‘try-on’ systems for consumers, personalised recommendations, conversational AI, sentiment analytics and smart inventory management to minimise both shrinkage in stores and disappointed customers. AI-led loyalty programmes are also being deployed to maximise customer satisfaction and minimise enterprise costs.

In a final domain example, the hospitality industry has seen a lot of churn with an old market leader, Thomas Cook, declaring bankruptcy in 2019 after having been a market leader in travel and tourism for over a century. Nimble players like Expedia, Airbnb and Booking.com internationally and Make My Trip in India have implemented dynamic pricing models, immersive metaverse experiences with VR, autonomous robotic hotel check-ins, facial recognition for security and guest service, and predictive analytics for loyalty programmes.

To conclude this section on the state of the art in AI applications, one can say that AI is emerging as the future of everything. Agriculture is being transformed with intelligent drones and crop planning. Systemic AI is transforming healthcare from diagnosis to clinical treatment to robotic surgery. Education and skills are becoming adaptive and personalised, and mobility and urbanisation are changing rapidly through intelligent automation. The future is all about our imagination.

The concern for leaders should be twofold: Are we using enough AI, and also are we becoming over-dependent on AI? As enterprises move beyond the sentence-completing GenAI to independent work done by AI agents and the inexorable move to AGI and ASI likely in the not-so-distant future, the leader must develop a comprehensive strategic planning and risk mitigation and governance framework for AI and finally embark on a new model of leadership to succeed.

Planning for ‘Game-Changing’ AI

Now that we have discussed every possible aspect of processes, technology, applications and future scenarios for AI, it’s time to focus on the role of the leader in delivering ‘game-changing’ AI to the organisation. Our research took us to in-depth discussions with three well-known leaders in diverse fields—Suresh Narayanan, chairperson and managing director of Nestlé India; Kiran Acharya, the former managing director of Sandvik Coromant; and Tapan Singhel, managing director of Bajaj General Insurance. A few vignettes from these discussions are presented here to set the scene for our analysis on the role of leadership for future AI-enabled organisations.

Suresh Narayanan, the former chairperson of Nestlé India, in a telephonic discussion with the authors in January 2025, stated his belief that analytics and digital technologies have been the real accelerators. They have helped in deconstructing market opportunities by geography, channels and consumer clusters and unblocked the obstacles that were not scientifically possible in India in an earlier era. Focus on analytics has enabled Nestlé to capture insights from thousands of distributors nationwide with algorithms that focus on ‘sell-through’ data. That data can be fed into the planning mechanisms of the company to improve the efficiency and effectiveness of demand forecasts, supply chain optimisation and factory productivity.

He believes that AI in the organisation is still at the experimentation and functional usage stage but fully AI-integrated planning models will be implemented over the next couple of years, giving speed, flexibility and insights. He believes the real role of AI will be insight generation, and many countries in the Nestlé global organisation have already started replicating the algorithms that have been implemented in India. A version of ChatGPT customised to the organisation, named NESTGPT, has connected people across geographies and functions to generate insights on brands, stock-keeping units, margins and profitability. He also suggests that while AI is a great generator of information and insights that can inform and inspire, there is still some hesitation in using it for external communications and commercials because the intellectual property rights of an AI-generated product are questionable.

Kiran Acharya believes that we should be clear what is a necessity, what is comfort and what is a luxury. Automation is quite mature in that sense, but AI will go a step further and improve the quality of decision-making. He also believes that in industries like machine tools, AI will be able to transform productivity not just at the tool level but across the entire system of production. Every machine action would be an algorithm, and the combination of digital, automation, AI and human intelligence and sensitivity would drive superior performance in the future.

Tapan Singhel believes that more and more processes will start moving to the front end, and over time, AI will make the back office in services organisations redundant. Clients will be enabled for direct access to systems, reducing the friction of an intermediary, and all processing will become straight through. While this is still in the future, the active use of copilots and bots has made every document an intelligent source of information, and AI is generating knowledge for use without duplication of effort. However, resilience, empathy and the solution-seeking approach will still be done by the ‘human in the loop’ with assistance at all levels from AI. He also sounds a note of warning that we should not be nodding to the use of AI without actually getting into details on the realised benefits and a real understanding on what problem is being solved beyond productivity improvement and what additional benefits are being targeted and realised.

Industry leaders have very positive views on the role changes for employees, managers and leaders of the future. Great employees of AI-ready organisations will have the capability to ask the right questions rather than providing all the answers. They will understand technology without being besotted by it. Managers will enable the transition of teams to being digitally friendly and AI-ready without pushing people beyond their comfort zones and encourage the development of multiple skills while retaining and sharpening their emotional sensitivity. They also caution that no manager or employee can get into the comfort zone of coming to the workplace every day and do what they have always been doing. They must use AI to challenge their assumptions and add value in new ways to their role, their teams and the organisation.

A clear area of consensus is that the leader is very much required, not as a data manager or a solution provider, which AI can do with speed and accuracy, but as an orchestrator of ideas and a strategist and future planner for a function or an organisation. As specialist roles in digital and AI disappear and every employee and manager is expected to feel comfortable with technologies in the way they think and work, the use of emotional intelligence will become essential. Leaders must continue to be curious and stay on the cutting edge of any kind of circumstantial change within or outside the organisation. Leaders must also have the wisdom to go beyond the hype, not getting carried away by multiple GenAI pilots blooming all over the organisation but continuing to ask what customer, employee or business benefit this will translate into. Leaders must play an even bigger role in designing the new roles of technology and humans and ensure that the organisation derives the best from the capabilities of both.

Drafting a comprehensive AI strategy for the organisation should be on top of every leader’s agenda. It would necessitate aligning ambitions with a reality check on the status of key pillars of AI, identification of potential barriers and a strong focus on data engineering and the data to GenAI and agentic AI journey. There has to be a balance between acceleration versus caution and we have to put in place strong measures for ROI at every stage of the implementation. Let us understand what this entails.