In a wide ranging conversation with MartechAI.com's Brij Pahwa, the Global CMO and Vice President of Lenovo’s Infrastructure Solutions Group (ISG), which is a 15 billion dollar business within Lenovo, spoke about how AI is reshaping marketing, why interoperable MarTech is more important than any single tool, how to deal with “dead data”, and what Indian marketers are getting right about AI. He also opened up about his love for travel, scuba diving and what his dream job would have been if he had not fallen in love with computers.
Q: You have seen marketing move from psychology, to data, to intelligence. From your vantage point at Lenovo ISG, how is marketing really changing in the AI age?
A: The first point is that nothing has been “replaced”. Psychology, data and intelligence are all still in play. What has changed is the scale and speed at which marketing teams can work if they use AI well.
Inside Lenovo, there have been wave after wave of advancements in data, data orchestration, the MarTech stack, AI tools, agents and studios. The team experiments a lot, tests and fails fast. The long term “North Star” is a world where every function in the company, from supply chain to marketing, has platforms with agents and decision tools that make work more productive and more intelligent.
However, we are not fully there yet. Some areas, like generating technical data sheets, have seen productivity gains of 40 to 50 percent. More complex, content rich assets such as white papers or podcasts have improved, but still need work. The view is that AI will augment and improve what marketing teams do, not replace them. Marketing teams are still essential in that future.
Q: Lenovo has spoken about AI enabled devices that know users and anticipate their behaviour. How do you see AI driven personalisation evolving, and where do you draw the line so it stays helpful and not intrusive?
A: The company’s overall strategy is what it calls “hybrid AI”. That means a mix of public AI, private AI and personal AI working together. Some models will run in the public cloud, some will sit in private environments to protect sensitive information like customer and patient records, and personal AI will live close to the user.
The future picture for personal AI is one private agent per person across their devices. For work, that could be a marketer, nurse or sales rep who has a personal agent on their work phone, laptop and other devices. This agent understands preferences, knows where customer records are, remembers previous meetings and can search, summarise and complete tasks, while connecting to the company’s private AI and to public AIs when needed.
The key is trust and privacy. Personal AI should keep personal and work data private, while making day to day tasks easier. Lenovo wants to enable that across its ecosystem, so one personal AI can follow a user across Motorola phones, tablets and PCs. As models get smaller and more efficient, more of this can run close to the user on their devices rather than only in large shared models.
Q: There is a lot of conversation around building AI versus using AI. You spoke about training and inferencing being different waves. How should marketers think about this shift?
A: The first big wave of AI investment has gone into “building AI” – training very large models, which are extremely capital intensive and rely on specialised chips and infrastructure. Only a small number of players will ever build models at that scale.
Most companies do not want to build their own giant AI. They want to use AI. Technically, that means a shift from training to inferencing. Training is about feeding massive datasets into models. Inferencing is about throughput – thousands of chatbot interactions, many agents running in parallel, package scanning, customer support and so on.
The next wave of innovation and capital will focus on using AI at speed and at scale inside organisations. That also means solving bottlenecks around memory, latency, security and energy. For marketers, this phase is where AI becomes more embedded in day to day tools and workflows rather than something that sits far away in a lab.
Q: With more than 15,000 MarTech tools in the market, many marketers want composable stacks. If you had to advise a CMO on building a future ready MarTech stack, what would you emphasise?
A: Different CMOs will give different answers, but one principle stands out: interoperability. The ideal stack is Lego like. Components should plug in and out and interoperate across layers, whether they are homegrown tools or major platforms like Salesforce.
In practice, many stacks today are built in ways where one proprietary piece locks in several other choices. That makes the stack brittle. The recommendation is to prioritise modularity, interoperability and flexibility by design. Everything in the stack should sit on an AI ready foundation, enhance workflows and decision making, and be built around strong first party data and privacy by design.
Companies can build internal tools in either modular or bespoke ways. The warning is against overly bespoke, proprietary approaches that create legacy overhang and slow down change when the next wave of tools arrives.
Q: Enterprises are generating massive amounts of “dead data” that is never used but still costs money to store. How should CMOs rethink data strategy so that marketing teams keep only what matters and use it well?
A: There is no simple or perfect answer, because data volumes are growing exponentially and new feeds appear every day. It is unrealistic to think that organisations can fully control what gets collected at the edge.
The practical answer starts with hygiene. Marketing and IT teams need to sit together and examine basic storage hygiene: do they have strong deduplication in place to avoid storing the same signals five times, are they using the right types of storage for different kinds of data, and are they optimising what sits on more expensive cloud storage versus what can be archived more cheaply.
Beyond hygiene, the more important change is how organisations look at data. Instead of trying to push everything into one lake and fight every silo, the focus should shift to observability and orchestration. Concepts like a global namespace allow teams to put a layer over multiple silos, see across them and run AI on top to search for patterns and answers. Seventy percent of data may never be touched directly, but there can still be value hidden in it. The goal is not only to reduce feeds, but to clean up, orchestrate and then unlock what is useful across silos.
Q: Many marketers complain that AI trained on similar datasets produces generic sounding campaigns, and that everything starts to look the same. You have spoken about staying grounded in real customer value. How do you keep AI powered marketing credible and outcome oriented instead of buzzword heavy?
A: The starting point is not the tool, it is the business. Marketing teams must understand the company’s true business strategy, where future revenue will come from, who the buyers are and what journeys they are on. That is not something a model or a generic playbook can do for them.
Marketers need to spend more time with sales leaders, identify which customers will drive growth, and understand their specific triggers, objectives and constraints. Generic campaigns often come from a shallow understanding of customers, not from the tools themselves. If two companies sell servers and AI solutions, their customer mix, priorities and paths to revenue can still be very different.
Once the business strategy and customer reality are deeply understood, tools can be used to gain productivity or scale. But teams should not “outsource their brains” to AI models or agencies. That is the only way to avoid generic, copycat work even when everyone has access to similar technology.
Q: You have called India an AI trailblazer for adopting and experimenting early. What have you learned from India’s AI journey, and what would you advise Indian marketers to focus on next?
A: Looking at Lenovo’s global surveys across 180 countries, India stands out for leaning in earlier and experimenting faster with AI. Marketers and enterprises in India are more willing to try, fail fast, adjust and try again. There is also a pattern of senior leaders mandating AI fluency and asking teams to go and learn what large language models can and cannot do.
The advice to marketers everywhere, including India, is to prioritise AI fluency and data literacy. Teams need to understand workflows, prompting, tool capabilities and limitations, and the principles of responsible AI, including bias and data handling. Many professionals still give only a nod to these ideas without doing the deep work of reading, training and practicing. Those who really lean in and build this fluency will be better placed to harness AI responsibly and creatively.
Q: Looking three years ahead, what big shift do you think will redefine global marketing, which most brands are not yet ready for?
A: The major shift will be where and how customers discover brands and consume marketing. Discovery is moving from classic search to answer engines and AI enabled interfaces.
Tools like Perplexity, for example, show a clear direction: they explain where they went to find information, and then synthesise an answer. That is a very different experience from traditional search. As answer engines and AI assistants become primary gateways, marketers will have to think about “answer engine visibility”, not just search rankings.
There is already work underway, including with major marketing cloud providers, on how to feed answer engines, much like search engine optimisation did for traditional search. Brands need to look ahead and ask where their future customers will go to learn about them, then start preparing content and systems so they can be found in those environments.
Q: On a more candid note, if you had not become a marketer, what path would your career have taken?
A: As a young man, the only goal was to find a job that paid for travel. Most of the 1990s were spent in Japan, Hong Kong and Taiwan, teaching English, working in import export and bartending. The dream job then was to be a travel writer, the kind who spends a year in places like Indonesia and comes back with guides to food and hidden spots. That is what would have happened if computers had not taken over as the main passion.
Q: What is something your team knows about you that the outside world probably does not?
A: A strong passion for scuba diving and warm beaches. Time off often means heading to clear water in places like the Maldives, combining travel with diving. That mix of travel, the ocean and faraway beaches is a real personal passion, even if it does not come up often in public conversations.
Q: And finally, where are you headed next for a break?
A: The next planned trip is to Anguilla in the Caribbean, part of the British West Indies. It is known for its wreck dives, including a 17th century galleon in about 80 feet of water and a Dutch trawler from the 1960s that has turned into a reef. It will be a classic beach and diving vacation.
The closing message to marketers is simple: lean in. Build AI fluency, experiment fast, understand your customers and your business deeply, and see AI as a powerful augmentation rather than a replacement for marketing judgement.
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