'AI models can hallucinate or misfire'
'We're investing in rigorous moderation infrastructure and developing new governance frameworks to mitigate these risks,' he said.
He added that deep AI expertise is scarce. 'While surface-level applications like RAG (Retrieval-Augmented Generation) are becoming common, true innovation requires depth in data science, ML infrastructure, and systems thinking—talent that's still hard to find.'
But our global presence in Bengaluru, San Francisco, and the UK gives us broader access to specialised talent pools, the co-founder said.
Talking about other key challenges, he said that AI infrastructure is expensive. Running advanced models at scale demands significant compute and energy. 'Our approach is rooted in frugality—we optimize model usage, leverage pre-processing, explore alternatives like TPUs (Tensor Processing Unit), and work closely with partners like Google to get the most out of every dollar,' he said.
InMobi views AI not just as a tool, but as a foundational shift and its roadmap over the next one to three years is anchored in three key areas. 'First, we are reimagining engineering productivity with AI—helping experienced engineers scale faster and empowering fresh talent to leapfrog traditional learning curves. AI is now embedded into every aspect of how we build—whether it's writing code, improving observability, or boosting efficiency,' he said.
'Second, we are building intelligent automation into our core business processes—moving from simple scripting to AI agents that can deconstruct complex workflows, predict outcomes, and take action. This isn't just automation; it's autonomous decision-making at scale. Third, we're embracing the rise of agentic architecture—where agents talk to agents, not APIs (Application Programming Interface), to get work done. This is the future of system communication, and are actively developing for it,' he further said.
InMobi is setting up a dedicated unit to track and accelerate engineering efficiency with AI, with a goal to complete most of the foundational work by year-end. The company is leveraging AI to generate high-impact formats—ranging from image-based ads to audio creatives—enabling brands to engage users across multiple touchpoints. It also uses AI to generate and summarize content at scale.
In the visual content space, he said the company is leveraging Contrastive Language-Image Pre-training (CLIP) to bridge the gap between AI-generated creativity and real-world commerce through its Glance AI product. 'By using CLIP, we're able to understand and interpret AI-generated fashion looks—essentially decoding the visual style and identifying apparel elements within the image. These elements are then matched to real products from our extensive catalogue of brand and retail partners,' he explained.
Even before the LLM (large language model) wave, the company has been leveraging AI for content generation at Glance. 'We're onboarding fresh engineering talent through structured bootcamps where AI adoption starts from day one—including access to AI assistants and hands-on experience with applied ML tools. Simultaneously, we're deepening our bench strength by hiring top-tier data scientists—we've onboarded over 50 employees in the past year alone, across domains like LLMs, DNNs (Deep Neural Networks), and imaging. We're also shifting our hiring lens—prioritising engineers with a strong aptitude in data science and statistical thinking. Our aim is that 80% of our workforce, both new and existing, to be highly AI- and ML-savvy in the next 1–2 years,' the co-founder and CTO informed.
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Pickett, 56, has dealt with his own anxiety by using AI chatbots as much as possible. He joined the company last August and said chatbots had helped him get up to speed in his role. He uses ChatGPT or Gemini to do research and receive advice about business moves, such as potential partnerships with other companies. He said it helped him "learn 10 times as much or test 10 times as many ideas in a very lightweight way." In the past, he said, "I would have had to ask the resident expert or somebody who worked with that company to really give me a debrief," Pickett said. "And instead, in five minutes, I'm like, 'Oh, OK, I get this.'" (He said he had also consulted people in his company, but now "the conversations are more productive.") Sarah Franklin, CEO of Lattice, a human resources software platform, said it can be difficult to get executives to use new tools, and in internal meetings she regularly asks, "Did you test that message with ChatGPT?" 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Many organizations, he added, have a "real failure of imagination and vision" when it comes to the power of these systems. "The main issue is that leaders have to take a leading role," Mollick said. "They all say AI is the future, use AI to do stuff. And then they don't make any decisions or choices." About half of companies do not have road maps for integrating AI, according to a Bain survey. Whitten at Bain said that about only 20% of companies were scaling their AI bets and that most didn't have benchmarks for how workers should use AI. At Mammoth Brands, Katz-Mayfield said that he and his team had discussed providing incentives to employees who use AI but that they hadn't needed to. The energy around experimenting is working for the company. In the last meeting it had five demos on the docket but didn't get to all of them because senior leaders were "asking so many questions and wanting to see different things." 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