“Many companies struggle with AI, not because the technology isn’t available, but because they can’t connect it to business outcomes,” said Mike Capone, CEO of Qlik, a United States of America-based software company offering analytics and AI platforms. The executive explained that the struggle with AI is mostly because these companies lack the right infrastructure and a culture that embraces AI-driven decision-making.
Capone, in an interview with indianexpress.com, shared his views on innovations with AI, challenges faced by organisations, the rise of open-source AI models, etc.
Talking about the rapid advancements in AI, Capone said that the world was moving towards smaller, more efficient and business ready models. He cited an IDC report suggesting that by 2026, 90 per cent of enterprises’ AI use cases will gravitate to practical and purpose-built AI from massive models. “That’s precisely what we’re enabling—AI that’s embedded seamlessly into workflows, making better decisions possible in real time. The future of AI isn’t about model size; it’s about making AI actually work where it matters,” he said.
A new era of AI
With the rise of companies like Chinese AI startup DeepSeek AI, the world is witnessing a shift. The recent developments around DeepSeek AI show that success in AI development is not solely dependent on the size of the investment. Capone explained that AI models are becoming commoditised with data quality and trust, and deployment becoming key differentiators. This shift also focuses on the significance of specialised AI models that are tailored for specific business needs rather than general-purpose solutions. “DeepSeek proves that AI isn’t just a game for the biggest spenders—it’s about who executes best.”
“DeepSeek’s success also highlights a major shift: smaller, specialised AI models are the future. Businesses don’t need sprawling, general-purpose AI—they need models tailored to their exact needs. The smartest companies won’t waste time chasing the ‘best’ model,” Capone told indianexpress.com.
Making AI accessible
Until a while ago, training AI models required massive amounts of investments. Today, more and more companies are working towards making AI accessible. When asked about the affordability factor, Capone said that affordability isn’t just about making AI more accessible but also about forcing AI out of the lab and into real business execution.
“Now, the real challenge isn’t just who can build AI—it’s who can apply it effectively. Lower costs are putting pressure on businesses to move beyond experimentation and integrate AI into daily workflows. The winners will be those that don’t just tinker with AI but embed it in decision-making, automation, and productivity at scale,” he said.
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According to him, for regions like India where businesses need scalability and cost-effectiveness, this shift of AI from an R&D experiment into a practical business tool is a game-changer. “In the new AI economy, success won’t be defined by who spends the most—it will be defined by who deploys AI the smartest,” he said.
On operationalising AI
During the interaction, Capone touched upon operationalising AI. “Operationalising AI means moving from isolated experiments to embedding AI into everyday decision-making. Many companies have invested in AI pilots, but few have successfully scaled AI across their business,” Capone said.
The CEO said that the greatest challenge, however, is not building models; it is connecting AI to real business outcomes. He highlighted that companies struggle with fragmented data, lack of AI expertise, and cultural resistance to AI-driven decision-making. “Many leaders still see AI as an IT project rather than a core business enabler.”
When asked how Qlik was making AI accessible to SMEs, Capone said that many SMEs lack the resources to build AI from scratch, and Qlik eliminates these barriers by embedding AI-powered automation and analytics into the tools businesses already use. “Our AutoML and AI-driven analytics allow companies to deploy machine learning models without deep technical expertise, reducing reliance on expensive data science teams.”
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On data strategy and open-source AI
Talking about data strategy, Capone emphasised the fundamental relationship between data quality and AI success: “Your AI is only as good as the data it’s built on. If your data is flawed, your AI will be too.” The CEO called for strong governance policies, clear data access protocols, and seamless integration into business workflows to generate actual value.
Looking ahead, Capone predicted some significant changes in the AI and data analytics landscape. “The AI arms race won’t be won by who builds the best models—it will be won by who integrates AI best,” he states. Gartner’s prediction that 40% of AI asset purchases will occur through marketplaces by 2028 suggests a future where AI model trading becomes commonplace.
Capone’s vision for AI’s future focuses on practical implementation over experimentation. “Too many companies are trapped in the AI hype cycle—spending millions on models without clear business goals,” he observed. Success in AI adoption will depend on treating it as a core business function rather than a side project.