At Davos this year, the tone around artificial intelligence has shifted. Boardrooms are no longer asking whether to invest in Artificial Intelligence (AI) — they already are.
Speaking to Moneycontrol at the World Economic Forum in Davos on January 20, Nigel Vaz, CEO, Publicis Sapient said that companies have moved out of the experimentation phase. “Last year was about proof of concept. Now, it’s about tying AI to real workflow transformation and business outcomes.”
The change is visible in how leaders think about adoption. AI is no longer seen as a collection of tools to automate individual tasks. Instead, it is becoming an enterprise-wide decision—about how organisations operate, how they make choices, and how much autonomy they are willing to build into their systems.
“We’re seeing a clear connection between workflow transformation and business results,” Vaz noted. “That’s the big shift of the past year.”
Yet, the road to scale is not frictionless. For many organisations, the real bottleneck is not the technology itself, but the foundations beneath it. Legacy data structures, outdated platforms and years of accumulated tech debt stand in the way of meaningful deployment.
“There’s a huge challenge with the amount of legacy technology organisations need to modernise to truly benefit from AI,” Vaz says. “The constraint isn’t just models or compute—it’s the enterprise itself.”
Historically, digital transformation was driven by capital expenditure—building platforms, migrating systems, modernising stacks. AI, however, is different. Vaz calls it a “secular trend” that cuts across the enterprise: improving operational efficiency while also unlocking growth.
In a volatile geopolitical and regulatory climate, companies are adopting central AI strategies but deploying them locally. “AI is a global idea, but regulation is deeply regional,” Vaz said. “What really helps businesses move faster is alignment between regulation and what enterprises are trying to achieve. Regulatory complexity, especially in an already tough business environment, slows momentum.”
Where companies have done the groundwork, the gains are tangible. Publicis Sapient’s software platform Slingshot, which spans everything from backlog to code deployment, is helping clients reduce tech debt and modernise faster. Its agentic platform, Bodhi, is enabling industry-specific autonomous agents inside enterprises.
But these tools perform very differently in organisations that are AI-ready versus those still dabbling with consumer-grade chatbots. “The difference is stark,” Vaz said. “Experimentation is not enterprise adoption.”
That distinction is reshaping budget conversations. Clients, he noted, are no longer spending on AI because it sounds futuristic. Every discussion now centres on value.
Clients are asking ---“Is this going to drive growth? Improve basket size? Help with credit risk decisions? Close customer issues faster?” Vaz noted. “Budgets are unlocked at the workflow level. Nobody is funding AI for the sake of AI anymore.”
Much of the public discourse, he argued, remains fixated on models, compute and eye-popping valuations. But the real economic impact of AI will play out in the application layer—inside businesses.
Cost reduction was the starting point. It was the low-hanging fruit. But that phase is giving way to a broader ambition: growth.
Vaz pointed to use cases already in play. In insurance, autonomous agents monitor air quality and trigger health interventions, from inhaler recommendations to real-time advice during wildfire conditions. In retail, AI agents help customers plan meals based on context and dietary needs, translating those plans directly into shopping baskets—linked to real inventory and supply.
“These are growth drivers,” he said. “But they require deep integration between AI platforms and enterprise data. That’s where the real work is.”
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