Exclusive: Google sees success with niche AI playbooks

Google is rolling out sector-specific artificial intelligence (AI) playbooks. This comes as enterprises move from experimentation and pilots with AI to production deployments, according to a senior executive at Google.

Faster deployment

Google's approach centres on layering AI capabilities over existing infrastructure rather than replacing it, allowing for quicker rollouts. Jain cited customer experience tools built on its Gemini platform as an example.

"It is not replacing their eCommerce platform or any of their underlying infrastructure. It's working on top of that, so it is quick to deploy," said Jain.

However, he stressed that rapid implementation does not eliminate the need for ongoing work.

"There is going to be constant evolution. Is your data ready? Is your merchant and product data ready to feed into those agentic experiences? There is still a lot of work to be done," he added.

The company is applying these capabilities across a broad set of industries. Jain's team oversees 19 verticals, including manufacturing, retail, healthcare, media and entertainment, and financial services.

"Every industry in 2026 is interested in AI. They are all thinking about it from either being disrupted or proactively transforming," said Jain.

Industry strategy

Rather than building entirely separate products for each sector, Google is developing reusable capabilities that can be adapted to different use cases, particularly in customer-facing environments.

"Customer experience is applicable to any organisation that is consumer facing. Banks need that, hospitals need that, media companies providing direct-to-consumer streaming services need that," said Jain.

The company organises its offerings across several layers, including AI, data cloud, infrastructure, security, and what it terms "agentic workforce transformation".

"Google Cloud has a wide range of products that you can think about as breadth and depth. AI cuts across all of them," said Jain.

At the application level, enterprises can either deploy pre-built solutions or assemble their own systems from underlying components.

"It's a bunch of Lego blocks. Let's help you decide which of these are relevant to your needs, and how do we assemble them so that you have the right capabilities to solve the problems you're trying to solve," he added.

Industry-specific strategies are informed by direct collaboration with customers and partners, with Google defining key priorities for each sector.

"In each vertical, there will be three or four categories of things that every enterprise cares about, and we line that up against products, solutions and partners that can help solve that problem," said Jain.

Partner role

"There's so much to be done. What we're doing is creating the capabilities. But they still need to be implemented, connected. There's a lot of business process transformation, change management needs to happen," said Anil Jain, Global Managing Director | Global Strategic Industries GTM, Google.

Jain pointed to the continued importance of systems integrators (SIs), even as AI platforms reduce the need for heavy custom software development. Traditional consulting firms and newer AI-native partners are converging, he said, combining long-standing enterprise relationships with emerging technical expertise.

"Between them, it's this convergence of what are you able to do to help customers move faster. A lot of the global SIs already have established relationships with these customers. They know their systems inside and out," added Jain.

Despite faster deployment cycles enabled by AI tools, enterprises remain constrained by legacy systems and organisational complexity.

"We can never start from scratch. We have data issues, organisational silos, tech debt, entrenched ways of doing business, supply chains and value chains that are interconnected," said Jain.

ROI focus

Enterprises are now entering a phase where demonstrating business value is critical, following earlier stages of experimentation and pilot programmes.

"We've gone from exposure and awareness to experimentation, to pilots, to moving everything into production, and now to business value realisation," said Jain.

Return on investment (ROI) varies significantly depending on the use case, with some benefits easier to quantify than others.

"In some cases, it's about time saved. What used to take weeks now can be done in a day. What used to take eight hours now can be done in eight minutes," said Jain.

Other gains, such as improvements in product quality, are harder to measure.

"How do I tell you the value of making better content or telling a better story because I now have this capability of improving my product?" he added.

Jain suggested that traditional business case models may need to adapt to the scale of change AI enables.

"There is so much opportunity to unlock value and create value," he said.

Workforce shift

A key driver of enterprise adoption is changing employee expectations, shaped by widespread use of AI-powered consumer tools.

"Every consumer is already using AI-powered capabilities. When you come into your workplace, the expectation is I can have that same generative ability," said Jain.

He argued that this shift is accelerating enterprise transformation, reversing a historical pattern where businesses led technological adoption.

"Consumers actually lead now. When you go into your enterprise and it still has old systems, as an employee you say, I know I can work faster. Why are things so slow?" added Jain.

For Google, the current phase of AI development represents both a technological and organisational inflection point.

"Where else would you want to be at this moment in time when the world is changing? If you want to be a change agent to help evolve the industries that you care about, Google is a great place to do it," said Jain.