AI Beyond the Hype: Insights From Google

Enterprises looking to gain real value from AI and implement it successfully should focus on the practical, systematic governance needed to create an AI workflow that embeds trust, transparency and interoperability.

The slew of AI models popping up every week can make AI feel like a big publicity cycle. This can be confusing for enterprises trying to decipher what they need to make their AI workflows effective. However, to move beyond the promise of AI models, tools and agents, enterprises should not place unrealistic expectations on technology. They should have the right foundation to build practical AI applications based on trust, transparency and interoperability.

“Interoperability and openness are two really key concepts, and transparency as well,” said Michael Clark, director of product management at Google Cloud, on the Targeting AI podcast from AI Business.

According to Clark, systems that can connect with other systems are key to the success of generative and agentic AI applications. For instance, for an AI agent to be successful in a customer service setting, enterprises need interoperability with disparate systems.

“In a lot of call centers, people do things very manually, not using APIs or not using other things when they’re solving customer problems,” Clark said. “So, it meant having either agents or having other context about what's happening in other parts of the business and being able to take actions."

Context can also be a key differentiator, defining an agentic application that works and one that doesn’t, Clark added.

“We see context being audio and video, even in some cases, things like lidar and other kinds of information, financial streaming data and other kinds of things to help agents make decisions daily,” he said.

Other than interoperability and context, enterprises need governance and trust.

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News Writer, AI Business

Senior News Director, AI Business