We spoke with Brajesh Jha, SVP and Global Leader of AI Value Transformation at Genpact, about Amazon Web Services’ launch of AgentCore marketplace and what it means for enterprise AI evolution.
Jha believes AI agents and tools marketplaces are forming a pivotal transition for the industry as it moves from experimentation into production.
Trust is key to success, but true transformation will only be possible for the organizations that also address challenges across people, processes, technology, and data.
Until now, the story of enterprise AI has been one of scattered, isolated experiments. Teams across nearly every business have spent the last few years launching countless demos and pilots, creating a chaotic landscape ripe for innovation. Amidst this flurry of activity, C-Suites are growing anxious about data compliance, security, and financial risk. For most, the path from demo to enterprise-ready AI deployment has been rocky at best.
We spoke with Brajesh Jha, SVP and Global Leader of AI Value Transformations at Genpact, a global professional services and advanced technology solutions company. As a founding member of Oracle Customer Success Services, Jha spent 17 years building the business from the ground up. Jha honed in on the July launch of an AI agents and tools marketplace called Amazon Bedrock AgentCore, and discussed how democratized access to tools is catapulting enterprise AI into the next stage of maturity.
The end of the beginning for enterprise AI: “This is one of those pivotal industry moments where things are no longer a ‘skunkworks’ project in your garage,” Jha said. “Now, you’re genuinely maturing. It’s an exercise in taking a very promising idea and transforming it into something that can actually mature and become integral to an organization’s existence.” The principle driving this transformation is the same one that powered similar technological revolutions in the past: trust. To illustrate the point, Jha compared the AWS launch to the arrival of the Apple App Store.
The Apple effect: Where earlier ecosystems failed, Apple’s success was possible because it offered a ‘stamp of approval’ in the form of a certified, trusted platform. Without backing from major vendors, users get nervous about data security, Jha said. Now, AWS is playing the same role for enterprise AI. “AWS Agent Core is doing what Apple does when it does its X number of verifications of an app in the iPhone,” Jha explained. “You can be certain that the governance layer and the data security and all the other core aspects of something considered to be enterprise-ready, because those are being added to it by AWS.”
A trusted platform is key to success with AI, Jha said, but the governance it enables mustn’t be fully outsourced. Because with the shift from isolated wins to horizontal orchestration come new challenges over ownership, liability, and politics. To navigate this new era, Jha explained how mature organizations are building a dedicated, centralized body to manage the entire AI lifecycle.
The AI leader: Across the largest enterprises and in Fortune 100 companies, Jha explained, “control is very much in the hands of the Chief Information Officer, the Chief Technology Officer, or the Chief Data Officer.” With assistance from experts like Jha, many are fundamentally reimagining business in the age of AI.
The AI value architect: Many are creating new roles, like AI architect, said Jha. “At Genpact, one of the roles we created is called an ‘AI value architect.’ They’re responsible for identifying valuable opportunities for automation or AI-led innovation, articulating the business case and supporting infrastructure, and then realizing the promise all the way from observability to governance and so on.”
The AI Center of Excellence: Most have an AI Center of Excellence, Jha noted, “which can help set aside fears like, ‘What is actually happening? How many agents are we deploying? And are those agents actually giving us what they were expected to provide?’ Now, all of those answers come from one single source, the AI CoE.” On one hand, this type of architecture creates a direct line to all of the innovation and automation opportunities coming from different functions, Jha continued. On the other, it links the results of AI deployments straight to the outcomes.”
But this new era of accessible, agentic AI also introduces a new risk: agent sprawl. As the pressure to adopt AI grows, Jha said, focus on vanity metrics tends to sharpen.
The vanity metric: “Boards put a tremendous amount of pressure on CIOs and CTOs. They ask questions like, ‘How many agents do we have in production?’ And that leads up to a ‘we don’t want to be left behind’ mentality.” By prioritizing quantity over quality, Jha said, businesses often miss the entire point of the transformation. “The core realities of running a business do not change. You still have to create value for your shareholders. You still have to demonstrate the business case for innovation.”
Jha’s final word of advice was a powerful warning against repeating the mistakes of past technology hype cycles. Instead, he urged leaders not to forget the foundational pillars of any successful transformation.
“The people, the process, the technology, and the data, those are the greatest, most persistent challenges. If companies can fix those, then they can realize AI’s full potential.”
