IBM thinks that over a billion new applications will be built with gen AI : Here’s how they’re going to help that happen with agentic AI

Enterprise AI in 2025 is moving from experimentation to implementation and deployments are evolving from AI assistants to AI agents.

That’s the primary theme of the IBM Think 2025 conference, which gets underway today. At the event, IBM is announcing an extensive list of new enterprise AI services as well as enhancements to existing technologies to help move more enterprise AI efforts into real-world deployment. The core of IBM’s updates are a series of updates for its watsonx platform that was first announced at Think 2023. At the Think 2024 event, the big theme was the introduction of orchestration and the ability to help enterprise build their own AI assistants. In 2025, AI assistants are table stakes and the conversation across the industry and in every enterprise is how to build, use and benefit from agentic AI.

IBM is announcing a series of agentic AI capabilities, including:

• AI Agent Catalog: A centralized discovery hub for pre-built agents.
• Agent Connect: A partner program for third-party developers to integrate their agents with watsonx Orchestrate.
• Domain-specific agent templates for sales, procurement and HR.
• No-code agent builder for business users without technical expertise.
• Agent development toolkit for developers.
• Multi-agent orchestrator with agent-to-agent collaboration capabilities.
• Agent Ops (in private preview) providing telemetry and observability.

IBM’s fundamental goal is to help enterprises bridge the gap between experimentation, real-world deployments, and business benefits.

“Over the next few years, we expect there will be over a billion new applications constructed using generative AI,” IBM CEO Arvind Krishna said in a briefing with press and analysts. “AI is one of the unique technologies that can hit at the intersection of productivity, cost savings and revenue scaling.”

The enterprise AI challenge: How to get real ROI

While there is no shortage of hype and interest in AI, that’s not what actually makes a real difference for an enterprise concerned with the bottom line. 

Research sponsored by IBM shows that enterprises only get the return on investment (ROI) they expect approximately 25% of the time. Krishna noted that several factors impact ROI. They include access to enterprise data, the siloed nature of different applications, and the challenges of hybrid infrastructure.

“Everybody is doubling down on AI investments,” Krishna said. “The only change over the last 12 months is that people are stopping experimentation and focusing very much on where is the value to the business.”

From AI experimentation to enterprise production

At the heart of IBM’s announcements is a recognition that organizations are shifting from isolated AI experiments to coordinated deployment strategies that require enterprise-grade capabilities.

“We’re trying to bridge the gap from where we are today, which is thousands of experiments into enterprise grade deployments which require the same kind of security governance and standards that we did demand on mission critical applications,” Ritika Gunnar, general manager data and AI at IBM, told VentureBeat in an interview.

The evolution of IBM’s watsonx Orchestrate platform reflects the broader maturity of AI technology. The platform was first announced by IBM in 2023, largely as a way to help build and work with AI assistants and automations. In 2024, as agentic AI first began to become mainstream, IBM started to add agentic capabilities and partnered with multiple vendors, including Crew AI.

With IBM’s new agentic AI components, the direction is now to help enable multi-agent collaboration and workflows. It’s about going beyond just the ability to build and deploy agents to actually figuring out how an enterprise can generate an ROI from agents.

“We really believe that we’re entering into an era of systems of true intelligence,” Gunnar said. “Because now we’re integrating AI that can do things for you and this is a big differentiation.”

The technology and protocols that enable enterprise agentic AI

The industry has no shortage of attempts to help enable agentic AI.

Langchain is a widely used platform for building and running agents and is also part of a wider effort alongside Cisco and Galileo for the AGNTCY open framework for agentic AI. When it comes to agent-to-agent communications, Google announced Agent2Agent in April. Then, of course, there is Model Context Protocol (MCP), which has emerged to become a de facto standard for connecting agentic AI tools to services.

Gunnar explained that IBM uses its own technology for the multi-agent orchestration piece. She noted that how agents work together is critical and is a point of differentiation for IBM. That said, she also emphasized that IBM is trying to take an open approach. That means enterprises can build agents with IBM’s tools, such as BeeAI, or those from other vendors, including Crew AI or Langchain, and they’ll all still work with watsonx Orchestrate.

IBM is also enabling and supporting MCP. According to Gunnar, IBM is supporting MCP by making it easy for tools with an MCP interface to automatically show up and be usable in watsonx Orchestrate. Specifically, if a tool exists with an MCP interface, it will automatically be available to use in watsonx Orchestrate.

“Our goal is to be open,” she said. “We want you to integrate your agents, regardless of whatever framework that you’ve built it in.”

Addressing enterprise concerns: Security, governance and compliance

As part of making sure agentic AI is ready for enterprise usage, there is a need to ensure trust and compliance.

That’s also a critical part of IBM’s push. Gunnar explained that IBM has built guardrails and governance directly into the watsonx portfolio.

“We’re expanding the capabilities that we have for governance of LLMs into agentic technology, ”  she said. “Just as we have evaluation of LLMs, you need to be able to have an evaluation of what it means for agent responses.”

IBM is also extending its traditional machine learning evaluation metrics to agent technologies. Gunnar said that IBM tracks over 100 different metrics for large language models, which it is now extrapolating and extending to agentic technologies as well.

Real-world impact

Agentic AI is already having real-world impact for many organizations.

IBM is using its own agentic AI to help improve its own processes. Gunnar noted that using its own HR agent, 94% of simple to complex requests at IBM are actually answered by an HR agent. For procurement tasks, IBM’s use of its own agentic workflows has helped to reduce procurement times up to 70%.

Another big group of organizations that are already benefiting from IBM’s agentic AI approach are the company’s partners. For example, Ernst & Young is using IBM’s agentic AI to build out a tax platform for its own clients.

What this means for enterprises

For enterprises looking to lead the way in AI deployment, IBM’s agentic AI direction provides a blueprint for moving from experimentation to deployment.

Simply building out an agent is not enough. If IBM’s CEO is right, the future will involve thousands of agents working on enterprise tasks. Organizations will build and consume agents and agentic services like MCP from many different sources.

IT leaders should evaluate the platform based on four critical factors:

1. Integration capabilities with existing enterprise systems.
2. Governance mechanisms for compliant and secure agent behavior.
3. Balance between agent autonomy and predictable outcomes.
4. ROI measurement capabilities for agent deployments.

It’s incumbent on enterprises to think now about how agents will all work together, how they will be secure and governed. IBM’s agentic AI ecosystem will appeal to its enterprise clients and the openness to connect other agentic AI systems means that organizations hopefully won’t be creating yet another silo.