IBM Predicts Over 1 Billion New AI Applications: How Agentic AI Will Transform Enterprise Technology

 

IBM Predicts Over 1 Billion New AI Applications: How Agentic AI Will Transform Enterprise Technology

The Shift from AI Experimentation to Implementation

Enterprise artificial intelligence in 2025 is at a pivotal turning point. Companies are moving beyond simply experimenting with AI to implementing real-world solutions. Even more significantly, we're seeing a major evolution from basic AI assistants to more powerful AI agents that can independently complete complex tasks.

This transformation forms the central theme of IBM Think 2025, the company's flagship technology conference that kicked off today. IBM is unveiling an extensive lineup of new enterprise AI services and enhancements to existing technologies, all designed to help businesses transition from AI experimentation to practical deployment.

What's the Difference Between AI Assistants and AI Agents?

Before diving deeper, it's helpful to understand a key distinction:

  • AI assistants respond to requests and provide information but typically require human guidance for each step
  • AI agents can independently take action, make decisions, and complete multi-step tasks with minimal human supervision

IBM's Vision: A Billion New AI Applications

IBM's leadership has bold predictions for the impact of generative AI on application development.

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

This statement underscores IBM's belief that we're entering a new era of software development, where AI-powered tools will dramatically accelerate application creation and deployment.

The Evolution of IBM's AI Strategy

IBM's approach to enterprise AI has evolved significantly over recent years:

  1. 2023: IBM introduced the watsonx platform, primarily focused on building AI assistants
  2. 2024: The company added orchestration capabilities and began incorporating early agentic features
  3. 2025: IBM is now fully embracing agentic AI with comprehensive tools for building, managing, and orchestrating multiple AI agents

This evolution mirrors the broader industry's progression from basic AI implementations to more sophisticated, autonomous systems that can handle complex workflows.

New Agentic AI Capabilities Announced at Think 2025

IBM's latest announcements center around a comprehensive suite of agentic AI tools and services:

For Business Users

  • AI Agent Catalog: A centralized hub where users can discover and access pre-built agents for various business functions
  • No-code agent builder: Tools that allow business users without technical expertise to create their own AI agents
  • Domain-specific agent templates: Ready-to-use templates for common business areas like sales, procurement, and human resources

For Developers

  • Agent Connect: A partner program enabling third-party developers to integrate their agents with IBM's watsonx Orchestrate platform
  • Agent development toolkit: Comprehensive resources for technical teams to build customized AI agents
  • Multi-agent orchestrator: Technology that enables multiple AI agents to collaborate and work together on complex tasks

For IT Operations

  • Agent Ops (currently in private preview): Systems providing telemetry and observability for monitoring and managing AI agent performance

The Enterprise AI Challenge: Bridging the ROI Gap

Despite widespread interest and investment in AI, IBM's research reveals a concerning statistic: enterprises only achieve their expected return on investment (ROI) approximately 25% of the time.

Krishna identified several factors contributing to this ROI gap:

  1. Limited access to enterprise data
  2. The siloed nature of different applications
  3. Challenges managing hybrid infrastructure (combining cloud and on-premises systems)

"Everybody is doubling down on AI investments," Krishna noted. "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."

This observation highlights a critical shift in enterprise AI strategy—moving from technology-driven experimentation to business outcome-focused implementation.

From Experiments to Enterprise-Grade Deployment

IBM's announcements reflect a deep understanding that organizations are transitioning 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," explained Ritika Gunnar, General Manager of Data and AI at IBM, in an interview.

Gunnar emphasized that IBM is helping businesses move beyond just building and deploying agents to actually generating measurable ROI from their AI investments.

"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 Behind Enterprise Agentic AI

The industry has developed several approaches to enable agentic AI:

  • Langchain: A widely used platform for building and running agents
  • AGNTCY: An open framework for agentic AI developed by Langchain, Cisco, and Galileo
  • Agent2Agent: Google's protocol for agent-to-agent communications announced in April 2025
  • Model Context Protocol (MCP): An emerging standard for connecting agentic AI tools to services

IBM is taking an open approach to these technologies. While using its own proprietary technology for multi-agent orchestration, the company is ensuring compatibility with tools and agents built using other frameworks.

"Our goal is to be open," Gunnar emphasized. "We want you to integrate your agents, regardless of whatever framework that you've built it in."

For example, IBM is supporting MCP by making it easy for tools with an MCP interface to automatically be available and usable in watsonx Orchestrate. This approach allows businesses to leverage existing AI investments while benefiting from IBM's enterprise-grade orchestration capabilities.

Addressing Enterprise Concerns: Security, Governance, and Compliance

As AI agents gain more autonomy and capability, questions of security, governance, and compliance become increasingly critical. IBM has built guardrails and governance directly into the watsonx portfolio to address these concerns.

"We're expanding the capabilities that we have for governance of LLMs into agentic technology," Gunnar explained. "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 extending its extensive evaluation metrics—tracking over 100 different factors for large language models (LLMs)—to agent technologies. This approach helps ensure that AI agents operate within appropriate boundaries and meet regulatory requirements.

Real-World Impact of Agentic AI

IBM isn't just talking about the potential of agentic AI—the company is actively using these technologies to improve its own operations:

  • HR processes: 94% of simple to complex HR requests at IBM are now answered by an AI agent
  • Procurement: IBM's use of agentic workflows has reduced procurement times by up to 70%

IBM's partners are also seeing benefits. Ernst & Young, for example, is using IBM's agentic AI to build a tax platform for its clients, demonstrating how these technologies can transform specialized professional services.

What This Means for Enterprise Leaders

For businesses looking to lead in AI deployment, IBM's agentic AI direction provides a blueprint for moving from experimentation to production. However, simply building an individual agent isn't enough.

If IBM's CEO is correct, the future will involve thousands of agents working on enterprise tasks. Organizations will both build their own agents and consume agentic services from various sources.

IT and business leaders should evaluate AI platforms 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

The Future of Enterprise AI

IBM's vision suggests we're entering a new era of enterprise AI—one where autonomous agents collaborate to solve complex business problems. This vision requires businesses to think strategically about how agents will work together and how they will be secured and governed.

The openness of IBM's approach, allowing connection to other agentic AI systems, suggests a future where interoperability is key. This approach helps ensure that organizations won't create yet another technology silo as they adopt these powerful new capabilities.

Key Takeaways About IBM's Agentic AI Strategy

  • IBM predicts over a billion new applications will be built using generative AI
  • Enterprise AI is shifting from experimentation to implementation
  • The evolution is moving from basic AI assistants to autonomous AI agents
  • New tools include agent catalogs, development kits, and orchestration capabilities
  • Enterprises only achieve expected AI ROI about 25% of the time
  • IBM is taking an open approach to integrate with various agent frameworks
  • Security, governance, and compliance are built into the platform
  • Real-world benefits include 70% faster procurement and 94% automation of HR requests

As businesses navigate this rapidly evolving landscape, IBM's comprehensive approach to agentic AI offers a potential pathway to realizing the true promise of enterprise AI—moving beyond the hype to deliver measurable business value at scale.

Open Your Mind !!!

Source: Venture Beat

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