Gen AI revolution accelerated by IBM

At the company's annual THINK event, IBM (NYSE: IBM) unveiled new hybrid technologies that break down the longstanding barriers to scaling enterprise AI – enabling businesses to build and deploy AI agents with their own enterprise data.

IBM estimates that over one billion apps will emerge by 2028, putting pressure on businesses to scale across increasingly fragmented environments. This requires seamless integration, orchestration and data readiness.

A new IBM CEO study shows that business leaders expect the growth rate of AI investments to more than double over the next two years, with most actively adopting AI agents and preparing to scale them. Yet their pace of investments has led to disconnected technology – and only 25% of AI initiatives have achieved the ROI they expected.  

IBM is combining hybrid technologies, agent capabilities and deep industry expertise from IBM Consulting to help businesses operationalize AI.  

Gen AI revolution accelerated by IBM

• Build AI agents in 5 minutes with industry’s most comprehensive set of agent capabilities
• Drive 176% ROI over three years by automating integration across hybrid cloud
• Turn enterprise data into the most powerful tool with new watsonx.data, which can lead to 40% more accurate AI agents
• Accelerate secured, scalable AI with 450 billion inference operations per day on new LinuxONE 5
   

"The era of AI experimentation is over. Today's competitive advantage comes from purpose-built AI integration that drives measurable business outcomes," said Arvind Krishna, Chairman and CEO, IBM. "IBM is equipping enterprises with hybrid technologies that cut through complexity and accelerate production-ready AI implementations."

Build AI agents in watsonx Orchestrate that work with 80+ leading business applications

AI agents are shifting from AI that chats with you to systems that work for you, yet many enterprises will struggle to integrate them across diverse environments, apps, and data. IBM is providing a comprehensive suite of enterprise-ready agent capabilities in watsonx Orchestrate to help businesses put them into action. The portfolio includes:

• Build-your-own-agent in under five minutes, with tooling that makes it easier to integrate, customize and deploy agents built on any framework – from no-code to pro-code tools for any kind of user.1
• Pre-built domain agents specialized in areas like HR, sales and procurement – with utility agents for simpler actions like web research and calculations.
• Integration with 80+ leading enterprise applications from providers like Adobe, AWS, Microsoft, Oracle, Salesforce Agentforce, SAP, ServiceNow, and Workday.
• Agent orchestration to handle the multi-agent, multi-tool coordination needed to tackle complex projects like planning workflows and routing tasks to the right AI tools across vendors.
• Agent observability for performance monitoring, guardrails, model optimization, and governance across the entire agent lifecycle.

IBM is also introducing the new Agent Catalog in watsonx Orchestrate to simplify access to 150+ agents and pre-built tools from both IBM and its wide ecosystem of partners, which includes Box, MasterCard, Oracle, Salesforce, ServiceNow, Symplistic.ai, 11x and more. For example, the catalog will include a sales agent for discovering and importing prospects that works with and is available in Salesforce's Agentforce and a conversational HR agent that can be embedded in Slack.

Forrester TEI projects 176% ROI over three years by automating integration of apps, APIs, events, and more across hybrid cloud

As AI adoption accelerates, integration remains a major challenge. Most enterprises rely on a patchwork of APIs, apps, and systems spread across on-prem and multi-cloud environments – many of which weren't built to work together.  

IBM is introducing web Methods Hybrid Integration, a next-generation solution that replaces rigid workflows with intelligent and agent-driven automation. It will help users manage the sprawl of integrations across apps, APIs, B2B partners, events, gateways, and file transfers in hybrid cloud environments.

An independent Forrester Consulting Total Economic Impact (TEI) study found that a composite organization representative of interviewed customers who adopted multiple webMethods integration capabilities realized over three years:

• 176% ROI, plus unquantified benefits such as ease of use, reduced training costs, and improved visibility and security posture
• 40% reduction in downtime
• 33% time savings on complex projects
• 67% time savings on simple projects
   

This complements IBM's broader automation portfolio, which spans application development and integration, infrastructure automation and technology business management. Integrations with HashiCorp – including Terraform for infrastructure provisioning and Vault for secrets management – will enhance automation across hybrid environments to support secure configuration, consistent policy enforcement, and scalable operations. Tools like IBM Concert Resilience Posture, along with watsonx and Red Hat technologies, give organizations an intelligent, unified way to manage operations and accelerate AI across hybrid clouds.

Unlocking unstructured data for generative AI

Unstructured data – buried in contracts, spreadsheets, and presentations – is one of the most valuable but underutilized resources in the enterprise. IBM is evolving watsonx.data to help organizations activate this data to drive more accurate, effective AI.

The new watsonx.data will bring together an open data lakehouse with data fabric capabilities – like data lineage tracking and governance – to help clients unify, govern, and activate data across silos, formats, and clouds. Enterprises will be able to connect their AI apps and agents with their unstructured data using watsonx.data, which tests show can lead to 40% more accurate AI than conventional RAG.

IBM is also introducing watsonx.data integration, a single-interface tool for orchestrating data across formats and pipelines, and watsonx.data intelligence, which uses AI-powered technology to extract deep insights from unstructured data. They will be available as standalone products, with select capabilities also available through watsonx.data – maximizing client choice and modularity.

To complement these products, IBM recently announced its intent to acquire DataStax, which excels at harnessing unstructured data for generative AI. With DataStax, clients can access additional vector search capabilities. Further, watsonx is now integrated as an API provider within Meta's Llama Stack, enhancing enterprises' ability to deploy generative AI at scale and with openness at the core.

IBM's new content-aware storage (CAS) capability is now available as a service on IBM Fusion, with support for IBM Storage Scale coming in 3Q. This provides ongoing contextual processing of unstructured data to make extracted information easily available to RAG applications for faster-time-to-inferencing.

Infrastructure for AI scale

IBM is launching IBM LinuxONE 5, its most secure and performant Linux platform for data, applications, and trusted AI – with the ability to process up to 450 billion AI inference operations per day. IBM LinuxONE 5's innovations include: 

• IBM's State-of-the-art AI accelerators, including IBM's Telum II on-chip AI processor and the IBM Spyre Accelerator (available 4Q 2025 via PCIe card), to enable generative and high-volume AI applications such as transactional workloads.
• Advanced security offerings with confidential containers to help clients protect their data and new integrations with IBM's pioneering quantum-safe encryption technology to address quantum-enabled cybersecurity attacks.
• Significant reductions in costs and power consumption, moving cloud-native, containerized workloads from a compared x86 solution to an IBM LinuxONE 5 running the same software products can save up to 44% on the total cost of ownership over 5 years.
   

IBM has also expanded its GPU, accelerator and storage collaborations with AMD, CoreWeave, Intel, and NVIDIA to provide new solutions for compute-intensive workloads and AI-enhanced data.