IBM Unveils Enterprise-Ready AI Agents and Tools to Accelerate AI Adoption

How IBM's new AI solutions ease deployment and integration for your business

IBM unveils a suite of enterprise-ready agents, data integration tools, and much more. IBM is holding its annual THINK conference this week, and, unsurprisingly, artificial intelligence is the star of the show. The common theme across IBM's broad swath of product unveilings is a focus on solutions that make it easier to scale enterprise AI in organizations, tackling challenges organizations face with AI deployment and integration. AI agents are the latest breakthrough in the AI space, taking the assistance that AI chatbots provide a step forward by actually performing tasks for people. Although agentic AI is a technology most enterprises should take advantage of, businesses face several implementation challenges, including finding ways to integrate it seamlessly into their various apps, data, and environments. Also: Why scaling agentic AI is a marathon, not a sprint To address these challenges, IBM unveiled a suite of enterprise-ready agents in watsonx Orchestrate. According to IBM, these AI tools enable businesses to build their own agents in under five minutes with both no-code and pro-code options; leverage pre-built agents for specialized use in specific domains such as HR, sales, and procurement; integrate with 80+ enterprise applications from the likes of Adobe, AWS, Microsoft, and more; orchestrate multi-agent, multi-tool coordination, and monitor agents with insights into performance, guardrails, governance, and more. IBM also announced a new Agent Catalog in watsonX Orchestrate, which lets businesses more easily identify and access the best agent for their business use case from 150+ agents and prebuilt tools made available through partners and IBM's offerings. IBM also introduced webMethods Hybrid Integration, a solution designed to help enterprises integrate AI into their business operations with agent-driven automation. According to IBM, this makes it easier to manage "integrations across apps, APIs, B2B partners, events, gateways, and file transfers in hybrid cloud environments." Based on interviews with several companies using webMethods, an independent Forrester Consulting Total Economic Impact (TEI) study created a model of a typical organization representative of those customers. The study found that over three years, this composite organization experienced a 176% ROI, a 40% reduction in downtime, 33% time savings on complex projects, and 67% time savings on simple projects. Generative AI applications require a lot of data, and the efficiency of the AI model depends on the quality of that data. However, getting data into the ideal condition is often a challenge for businesses, as this typically takes lots of manual work to locate the unstructured data and then organize and structure it in a way that is most helpful for models. IBM's new watsonx.data seeks to help with that issue by combining an open data lakehouse with data fabric capabilities to help businesses unify and activate data across different formats and silos. According to IBM, wastsonx.data will help users connect their unstructured data with AI apps and agents, which would lead to 40% more accurate AI than when using the conventional RAG method. Also: RAG can make AI models riskier and less reliable, new research shows To further help enterprises work with unstructured data, IBM also launched watsonx.data integration, a single interface where users can access, manage, and work with data from different sources or locations, and watsonx.data intelligence, which leverages AI to yield deep insights from the unstructured data. IBM also unveiled a new content-aware storage (CAS) capability available as a service in IBM Fusion with IBM Storage Scale. This capability can continuously analyze unstructured data and extract relevant information, which is then made available to RAG applications for faster processing.

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

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 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: 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: 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.

IBM unveils capabilities meant to accelerate AI agent adoption - SiliconANGLE

IBM unveils capabilities meant to accelerate AI agent adoption IBM Corp. is using its Think 2025 conference in Boston this week to introduce a sweeping set of artificial intelligence and hybrid cloud technologies aimed at accelerating enterprise adoption of AI agents. The venerable tech giant said it's focused on making the new breed of autonomous software that can take actions independently while working toward a specified goal, easier to build and orchestrate. The new technologies are designed to address what Chief Executive Arvind Krishna called the "fragmentation of infrastructure" that has stalled many AI initiatives. He cited internal research that found that only 25% of enterprise AI projects have delivered expected returns on investment. IBM is tackling this shortfall with tools for building and orchestrating AI agents, unifying enterprise data and scaling AI inference, the process of running trained models. Central to IBM's announcements is watsonx Orchestrate, a platform the company claims enables businesses to build AI agents in less than five minutes. The system includes over 150 prebuilt agents, integrations with more than 80 enterprise applications, and orchestration tools that enable multiple agents to collaborate across complex workflows. "We think this will appeal to both low-code and pro-code developers," Krishna said in a press briefing. "Agents are going to form an integral part of how generative AI is used in enterprises." He said the rise of AI agents will trigger a shift in management thinking. "This is a new way to work," he said. "C-suite leaders are already using these tools to streamline operations and make decisions. We expect agents to become the interface layer for enterprise users." IBM is also launching an Agent Catalog within watsonx Orchestrate to support the discovery and reuse of existing agents. It allows organizations to build domain-specific agents for functions like human resources, sales and procurement and coordinate them internally as well as interact with external applications from vendors like Salesforce Inc., Oracle Corp. and ServiceNow Inc. IBM Senior Vice President of Software Rob Thomas said IBM is addressing a "big gap in the market for connecting agents." It has the tools in place, said Brent Ellis, Forrester Research Inc.'s principal analyst serving technology, architecture and delivery professionals, "but the art will be putting those parts together. That will be the hard work of taking things like MCP [Model Context Protocol] and A2A [Agent2Agent Protocol] and generalizing them, as well as adding in key enterprise elements like the ability to pass encrypted data in the model context, or use session keys across multiple platforms," he said. "Right now, they are doing this in a bespoke way in their consulting organization." To address the complexity of enterprise landscapes, IBM introduced webMethods Hybrid Integration, a platform that automates the integration of applications, application program interfaces, events and data across on-premises and multicloud environments. IBM said it can replace rigid workflows with intelligent automation powered by agents. It cited a study conducted by Forrester Consulting that found that organizations using webMethods achieved a 176% return on investment over three years, a 40% reduction in downtime and up to a 67% time savings on routine projects. "Integration remains one of the most difficult problems in enterprise IT," Krishna noted. "By using agent-driven integration, we can dramatically cut the time and complexity of those efforts." IBM is also revamping watsonx.data, a governed data store designed to scale AI and analytics workloads, with new features that help enterprises extract value from unstructured data such as documents, spreadsheets, presentations and multimedia. Enhancements include open data lakehouse support, data fabric capabilities, lineage tracking and AI-powered tools for orchestrating and analyzing data. Krishna said connecting AI applications and agents with unstructured data can improve the accuracy of AI applications by up to 40% over conventional retrieval-augmented generation or RAG methods. IBM also introduced content-aware storage for ongoing contextual processing of unstructured data to support real-time inference. All these capabilities are undergirded by technology IBM recently acquired with DataStax Inc., whose database-as-a-service offering based on the open-source Apache Cassandra database management system is built to handle vast amounts of information distributed across multiple locations and environments. IBM said DataStax brings vector search and real-time data capabilities to its stack. With the introduction of a new generation of mainframes imminent, IBM highlighted the launch of LinuxONE 5, a high-performance Linux platform that it said can process up to 450 billion AI inference operations per day. Powered by the company's Telum II AI processor and the forthcoming Spyre Accelerator, LinuxONE 5 is aimed at compute-intensive applications like fraud detection and medical imaging. The platform also introduces confidential containers for secure AI operations and encryption technologies that can't be easily compromised by quantum processing. IBM claimed a mainframe running LinuxONE 5 delivers a 44% cost savings over five years compared with x86-based systems running equivalent workloads. Krishna said the announcements collectively reflect IBM's belief that AI must be embedded into the core of enterprise operations as businesses move beyond experimentation to focus on measurable outcome. "The era of AI experimentation is over," he said. "Enterprises want integration, governance and real ROI." Ritika Gunnar, IBM's general manager for data and AI, said agents are critical to the transformation. "We are moving from AI that generates content or chats with users to AI that acts on behalf of users," she said. "These agents must integrate with existing systems, workflows and governance requirements." She said IBM expects more than 50% of enterprises will embed agents in essential systems by 2027. Asked what it plans to do about AI hallucinations and bias, IBM said its governance tools have been extended to support agents and that the company is investing in lifecycle management, observability and traceability for agentic workflows. "Just like we evaluate and govern large language models, we're doing the same for agents," Gunnar said. IBM supports open standards such as MCP developed by Anthropic PBC and has open-sourced its Agent Collaboration Protocol to promote interoperability and agent-to-agent communication. The company has also open-sourced its Granite model family and continues to partner with open model developers. "We think the future of AI is open," Thomas said. Forrester's Ellis said that though few companies outside of the IBM customer base have adopted watsonx or Granite at this point, the company's commitment to openness should resonate with buyers in the long term. "IBM's revenue model is on the services and infrastructure used to run these models, and it would make sense for them to keep their models open," he said. "The need for models that address governance concerns like security and indemnity will grow, as will the need to ingest historical data for use within AI models. IBM can address both of those trends. This looks good for IBM."

IBM Unveils Hybrid AI Tools to Help Enterprises Scale with Speed | AIM

The tools will allow companies to build AI agents in five minutes and integrate them across over 80 applications. IBM is slated to announce a new suite of hybrid AI technologies at its annual Think conference, aimed at helping enterprises deploy generative AI agents across complex environments. The tools will allow companies to build these agents in five minutes, integrate them across over 80 applications, and scale operations on new platforms capable of 450 billion daily inference operations. The announcement responds to growing demand as businesses face a projected surge in application development, which is over one billion apps expected by 2028. According to a recent study by IBM, leaders are doubling AI investments but struggling with disconnected systems and limited return on investment. "The era of AI experimentation is over," IBM chairman and CEO Arvind Krishna said. "IBM is equipping enterprises with hybrid technologies that cut through complexity and accelerate production-ready AI implementations." IBM's watsonx Orchestrate platform now offers a comprehensive suite of AI agent capabilities, including pre-built domain agents for HR, sales, and procurement, alongside tools to build custom agents without code. The platform also integrates with major enterprise systems such as Microsoft, Salesforce, Oracle, SAP, and AWS, and provides agent orchestration, monitoring, and optimisation features. A newly introduced Agent Catalog in watsonx Orchestrate includes over 150 agents and tools developed by IBM and partners like MasterCard and Salesforce, designed to support use cases ranging from prospect discovery to HR automation. To address integration challenges, IBM also launched webMethods Hybrid Integration, which is designed to automate workflows across cloud and on-premise systems. For data management, IBM evolved its watsonx.data offering to activate unstructured data such as contracts and spreadsheets. Enhancements include new orchestration tools and AI-powered insight engines. Testing indicates this leads to AI agents that are up to 40% more accurate. IBM also introduced IBM LinuxONE Emperor 5, designed to support AI at scale. The platform includes built-in AI accelerators and quantum-safe encryption technologies, and promises a 44% reduction in total cost of ownership over five years compared to equivalent x86 systems. These releases are supported by expanded partnerships with AMD, Intel, NVIDIA, and others, reinforcing IBM's strategy to help enterprises operationalise AI across hybrid cloud environments.

IBM Think 2025: The Biggest News In AI, Linux

'One of the biggest reactions we've gotten on Watsonx.orchestrate and agents is from services partners. They want to build agents. They want a way to have them land on a platform like Orchestrate, where they can leverage multiple models, multiple data repositories,' says IBM Senior Vice President of Software and Chief Commercial Officer Rob Thomas. New domain-specific artificial intelligence agents. Advancements in AI orchestration tools. And details on the latest version of IBM's LinuxOne. These are some of the biggest announcements coming out of the Armonk, N.Y. cloud, mainframe and AI vendor's annual Think conference. Think 2025 runs through Thursday in Boston. During a virtual press conference, IBM Senior Vice President of Software and Chief Commercial Officer Rob Thomas told CRN that solution providers are "a critical part of how clients acquire and consume technology" and represent "one of the biggest investments we've made" over the last four years. He put services at north of 80 percent of the $5 trillion a year in projected IT spending and credited solution providers with Watsonx.orchestrate adoption. "One of the biggest reactions we've gotten on Watsonx.orchestrate and agents is from services partners," Thomas said. "They want to build agents. They want a way to have them land on a platform like Orchestrate, where they can leverage multiple models, multiple data repositories. And so we've got a lot of momentum with systems integrators building agents using Orchestrate." [RELATED: What IBM's Earnings Say About Tariffs, Consulting, AI] In response to another question during the conference, IBM Chairman and CEO Arvind Krishna said that AI demand has not fallen amid economic uncertainty as countries negotiate global tariffs. "Everybody is doubling down on AI investments, but they're now looking for that return on AI," he 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 right now?" Perhaps of importance to solution providers, Krishna said that consultancies and services businesses can leverage new AI software to create platforms from previously conducted knowledge work, using EY's tax question-and-answer product built on Watsonx.ai as an example. "Now their consultants can be more productive," he said. "It actually allows them to scale also in going more downmarket because if the cost of providing this consulting is now cheaper, you can scale to many smaller clients. ... Once the platform is built out, it offers them cost savings." Heather Domin, vice president and head of office for responsible AI and governance at India-based IBM partner HCLTech, told CRN in an interview that IBM investing in not just AI applications but the underlying technology making AI usable for enterprises is important for her practice. She cited the Watsonx.governance platform and Guardium real-time activity monitoring product. "We are seeing a lot of interest with our clients around responsible AI and, of course, IBM brings just a tremendous amount of expertise in that space," said Domin, who previously served as IBM's global leader for responsible AI initiatives. "One of the things that I love about IBM is that there really is a strong awareness of where the technology is heading in general, and then what are the types of guardrails that we need to have in place. And I'd love to see that in all partners and vendors that we work with." The news helps illustrate the ways IBM and its partners are going after the $4.4 trillion annual economic value that GenAI is expected to generate across industries. AI is also the way to scale across an estimated 1 billion-plus applications that will be in existence by 2028, according to IBM. That many apps means countless fragmented environments. Data that IBM shared during Think shows that CEOs expect the AI investment growth rate to more than double over the next two years. But so far, only 25 percent of AI initiatives have achieved the expected ROI.

IBM Think 2025: CEO Krishna Says Customer AI Success Rate Can Triple With Big Blue Approach

The human is never going to be out of the loop. The human is always going to be there for the most, either, complex or the edge cases where the AI does not have confidence in its own answer. The precursor to agents was assistants. If you look at assistants, I think at last count, we had over 20,000 deployments. While there are some difficulties, there is also a lot of eagerness, excitement and enthusiasm about adopting these technologies. If I look at proof of concepts, I think our teams collectively do ... 4(000) or 5,000 each year in terms of kicking the tires. And about half of those, by the way, do make their way into some form of production. I also think that we should understand that everybody-our partners, from SAP with their Joule technology, Salesforce with their Agentforce technologies, ServiceNow with their agentic workflows, Adobe with their agents on Experience, Workday with their agents (making) the HR process more effective, Oracle with some of their agents around both HR as well as payroll. I can go on. Those are being extremely well adopted in all of these cases, including our own, across many, many clients, because they help. Now, these are not general-purpose agents. Almost all the examples I mentioned, they do a small but very precise set of tasks extremely well. I do not believe that sensitive financial information, which is often in the context of both the C-suite and the board of directors, is going to be in the purview of something that is completely outside the domain of your CIO (chief information officer) and CISO (chief information security officer). I think we are far, far from those days. However, could you be using third-party tools to augment your own learning, to maybe figure out what the competition is doing, to maybe figure out what is going on in the public domain. Absolutely, I believe, and those may be tools that are third-party tools. That would be like asking, 'Do these people today use Google search to do something?' Of course, they do. But you normally don't put your most sensitive information into the Google search bar. Will the most senior people in the organization start using agentic AI and AI tools to make themselves more productive? I believe that that answer is a resounding yes. Bias actually has been a topic we've been working on for many years. It's one of the reasons that our R&D (research and development) teams worked on something called the AI Fairness toolkit, which allows you to check your models for bias. It is also a huge part of the tests that we do as we build our own Granite models before we put them out. It has also become part of the guardrails that you can put around different models that you bring in, also, from other parties. Bias is something that can be handled, and there are known techniques. (They) may not take it down to zero, but it can take it down to really minimal levels. Hallucination is a little bit different. This is also something that is correlated, not perfectly, but it's correlated with size of models. As the amount of data you ingest and as the model sizes tend to go into the hundreds of billions of parameters, we can see that hallucination does increase. So it now depends on what you're trying to do. The technique that people use to take care of hallucination is that you use a verifier model. I'm actually going to go a little bit tongue in cheek and tell you I don't think hallucination will ever go to zero. At least when I talk to my children, they are often convinced that they said or did something or didn't, when actually at least 15 percent of the time, my observation is they are dead wrong, but they are convinced that they are correct. I'm going to take that as a proxy for most humans and sort of extrapolate it there and say, maybe AI is going to be better than that, but probably not at zero. AI is the source of productivity for this era, and that going back to the competitive advantage for a business means that that is the heart of your competitive advantage. But not all AI is built the same. And not all AI is built for the enterprise. Why do I make that claim? Ninety-nine percent of all enterprise data has been untouched by AI. If you need to go unlock the value from that 99 percent, you need to take an approach to AI that is tailored for the enterprise. And if you can unlock the value from that data, that's a huge opportunity for the enterprise. If you think about the massive general-purpose models, those are very useful. But they are not going to help you unlock the value from all of the data inside the enterprise. To win, you are going to need to build special-purpose models, much smaller, that are tailored for a particular use case and that can ingest the enterprise data and then work. What about accuracy? Go look at the leaderboards. Smaller models are now more accurate than larger models. What about intellectual property? That is where the open nature of some of the smaller models comes in. And if you think about specific domains, it's a lot easier to build a smaller model that can go after that. You can think about these models that are 3 (billion), 8 (billion), 13 (billion), 20 billion parameters, as opposed to 300 (billion), 500 (billion). They're incredibly accurate. They are much, much faster. They're much more cost effective to run. And you can choose to run them where you want. You put all that together, and that is why, for example, we invest in our Granite family of models. It's not a substitute for the larger models. It's an 'and' with the larger models. You can now tailor these models specifically to your enterprise needs. At 30 times (reduction in inference costs), you begin to get a huge advantage in cost and in what you can begin to do with them. As technology comes down the cost curve, it opens up many, many more opportunities. If you think about how expensive storage and compute were in the '90s, and you compare it to today, in that case, I think it's 100 to 1,000x cheaper. As that happens, you can throw it at a lot more problems, and that is the advantage.

IBM Accelerates Enterprise Gen AI Revolution with Hybrid Capabilities

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 Today at the company's annual THINK event, IBM (NYSE: IBM) is unveiling 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. "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.2 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.3 IBM is also introducing the new Agent Catalog in watsonx Orchestrate4 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 webMethods Hybrid Integration5, 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 years6: 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.7 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.8 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.9 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.10 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.11 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. To see all THINK news, visit the IBM Newsroom. Statements regarding IBM's future direction and intent are subject to change or withdrawal without notice and represent goals and objectives only. About IBM IBM is a leading provider of global hybrid cloud and AI, and consulting expertise. We help clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs, and gain a competitive edge in their industries. Thousands of governments and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently, and securely. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and consulting deliver open and flexible options to our clients. All of this is backed by IBM's long-standing commitment to trust, transparency, responsibility, inclusivity, and service. Visit www.ibm.com for more information.

IBM : AWS and IBM Continue Deep Collaboration to Deliver New Agentic AI Capabilities

AI is quickly moving beyond a tool to simply enhance productivity, ushering in a new era defined by autonomous, reasoning agents. AWS and IBM are making it easier for customers to adopt these... AI is quickly moving beyond a tool to simply enhance productivity, ushering in a new era defined by agents that can reason, act and learn autonomously. AWS and IBM are working to make it easier for customers to adopt these agents no matter what their experience level. During IBM Think, IBM is introducing new tools and frameworks to build and manage networks of AI agents, along with new pre-built domain agents with ready-to-use skills - integrated with key technologies available on AWS Marketplace. We're also expanding access to Granite models, as well as the availability of automation software via AWS. A Powerful Foundation for Trustworthy Agentic AI IBM's focus on enterprise-grade, trustworthy AI and the integration with the AWS technology stack form the ideal foundation to enable generative AI to scale. We're excited to announce a new planned integration between Amazon Q index and IBM watsonx Orchestrate, IBM's agentic AI and automation solution that helps create, deploy and manage AI agents and assistants. The Amazon Q index enables customers to create a central repository of data based on multiple third-party applications. The index helps to fuel AI decision-making and serves as a powerful foundation for retrieving relevant content across various enterprise data sources. Watsonx Orchestrate also offers IBM pre-built agents that are specialized in critical business domains such as HR, Procurement and Sales. By powering these agents with Amazon Q index, customers can enhance their generative AI with data from critical applications without having to manually access individual information sources. Through integration with Amazon Q index, customers will have the ability to enable any IBM watsonx agent to use domain-specific data from applications such as Adobe, Salesforce, Slack and Zendesk for more personalized experiences. A preview of this new integration is on display at IBM Think with expected availability in second half of this year. "With Amazon Q Index, we're connecting ISVs' Generative AI solutions to enterprise applications, enabling a secure, personalized experience for every enterprise user," said Dilip Kumar, AWS Vice President, Q for Business. "This new integration with IBM watsonx Orchestrate will make it even more powerful and enable customers to begin getting value from AI across their organization quickly and securely." Providing flexible delivery options, IBM watsonx Agents and watsonx Orchestrate are now available as SaaS and on AWS Marketplace in the U.S., Europe and Singapore, and coming in June to India. "As labor markets tighten and skills gaps widen, agentic AI will help advance the intelligent workforce and have a profound impact on ecosystems and economies," said Malcolm Adams, CEO of Avid Solutions, a research and development company specializing in technology for sustainable food production. "We've used watsonx Orchestrate on AWS to automate repetitive tasks for customer service, project management and accounting, but we're most excited about the enormous impact agentic AI and networks of agents working together will have on large scale challenges such as global supply chains. Watsonx provides the foundation for trustworthy AI while AWS provides the power to scale globally to transform entire industries." To provide a foundation of trust for agentic AI on AWS, IBM is adding new capabilities to IBM watsonx.governance to enable agentic lifecycle governance, cataloging and evaluation. New capabilities such as comprehensive model evaluation and monitoring for bias detection and drift management will soon be part of the offering on AWS Marketplace to help enable responsible, transparent and explainable AI models throughout their entire lifecycle. IBM also established AI Integration Services to help clients transform end-to-end processes with agentic AI. IBM consultants bring expertise in business domains across industries and AWS technology plus new AI-powered capabilities in IBM Consulting Advantage to reinvent clients' processes with agentic AI on AWS. IBM Consulting Advantage for Agentic Applications includes a library of pre-built templates customized to transform complex enterprise workflows like customer service and finance on AWS. It also includes tools to accelerate building and deploying agentic AI leveraging AWS native technologies like Amazon Q and Amazon Bedrock. Granite 3.2 Models Available IBM's Granite 3.2 large language models are currently available on Amazon Bedrock Marketplace and Amazon SageMaker JumpStart, allowing customers to deploy their dedicated model with just a few clicks. Additionally, these models are planned to be available through Amazon Bedrock Custom Model Import (CMI) in the third quarter of 2025. CMI provides a serverless deployment option that allows customers to bring their own models to Amazon Bedrock without managing the underlying infrastructure. IBM Advances Software Momentum on the AWS Marketplace Customers and IBM Business Partners alike are benefitting from IBM Automation Software available on the AWS Marketplace. With the addition of Apptio and Kubecost software, we're providing customers more choice than ever. Software from HashiCorp, an IBM Company, will be available via IBM on AWS Marketplace later this year. Also in June, IBM webMethods Hybrid Integration, a next-generation integration solution that will replace rigid workflows with intelligent, API and agent-driven automation, will be available on AWS Marketplace. Meanwhile, IBM Concert, which provides generative AI-driven insights by identifying vulnerabilities and suggesting fixes to help enhance resilience and mitigate risk across the entire application ecosystem, is already available via AWS. IBM currently offers more than 70 IBM Software solutions in 89 countries on AWS. Beginning in July, IBM will automatically enable resellers within IBM Partner Plus in 22 countries to sell eligible IBM Software on the AWS Marketplace as part of their IBM Business Partner Agreement. As new assistants and agents proliferate across the enterprise, these are just a few of the latest examples of how AWS and IBM are helping organizations easily orchestrate and manage agents, enhance automation and implement robust AI governance and responsibility. To learn more, please visit https://www.ibm.com/aws or join us at IBM Think and check out the AWS booth #413 and these sessions hosted by AWS experts. Session #1011: Power of Partnership: How AWS and IBM Collaborate to Solve Enterprise Challenges Session #1489: Empowering partners to reach more clients with the value of hybrid cloud and AI with IBM and AWS Marketplace Statements regarding IBM's future direction and intent are subject to change or withdrawal without notice and represent goals and objectives only.

IBM : and Salesforce Activate Enterprise Data on IBM Z, Preview New Agents to Help Customers Unleash Their Data for Agentic AI

Enterprise data is a crucial source of intelligence for AI models and agents to learn from. For decades, businesses have entrusted IBM Z mainframes, renowned for their reliability and security, to... Enterprise data is a crucial source of intelligence for AI models and agents to learn from. For decades, businesses have entrusted IBM Z mainframes, renowned for their reliability and security, to run their mission-critical transactions. To use it as a catalyst for agentic AI, IBM and Salesforce are providing customers access to their business data in IBM Z mainframes and Db2 databases so it can power AI use cases on the Salesforce Agentforce platform, the digital labor platform for augmenting teams with autonomous AI agents. Also at IBM Think, we will introduce new IBM agents built with IBM watsonx Orchestrate that work with Salesforce technologies, and IBM Granite model performance. Expected to be available in June, the new integration will enable AI agents to complete back-to-front office connections using data from IBM Z mainframes, relied upon to run 70% of the world's transactions by value*. This leverages Salesforce Zero Copy via an integration between IBM watsonx.data and Salesforce Data Cloud, a hyperscale data engine natively integrated within the Salesforce Platform, making high speed data transfer possible without having to move or copy data. IBM is the first to offer Salesforce customers this data flow between IBM Z and Salesforce Data Cloud for the purpose of analytics and AI, which is especially impactful for organizations in regulated industries, such as banking and financial services, enabling businesses and their agents to unlock new opportunities for innovation. "In today's dynamic landscape, data fluidity is paramount. Data Cloud's Zero Copy Partner Network helps address this need by providing highly secure and rapid connectivity to their IBM Z data," said Rahul Auradkar, EVP & GM, Unified Data Services & Einstein, Salesforce. "This eliminates the friction of traditional data movement, enabling utility and value for previously trapped information. By unlocking this access, we're helping customers derive real-time insights and activate the full potential of their data ¾ regardless of where it resides to drive AI-powered customer experiences with Agentforce, across applications and the broader ecosystem." Sales Prospecting AgentCollaborates with other Agents to Make the Sale IBM and Salesforce are also collaborating on a series of pre-built sales agents that bring together the power of watsonx Orchestrate and Agentforce. The first of these agents is the IBM watsonx Sales Prospecting agent, which enables sellers to effectively prospect and increase sales conversion rates by helping with product targeting, fetching account contacts, or developing a sales pitch. It uses a conversational interface and interacts across Agentforce, watsonx Orchestrate and third-party agents. It is expected to be available via watsonx Orchestrate and the Salesforce Agent Exchange in coming months. Unleashing HR Productivity in Slack with IBM watsonx Employee Support Agent Slack is the work operating system that brings together people, apps, processes, data, and agentic AI to fuel productivity. By bringing AI agents into Slack, users can reduce workloads and focus on what truly matters. The IBM watsonx Employee Support Agent, built with watsonx Orchestrate, answers HR questions and can take action through Slack's conversational interface. Using IBM's expertise with its own HR agent over the past three years, IBM employees use it to answer 94% of company-wide, lower-level HR queries, freeing up HR professionals to focus on more complex issues. The IBM watsonx Employee Support Agent is expected to be available in the Slack Marketplace in June. IBM Granite Models in Top Salesforce CRM Benchmarks For customers who want a choice of smaller, purpose-built LLMs, IBM Granite models, the IBM flagship family of AI models built for business, are available on the Salesforce Platform as a Bring-Your-Own-Model and can be easily accessed in Agentforce. These smaller, specialized AI models for enterprises continue to outperform in testing. Granite 3.1 8B model yielded high marks on accuracy in the Salesforce LLM Benchmark for CRM, the world's first LLM benchmark for CRM to assess the efficacy of generative AI models for business applications. IBM Consulting Helps Clients Navigate Evolving Data and AI Demands As organizations navigate complex CRM and AI landscapes, IBM Consulting can help clients unlock greater returns from their Salesforce investments. Leveraging Agentforce, watsonx and IBM Consulting's deep expertise, we are uniquely positioned to deliver differentiated global Salesforce transformations. Our AI-powered delivery platform IBM Consulting Advantage includes Salesforce-specific AI agents, trained on specific Salesforce knowledge bases and fueled by a proprietary model, that support our consultants in delivering greater value, faster. With these capabilities, we can orchestrate the agentic ecosystem and help drive long-term platform extensibility, supportability and reduced ownership costs. To learn more about Zero Copy Integration with IBM Z mainframes and Db2 databases or new IBM pre-built agents for Salesforce and Slack, please visit: https://www.ibm.com/salesforce.# * IBM mainframes run an estimated 70% of all world transactions by value. Source: Mitigating Fraud in the AI Age, Celent, 2025. Statements regarding IBM's future direction and intent are subject to change or withdrawal without notice and represent goals and objectives only.