Ibm Leans On Ai Agents To Scale Execution Across The Enterprise
Emerging AI agents represent a fundamental tipping point in the AI revolution, shifting from AI that can chat with you, to AI that can do work for you—with unprecedented autonomy. For decades, interacting with powerful enterprise systems required specialized knowledge to navigate a vast network of complex interfaces. AI agents will lower the barrier to accessing AI's power. By enabling interaction through conversational interfaces, users can simply state their goals—while networks of agents take on the necessary actions across backend systems. This democratization means every person, regardless of their technical expertise, can leverage AI to automate complex tasks, gain insights and drive productivity across functions like HR, finance, IT and customer service. We believe this new operating model will be the catalyst that ignites the massive economic potential anticipated for generative AI—which has been estimated to generate up to USD 4.4 trillion economic value annually across...
But to make it a reality, agents must be able to work seamlessly across the vast web of applications, data and systems that underpin today's complex enterprise technology stacks. Which means that orchestration, integration and automation are the secret weapons that will move agents from novelty into operation. That’s why IBM isn’t just making agents—we're re-imagining the underlying technology stack that will power the agent era. AI agents are emerging as the next evolution in enterprise intelligence, moving beyond copilots to autonomous systems capable of driving outcomes. As companies adopt them across departments, complexity surges and execution risks rise. IBM Corp.
is focused on developing AI agents that execute across systems rather than merely assist at the edges. These agents are designed to integrate with legacy and modern tools, orchestrating processes across the full sprawl of enterprise infrastructure, according to Ritika Gunnar (pictured), general manager for data and artificial intelligence at IBM. IBM’s Ritika Gunnar talks with theCUBE about how agent frameworks are reshaping enterprise automation. “Now we’re in the realm of AI that can do for you,” she said. “This is a very powerful new chapter … we’re hearing that from a lot of our clients and the use cases that they’re driving and how they’re actually leveraging the technologies. This is a very, very poignant time in technology, and we really think that there is a new era, what we’re calling the era of systems of intelligence.”
Gunnar spoke with theCUBE Research’s Dave Vellante at IBM Think, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how agent frameworks reshape enterprise automation, why orchestration matters and how IBM addresses unstructured data. (* Disclosure below.) In a groundbreaking move toward AI integration at scale, IBM has announced a staggering $3.5 billion productivity boost achieved through the use of AI agents across 70+ business areas. By adopting a "Client Zero" approach and rolling out its agentic AI strategy internally to over 270,000 employees, IBM sets a new benchmark for enterprises seeking to transform operational efficiency. This transformation emphasizes the power of AI agent development and the need for businesses to strategically build AI agent ecosystems.
IBM’s innovative "Client Zero" model positions the company itself as the first adopter of its AI technologies, showcasing real-world use cases and tangible outcomes. This internal deployment allowed IBM to develop AI agents tailored to its specific business functions, validate their performance, and gather invaluable insights before offering solutions to clients. The success of this strategy highlights the importance of businesses first looking inward to develop AI agents that solve core operational challenges. IBM's AI implementation wasn’t limited to one domain. The company leveraged digital agents in key departments like HR, IT, finance, and sales: AskHR Agent: Streamlined human resources operations by automating 94% of simple tasks, such as handling vacation requests and generating pay statements.
AskIT Agent: Reduced IT support calls and chats by 70%, significantly enhancing response times and freeing up human experts for complex issues. IBM sees an opportunity to help customers build, run and manage AI applications and agents across their distributed, hybrid environments at a time when enterprises are experiencing some implementation frustrations. This week at its annual Think Conference in Boston, IBM is rolling out new technologies aimed at this burgeoning world of AI agents and workloads. “We expect there will be over a billion new applications constructed using generative AI,” IBM CEO Arvind Krishna said in a press call this week. “Our clients are expecting to double or even increase the investments on AI, [but] they’re finding that only about 25% of the time are they getting the ROI that they expect, driven by a... Core to what IBM is introducing is the ability to develop and manage AI agents, which IBM says will handle the heavy lifting for future AI systems.
“For decades, interacting with powerful enterprise systems required specialized knowledge to navigate a vast network of complex interfaces. AI agents will lower the barrier to accessing AI’s power,” wrote Ritika Gunnar, general manager of product management for data and AI at IBM, in a blog post about the news. “By enabling interaction through conversational interfaces, users can simply state their goals—while networks of agents take on the necessary actions across backend systems. This democratization means every person, regardless of their technical expertise, can leverage AI to automate complex tasks, gain insights and drive productivity across functions like HR, finance, IT and customer service.” To that end, IBM is announcing a suite of agent capabilities in watsonx Orchestrate, a natural language processing platform that can manage and automate agent activity. IBM ramps up its efforts to push AI deeper into the enterprise by expanding its Watsonx platform and unveiling new integration and data tools that aim to address one of the technology’s biggest obstacles:...
The announcement, made during the company’s Think event, outlines IBM’s strategy to help organisations design and deploy AI agents that integrate with complex digital environments and fragmented data sources. IBM says a key issue hindering enterprise AI is the failure to integrate systems across hybrid and multi-cloud setups. The company’s internal research shows that although interest in AI continues to grow, only 25% of AI initiatives have met expectations in terms of return on investment. IBM points to technology sprawl and a lack of orchestration as reasons for this shortfall. As the initial frenzy around Generative AI shifts to new agentic and automation use cases, enterprises must embrace a holistic and orchestrated approach to achieve meaningful productivity gains. Enterprises are adapting and deploying GenAI and AI agents across the enterprise.
Gartner estimates that by 2028, 33% of enterprise software will include agentic AI, up from less than 1% in 2024. This means 15% of day-to-day work decisions could be made autonomously, according to the research. The benefits are clear: AI agents facilitate autonomous decision-making and the ability to perform tasks independently. At the same time, they also augment human workers to empower decision-making and drive greater efficiency. Despite the many upsides, however, many companies are struggling to turn the performance gains of individual workflows into systematic enterprise-scale productivity advantages. As agentic AI proliferates, the lack of effective governance, integration, and orchestration strategies can lead to increased fragmentation and sprawl, making it harder to achieve impactful efficiencies and measurable business value.
With heterogeneous business applications and systems in the mix, it’s difficult to seamlessly integrate AI agents and automation across diverse workflows and data silos. IBM’s focus on AI assistant and agent orchestration, and prebuilt integrations weaves agentic AI into the enterprise fabric. This elevates individual use cases and tasks to solutions that work collectively to deliver business value. Critical to IBM’s agentic AI vision is an orchestration layer that facilitates integration, communication, and information sharing. The orchestration layer allows for the right data to be sent to the right agents at the right time, encapsulated with the proper context and security models. This is crucial to unleashing multiple agents to successfully solve problems and complete complex tasks.
AI agents are transforming revenue team efficiency, boosting productivity by over 50%. By automating tasks like lead management and outreach, AI enhances human capabilities, allowing teams to focus on strategic initiatives. Discover how CloudApper AI RevOps bridges the gap between automation and personalization for optimal business growth. The post How a Simple Conversation Changed My Perspective on AI in Revenue Operations appeared first on Clo… AI is transforming how we work, live, and interact with technology. But as the ecosystem evolves, it’s essential to understand the nuances between similar-sounding concepts—especially when those differences can determine a project’s success or failure.
One pair that’s often confused: Agentic AI vs. AI Agents. Agentic AI vs. AI Agents is more than just a technical distinction—it’s strategic. At a glance, these terms seem interchangeable. After a…
AI agents are emerging as the next evolution in enterprise intelligence, moving beyond copilots to autonomous systems capable of driving outcomes. As companies adopt them across departments, complexity surges and execution risks rise. IBM Corp. is focused on developing AI agents that execute across systems rather than merely assist at the edges. These agents are […] The post From copilots to orchestrators: AI agents reshape IBM’s automation play… To view factuality data please Upgrade to Premium
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Is Focused On Developing AI Agents That Execute Across Systems
is focused on developing AI agents that execute across systems rather than merely assist at the edges. These agents are designed to integrate with legacy and modern tools, orchestrating processes across the full sprawl of enterprise infrastructure, according to Ritika Gunnar (pictured), general manager for data and artificial intelligence at IBM. IBM’s Ritika Gunnar talks with theCUBE about how ag...
Gunnar Spoke With TheCUBE Research’s Dave Vellante At IBM Think,
Gunnar spoke with theCUBE Research’s Dave Vellante at IBM Think, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how agent frameworks reshape enterprise automation, why orchestration matters and how IBM addresses unstructured data. (* Disclosure below.) In a groundbreaking move toward AI integration at scale, IBM has announced a staggering $3.5 b...
IBM’s Innovative "Client Zero" Model Positions The Company Itself As
IBM’s innovative "Client Zero" model positions the company itself as the first adopter of its AI technologies, showcasing real-world use cases and tangible outcomes. This internal deployment allowed IBM to develop AI agents tailored to its specific business functions, validate their performance, and gather invaluable insights before offering solutions to clients. The success of this strategy highl...