IBM’s AI strategy: Operationalizing enterprise intelligence

Artificial intelligence is no longer confined to back-end tasks or experimentation. It’s becoming the connective layer that reshapes how workflows function, how decisions are made and how value is delivered across the enterprise. For businesses navigating economic pressure, talent disruption and performance mandates, IBM Corp.’s AI strategy makes intelligence not optional, but operational.

That shift came into focus during this year’s “AI-Powered Business Operations: Strategies for End-to-End Transformation” event. Across a series of candid conversations with IBM leaders and enterprise decision-makers, theCUBE explored how the company is reimagining the structure of digital work itself, from sourcing and sustainability to customer service and skills development. That enterprise-wide scope reflects IBM’s AI strategy to embed intelligence throughout operations, not just in isolated systems.

“We’re entering the golden age of AI, a pivotal moment in how enterprises operate, create value and engage the world,” said theCUBE Research’s Scott Hebner. “This shift will redefine everything from how technology is applied to how labor is deployed, igniting a new super-cycle of innovation and productivity. Businesses such as IBM that embrace this future now won’t just keep up, they’ll lead.”

Since the event, IBM has moved swiftly to operationalize its vision. The company has introduced tightly scoped AI models tailored for enterprise complexity, integrated trust and observability as core design principles and doubled down on orchestration strategies to scale AI safely and efficiently. Its direction is clear: AI isn’t a sidecar, it’s the system, and IBM is building the infrastructure to make that system work across every layer of the modern business.

This feature is part of SiliconANGLE Media’s exploration of IBM’s market impact in AI-powered business operations. (* Disclosure below.)

AI becomes the operating model, not just the accelerator

What sets IBM apart isn’t just its AI investments; it’s the way the company threads AI through operational decisions, cross-functional models and business value delivery. During the March event, IBM leaders showcased how AI now shapes the structure of work, not just the speed of it. From outcome-based sourcing and real-time customer interaction to sustainability-linked financial strategies, AI is being embedded across the enterprise as a coordinating force, according to Tony Menezes, global managing partner of business process operations, IBM Consulting, at IBM. These changes illustrate IBM’s AI strategy to turn AI into a decision-making layer, not just a productivity boost.

“Reimaging the way work gets done is a harder part of the discussion,” Menezes told theCUBE. “It’s an easier presentation or a sales pitch to make to a given client. It’s them internalizing it and getting it into their entire organization that is the harder yards to walk through.”

Customer-facing operations are also being rebuilt from the inside out. IBM’s use of agentic AI in contact centers shows how generative models can go beyond basic automation to create contextual, real-time user experiences at scale. Agentic systems shorten deployment cycles, reduce labor strain and even outperform traditional call centers in user satisfaction, signaling a shift in both client expectations and internal delivery models, according to Glenn Finch, global managing partner of cognitive and analytics at IBM. This reimagining of user experience is a direct outcome of IBM’s AI strategy to scale contextual intelligence into client-facing operations.

“A lot of clients are saying, ‘OK … we’re going to let, not a chatbot, but a digital virtual agent that can have a generative conversation with you, we’ll let them take calls,’” he told theCUBE. “The strange part is what we’re finding is that the Net Promoter Score, the client-perceived value of those channels is actually higher than the call center.”

The same logic applies to sustainability initiatives. Cemex Inc., working in partnership with IBM, now uses AI and middleware analytics to integrate decarbonization targets into its core financial operations. Executive compensation is tied to emissions metrics, data lakes feed middleware models and environmental performance is inseparable from long-term profitability, according to Maher Al-Haffar, chief financial officer of Cemex. By aligning environmental goals with financial structure, IBM’s AI strategy demonstrates a new kind of purpose-built operational alignment.

“We’re just scratching the surface,” he told theCUBE. “I’m very excited about the future of using technology and particularly AI. The faster computing gets and the cheaper it becomes, the more we can use it to almost act as a second brain for us while we’re conducting our business.”

Rather than fearing AI, IBM’s HR teams have leaned into it, building cross-functional fluency and creating so-called “purple squirrels” that speak both technical and business languages, according to Jill Goldstein, global managing partner of HR and talent transformation at IBM. These employees are critical to embedding AI across workflows and accelerating adoption, and these talent transformation efforts support IBM’s AI strategy to cultivate AI fluency across the workforce.

“Inside of IBM, one of the first things that we did when generative AI really became very popular is [that] we challenged our workforce to opt into a workforce challenge, an old-school hackathon,” Goldstein said during the March event. “We asked them to roll up their sleeves and reimagine how they would work with a digital buddy, with generative AI helping them in their day-to-day activities.”

AI strategy: IBM’s path to trustworthy, scaled AI

After setting the foundation for AI as the operating model in March, IBM’s AI strategy advanced in early May during MWC25, which was all about making that vision actionable. Across telecom, enterprise workflows and application management, the company is moving beyond broad large language models toward small, specialized and goal-driven models — some tailored specifically for time-series forecasting and event prediction. These tightly scoped systems are built to predict, decide and act at enterprise scale, according to Andrew Coward, general manager of software networking at IBM. These innovations reflect IBM’s AI strategy to address gaps in time-series prediction and domain-specific modeling.

“Many people don’t understand that LLMs really don’t understand time-series data, because it doesn’t understand the difference between what happened yesterday versus what happened 10 years ago,” Coward told theCUBE during MWC. “There’s new models, and IBM’s built one called Tiny Time Mixer. Very small parameters, [a] million parameters, and they understand time. We can take network data, and then we can apply it to weather information or TV schedules. Then we can make predictions about what’s likely to happen.”

Also in early May, during the AI Agent Builder Summit, IBM explored how causal AI can give autonomous agents the ability to understand why an event occurred, not just what happened. This causal capability helps AI systems provide not only next-step recommendations but also justifications grounded in enterprise context, bringing deeper decision intelligence to workflows, according to Stuart Frost, founder and chief executive officer of Geminos Software, who was joined by Michael Garas, AI partnerships leader at IBM, during and interview with theCUBE. Cause-and-effect reasoning is a key component of IBM’s AI strategy to deliver explainable, contextualized decision intelligence.

“What we’re finding is that we need to underpin all of this with causal AI, which really gives you, for the first time, the ability to make truly data-driven decisions and put them in the context of the overall enterprise challenge,” Frost told theCUBE during the event. “What we’re doing is underpinning the agents and the LLMs with that causal information so that they can help us make some of the easier decisions. Not all decisions are complex, but we do need that foundation.”

IBM is also reinforcing operational trust through AI-powered observability. At this year’s Red Hat Summit, IBM detailed how its Instana Observability platform integrates with watsonx to generate plain-English summaries of issues and trigger automated remediation. By shortening the path from detection to resolution, these tools boost uptime and resilience, according to Chris Farrell, group product manager of Instana Observability at IBM.

“Last month, we released a new version of that, where you can have watsonx generate actions for you, for something that you might not have seen before and that we haven’t necessarily curated for you,” Farrell told theCUBE in May. “That way, you can start taking advantage of what we’ve learned and know from previous incidents and actions that we’ve taken.”

These trust-first foundations paved the way for IBM’s next leap: Orchestrating AI agents at scale, a core element of IBM’s AI strategy for delivering trustworthy automation.

Systemic intelligence at scale: IBM’s AI strategy to unify the digital enterprise

At IBM Think 2025, the company moved from foundational infrastructure to full-scale orchestration, unveiling a bold vision for agentic AI across hybrid enterprise environments, according to Hebner. Rather than simply theorizing about the future of AI, IBM’s AI strategy came to life through semantic tooling, open frameworks and a growing partner ecosystem designed to unify digital labor at scale.

“We’re seeing a critical inflection point,” Hebner observed. “IBM is applying the same interoperability playbook that made it a leader in e-business and hybrid cloud.”

Central to this strategy is watsonx Orchestrate, IBM’s semantic control plane for managing autonomous agents across on-prem, cloud, and software-as-a-service systems. Through its new Agent Connect partner program, IBM is inviting SaaS vendors, integrators and developers to contribute agents built on any stack, plugging them directly into the orchestration framework and unlocking observability, governance and semantic interoperability. This modular architecture targets a stark enterprise reality: Labor represents over 60% of operational costs, while software spend lingers near five percent, according to Hebner.

“Rather than forcing a top-down agent architecture, IBM is enabling composability,” Hebner said. “That’s a future-proof play.”

IBM’s growth strategy also leans heavily on its partner ecosystem, which includes resellers, independent software vendors, integrators and startups, according to Zeus Kerravala, principal analyst at ZK Research. These partners contribute to use case development, ideation and enterprise adoption, especially as AI and quantum technology mature.

“There’s no question that IBM does enterprise well, and because of that, its partner ecosystem is filled with other companies that target large companies,” Kerravala wrote in a SiliconANGLE.com article. “However, startups are often more innovative and agile and can help create new use cases for emerging technology. As AI matures and quantum becomes real, I’m expecting to see IBM continue to diversify its partner ecosystem to bring new use cases to companies large and small.”

IBM’s orchestration strategy extends beyond agent infrastructure into full-stack integration. The company’s watsonx portfolio — spanning data, AI and governance — anchors a modular architecture that supports business-ready orchestration across hybrid environments, according to Sanjeev Mohan, principal analyst at SanjMo.

“Although there are a lot of new products … I think with watsonx.data, watsonx.governance [and] watsonx.ai, they’ve got the three pillars,” Mohan said. “They’ve got data, AI and governance. Watsonx Orchestrate is the layer they’re putting on top to orchestrate different pieces and help business users develop AI pieces.”

As enterprises pursue more complex agentic AI use cases, platform maturity, interoperability and embedded decision intelligence are becoming decisive factors. IBM’s commitment to openness, governance and orchestration positions it as a strong contender in this evolving space, but success hinges on delivering trusted AI agents that can reason, adapt and integrate across a fractured digital estate, according to Hebner. These long-term moves affirm IBM’s AI strategy to unify digital labor, infrastructure and intelligence under one modular, scalable architecture.

“IBM’s unique position in enterprise AI and cloud didn’t emerge overnight,” Hebner said. “It’s the result of a deliberate, long-term strategy. IBM made hybrid cloud the backbone of its vision with Red Hat in 2019. Now, they’re doubling down on enterprise software, leveraging over a decade of innovation in AI for business to integrate, orchestrate and automate complex business operations. Add in its consulting practices and expanding partner ecosystem, and it’s clear that IBM is applying systemic intelligence at scale to unify the digital enterprise.”

(* Disclosure: TheCUBE is a paid media partner for the “AI-Powered Business Operations: Strategies for End-to-End Transformation” event. Neither IBM Corp., the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

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