Enterprises are moving beyond AI pilots, putting greater emphasis on governance, production readiness and measurable business outcomes.

Research from Deloitte highlights a significant gap between enterprise AI ambition and execution: 54% of organizations plan to move at least 40% of their AI initiatives into production within six months. Yet only 25% have done so today. IBM is betting on its strengths —  mainframe hardware, hybrid computing assets and a legacy of strong governance — to gain a leg up on the rest of the market, according to theCUBE Research’s Dave Vellante.

“In our opinion, IBM has found a differentiated path in enterprise AI by productizing workflow-level outcomes across a stack that, while sometimes confusing, customers generally trust,” he said. “Client Zero proof, a workload-first hybrid design, durable platforms reimagined for agents and a pragmatic quantum roadmap create a credible basis for sustained advantage.” 

This feature is part of SiliconANGLE Media’s exploration of how enterprise AI has moved beyond the race for model supremacy, with major technology players now focused on enabling governed enterprise AI that delivers trust, reliability and predictable business outcomes. (* Disclosure below.)

Building toward an enterprise AI operating model

IBM already has a substantial book of business around generative AI, with AI-related software sales and consulting engagements totaling more than $12.5 billion as of Q4 2025, up from $9.5 billion the previous quarter.

Through a series of investments, acquisitions and product launches, IBM is gathering assets for an enterprise AI operating model with four pillars: agent orchestration, trusted data, intelligent operations and governance.

Many of these capabilities were rolled out as part of a steady drumbeat of announcements leading up to IBM Think 2026 in May. In February, the company announced a partnership with Deepgram Inc. to integrate voice capabilities into watsonx Orchestrate, its enterprise platform for building, deploying and managing agents. Two months later, it made IBM Bob — an AI-assisted coding tool that helps developers plan, write, test, modernize and deploy code —  globally available.

In May 2026, IBM completed its acquisition of Confluent, the data streaming platform, to enhance watsonx.data. By integrating streaming and real-time data, IBM aims to make watsonx.data the trusted data foundation, providing AI agents with continuously updated context. 

At Think 2026, the company repositioned Concert from a point product into an agentic IT operations platform designed to unify observability, optimization, security and automation across hybrid environments. The move positions Concert as the intelligent operations layer of IBM’s broader enterprise AI strategy. 

The IBM Sovereign Core platform announced at Think 2026 is designed to address the final pillar: governance. It helps enterprises build and operate AI-ready sovereign environments by embedding governance, compliance and operational control directly into the underlying infrastructure.

June brought more AI-related announcements: IBM joined OpenAI’s Daybreak Cyber Partner Program, which aims to identify AI-driven cybersecurity threats. IBM has partnered with Google Cloud to help IBM consultants design, build and govern enterprise-grade agents on Google Cloud.

All these announcements paint a picture: IBM wants to be the one-stop shop for enterprise AI, and to do so, it is investing significantly in new capabilities while leaning on its reputation for trusted enterprise security and governance, according to theCUBE Research’s John Furrier.

“While others chase frontier models, IBM is betting on something harder: trusted AI in production,” he told theCUBE. “The question coming out of Think is simple — are they becoming the system of record for enterprise AI or just another layer in the stack?” 

IBM’s legacy infrastructure finds new relevance in the AI era

In a media briefing ahead of Think 2026, IBM Chief Executive Arvind Krishna pointed out that over 70% of all enterprise data remains locked up in on-premises systems. That makes hybrid cloud a critical component of IBM’s enterprise AI operating model, allowing enterprises to use corporate data for AI workloads without abandoning existing infrastructure.

Recent earnings reports appear to reinforce IBM’s bet on hybrid cloud in the AI era. Red Hat Inc., whose OpenShift Kubernetes platform underpins IBM’s hybrid cloud software portfolio, saw revenue increase 13% year over year. Meanwhile, revenue from IBM Z, the company’s mainframe platform, saw revenues grow 51% year over year, underscoring the continued importance of mainframes in IBM’s infrastructure business.

IBM is embedding AI technology directly into IBM Z mainframes to support demanding AI workloads, according to Ric Lewis, senior vice president of the Infrastructure Division at IBM.  This strategy has resonated with clients and led to 20%-30% program-to-program growth for almost a decade. Z mainframes, a key priority for enterprises looking to deploy AI while maintaining control over their data and systems, are also a pillar of IBM’s sovereignty strategy.

“Sovereignty is our home court. We do 70% of the world’s financial transactions,” Lewis told theCUBE. “The most important data that travels around the world is in our Z systems. Those are sovereign systems. They’re on-prem, they’re in environments, they’re in banks, they’re in financial institutions, they’re in insurance. Sovereign is our home court, and we know this space extremely well.”  

Sovereignty becomes a prerequisite for enterprise AI

IBM’s view of sovereignty centers on customer control: keeping critical data and AI workloads in environments customers own and govern, rather than depending entirely on third-party infrastructure. Javier Olaizola, global managing partner for hybrid cloud and data at IBM Consulting, explained that the topic is coming up earlier in client engagements.

“Sovereignty and exposure was coming in on slide 40 in a PowerPoint pitch,” he told theCUBE. “Now it’s coming up front: ‘How much is my exposure?’ That is coming up with a new set of conversations that we are having with clients.” 

Sovereign Core is the company’s answer to those questions, according to Sripriya Srinivasan, general manager for Core and ALM software products at IBM. Its goal is to help companies maintain operational consistency even as AI regulations and geopolitics constantly shift. It also helps companies contend with massive AI agent sprawl.

“The proliferation of agents is real in enterprises. Everybody is starting to build agents,” Srinivasan told theCUBE. “How do you, as a [chief information officer], make sure that there’s a level of standardization, consistency, governance, orchestration? All of these are internal fears, not necessarily coming, just based on regulations.”  

Putting the enterprise AI operating model to work

How does this all translate to real-world results? It’s useful to look back at Krishna’s comments from Think 2026, where he presented IBM’s point of view on what makes a successful enterprise AI strategy. 

“The enterprises pulling ahead are not deploying more AI; they’re redesigning how their business operates,” Krishna said during a media briefing. 

IBM sets its own example by viewing itself as Client Zero for its enterprise AI strategy, according to James Kavanaugh, senior vice president and chief financial officer of IBM.

“AI is now embedded across our business, from how we deliver services to our software portfolio to the capabilities we are adding to our infrastructure platforms and how we drive our own productivity,” he said.

IBM reports that more than 80,000 of its employees use IBM Bob, with users reporting more than a 45 percent increase in productivity. Additionally, IBM Consulting has deployed more than 4,000 digital workers across 450 active projects, including agents from watsonx, Anthropic PBC and OpenAI Group PBC.

To keep this digital workforce under control, the company relies on a management philosophy that treats AI agents like human workers, an idea that Krishna himself suggested, explained Mohamad Ali, senior vice president and head of IBM Consulting.

“Arvind called me, and we had a conversation about three years ago, and he said, ‘I want you to come over, and I want you to be a consulting business,’” he said in an interview with theCUBE. “‘What is digital labor? It’s a whole bunch of bits of software. I need you to come in, and I need you to build this set of software and do it in a way that it could be managed. You could think of this as HR management … but now you have to HR manage human workers and digital workers.’”  

IBM customers are also putting elements of the enterprise AI operating model into practice. Global bank BNP Paribas SA has nearly 1,000 AI use cases underway across its operations, using a group-level framework with governance distributed across business units and functions.

“The strategy is to use AI, not to become AI — to use AI to better serve our customers and to give more value to them,” Jean-Michel Garcia, group chief technology officer at BNP Paribas, told theCUBE. “But also internally, for employees, to give them more capabilities to focus on the right set of tasks and to make them more efficient and more focused on what they do.” 

(* Disclosure: TheCUBE is a paid media partner for the IBM Think event. Neither IBM, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

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