IBM's Enterprise AI Strategy: Trust, Scale, And Results
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Watsonx AI IBM generative AI platform displayed on a smartphone. On 10 August 2023 in Brussels, ... More Belgium. (Photo illustration by Jonathan Raa/NurPhoto via Getty Images)
BM has rapidly established itself as a serious enterprise AI contender. It combines a full-stack platform strategy, proprietary models, deep integration with Red Hat hybrid cloud infrastructure, and global consulting scale. It's executing a multi-pronged approach that is already delivering operational leverage and financial upside.
Its approach is paying off. In its most recent earnings, IBM disclosed that it'd grown its book of AI-related business to $5 billion in less than two years, with approximately 80% of that stemming from consulting engagements and the remainder from software subscriptions.
IBM detailed its AI strategy at its recent investor day. It's a strategy centered on a pragmatic, enterprise-first approach that can deliver trusted, efficient, and domain-relevant AI solutions. IBM's AI straetgy brings together infrastructure software from Red Hat, foundation models from IBM Research, customer enablement capabilities from IBM Consulting, and integration with a broad ecosystem of partners.
Unlike some competitors focused on developing massive general-purpose models, IBM's bet is on smaller, specialized models, deployed across hybrid cloud environments, and tightly integrated with its consulting services and data platforms. The goal is to help businesses operationalize AI in a way that's scalable, secure, and aligned with real-world enterprise needs.
This is an approach particularly well-suited for companies in regulated industries — such as financial services, healthcare, and government — where data security, governance, and compliance concerns are paramount.
At the core of IBM's AI stack is watsonx, an end-to-end platform designed to support the entire AI lifecycle. Watsonx allows businesses to build and train models using both IBM's proprietary tools and open-source models while also enabling them to fine-tune those models using their proprietary data.
One of the most critical components of this platform is Granite, IBM's family of smaller, purpose-built foundation models tailored for enterprise use cases like code generation, document processing, and virtual agents.
These cost-efficient, interpretable models are built to perform well in sensitive, highly regulated environments. IBM has even open-sourced several Granite models to support transparency and community-led development.
IBM's AI technology is further strengthened by its integration with Red Hat's hybrid cloud tools. OpenShift AI and RHEL AI provide the infrastructure to build, deploy, and manage AI applications across on-premises, private, and public cloud environments. This hybrid model offers flexibility for enterprises that need control over their data while still wanting the agility of cloud-native services.
Global system integrators are integral to helping IT organizations navigate complex new technologies, especially enterprise AI. Enterprises often struggle to understand the new technology while also attempting to extract value quickly. GSIs thrive in this market, promising quick time-to-value for AI transformation projects.
A defining strength of IBM's approach is the synergy between its AI stack and its global consulting business. IBM Consulting, with its 'hybrid by design' approach, is central in driving client adoption of watsonx and Granite. This helps enterprises bring AI into mission-critical workflows across HR, procurement, customer service, and supply chain operations.
IBM Consulting competes directly against companies like NTT DATA, Deloitte, Cognizant, and Capgemini. Each of these companies has AI platforms in place and AI-specific engagement models that offer a compelling choice for enterprises.
Partnerships play a critical role in IBM's AI strategy. The company has built a rich ecosystem of collaborators that includes hyperscalers, chipmakers, open-source communities, and enterprise software vendors.
Rather than trying to build and control every component internally, IBM focuses on integrating and orchestrating AI capabilities across a broad range of technologies. This strategy enables IBM to deliver value through its innovations and the strength of its partner network.
A example of this is IBM's integration of watsonx with platforms like SAP, Salesforce, and ServiceNow. Operating within familiar business applications allowsd customers to leverage IBM's AI without disrupting existing workflows. T
Collaboration extends to the systems integrators and hardware vendors that form the backbone of many enterprise deployments. IBM is working alongside companies like Dell, Lenovo, and Nokia to deliver AI-ready infrastructure, and has formed go-to-market alliances with integrators and resellers to accelerate customer adoption.
Financially, IBM's AI bets are translating into real momentum. In its latest earnings release, the company reported that its book of AI business has grown to over $5 billion, and its software division posted double-digit growth in 2024 — its strongest in years — mainly fueled by demand for AI and hybrid cloud solutions.
Free cash flow climbed to $12.7 billion, and IBM reports that for every dollar spent on watsonx, clients invest five to six dollars more across IBM's broader software and consulting portfolio. This multiplier effect highlights the strength of IBM's integrated offerings.
Most AI-related revenue still comes from consulting, reflecting the power of IBM's services-led go-to-market model. However, the company's strategy of combining Red Hat infrastructure, watsonx software, and consulting expertise is clearly gaining traction.
The tight integration of its software, infrastructure, and services sets IBM apart in the enterprise AI space. Red Hat's OpenShift and RHEL AI form the infrastructure foundation of IBM's AI strategy, powering the deployment of watsonx across diverse cloud and edge environments.
IBM Consulting brings the human element, delivering AI solutions tailored to industry-specific challenges in sectors such as banking, healthcare, manufacturing, and government. Together, these arms of IBM provide the technological muscle and domain expertise needed to bring AI from concept to production at enterprise scale.
IBM's end-to-end approach, spanning model development, deployment, governance, and business transformation, is a strategy that's clearly working. It's also a strategy that's difficult for competitors to match.
As bookings grow, platform adoption accelerates, and ecosystem partnerships deepen, IBM is reshaping its identity around AI, hybrid cloud, and consulting. The company's ability to commercialize AI through a tightly connected stack of products, platforms, and people makes it one of the most interesting and credible enterprise AI players today.
Disclosure: Steve McDowell is an industry analyst, and NAND Research is an industry analyst firm, that engages in, or has engaged in, research, analysis and advisory services with many technology companies; the author has provided paid services to many of the companies named in this article in the past and may again in the future, including IBM. Mr. McDowell does not hold any equity positions with any company mentioned.
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