IBM Just Admitted What Most Consultants Won't

The company that's been selling AI transformation to enterprises for years just revealed it had to build its own internal playbook before it could credibly help anyone else. That's either reassuring honesty or an indictment of how the industry has been operating. Possibly both.

IBM this week announced IBM Enterprise Advantage, a new consultancy service that gives enterprise clients direct access to the same AI platform its 160,000-plus internal consultants have been using since January 2024. The platform — called IBM Consulting Advantage internally — provides access to proprietary and third-party large language models, ready-to-deploy applications, AI agents, and industry-specific starter kits. Until now, clients got the consultants. Now they get the tools too.

IBM Consulting SVP Mohamad Ali acknowledged the logic plainly: "Most organizations are eager to invest in AI but struggle to achieve any real value from it. IBM faced similar challenges early on, but solved them with IBM Consulting Advantage. Enterprise Advantage brings this framework to clients."

A global consulting firm admitting it struggled with AI before figuring it out is either a moment of unusual candor or a very effective sales pitch. Given the PwC data showing 56% of companies globally getting nothing from AI investments, it's probably both.

What IBM Is Actually Offering

The architecture of IBM Enterprise Advantage is worth understanding precisely because it addresses the specific failure mode that's sinking most enterprise AI programs: the gap between pilots and scale.

Clients can redesign existing workflows and enhance them with AI agents and automations without major refactoring — meaning workloads stay on whatever cloud infrastructure is already in place, whether AWS, Google Cloud, Azure, or other platforms. That's a deliberate decision to remove the "rip and replace" objection that stalls most enterprise AI projects before they start.

The service is also hybrid by design: IBM's expert consultants remain available, but clients can take a more hands-on role using the platform directly. It's a recognition that the all-or-nothing model — either full consultancy dependency or go-it-alone implementation — has been failing organizations at scale.

Early clients include Pearson, the educational publishing firm, which used the platform to build a customized AI system combining human expertise with agentic AI assistants for daily operations and decision-making. An unnamed manufacturing client has moved from identifying high-value AI use cases to deploying prototypes within a governed, regulatory-compliant environment — the exact progression that most enterprises describe as aspirational and rarely achieve.

The Broader Signal: Enterprise AI Is Becoming Infrastructure

IBM's move reflects something happening across the industry: the conversation is shifting from "should we adopt AI" to "how do we make it actually work at the organizational level." The vendor landscape is consolidating around platforms that can bring models, deployment infrastructure, and services together in one place — rather than expecting enterprise clients to assemble the stack themselves.

Constellation Research analyst Holger Mueller framed it directly: "Such companies need three things: proven AI models, an AI platform and AI services — and that is essentially what IBM is offering here. Platforms that can bring AI models and services together are going to be critical for enterprise adoption."

That's the same conclusion the PwC data points to: the companies seeing real AI returns aren't the ones with access to better models. They're the ones with better implementation infrastructure and clearer governance. IBM is betting that selling that infrastructure — rather than just consulting hours — is the growth play for the next five years.

What This Means for Mid-Market and Growth Organizations

IBM Enterprise Advantage is built for enterprise scale, which means the immediate audience is large organizations with existing cloud infrastructure and complex workflows. But the template it represents is instructive for any organization building an AI strategy: the companies that are seeing returns aren't purchasing AI tools and hoping for the best. They're investing in the combination of platform, governance, and expertise that makes tools actually work.

For growth leaders, the takeaway isn't "hire IBM." It's that the execution layer — the workflows, governance frameworks, and human expertise that surround AI tools — is where the investment needs to go. The models are largely commoditized. The implementation strategy is where competitive advantage lives.

IBM figured that out internally before selling it externally. Most organizations are still waiting to figure it out at all.

Winsome Marketing helps growth leaders build AI strategies with the infrastructure to actually deliver — not just the ambition. Let's talk.