Today’s signals say the next enterprise winners will pair governed agentic playbooks with real infrastructure and a clearer story about how work actually changes

If you squint, most AI news falls into two buckets. People building bigger engines, and people figuring out how to steer them without driving into a wall. Today we got both. IBM wants to be the adult in the room for enterprise AI scale. McKinsey is trying to reframe AI from replacement to amplification. Operators are being reminded that shipping “more AI” is not the same as making better decisions. And the infrastructure arms race keeps accelerating, even when the headlines can’t decide whether they are about factories or molecules.

IBM is packaging consulting, governance, and reusable agent assets into a cloud and model agnostic platform service that looks built for the messy middle of enterprise adoption.

IBM announced IBM Enterprise Advantage, an asset based consulting service designed to help organizations build, govern, and operate internal AI platforms at scale, available January 26, 2026. The positioning is pragmatic. You do not need to change your cloud. You do not need to swap models. You do need a way to turn scattered pilots into something secure, repeatable, and actually shippable across business units.

Under the hood it rides on IBM Consulting Advantage, IBM’s internal AI powered delivery platform that IBM says has supported more than 150 client engagements and delivered up to a 50 percent productivity increase for consultants using it. Enterprise Advantage also includes a marketplace of industry specific AI agents and applications, which is IBM quietly acknowledging what buyers want now: not another model, but a catalog of governed capabilities that can be deployed with less reinvention and fewer compliance surprises.

For founders and investors, this is IBM making a serious bid in the post foundation AI consulting wars where the prize is not “who has the best LLM,” but “who owns the operating system for enterprise change.” Watch for two things next. First, whether IBM can translate marketplace agents into measurable time to value for specific vertical workflows. Second, whether cloud and model neutrality holds up once clients ask for deeper performance tuning, cost controls, and observability across mixed stacks like AWS, Azure, Google Cloud, and watsonx.

A CIO.com argument lands at the right moment because the competitive edge is shifting from rollout volume to decision quality and velocity.

The article’s core warning is simple: enterprises can deploy AI everywhere and still lose if their decision making does not improve. That sounds obvious until you’ve watched a company celebrate “thousands of Copilot seats” while approvals, pricing, hiring, and incident response remain as slow and political as ever.

This matters because the next wave of enterprise software positioning is moving up stack. Infrastructure and tools are table stakes. The durable moat is decision advantage: who can sense changes sooner, recommend better actions, and shorten the loop from signal to action without creating new risk. That is less about model IQ and more about how you instrument the business, define decision rights, and encode policies into workflows that humans actually trust.

What to watch is vendors and internal platform teams racing to own that layer. Expect more talk about decision intelligence, evaluation frameworks tied to business outcomes, and systems that can justify recommendations with traceability rather than vibes. If your product cannot show how it changes a specific decision and improves a measurable KPI, you are competing in the land of features and procurement fatigue.

OutSystems CEO Woodson Martin says the real blockers are talent scarcity, confusion about use cases, and the internal narrative required to get adoption.

Martin’s thesis is refreshing because it is operational, not theoretical. First, there is a deep hunger for talent that can build applications with a deep understanding of AI, meaning not just prompt literacy but the mix of domain knowledge, workflow design, and risk awareness that turns models into products. Second, vendor messaging has become so broad that it is creating confusion, collapsing meaningful distinctions between platforms that enable AI and tools that merely attach it.

The third blocker is the most painful and the most real: internal traction. CIOs often need help evangelizing, building a credible story for P and L owners, and proving momentum. Martin even quotes a CIO asking for help positioning OutSystems as an agent platform, with Workbench as a way to get traction with an agentic system. Translation: the buyer is not only purchasing software, they are purchasing political cover and a narrative that makes the business say, “wow, I could dramatically improve productivity.”

For operators, the takeaway is that enablement is becoming part of the product. The winning vendors will not just ship agent builders, they will ship repeatable demos that map to business outcomes, implementation paths that reduce perceived risk, and proof points that help champions sell internally. For founders, this is a go to market reminder. Your buyer needs a story that survives the finance meeting, not just a slick sandbox.

McKinsey frames the winning enterprise pattern as AI that amplifies employees, with leadership action as the limiting reagent.

McKinsey’s 2025 report on superagency argues that the dominant model is not replacement, it is empowerment, and it backs that with survey data from 3,613 employees and 238 C level executives collected in late 2024. The headline stats are telling. Ninety two percent of companies plan to increase AI investments, yet only 1 percent of leaders call their companies mature in AI deployment. Employees are also three times more likely than leaders estimate to use gen AI for at least 30 percent of daily work, which suggests adoption is already happening, just not through official channels.

The report also highlights the trust and measurement gap. Cybersecurity and inaccuracy top employee concerns, and only 39 percent of companies use benchmarks to evaluate AI systems. Even among those that benchmark, only 17 percent focus on fairness, bias, transparency, and compliance. That is a lot of spend chasing ROI with limited instrumentation, which is a polite way of saying many companies are driving fast with foggy windshields.

What to watch is whether “superagency” becomes a practical operating model rather than a keynote theme. McKinsey points toward executive alignment, federated governance, workforce training, and modular architecture as prerequisites to scale. For founders selling into enterprise, the implication is clear: products that slot into human workflows, provide explainability, and support governance will scale faster than tools that assume the organization will reorganize itself around your UI.

Even when the details blur, the direction is consistent: specialized infrastructure and tight hardware software integration are becoming competitive weapons.

The stated theme is an expanded collaboration to scale AI factories, meaning data centers purpose built for accelerated AI computation. The strategic implication is straightforward. Demand for AI workloads keeps rising, and the winners will be the ones who can deliver capacity with predictable performance, cost, and deployment speed.

The provided source details also highlight NVIDIA BioNeMo adoption in life sciences, including companies like Amgen and AstraZeneca, running on NVIDIA DGX Cloud. That may read like a different story, but it reinforces the same infrastructure point: NVIDIA is not just selling chips, it is selling vertically integrated stacks where the platform, tooling, and compute come bundled into something closer to an outcome. In pharma, that outcome is faster model development and inference for drug discovery workflows.

For investors, the thread to pull is how “AI factory” providers like CoreWeave change the hyperscaler calculus. Vertical integration and workload specific optimization can win share when speed to capacity matters more than vendor consolidation. For founders, this is a procurement reality check. Your inference cost curve, latency, and security posture may depend less on model choice and more on where and how you run it.

The next advantage is not just intelligence. It is industrial grade delivery of it.