Hi everyone—welcome back to EdgeBytes from The Enterprise Edge, where you get signal over noise in the enterprise AI era.

These days, we are witnessing a structural realignment of enterprise software economics—and if you’re still evaluating vendors on features alone, you’re already behind.

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In the span of a few days, three moves landed that, taken together, redraw some of the lines on the enterprise AI map: SAP’s acquisition of Reltio, IBM closing its $11 billion acquisition of Confluent – the largest deal of 2026 so far, and IFS introducing an asset-based pricing model explicitly designed to accelerate Industrial AI adoption.

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Individually, each is logical. Collectively, they expose where value is actually being created—and how vendors intend to capture it.

I’m Mark Vigoroso, founder & CEO of The Enterprise Edge, and today we’ll quickly break down the significance of these events and what customers, partners, and competitors should take away.

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Let’s start with IFS, because this is the most commercially disruptive.

IFS has eliminated user-based pricing in favor of asset-based pricing—charging based on the physical or operational assets a company manages rather than the number of users interacting with the system. As CEO Mark Moffat put it, “We’re not pricing the workers. We’re pricing the work.”

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That’s not a pricing tweak. That’s a redefinition of the economic unit of enterprise software.

For decades, SaaS scaled revenue with headcount. More users meant more licenses. But AI breaks that logic. In an agentic environment, value is no longer tied to human interaction—it’s tied to autonomous execution across systems, assets, and workflows.

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IFS is aligning its revenue model to that reality.

And critically, they’re removing the friction that has quietly constrained AI adoption inside industrial enterprises: the fear that scaling automation will explode software costs. Their model flips that dynamic—predictable cost tied to assets, not usage, not seats, not automation volume.

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That unlocks something bigger: enterprise-wide deployment without economic hesitation.

Analysts are lauding this model, saying it “helps companies operationally scale their investment to the value levers it needs to run the business.”

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That phrase—value levers—is doing a lot of work. Because it yet another signal of the shift from software as a tool to software as an operating system for outcomes.

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Now contrast that with SAP.

SAP’s acquisition of Reltio is about control over data coherence at scale. Reltio’s strength is master data management across fragmented enterprise landscapes—customers, suppliers, products, assets. SAP is effectively saying: if AI is going to drive decisions, then the integrity of the data layer becomes non-negotiable.

This is SAP reinforcing its Clean Core narrative—but extending it into AI readiness.

Because here’s the constraint SAP is solving: you can’t scale intelligent automation on inconsistent, duplicated, or poorly governed data. And SAP knows that in large enterprises, especially those mid-transition to S/4HANA, that problem is everywhere.

So Reltio becomes a strategic control point—not just for governance, but for enabling SAP’s Joule AI and broader Business Data Cloud strategy to operate on trusted, unified data.

SAP is betting that the winner in enterprise AI is not the model—it’s the system that ensures the model is operating on reliable context.

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Now bring in IBM.

IBM’s $11 billion acquisition of Confluent is about something different: real-time data movement and event streaming at enterprise scale.

Confluent isn’t just Kafka infrastructure—it’s the connective tissue for live, continuous data flows across applications, systems, and environments. And that matters because AI doesn’t create value in batch mode. It creates value in motion—when decisions are made in real time, based on continuously updated signals.

IBM is assembling an architecture where watsonx sits on top of a real-time data backbone. That’s the play.

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So now step back.

IFS is aligning pricing to operational outcomes.
SAP is consolidating control over data integrity.
IBM is investing in real-time data velocity.

Three different layers of the same equation: value is created when data moves fast, stays clean, and drives action at scale—and when the commercial model doesn’t inhibit that process.

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Now let’s talk about relative positioning.

IFS has momentum in asset-intensive industries—energy, aerospace & defense, manufacturing, field service—because it is closest to where physical operations meet digital systems. Their asset-based pricing model is likely to resonate strongly in environments where AI drives maintenance, scheduling, and production outcomes. Expect accelerated adoption in industries where asset utilization directly ties to revenue.

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SAP remains structurally dominant in large enterprises because of its installed base and control over core financial and operational systems. The Reltio acquisition strengthens SAP’s ability to unify data across that landscape, which is essential for scaling enterprise AI. But SAP still carries the weight of complexity—its success depends on how quickly customers can operationalize that data layer post-S/4 transition.

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IBM is positioning itself as the enterprise AI infrastructure provider—trusted, scalable, and increasingly real-time. The Confluent acquisition fills a critical gap in IBM’s stack: data in motion. But IBM’s challenge remains execution velocity—integrating these capabilities into a cohesive, consumable platform that enterprises can deploy without heavy lift.

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Now, what does this mean for the market?

First, pricing models are going to fragment—and then converge around value realization.

IFS has taken a decisive step in decoupling revenue from user counts – hooking to assets, not outcomes or consumption - a multi-threaded trend my April 1st post explores in further detail. Expect others to follow—but unevenly. Vendors with legacy pricing models tied to seats or transactions will face increasing pressure as AI reduces the relevance of human interaction as a pricing anchor.

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Second, data architecture is becoming the gating factor for AI ROI.

SAP and IBM are both making moves that acknowledge this: without clean, unified, real-time data, AI remains a pilot—not a production system.

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Third, the competitive battlefield is shifting from applications to systems of action, evidence of which builds with each passing day.

It’s becoming crystal clear that the vendors that win will not be those with the most features—but those that can orchestrate decisions, automate workflows, and align commercial models with outcomes.

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Now let’s bring this down to what actually matters for buyers.

For CEOs: your question is no longer “Which vendor has the best AI?” It’s “Which system allows AI to scale without economic or operational friction?” If your software model penalizes usage, or your data architecture limits deployment, you will not capture the full value of AI—no matter how advanced the technology.

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For CFOs: the shift is from fixed licensing to dynamic value alignment. You need to evaluate whether pricing models scale with outcomes or with activity. According to IDC, enterprise AI spending is projected to grow at over 20% CAGR through the decade—your job is to ensure that growth translates into measurable economic return, not just expanding cost bases. Models like IFS’s asset-based pricing introduce predictability—but they also require new financial instrumentation tied to operational performance.

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For CIOs: your leverage has increased—but so has your accountability. You are now orchestrating three critical layers simultaneously: data integrity, data velocity, and system execution. SAP’s data consolidation, IBM’s real-time streaming, and IFS’s operational alignment each solve part of the equation—but no single vendor solves all of it. Your architecture decisions will determine whether AI remains fragmented or becomes systemic.

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And here’s the closing reality:

Enterprise AI value is not determined by how much technology you deploy. It’s determined by how quickly and cleanly that technology translates into operational outcomes—without introducing friction into cost, data, or execution.

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IFS is removing cost friction.
SAP is addressing data friction.
IBM is reducing latency friction.

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The companies that understand how to integrate all three will not just adopt AI—they will operationalize it at scale.

And that’s where the advantage will show up—in revenue, in margin, and in time-to-value.

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That’s all for now. Thank you for being with us. Would love to hear your reactions, experiences, and other thoughts. Leave a like, share this video or drop a comment below. See you on the next episode of EdgeBytes. Signal over noise.