Enterprise Digital Transformation Weekly: AI-Native Networks and Process Mining Set the Pace (Feb 26–Mar 5, 2026)

Digital transformation talk often collapses into two clichés: “move to the cloud” and “add AI.” This week’s enterprise technology and cloud-services news shows what it looks like when organizations try to operationalize both—at scale, under real constraints, and with legacy gravity pulling hard.

On one side, Mondelēz International’s SAP modernization highlights a pragmatic truth: transformation is less about swapping software labels (ECC to S/4HANA) and more about understanding how work actually flows across a fragmented enterprise. By selecting Celonis as a vendor-neutral process backbone to model business processes during its SAP migration, Mondelēz is effectively treating process visibility as a prerequisite for system change—not a byproduct of it. That’s a notable shift in sequencing: map reality first, then migrate. [2]

On the other side, Mobile World Congress Barcelona 2026 brought a telecom-flavored but enterprise-relevant message: AI is being pushed down into the infrastructure layer. ZTE’s “full-stack AI” positioning and its AIR MAX framework argue that networks themselves must become AI-native—optimized not just for throughput, but for autonomy, efficiency, and new monetization models. The pitch is that AI-era services will demand AI-era connectivity and operations, with AI embedded into network architecture and day-to-day running. [1][3][4]

Taken together, these stories point to a maturing phase of digital transformation: less “pilot AI,” more “re-architect the systems and processes AI depends on.” The week’s signal is clear—enterprises are moving from experimentation to structural change, where process mining and AI-native infrastructure become foundational tools rather than optional add-ons. [1][2][4]

Mondelēz’s SAP Overhaul: Process Mining as the Transformation Backbone

Mondelēz International’s decision to use Celonis to model business processes during its migration from SAP ECC to S/4HANA is a telling move in how large enterprises are de-risking ERP transformation. Rather than relying solely on the ERP vendor’s view of the world, Mondelēz is leaning on a vendor-neutral process mining platform to understand and optimize operations that are described as complex and fragmented. [2]

What happened is straightforward: Celonis is being used as the process “backbone” for the SAP overhaul—an explicit acknowledgment that the hardest part of ERP modernization is not the technical cutover, but the operational truth-finding that precedes it. Process mining, in this framing, becomes a discovery and governance layer: it models how processes run today, where they diverge across business units, and where bottlenecks or inconsistencies might undermine a clean migration. [2]

Why it matters for enterprise technology and cloud services is that S/4HANA migrations are often paired with broader cloud and platform decisions, but those decisions can be undermined by poor process clarity. A vendor-neutral tool can help avoid “ERP-shaped blind spots,” where teams assume the new system will fix process issues that are actually organizational or cross-system in nature. [2]

The expert takeaway embedded in this move is sequencing: treat process intelligence as a first-class artifact of transformation. If you can’t model the process, you can’t reliably standardize it; if you can’t standardize it, you can’t fully capture the value of a modern ERP platform. In real-world terms, this approach aims to reduce surprises—like discovering late that different regions run materially different order-to-cash flows—when the migration clock is already ticking. [2]

ZTE’s Full-Stack AI at MWC: AI-Native Connectivity as a Digital Foundation

At MWC Barcelona 2026, ZTE showcased what it described as comprehensive AI solutions under an “All in AI, AI for All” strategy, emphasizing AI-native connectivity, efficient computing infrastructure, and smart home devices—positioned toward an open, secure, and inclusive digital future. [1] While the venue is telecom-centric, the implications land squarely in enterprise digital transformation: connectivity and compute are being reframed as AI-era primitives, not just utilities. [1]

What happened this week is ZTE’s attempt to present AI as a full-stack proposition—spanning network connectivity and computing infrastructure—rather than a set of isolated applications. That matters because enterprise AI initiatives increasingly fail or stall not due to model choice, but due to infrastructure mismatch: latency, reliability, and operational complexity become the limiting factors when AI moves from demos to production. ZTE’s framing suggests the network itself must be designed with AI workloads and AI-driven operations in mind. [1]

Why it matters to cloud services is the implied shift in where “cloud-like” capabilities live. If connectivity becomes AI-native, then the boundary between cloud platform and network platform blurs: performance, policy, and automation can be enforced closer to where data is generated and consumed. ZTE’s emphasis on efficient computing infrastructure also underscores that AI-era transformation is constrained by cost and efficiency, not just ambition. [1]

The expert take: “full-stack AI” is as much an organizational claim as a technical one. It signals a push toward integrated stacks where vendors aim to supply more of the end-to-end path—from infrastructure to operations. For enterprises, the practical impact is a renewed need to evaluate how network strategy, compute strategy, and AI strategy interlock, because weaknesses in any layer can cap the value of the others. [1]

AIR MAX and the Push Toward Autonomous, Efficient Networks

ZTE’s AIR MAX announcements at MWC Barcelona 2026 sharpened the infrastructure story into a specific operational thesis: mobile networks should be optimized “for the AI era” by improving energy efficiency, labor efficiency, and investment efficiency, while integrating AI deeply into network operations. The stated ambition is to help transform telecom operators into technology companies. [3]

A companion description of AIR MAX frames it as a three-tier, full-stack AI capability aimed at upgrading mobile network architecture, operations, and business models. The framework includes AI-native infrastructure, L4 autonomous network operations, and monetization engines—positioned to turn mobile networks into “AI engines.” [4] Even if your enterprise isn’t a carrier, the pattern is familiar: autonomy, efficiency, and new revenue models are being bundled into a single transformation narrative. [4]

Why it matters for digital transformation is that “autonomous operations” is no longer confined to data centers and cloud management consoles. The same logic—use AI to reduce manual toil, improve reliability, and optimize cost—has moved into the network layer. AIR MAX’s focus on labor efficiency is especially telling: it treats operational staffing and complexity as a core constraint, not an afterthought. [3]

The expert take is to read “L4 autonomous network operations” as a maturity marker: vendors are now marketing specific autonomy levels, implying a roadmap from assisted operations to more self-directed systems. [4] For enterprises consuming network services, the real-world impact could be indirect but significant: if networks become more autonomous and efficient, service quality and cost structures may shift, and new AI-era services may become feasible. But it also raises governance questions—when AI is “deeply integrated” into operations, accountability and control mechanisms must keep pace with automation. [3][4]

Analysis & Implications: Transformation Is Moving Down the Stack—and Up the Process Chain

This week’s developments sketch a two-front digital transformation: enterprises are moving “up the process chain” to gain operational truth, while infrastructure providers are moving “down the stack” to embed AI into connectivity and operations.

Mondelēz’s Celonis-backed SAP migration approach is a reminder that transformation programs fail when they treat process as documentation rather than data. By using a vendor-neutral process mining tool to model processes during the ECC-to-S/4HANA transition, the company is prioritizing visibility into fragmented operations before committing to a future-state design. [2] That’s a governance move as much as a technology move: it creates a shared reference for what must be standardized, what can remain differentiated, and where optimization is possible.

Meanwhile, ZTE’s MWC messaging argues that AI-era transformation requires AI-native connectivity and AI-driven operations. The “full-stack AI” framing and AIR MAX’s three-tier architecture (AI-native infrastructure, L4 autonomous operations, and monetization engines) suggest vendors see AI not as an application layer, but as an organizing principle for the entire service stack. [1][4] AIR MAX’s explicit focus on energy, labor, and investment efficiency also grounds the AI conversation in operational economics—an important correction to AI hype cycles that ignore run costs and operational burden. [3]

Put together, the connective tissue is operationalization. Process mining operationalizes transformation planning by turning messy reality into analyzable models. AI-native networks operationalize AI-era services by making the underlying infrastructure more autonomous and efficient. [2][3] Both approaches also hint at a shift in buyer expectations: enterprises will increasingly demand measurable efficiency and reliability outcomes, not just feature checklists.

The broader implication for enterprise technology and cloud services is that “digital transformation” is becoming less about adopting a single platform and more about aligning multiple layers: process intelligence, core systems modernization, and AI-ready infrastructure. This alignment pressure will likely intensify as organizations try to scale AI beyond pilots—because scaling AI stresses both the business process layer (where value is realized) and the infrastructure layer (where performance and cost are determined). [1][2]

Conclusion: The New Baseline Is Measurable Operations

This week’s signal is that digital transformation is settling into a new baseline: measurable operations, not aspirational architecture diagrams.

Mondelēz’s use of Celonis during its SAP ECC to S/4HANA migration underscores that enterprises are treating process understanding as a prerequisite for modernization—especially when operations are complex and fragmented. [2] In parallel, ZTE’s MWC Barcelona 2026 announcements push AI deeper into the infrastructure narrative, arguing for AI-native connectivity and increasingly autonomous network operations designed around efficiency and new business models. [1][3][4]

The takeaway for enterprise leaders is to watch where the “center of gravity” is moving. Transformation value is increasingly created (and lost) in two places: the process layer that determines how work happens, and the infrastructure layer that determines whether AI-era services are feasible at scale. This week didn’t deliver a single silver bullet—but it did show a consistent direction: the winners will be the organizations that can instrument reality, automate responsibly, and tie modernization to operational outcomes that can be measured and improved. [2][3]

References

[1] ZTE Unveils Full-Stack AI at MWC Barcelona 2026 — The Register, March 2, 2026, https://www.theregister.com/2026/03/02/zte-unveils-full-stack-ai-at-mwc-barcelona-2026/?utm_source=openai
[2] Mondelēz Picks Celonis as Process Backbone for SAP Overhaul — The Register, February 27, 2026, https://www.theregister.com/2026/02/27/mondelez_sap_migration_celonis/?utm_source=openai
[3] ZTE Launches AIR MAX Solution to Build the Optimal Mobile Network for the AI Era at MWC Barcelona 2026 — The Register, March 4, 2026, https://www.theregister.com/2026/03/04/zte-launches-air-max/?utm_source=openai
[4] ZTE AIR MAX: Reshaping Mobile Network Paradigm in the AI Era — The Register, March 3, 2026, https://www.theregister.com/2026/03/03/zte-reshaping-mobile-network/?utm_source=openai