Stop buying AI and start operationalizing how work gets done

Enterprise AI is stuck in its “demo decade.” Generative AI models are improving rapidly, and copilots are everywhere. Yet for leaders who own the operational core across procurement, finance, supply chain, and IT, there are pilots everywhere, but no compounding value. HFS estimates that around 75% of firms remain stuck in pilot purgatory. This POV is for enterprise leaders accountable for how work actually gets done across those functions, typically COOs, CFOs, CIOs, and heads of shared services.

Should you keep funding isolated AI pilots and point solutions, or build an execution layer that ties operational truth to governed action? We argue you should stop buying more AI pilots and start funding “Enterprise AI Execution” as the missing operating layer.

Exhibit 1: Bolt GenAI onto fragmented data, and you get AI theater

Source: HFS Research, 2026

Define the missing layer: Enterprise AI Execution

More capable GenAI models (LLMs) aren’t the only solution. For any real workflow—whether inventory, fulfillment, cash, or service—critical facts are scattered across ERP instances, planning tools, logistics systems, CRM, ITSM, spreadsheets, emails, and inconsistent master data. Bolt GenAI onto the surface, and you get AI theater: fluent answers without operational grounding and agents that can recommend but cannot execute safely.

The market doesn’t need another process mining vs. automation fight. It needs an overlay that makes operations legible to machines and actionable for humans while retaining the application stack.

An emerging operating layer, Enterprise AI Execution (EAIE), is taking shape. EAIE is a control plane that turns multimodal enterprise data into governed, real-time decisioning and executed actions across systems and teams. Enterprise AI only becomes real when it is grounded in operational truth, informed by a semantic representation of the business, and delivered through composable solutions and orchestrations that drive collaborative execution.

Exhibit 2 shows EAIE as the operating control plane that sits between fragmented enterprise data and executed decisions. The five layers are also a maturity path. Organizations that attempt to implement the top layers without the foundational lower layers end up with AI theater instead of compounding value. Layer 1, a real-time data core, is the foundation; governance and learning comprise the most mature layer.

Exhibit 2: Enterprise AI Execution bridges enterprise data and context while governing action

Source: HFS Research, 2026

How Celonis maps to the Enterprise AI Execution layer

Celonis is best known for process mining. Its recent product direction broadens that foundation into a process intelligence and orchestration platform, illustrated in Exhibit 3, built around an object-centric Process Intelligence Graph (to connect data, objects, and events) and an Orchestration Engine (to route, coordinate, and automate actions for people, systems, and AI agents). In EAIE terms, Celonis is evolving from insight to governed execution in the flow of work.

Exhibit 3: The Enterprise AI Execution stack and how Celonis maps to each layer

Source: HFS Research, 2026

Competitors can claim parts of the stack (process modeling, mining, integration, workflow, RPA, and copilots). However, EAIE will be won at the seams by a provider who can connect objects across systems, inject context as guardrails, define what can be “hands-off,” and execute in a closed loop with governance.

Enterprise AI Execution in practice: Proof points across value streams

Supply chain, especially in manufacturing, is one of the most fragmented and complex enterprise functions, making it a strong proving ground for EAIE. Complexity is unavoidable, decisions are exception-driven, trading policies fluctuate constantly, and hallucinations become physical costs, including downtime, stockouts, excess inventory, and expedited freight. The same EAIE pattern also appears in finance and shared services, where value streams such as order-to-cash span multiple systems and teams.

The proof points in Exhibit 4 show what changes when you stop bolting AI onto fragmented data (Exhibit 1) and instead put an execution layer in place that connects operational truth, context, orchestration, and governance (Exhibit 2). In each case, the differentiator is the ability to connect operational truth to governed action in the flow of work.

Exhibit 4: Enterprise AI Execution in practice with proof points mapped to the value pyramid

Source: HFS Research, 2026, based on publicly shared customer examples

Manufacturing and supply chain: Protecting throughput and working capital

Mercedes-Benz protected service levels and throughput across a multi-plant manufacturing and logistics network

• Before EAIE: Operational data and exceptions were distributed across production and logistics systems, making it hard to spot deviations early and respond consistently before issues cascaded into missed deliveries, premium freight, or production disruptions.
• EAIE in action: Celonis and Mercedes-Benz described connecting major production and logistics systems into the MO360 environment to create an end-to-end, governed view across orders, parts, and process state across 30+ global plants. Teams can detect deviations earlier and trigger coordinated interventions (for example, resequencing, logistics adjustments, service-part actions, and quality containment) using shared guardrails and a common operational truth.

• Enterprise takeaway: EAIE is about more than visibility. It creates a governed, shared reality that enables consistent cross-functional responses, reducing late-stage firefighting while protecting on-time delivery and throughput.

Smurfit Westrock turned spare-parts chaos into governed inventory action after acquisitions

• Before EAIE: Acquisitions left fragmented master data and limited inventory visibility across hundreds of thousands of spare-part numbers, driving duplicate buying, excess stock, and higher downtime risk when critical parts could not be found quickly.
• EAIE in action: Using process intelligence and LLM-based analysis, Smurfit Westrock harmonized master data and connected purchase orders, inventory, and usage patterns into a governed parts view. Within two months, it identified purchase orders raised for parts already in stock and found significant numbers of parts unused for more than eight years. The team moved from diagnosis to prevention by canceling unnecessary purchases and putting controls in place. Engineers can query inventory in natural language and receive options anchored to governed part definitions. In one reported incident, a needed part was located at another regional plant, and a transfer order was scheduled in under a day.

• Enterprise takeaway: EAIE turns “messy reality” into controlled spend and risk by pairing semantic object-level truth with guardrails that enable safe action (cancel, transfer, route) rather than one-off heroics.

MAHLE governed inventory decisions across 148 plants instead of improvising site by site

• Before EAIE: Local teams often managed inventory with limited visibility, leading to build-ups, slow issue detection, and reactive firefighting across locations.
• EAIE in action: By connecting 148 plants, MAHLE reported up to a 20% reduction in inventory across locations, driven by global visibility, proactive alerts, and interventions designed to prevent inventory build-up. The approach relies on shared definitions, thresholds, and action playbooks so sites can act consistently on the same signals.

• Enterprise takeaway: At scale, EAIE’s value is repeatability. It standardizes how exceptions are detected, prioritized, and acted upon, driving working-capital impact without constant central micromanagement.

Across these manufacturing examples, the pattern is consistent: connect operational truth to context, orchestrate action in the flow of work to improve outcomes, then improve repeatedly.

Finance and shared services: Enterprise AI Execution beyond supply chain

The same interoperability problem shows up in finance, just with a nuanced approach: order-to-cash is not a single workflow; it is a value stream spanning sales, credit, invoicing, disputes, and collections.

Malvern Panalytical built the bridges for end-to-end order-to-cash execution

• Before EAIE: Order-to-cash signals were spread across separate systems, making it difficult to see bottlenecks, align teams on the same truth, and standardize responses across sales, credit, billing, disputes, and collections.
• EAIE in action: After connecting Celonis to three separate data systems, Malvern Panalytical described it as “building the bridges” needed to see bottlenecks and standardize responses. Reported results include a 50% reduction in Days Sales Outstanding, millions of pounds in freed-up working capital, and a 17x ROI within a year. The value comes from connecting orders, invoices, disputes, and collection actions and then orchestrating who does what, when, under which policies.

• Enterprise takeaway: Without an execution layer that connects objects and handoffs and governs actions end-to-end, AI will keep optimizing local tasks while enterprise outcomes (cash, risk, customer experience) stay stuck.

Enterprise AI Execution is not the answer to all enterprise scenarios

Enterprise AI Execution platforms, such as Celonis, create the most value where work is repeatable, event-driven, and anchored in operational systems. They are not the primary answer for these situations:

• Early-stage product development and R&D discovery work that is intentionally non-linear.
• Creative strategy and other knowledge work where outcomes are not driven by repeatable flows.
• Low-volume or poorly instrumented processes where objects and events are not reliably captured in systems of record.

Even in these environments, EAIE can still improve the operational value streams that support innovation; for example, procurement, supplier quality, change control, compliance, and service.

The Bottom Line: Enterprise leaders should fund Enterprise AI Execution first, then deploy AI agents inside its guardrails.

Enterprises that skip the execution layer will keep adding AI to broken processes, increasing risk and cost without moving the needle on margin, growth, or resilience. Direct investment and executive attention to the data, semantic, guardrail, and orchestration capabilities that make EAIE real across procurement, finance, supply chain, and IT. In RFPs and renewals, require vendors to show how they connect to that execution layer and govern AI-driven actions end-to-end.