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The Plateau After the Pilot

Most enterprises can point to one or two early AI wins—perhaps a recommendation engine or a fraud detection model. But success with the first few use cases often gives way to a frustrating stall. Scaling AI across the enterprise proves elusive. According to a 2025 IDC survey, only 20% of firms have successfully scaled more than three AI use cases.

The issue isn’t a lack of ambition or technical skill. It’s a combination of fragmented priorities, unsustainable development practices, and missing foundational enablers. To understand how to push past this plateau, enterprises must unpack the root causes of scaling failure—and learn from those who’ve crossed the chasm.

1. Disconnected Use Cases: Islands of Innovation

Initial AI deployments tend to be isolated experiments—developed by small teams with limited business input. These projects often deliver results, but they’re not built for reuse or long-term integration.

• Siloed Execution: Teams build point solutions without thinking about how they’ll interoperate with others.
• Inconsistent Data Sources: Different models draw from inconsistent or overlapping datasets, often duplicating effort.
• Tooling Chaos: Each team picks its own stack—languages, frameworks, deployment methods—leading to an unmanageable sprawl.

Case in Point: A European insurer had seven separate models for customer segmentation—each built by different units with conflicting assumptions. This resulted in contradictory messages sent to the same customer segments, reducing campaign credibility and customer trust.

✅ What Works

• Use-case rationalization: Develop a strategic view of existing and upcoming use cases to avoid duplication.
• Reusable components: Adopt shared feature stores, standard APIs, and centralized data access policies.
• Shared playbooks: Document successful architectures and operationalization strategies for other teams to replicate.

2. Talent Bandwidth: Same People, More Projects

In many organizations, a single AI team is responsible for both pilot development and enterprise-wide scaling. The result is a stretched and reactive workforce.

• Role Dilution: Data scientists become de facto DevOps engineers, UI designers, and stakeholder managers.
• Drop in Quality: Under pressure to deliver quickly, long-term maintainability and documentation are sacrificed.
• Attrition Risk: High-performing individuals often leave when strategic clarity and resources are lacking.

Case in Point: A telecom giant launched a churn prediction model with impressive accuracy. Encouraged, leadership assigned the same two-person team to replicate similar models for upsell, retention, and pricing—none of which reached the same success due to rushed development and poor follow-through.

✅ What Works

• Dedicated scale squads: Separate teams for experimentation (innovation) and industrialization (scaling) reduce context-switching.
• AI Product Managers: Assign owners focused on aligning model outputs with business outcomes, timelines, and user needs.
• Automation: Incorporate CI/CD, MLOps, and automated monitoring to reduce manual intervention and accelerate delivery.

3. No Platform Thinking: Scaling on Spaghetti

One-off models often become liabilities when they lack centralized infrastructure and oversight. Without platform thinking, enterprises find themselves managing a tangled web of incompatible models, systems, and deployment processes.

• Spaghetti Infrastructure: Each use case reinvents the wheel—from data ingestion to model serving—leading to inconsistent and fragile systems.
• Missing Guardrails: Without governance, models drift, lose accuracy, or even introduce regulatory risks.
• Vendor Overload: Tool proliferation causes cost overruns and integration complexity.

Case in Point: A retail group with operations across four countries tried to unify AI workflows. Each region had locked-in tools and preferred vendors, creating barriers to consolidation. A six-month internal alignment initiative, driven by the enterprise architecture team, eventually established a shared model registry and observability stack.

✅ What Works

• Platform-first mindset: Build shared infrastructure before scaling—feature stores, model registries, inference APIs, and observability tools.
• Governance: Introduce standardized access controls, approval workflows, and audit trails for all production models.
• Vendor rationalization: Select tools based on extensibility, interoperability, and enterprise alignment—not team preferences.

4. Business-Model Misalignment

AI initiatives often stall because they fail to anchor themselves in measurable business goals. Scaling is deprioritized when impact is unclear.

• Unclear Value Capture: Teams deploy models without frameworks to quantify ROI.
• Limited Executive Buy-in: AI is still seen as a tech experiment, not a business lever.
• Misaligned KPIs: IT and data science teams optimize for model metrics, not outcomes that drive P&L.

Case in Point: A B2B manufacturing company built a predictive maintenance model that successfully identified at-risk machines. However, the operations team never acted on the alerts, citing lack of clarity on ownership and risk prioritization. The model quietly decayed without usage.

✅ What Works

• Value framing: Define success in business terms from day one—whether it’s reducing downtime, increasing conversion, or speeding up compliance.
• Executive sponsorship: Ensure continuous visibility and alignment at the C-level.
• Incentive alignment: Tie AI adoption to operational KPIs and performance metrics across departments.

5. Fragile Governance and Scaling Fatigue

As models proliferate, governance becomes critical. Without visibility, standards, and lifecycle oversight, scaling efforts collapse under their own weight.

• Invisible Models: Many models operate without documentation, version control, or owner accountability.
• No Retraining Plan: Performance degrades, but retraining processes are ad hoc or nonexistent.
• Scaling Fatigue: After a few rushed deployments, business teams lose trust and enthusiasm.

Case in Point: An energy utility scaled AI to over 40 use cases in 24 months. But 60% of the models were either unused or outdated within a year. The organization launched an AI governance council to assess model lifecycle maturity and decommission low-impact models.

✅ What Works

• Model lifecycle management: Track model versions, owners, training datasets, and business impact metrics.
• Monitoring and alerting: Use real-time drift detection and performance dashboards.
• Regular audits: Evaluate models quarterly to retire, retrain, or enhance.

From Use Case to Use Culture

Scaling AI isn’t about building more models—it’s about building an ecosystem where models thrive. That means:

• Investing in horizontal platforms, not just vertical use cases
• Developing repeatable practices, not one-off wins
• Thinking about AI as a lifecycle, not just a delivery phase

Enterprises that treat scaling as an intentional, multi-dimensional discipline—not a continuation of pilot-mode experimentation—are the ones moving AI from the lab to the core of the business.