Executives are pouring millions into AI, yet a 2025 BCG study found that only about 5% of companies are getting measurable value from AI at scale, while most see little or none. At the same time, multiple surveys show that over half of AI projects never reach production or get abandoned after proof of concept because of poor data, weak governance, and unclear business value.
The problem is not a lack of clever models. The problem is how those models are run, owned, and maintained day after day. In other words, AI operations are where most of the risk and most of the upside sit.
This guest post looks at why scaling AI fails so often, what goes wrong in the trenches, and how an operations-first approach changes the trajectory.
Why does scaling AI fail for most enterprises?
Most large organizations have no shortage of AI experiments. McKinsey’s latest State of AI survey shows that nearly all respondents report using AI somewhere, yet only a small minority are seeing sustained enterprise-level impact.
What happens in practice:
- Dozens of proofs of concept are launched across business units
- A handful look promising in a demo
- Very few survive security reviews, integration work, and real user feedback
Underneath this pattern are some predictable issues:
- AI as a one-off “initiative” instead of an operating capability
AI is treated like a project with a start and end date. There is a budget cycle, a vendor, a dashboard, a presentation. What is missing is a view of AI as a product that needs a roadmap, ownership, and a run budget. - Pilots that ignore the production environment
Many pilots quietly depend on hand-curated datasets, manual feature engineering, or a single power user. None of that exists in the live ecosystem. When teams try to move the same artefact into production, everything from data access to latency behavior changes at once. - No economic view of scaling
Boards hear stories about 10x productivity. What they rarely see is a costed view of infrastructure, observability, model updates, and change management. Without that, expectations spiral and AI ends up on the “failed innovation” list when the first wave of projects disappoints.
Most playbooks for enterprise AI scaling still assume that once you pick the right model and platform, the rest is mainly execution detail. In reality, the way you design and run AI operations often matters more than which large language model you picked in the first place.
Common operational pitfalls
When I look at failed or stalled AI initiatives, I almost always find the same operational patterns.
Pitfalls you see in the wild
| Symptom in production | What you see in week 1 | Root cause in operations |
|---|---|---|
| Model works in a lab, breaks in production | Latency spikes, timeouts, or missing features | No environment parity, ad-hoc infrastructure |
| “Black box” outputs users stop trusting | Complaints about weird edge cases and bias | No clear feedback loop, no model behavior documentation |
| Endless firefighting after go-live | Data scientists pulled into incident channels | Monitoring focused on infra only, not model behavior |
| Model updates take months | Release freezes every time a change is proposed | Treating model deployment as a bespoke project each time |
Behind these symptoms, a few structural issues keep showing up:
- Fragmented data supply chains
Data for training, testing, and serving comes from different paths, but data management services unify these pipelines to reduce drift and instability. Models behave well in tests, then misbehave in production because the input distribution and freshness are completely different. - Throw-over-the-wall collaboration
Data scientists own notebooks. Platform teams own clusters. Business owners own KPIs. Nobody owns the full lifecycle from concept to retirement. Every handoff introduces delays, rework, and subtle mismatches in expectations. - Operational risk treated as an afterthought
Legal, compliance, and security get pulled into the conversation once something is close to launch. They see a finished solution, raise legitimate concerns, and the project stalls. It feels like “governance is blocking AI” when the real problem is late involvement.
Without a strategy for AI operations, pilots stay stranded. You end up with pockets of interesting work that never join the fabric of how the company runs.
MLOps as the missing link in AI operations
MLOps is often described as “DevOps for machine learning.” That definition is technically correct, but it undersells what is going on. In practice, MLOps is the discipline that turns models into run-ready systems and ties them to real business outcomes.
You can think of AI operations as three layers that MLOps has to hold together:
- Assets
Research on MLOps adoption shows that practices like workflow orchestration, reproducibility, versioning, and monitoring all correlate with higher user satisfaction and better outcomes. This sounds abstract until you notice how concrete the failure modes are when those practices are missing.
MLOps is not a tool category you buy once. It is the operational spine that lets your data science, platform, and product teams act as one system. That is why it sits at the heart of serious AI operations programs.
Governance and monitoring that work in real life
Many enterprises respond to AI risk by writing long policy documents. Fewer manage to turn those documents into day-to-day routines for teams that build and run models.
Mature AI operations tend to build governance into three practical loops:
- Technical monitoring loop
Recent industry analysis points out that poor data governance and weak AI oversight are already the main reasons many AI projects are predicted to fail or get canceled in the next 1–2 years.
The most successful organizations I work with treat these loops as part of their AI operations playbook, not separate “risk initiatives.” They automate as much as possible (data lineage, access control checks, drift detection) and spend human time where judgment is needed.
Case studies on scaling AI successfully
To make this concrete, let us look at two anonymized patterns that come up often.
Case study 1: From proof-of-concept theater to production AI
A global retailer had more than 40 AI use cases in various pilot stages: demand forecasting, dynamic pricing, marketing personalization, and store operations. Only two were live at any point, and both required constant manual intervention.
Key problems:
- Each team built its own pipelines and infra patterns
- No shared standards for monitoring, data access, or model deployment
- Business owners saw AI as “IT’s project,” not as part of their P&L
The company changed tack and created a small central AI operations group with three responsibilities:
- Define and maintain a reference MLOps stack (data ingestion patterns, training and serving pipelines, experiment tracking, model registry).
- Set and enforce standards for observability, governance, and cost reporting.
- Coach business teams to treat AI use cases as products with owners, success metrics, and roadmaps.
Within 18 months:
- Time from idea to first production release dropped from 9–12 months to about 8 weeks
- More than 20 models were running with shared tooling, instead of bespoke scripts
- Quarterly reviews linked each use case to measurable impact on margin and inventory
The interesting part is what did not change. The underlying models stayed fairly similar. The step change came from disciplined enterprise AI scaling through shared operations, not from exotic new algorithms.
Case study 2: Industrial AI that survives contact with reality
An industrial manufacturer tried to use predictive maintenance models for critical equipment. The first attempt failed. Models trained on historical sensor data looked accurate in offline tests, yet in production they produced too many false alarms. Technicians stopped paying attention.
An internal review found three root causes:
- Training data had been cleaned in ways that did not reflect real sensor noise
- The live pipeline was missing two key signals that had been present in training
- No one had mapped how model predictions would change technician workflows
On the second attempt, the team re-framed the work as an enterprise AI scaling problem rather than a data science contest.
They:
- Defined a clear “data contract” for sensor streams, with guarantees around sampling frequency, units, and missing data handling
- Implemented a unified MLOps pipeline from ingestion to serving, so retrained models could move into production with minimal friction
- Included technicians in design, with thresholds and alert formats tuned to their reality
Monitoring now included both drift indicators and field feedback. When the model started to degrade, retraining was handled through the same standardized pipeline instead of a one-off rescue project.
Within a year, unplanned downtime in the targeted asset class fell meaningfully. The change that mattered most was the reliability of the full pipeline, not a dramatic jump in model accuracy.
Where to go from here?
If you are serious about scaling models, start by treating AI operations as a first-class discipline:
- Map the full lifecycle of 2–3 high-value use cases from data ingestion to retirement
- Identify every manual step, handoff, and “shadow process” that keeps models alive
- Decide which elements of your MLOps stack will be shared, opinionated defaults
- Build governance and monitoring into those defaults instead of layering them on top
The organizations that will matter in the next wave of AI are not the ones with the flashiest demos. They are the ones that can quietly run and evolve dozens of production models without drama, month after month. If you can get AI operations to that level of maturity, the rest of your story starts to take care of itself.