Pragati Awasthi, Assistant Teaching Professor, Drexel University College of Computing & Informatics
Artificial IntelligenceMachine Learning

From Models to Impact: Tackling ML Operational Challenges in the Enterprise

cxomagazine
By Pragati Awasthi, Assistant Teaching Professor, Drexel University College of Computing & Informatics

Machine Learning has made the leap from hype to high-stakes, becoming a core agenda item in boardrooms worldwide. Retailers personalize recommendations, banks detect fraud in real-time, and manufacturers predict equipment failures before they occur. Creating a model is the easy part; making it work consistently in production is what separates the pros from the rest. This “last mile” challenge deploying, monitoring, and scaling models, is where many companies stumble. Industry surveys suggest that over 90% of ML models development failures stem from poor productization practices and difficulties integrating models into production systems (McKinsey et al., 2024). Below, let’s unpack the most common hurdles organizations face when operationalizing Machine Learning, along with practical ways to address them.

1. Bridging the Research-to-Production Gap

Data scientists focus on accuracy; production environments demand scalability, latency guarantees, and seamless integration with existing systems. This mismatch often results in impressive prototypes that never graduate to production (Sculley et al., 2015).

What helps:

  • Collaborate early. Pair data scientists with engineers, product managers, and DevOps from the start to align expectations.
  • Standardize packaging. Frameworks like MLflow, Kubeflow, or Vertex AI help teams move experiments into deployable artifacts.
  • Adopt ML-aware CI/CD. Traditional DevOps pipelines need adjustments to handle models, data, and retraining cycles.

Operationalizing Machine Learning is less about installing the right tools and more about cultivating the right mindset.

2. Keeping Pace with Data Quality and Drift

Even the most accurate model is only as good as the data it sees today. In the real world, inputs don’t stay static, customers shop differently during holiday seasons, an IoT sensor may start reporting in a new format, or a payment system might quietly change its logs. These shifts, known as data drift (input changes) or concept drift (changing relationships between inputs and outcomes), silently erode performance over time (Lu et al., 2018).

What helps:

  • Watch the streams, not just the scores. Monitor variable distributions, correlations, and output patterns over time.
  • Keep a data paper trail. Tools like DVC or Delta Lake enable dataset versioning for traceability.
  • Automate adaptation. Threshold-based alerts and retraining pipelines can adapt models before business impact is felt.

The goal isn’t to stop drift, it’s to detect and adapt faster than it hurts business outcomes.

3. Monitoring and Governance

Unlike deterministic software, ML models are probabilistic. They won’t always be right, and defining “acceptable performance” is often tricky. With regulatory attention on AI increasing (NIST AI Risk Management Framework, 2023; EU AI Act, 2024), monitoring is no longer just a technical concern but a governance requirement.

What helps:

  • Link metrics to business outcomes. Don’t just track accuracy, monitor fraud prevented, conversions gained, or downtime reduced.
  • Bake in transparency. Tools like Evidently AI or WhyLabs support bias detection and explainability.
  • Define ownership. Model ownership, assumptions, and compliance checks should all be made clear via governance frameworks.
4. Scaling Infrastructure and Costs

Training and serving ML models require compute-intensive resources. Without a plan, organizations risk runaway cloud bills or latency bottlenecks.

What helps:

  • Go cloud-native. Elastic scaling ensures you only pay for resources when you need them.
  • Optimize for inference. Techniques such as quantization, pruning, and distillation reduce resource consumption while maintaining accuracy.
  • Match serving to value. Real-time inference is expensive batch predictions may be cheaper and perfectly adequate for many cases.
5. Breaking Down Silos and Skill Gaps

Technology is only half the challenge. Many organizations underestimate the cultural and skill shifts required for successful ML adoption. Teams often work in silos, data scientists, engineers, compliance, product, leading to delays, misalignment, and stalled AI/ML adoption. Recent surveys highlight this challenge: nearly 48% of organizations cite lack of AI/ML skilled talent as a top barrier, while 35% report difficulties integrating models into existing workflows due to cross-team misalignment (InfoQ, 2025). Similarly, 92% of middle-market firms using generative AI report rollout challenges tied to internal skill gaps and siloed operations (RSM US, 2025).

What helps:

  • Create MLOps champions. Empower individuals who bridge data science and engineering skill sets.
  • Invest in cross-training. Help engineers understand ML basics, and data scientists understand production realities.
  • Think product, not project. Treat models as evolving products that need updates, monitoring, and feedback, not one-off deliverables.
Looking Ahead

Operationalizing Machine Learning is less about installing the right tools and more about cultivating the right mindset. Companies that succeed don’t treat ML as a side experiment; they treat it as a living system, with processes for monitoring, governance, and iteration.

The payoff is clear: models that not only impress in demos but deliver sustained value in the messy, unpredictable world of production.