The technical work is done. You’ve chosen an open-source AI model, benchmarked its performance, and determined its cost. You know exactly how it’ll be used, and you’ve defined what success looks like.
But how do you scale it without causing chaos?
Many enterprises face this binary trap. Either they implement rigorous one-off approval processes that kill velocity, or they let teams operate without governance. Both are costly mistakes. Moving too slow means you lose ground to competitors, while moving too fast jeopardizes data security. Most enterprises fall into the second camp: 78% of them use AI, but only 27% have governance in place.
That means they’re accumulating risk faster than they’re building capabilities.
Fortunately, you don’t have to choose between speed and governance. By building a solid foundation upfront—and iterating on it over time—you can do both.
Start with these three strategies:
1. Align Stakeholders and Define Ownership
When you selected your first AI model, did you do it in a vacuum? (Probably not, right?) You likely solicited input from key stakeholders to determine what makes the most sense across the enterprise.
To scale AI effectively, you’ll need to expand that collaboration into a formal structure. Research shows AI governance projects with broad stakeholder engagement are twice as likely to succeed, and organizations with well-defined roles reduce AI project failures by over 40%.
Start by assembling representatives from multiple departments, including SecOps, infrastructure, legal, finance, and potentially HR, depending on your data types. Each brings a critical perspective to your working group: SecOps identifies security requirements, legal clarifies compliance obligations, and so on.
Next, define clear roles and accountability. Each member of your working group should know how to answer key ownership questions, such as:
- Who approves new AI applications?
- Who monitors ongoing performance?
- Who decides which AI use cases get prioritized?
- Who handles escalations when issues arise?
- Who maintains solutions over time when data drift occurs or models need updates?
Document these decisions explicitly. Having this level of clarity prevents confusion when ownership questions arise during scaling.
2. Establish Governance to Prevent Shadow AI
As AI usage expands beyond your initial deployment, more employees will want to experiment with AI tools. That’s a good thing: Workers who use AI say they’re 40% more productive, with some studies showing as much as 66% higher throughput for knowledge work.
But without clear pathways to approved tools, employees will find their own. They’ll use personal ChatGPT accounts, download unvetted models from public repositories, or adopt unsanctioned solutions.
It’s already happening, too: Research shows organizations now manage an average of 490 SaaS applications, only 47% of which are authorized. Worse, nearly 8 in 10 AI users admit they’re bringing their own tools to the workplace. This exposes organizations to data breaches, compliance violations, IP issues, and security vulnerabilities.
Effective governance means creating a centralized catalog that lets teams access vetted models, ideally without waiting for additional security reviews or legal approvals. Your catalog should contain documentation for each model, including approved use cases, licensing terms, and resource requirements.
Of course, building a catalog in-house will demand significant investment and ongoing maintenance. To reduce the expense of creating and maintaining that catalog, consider solutions like Anaconda AI Catalyst—a curated catalog of pre-validated models with comprehensive governance controls and documentation. By centralizing model information and governance, your teams can focus on building applications rather than managing catalog infrastructure.
3. Build an Evaluation and Feedback Loop
AI applications degrade over time. In fact, more than 9 in 10 machine learning models experience drift. This is normal—data patterns are prone to change, and business requirements are bound to evolve.
But problems arise when degradation goes unnoticed. Over time, applications become less accurate, and users lose trust. By the time issues get flagged, they’ve already harmed your operations.
Here’s how to catch problems early:
Establish Baseline Metrics
Start by measuring your model’s performance immediately after deployment. Pick 2-3 key metrics you can track over time—things like accuracy for classification tasks, response quality for chatbots, or task completion rates for automation.
Keep a close eye on these, since they’ll be your reference point for detecting problems later.
Set Up Automated Monitoring
Next, log your model’s predictions so you can compare them to actual outcomes when they become available. Use monitoring tools like Databricks (through MLflow) or Snowflake ML Observability to track your metrics continuously.
To stay on top of major shifts, set thresholds that trigger alerts when performance deviates. Google’s Vertex AI, for example, uses a 30% deviation from baseline as its default threshold, though your specific limit should reflect your business tolerance for degradation.
Create Clear Escalation Paths
Route different alerts to the right teams. Data quality issues might go to engineering, for instance, while system performance concerns may get routed to IT ops.
When alerts fire, who investigates? If accuracy drops 30%, does your data science team get paged immediately? If users report unexpected results, how do those reports reach the people who can fix them?
Tools like Grafana can help you visualize performance trends over time, while platforms like MLflow or Weights & Biases help track what changed and when.
Conclusion
Scaling AI is all about balancing speed with control. The strategies outlined here—aligning stakeholders, establishing model governance, and building evaluation loops—create the foundation for systematic growth.
Organizations that do these well move faster than their competitors and avoid the security risks of ungoverned adoption. Their teams get immediate access to approved models, and their leadership gets 24/7 visibility into what’s deployed.
Anaconda AI Catalyst supports this structured approach with flexible deployment options that work across your entire organization, from developers prototyping locally on laptops to production deployments in your VPC. Combined with comprehensive AIBOMs and built-in governance controls, AI Catalyst can help you scale confidently without sacrificing security or speed.
Explore the model catalog in AI Catalyst to see how enterprise-ready open-source models can accelerate your AI scaling efforts.