Stop experimenting with AI, start operating with it

Your AI experiments are going great. Everyone’s excited. The demos look amazing. Executives are impressed.

And you will get zero business value from any of it.

Gartner found that 54% of AI projects never make it from pilot to production. When I dug into MIT’s research, the number got worse - about 5% of AI pilots achieve rapid revenue acceleration. But here’s what nobody’s saying: experiments are designed to stay experiments. They optimize for learning. Operations optimizes for delivery. Those are fundamentally different games.

Why experiments never become operations

Experiments feel safe. Low risk, high learning. Nobody gets fired for running a pilot. You get three months to play with technology, produce some slides, maybe write a report. Done.

Operations? Terrifying. People depend on it working every single day. When it breaks, customers notice. When it’s slow, productivity drops. When it’s inconsistent, trust evaporates.

I’ve watched companies at Tallyfy run six-month pilots on workflow automation, produce excellent results, then… nothing. The pilot proved it works. But moving to operations meant integrating with their actual business processes, training their actual teams, handling their actual edge cases. The exciting part was over. The hard part was just beginning.

BCG’s research nails this - 70% of AI implementation challenges stem from people and process issues. Only 20% are technology problems. But experiments only test technology. You never discover the real problems until you try to operate.

The resource allocation pattern tells the story. Companies fund experiments generously. Cool technology, smart people, flexible timelines. Then the pilot succeeds and suddenly you’re asking for ongoing budget, dedicated support, change management resources. Everyone moves on to the next shiny pilot instead.

What operational AI actually looks like

Let me paint two pictures.

Experimental AI: Data scientist pulls a clean dataset. Builds a model. Gets 92% accuracy in testing. Shows impressive demo to executives. Project marked “successful.” Team moves to next experiment.

Operational AI: Same model, but now it runs against yesterday’s data at 6 AM every morning. When upstream systems change their schema without warning, it needs to handle that gracefully. When the network is slow, it can’t just fail - there’s a retry strategy, fallback options, clear error messages. When it produces unexpected results, there’s monitoring that catches it before customers do. When someone new joins the team, there’s documentation that lets them understand and maintain it.

See the difference?

I came across this McKinsey piece that stopped me cold: nearly two-thirds of organizations have not yet begun scaling AI across the enterprise. They’re stuck in pilot paralysis. Not because the technology doesn’t work. Because operations is hard.

When we work with mid-size companies implementing AI experiments to operations transitions, the real work is not the AI. It’s the operational wrapper around it. The monitoring, the logging, the error handling, the integration points, the rollback procedures, the performance optimization, the documentation. That’s where 80% of the effort goes.

Research from IDC found only 4 out of every 33 AI POCs graduate to production. The ones that make it? They’re designed for operations from day one. Not retrofitted later.

The transition framework that actually works

Here’s what I’ve seen work for moving AI experiments to operations in mid-size companies.

Start with operational thinking during the experiment. Not after. During.

When you’re running your pilot, ask operational questions: Who will support this when the data scientist moves on? What happens when this runs against real-time data instead of the curated test set? How will we know if it’s working correctly next Tuesday at 3 AM?

The companies that succeed treat pilots like product development, not science experiments. There’s solid research showing that incremental rollout builds confidence, reduces risk, and ensures each step forward reinforces the overall system.

Build the operational infrastructure alongside the model. Most teams build a great model, then scramble to operationalize it. Backwards. While you’re developing the AI, develop the monitoring. Develop the logging. Develop the integration layer. Develop the documentation.

This sounds like extra work. It is. But the alternative is building something you can’t actually use.

Test operational scenarios, not just accuracy. Your model gets 95% accuracy? Great. Now test it with incomplete data. Test it when the API is slow. Test it when someone feeds it garbage inputs. Test it at 10x the expected volume. Test it when the person who built it is on vacation.

BCG’s data shows that 74% of companies struggle to achieve and scale value from AI initiatives. The pattern is consistent: experiments test for accuracy, operations demands reliability.

Assign operational ownership before you start. Who owns this when it’s running in production? Not “the AI team.” A specific person who will be responsible for keeping it running, fixing it when it breaks, improving it over time.

Mid-size companies have an advantage here. You can make these decisions quickly. You don’t have six layers of approval. You can assign ownership, allocate resources, and move forward. But you have to do it deliberately.

Operational requirements experiments ignore

Experiments run in controlled environments. Operations runs in chaos.

Here’s what never gets tested in pilot phase but becomes critical in operations:

Consistency under varying conditions. Your experiment ran on three months of data from Q2. Beautiful results. Then you deploy it and discover that Q4 data has completely different patterns. Or that Mondays look nothing like Fridays. Or that the model trained on US data falls apart on European data.

I’ve seen this kill deployments. The pilot succeeds because you controlled the conditions. Operations fails because reality is messier than your test environment.

Integration with existing tools and processes. During experiments, people are willing to use new interfaces, learn new systems, change their workflow. In operations, the AI needs to fit into how they already work. If it requires five extra steps, they won’t use it. If it lives in a separate tool they have to remember to check, they won’t use it.

Research on AI operationalization emphasizes this: you’re not just deploying technology, you’re changing how people work. Unless you solve for the integration, the technology sits unused.

Performance under real usage patterns. Your pilot processed 1,000 records overnight. Operations needs to handle 50,000 records by 8 AM, because that’s when people need the insights. Your experiment took 30 seconds to return results. Operations needs sub-second response because users won’t wait.

The performance requirements change completely when you move from experiment to operations. And they can’t be retrofitted - you need to design for them.

Support and troubleshooting capabilities. What happens when the AI produces a result that doesn’t make sense? In experiments, the data scientist investigates. In operations, the business user needs to either understand why or have a clear path to get help.

Building operational discipline means creating the support infrastructure before you need it. Documentation that actual humans can follow. Error messages that explain what went wrong and what to do about it. Monitoring that catches problems before users report them.

Building operational discipline around AI

The hardest part of moving AI experiments to operations is not technical. It’s cultural.

You need to shift from “move fast and break things” to “move deliberately and keep things running.” Both are valuable. But they require different mindsets, different processes, different measures of success.

Here’s what operational discipline looks like for AI:

Standard operating procedures for AI-enhanced processes. Not documentation that sits in a wiki. Actual procedures that people follow. What do you do when the model flags something as high risk? What’s the escalation path when results look wrong? Who do you call when it stops working?

Write these procedures while you’re building the system, not after it breaks in production.

Quality standards and performance metrics. Experiments measure accuracy. Operations measures uptime, response time, error rates, user satisfaction, business impact. Define these metrics before you deploy. Set thresholds. Build alerts. Create dashboards that show operational health, not just model performance.

Research on MLOps shows this clearly: successful operationalization requires automating the entire model lifecycle - development, testing, continuous delivery, monitoring. You can’t bolt this on later.

Operational expertise and troubleshooting capabilities. Someone needs to own the operations. Not the data science team - they’re building the next model. The operations team. The people who will keep it running, diagnose problems, implement fixes, manage the performance.

This means training. Documentation. Knowledge transfer. The expertise to run experiments does not transfer automatically to the expertise to operate systems.

Continuous improvement processes for operational AI. Operations is not “set it and forget it.” The world changes. Data patterns shift. Business requirements evolve. You need processes for monitoring performance over time, identifying degradation, updating models, testing changes, deploying updates.

The companies that excel at moving AI experiments to operations treat it like product management, not project management. It’s never “done.” It’s continuously improving.

Gartner’s research on AI maturity breaks this down across seven key areas: strategy, product, governance, engineering, data, operating models, and culture. At operational maturity, you have best practices, accessible expertise, executive sponsorship, and dedicated budget. Getting there requires building operational discipline deliberately.

For mid-size companies specifically: You have an advantage. You can move faster than enterprises. You don’t have layers of committees and approval processes. You can make the decision to transition AI experiments to operations and actually execute on it.

But you also have a constraint: operations must work without massive support teams. You can’t throw 20 people at maintaining an AI system. You need operations that run reliably with minimal intervention.

This means:

Design for operational efficiency from day one. Build systems that mostly run themselves. Invest in good monitoring so small problems get caught before they become big problems. Create clear documentation so anyone on your team can understand what’s running and why.

Choose your operational battles carefully. Not every experiment should become operational. Some things work great as pilots but aren’t worth the operational overhead. Be ruthless about deciding what’s worth scaling and what should stay experimental.

Build operational capability incrementally. Don’t try to operationalize five AI systems simultaneously. Pick one. Get it running well. Learn from it. Then scale the operational practices to the next one.

The companies winning with AI are not the ones with the most experiments. They’re the ones who mastered the transition from experiments to operations. They figured out how to take promising technology and turn it into reliable business capabilities.

That’s where the value lives. Not in the demo. In the daily operation.