Scaling AI to Production (and what that really means)

Let's say you've built an AI prototype that works great in controlled tests. Everyone's excited about the potential. The demo went well.

Now what?

If you're like most organizations, this is where things get tricky. Moving from a working prototype to a fully operational, scaled AI system isn't about making things "bigger". Instead, you’re going to play an entirely different game.

Today, I'll share the typical pitfalls at this stage, and how you can navigate this transition successfully.

Let's dive in!

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Understanding the AI Product Lifecycle

When we talk about "scaling AI", what we're really discussing is the transition between two distinct phases of the AI product lifecycle: discovery and delivery.

Discovery is all about validation. You're testing assumptions, building prototypes, and figuring out if your AI solution is technically feasible and actually valuable. The focus is on learning fast and pivoting when needed – not building perfect systems.

Delivery is about transforming validated ideas into robust, reliable, and operational systems. Your focus shifts from exploration to stability and integration.

Here's how these phases compare:

How do you even know which phase you're in and when it's time to shift? Here's a rough checklist I wrote for an upcoming book to help you decide:

In short: it’s about proving plenty of value and the absence of red flags.

When moving to production, remember that some components from your AI solution prototype can be reused, others need enhancement, and some must be rebuilt entirely

AI Scaling Goals

When scaling AI solutions, you typically work to achieve the following scaling goals to ensure your system delivers sustainable value:

Scalability: True scalability means your AI solution can grow without requiring proportional increases in resources or management overhead. Ask yourself: Can your system handle 2x, 5x, or even 10x the current volume without a complete redesign? Will costs scale linearly with usage, or can you achieve economies of scale?

Reliability: In production, inconsistency kills adoption. One bad experience can undo months of built-up trust. Users need to trust that your AI will deliver predictable results. This means building fault tolerance, implementing proper error handling, and ensuring your system recovers gracefully when things go wrong.

Performance isn't just about speed – it's about efficiency under real-world conditions. Your prototype might work perfectly with 10 simultaneous users, but what happens with 100? 1,000? Will response times remain acceptable? Will costs stay manageable? What level of performance do you even need?

Maintainability: AI systems require ongoing updates, monitoring, and retraining. There isn’t anything such as "set and forget". That’s why scaling also means creating clear documentation, modular designs, and straightforward testing processes that make it easy to evolve your solution over time.

Security: When your AI solution processes more sensitive data and becomes more integrated with critical business processes, this will put tougher requirements on the security front as well. Proper access controls, data protection measures, and compliance with relevant regulations aren't optional – they're essential.

Each of these principles plays a critical role. The question is:

How do you actually achieve them?

5 Levers for Scaling Success

There are 5 critical levers you can use to - quite literally - pull these goals off (and keep them up).

Here’s my mental picture for this:

As with any lever, they work best the larger they are (which happens when you use the key pieces together):

People

During prototyping, you probably had a small, agile team where everyone wore multiple hats. But scaling requires specialized expertise and clearer role definition.

This means expanding from a 2-Pizza team to something that requires more of a full buffet:

In Delivery, you typically need to “feed”:

• Data scientists / ML engineers who focus on model accuracy and reliability

• Data engineers who build robust data infrastructure

• IT specialists who handle system performance and security

• Domain experts who ensure business alignment

• Governance teams who manage compliance requirements

• etc.

The skills that build great prototypes aren't always the same ones needed for successful production deployment. This doesn't necessarily mean replacing your team – but means augmenting it with the right expertise at the right time.

Just throwing additional roles at the existing headcount does not work.

Processes

Your prototype probably thrived on informal, flexible processes that enabled quick pivots. In production, you'll need more structure – but not at the expense of agility.

In reality, this often means adhering to frameworks like Scrum or SAFe that provide enough structure for coordination while preserving the ability to adapt and iterate (in theory, at least).

The key is to strike the right balance and adopt what works in your organization. Adopting both a new AI solution and a new delivery process model can be too much to chew.

Data

In the prototyping phase, data handling was likely manual and ad-hoc. Maybe you exported a CSV file once, cleaned it up in a notebook, and ran with it.

For production, you need automated, resilient data pipelines that can:

• Pull data reliably from source systems

• Handle inconsistencies and anomalies gracefully

• Maintain proper version control

• Ensure compliance with data governance policies

• Scale to handle growing volumes

While the conceptual flow of your data pipeline might remain the same, the technical implementation almost always needs a complete rebuild for production.

Technology

The more technology you can re-use from your prototype, the better. This doesn’t mean using low-performing tools in production, but instead using production-grade platforms that allow rapid prototyping from day 1 (tools like Azure ML Studio or Vertex AI are popular, but by no means only options)

AIOps

Once your AI system is live, the journey doesn’t end, but just beings. AIOps (a.k.a. MLOps) is the lever that helps you to ensure operational capability which includes a continuous cycle of monitoring, maintenance, and optimization. Models naturally degrade over time due to changing conditions, new data patterns, or evolving business needs.

Concretely, AIOps practices help you:

• Monitor performance continuously

• Detect data drift before it impacts results

• Maintain pipeline health

• Manage model versions and updates

• Automate routine maintenance tasks

These 5 levers – People, Processes, Data, Technology, and AIOps – provide the foundation for successful AI scaling. Each reinforces the others, creating a robust framework that supports your scaling goals.

Special Attention Areas

Beyond the 5 key levers, there are (at least) 3 specific areas that deserve special attention:

1) Answer the “What’s in for me?” question

No matter how brilliant your AI solution is technically, it won't deliver results if people don't use it. Employees rarely resist AI because they don't understand its benefits. They resist because they're not convinced those benefits apply to them. Recap the AI Paradox for this.

2) Managing Costs

Unlike traditional IT projects where costs typically flatten more or less after implementation, AI projects introduce oscillating costs (retraining, fine-tuning, etc.) that often scale in proportion with usage. To manage this, I use the value threshold and cost cap approach where we define a minimum business value your AI must generate as well as a maximum budget allocated to the solution. If your solution stays below the cap and above the threshold, it's justified. If either side changes, reassess.

3) Monitoring

Monitoring AI solutions would deserve a blog of its own (which I'll probably write at some point), but just as a quick heads up, Monitoring shouldn't be an afterthought. AI systems don't just consume data - they generate it. Production-grade monitoring should track technical, operational, and strategic metrics to ensure that your solution remains not just functional, but valuable. It's less about catching failures and more about continuous optimization to ensure your AI continues to meet business goals.

Moving forward

So wow do you move forward without overextending your resources or losing focus? Similar to prototyping, continue to work in complete, contained increments. Rather than trying to industrialize AI across the entire company at once, scale in layers. Prove value in one area, stabilize it, monitor performance, and then move to the next.

Keep asking these alignment questions:

• Are we still hitting the value threshold?

• Are we within the cost cap?

• Are users actively engaging and finding the solution helpful?

• Are we learning from real usage data and adjusting where needed?

• Do we know what "scaling" looks like for this solution?

If you're consistently answering "yes", you're scaling AI in the way that matters most: sustainably, responsibly, and in alignment with what your business really needs.

Remember: at scale, AI isn't just something you deliver. It's something you live with.

Until then, keep innovating!

See you next Friday,
Tobias