Planting Your AI Prototype with Production in Mind From Day One

I recently shared the stage with a leader from a European public mobility company. They'd sunk 7-figures into developing an AI solution to optimize infrastructure maintenance.

The AI solution looked brilliant on paper. It worked flawlessly in testing. But in production? It flopped. Why? Because the system completely ignored the workflows of the actual maintenance teams. The tech was sleek and fast to build – but a nightmare to integrate.

I've seen this story play out again and again (and yep, I've made this mistake too).

Your prototype isn't just a test. It's a seed you're going to plant into your production system. If you don't plan for production from day one, your AI solution will never grow into success.

Today's newsletter is about thinking backwards – building AI that thrives in your business, not just in a lab.

Let's dive in.

Why AI Prototypes Are Different (and Why That Matters)

If you're used to regular software, you might see prototypes as throwaways.

You build a quick version to test an idea, then rebuild it later "properly" with better architecture, security, and performance in mind.

But AI doesn't play by those rules. Your AI prototype's core strengths and weaknesses –like how accurate it is, how it fits with your team, and what it costs to run – carry over to production.

Take a look at this graphic showing the journey from idea to product (credits to Willi Weber, taken from our last book):

See that "exit probability" spike at the prototype-to-pilot stage?

That's where AI projects die.

Teams that realize too late that their prototype has fundamental flaws – such as poor user fit or exploding costs – that can't be fixed for production risk wasted budgets, frustrated users, or even legal headaches.

So the best way to think about it is instead of treating each stage as an isolated project (with different responsibilities and entirely disconnected tech stacks), treat it as a continuous growth journey:

What Stays the Same vs. What You Can Improve

Let's break down what you're working with. Some things in your AI prototype won't change much:

• Core accuracy: If your AI makes mistakes in testing – like a chatbot giving wrong answers – it'll do the same in production (given the same data and model). If you can improve the AI in production, you can also improve it in the prototype (not necessarily vice versa).

• User fit: If your prototype doesn't match how your team works, production won't fix that. The mobility company's AI failed because it ignored actual maintenance workflows.

• Cost needs: If your prototype needs heavy computing power, so will your production system. I've seen companies shocked by giant cloud bills after testing on fake, easy data.

But some things can get better:

• Scale: You can technically grow it to handle more people – think thousands to millions of users.

• Integration: You can smooth out connections with existing tools.

• Safety nets: You can add checks to spot mistakes – do it. Real user data beats test data every time.

In short: Your prototype is a seed of your production reality. Don't dodge its weak spots – plan(t) for them.

How to Build with Production in Mind from Day One

So how do you avoid a multimillion-dollar flop? Start thinking about production from the very beginning. It's a principle called Thinking Backwards.

Here's how that works:

• Pick tools that fit what you've got: Don't go for the shiniest option. Choose AI solutions that fit smoothly within your current setup.

• Involve users early: Don't wait until it's done to find out they hate it. A rough first try still shows if they'll buy into the idea.

• Test it in real-life chaos: Make sure your AI solution can handle the mess it'll face later. Real-world data is wild. Your solutions should embrace, not fight it.

• Build on something that grows: To stay with the seed metaphor – if your solution can't grow then what's the point? Don't bring in a tree (which rarely works), but neither put a seed into non-fertile ground. Put a seed into a fertile ground and provide the right growing conditions.

Be aware that thinking backwards does not mean overloading your initial prototype with huge requirements or fancy bells and whistles. You still want to ship Atomic AI use cases that allow for fast and easy iterations. You just want to avoid painting yourself into a corner. As you build your AI solutions, you want more options down the road, not fewer.

To see this in action, let's imagine a company wants to build a customer support AI chatbot:

Thinking Backwards Approach
✅ Uses tools that work with what's already there.
✅ The team tries it early, spots problems.
✅ It gets tested with rude, messy questions.
✅ It's built to grow without breaking.
✅ Starts basic, but keeps future options.

Not Thinking Backwards Approach
❌ Fancy tool picked, that promised "the best chatbot solution"
❌ Trusting vendor demos to fix issues later
❌ Tested it with users only after the solution was bought
❌ Ignored hard questions because the decision was already made
❌ Started big, hit a wall, fast

Practical Strategies to Bridge the AI Prototype-to-Production Gap

Here are some concrete ways to make sure your AI doesn't crash and burn when it hits production:

• Start messy, but know you can tidy up: It's fine to use quick tools if you can fix them fast. I like Google Colab or n8n for early tests – it's easy to share and runs almost anywhere. Find your own "Colab" – maybe an internal playground or a simple drag-and-drop tool. Just make sure it gets you going without trapping you in. Rule of thumb: pick flexible platforms over rigid tools.

• Embed in workflows: Don't treat user experience as an afterthought. The end goal should be to get your AI solutions in front of users as seamlessly as possible. Early adopters might tolerate picking a new tool. But most mainstream users won't. Apply TRICUS principles of effective workflow augmentation.

• Add feedback loops: Treat your AI like a data machine. If you can't watch what it's doing, it'll fail fast. Don't overcomplicate this with fancy tech – speaking to with your first 20 users regularly can reveal tons. Ask how they use it, what's annoying, what's great. Simple works.

• Roll out slowly: Skip the huge, all-at-once launch. Get in front of users quickly, but not in front of everyone. The attention span for the typical business user for "new" things is pretty short. Test with a few, tweak it, then grow. Don't dump it on users and beg for buy-in after.

• Lean on MLOps ideas: Running AI models in production isn't exactly brand new, and the "MLOps" world has solid tricks to borrow. Your AI is just a model with a job – don't start from scratch. Have someone tech-savvy on your team wear the "MLOps" hat from day one.

These steps create a smooth path from prototype to production, not a painful leap.

Work 1:1 with me to find and grow AI opportunities in your business?

I've opened up 5 consulting spots starting Q2 to help businesses get on the right track with their AI journey and find AI solutions that will add $10K+ profit per week, month, or year (depending on your business) within the next 90 days.

3 spots are already taken, 2 are still available.

Interested? Just reply "Details" and I'll share how we can work together.

Tailoring the Approach to Your Business

I'm known for simplifying things. But he old phrase "it depends on your business" is true. In particular, here are some aspects where you really need to tailor this to your situation:

• Your industry: In regulated industries like healthcare, insurance or finance, there are rules that will slow you down. You know your regs – don't skip them, work with them.

• Your team: If you're on a team that's spent years learning to work (really!) agile and in cross-functional teams, you can rely on that knowledge now. If not, there's a whole learning curve you have to go through.

• Your balance: If you're in marketing, you might be leaning towards quick wins. If you're in manufacturing, you might prioritize long-term reliability of your solution. There's no blueprint – go with what feel right.

Match your plan to your goals, and you're setting yourself up to win.

Getting started

Your AI prototype isn't just a test – it's the seed of your production system. The best AI projects I've seen don't start over, but they grow from a strong base. They set clear expectations early, and plan for growth from day one.

Here's your next step:

Ask your team that is working on an AI solution today:

Their answer might surprise you – and could determine whether your next AI project becomes a success story… or a cautionary tale you share on stage.

See you next Friday,
Tobias

PS: If you want to work 1:1 with me to uncover AI opportunities worth $10K+ in your business, just reply Details and I’ll share how we can work together.