This week's episode is brought to you by Danny Wood, Lead AI Research Scientist at Fuzzy Labs.
Ahoy there 🚢,
Go to LinkedIn and it seems like every business is solving their problems with AI. The enthusiasm is infectious - and honestly, it's exciting to be part of this field. But what does it truly mean to "embrace AI"? Are companies really embracing it, giving AI a proper bear hug, or are we only seeing part of the picture?
Think about renovating a house. You might be buzzing with excitement about installing smart home technology - automated lighting, AI-powered security, voice-controlled heating. But if your home isn’t set up for those systems or you don’t even have power going to every room… then it won't just fail to work properly - they'll create more problems than they solve.
The same principle applies to AI projects.
Setting up for success
Let's say that you're a software engineer in a relatively small business and your CEO has decided that now is the time to "adopt AI." Sure, your salespeople have been using ChatGPT to help get the tone right on follow-up emails, and you've been using Cursor for some of your more tedious refactoring, but now it's time for the business to get systematic with how it's using machine learning.
Whether they want to start using a simple machine learning model for forecasting or they want a fully-agentic chatbot that is able to deal with customer queries, you've been tasked with making sure that when the data scientists and machine learning engineers come in, they have the best chance of making a big impact.
To get this right, you need to get things up and running quickly. Maybe you're hiring some consultants for a few weeks, or you're setting up an entirely new team. You know that enthusiasm will wane if your managers don't see results soon. So how do you set up for success?
There are a few things that you can do to make sure that money spent investing in AI is money well-spent.
The companies that get this right share something in common: they've been getting their data house in order long before they started shopping for AI solutions. They've got their tools ready, paint colours picked out, a new floorplan fully mapped - rather than winging it and hoping for the best.
What problem are you trying to solve?
Sometimes, there is a very specific problem that AI is being brought in to solve or a particular process that AI is going to be used to automate. Other times, the brief might be as vague as "We need to start using AI or we'll fall behind." The further you can move towards having concrete problem statements, user stories or use cases, the more likely your project is to be a success.
You need to be specific about your goals before you start.
If you're starting out with something as vague as "apply AI to our manufacturing process," think, can you hone that down to "we want to be able to predict our output over the next four weeks" or even better, something super specific like "We know that the data from this component changes in a characteristic way in the days before it fails, we'd like to automatically flag that."
Just like you can't build an extension if you haven't got the land for it, you can't build effective AI systems without knowing exactly what problem you're solving.
Early on, there's huge benefit in figuring out where simple but concrete challenges are that can be claimed as early wins.
Getting your data in order
If you want data scientists or machine learning engineers to build something quickly, give them enough data straight away! You might not know what data they will need but it's a safe bet that they will need as much data as you can give them and will constantly ask for more.
There will be times when you need specialist AI engineers or data scientists to tell you exactly what data they need, but in general, the best time to start collecting data is now! For a forecasting model how much data on past trends can you collate? For an LLM chatbot with RAG, do you have an archive of the kinds of documents it's going to be reading? For a computer vision model, are you already collecting images and are they categorised/labelled?
Take an (appliance) company we worked with. They were trying to predict when certain components were going to fail in their systems. They had months and months of sensor data from all of their refrigeration systems, which was a great starting point.
But the real breakthrough came when they could show us labelled examples - specific points where components had failed, connected to what the data looked like beforehand. That's like having a proper inventory system that tells you not just "we have pipes" but "these are the pipes that work well, these are the ones that failed, and here's why they failed."
Because if you just have logs from all your systems but no context about when things went wrong, you can't build predictive models. You need those examples that show what it looked like when components were about to fail. Just like you need to know the shape of the room before you start fitting the carpet.
Subject matter experts
Sometimes, making accurate predictions is just a case of pointing the right model at the data and hitting train... but this is rare. And even then, sometimes the hard part is knowing what the useful thing to predict even is. More often than not, finding the right subject matter experts in your company is going to be key to success.
Talking to subject matter experts is also vital in working out whether what is being asked for is possible. If your subject matter expert can't do a task, do you have good reason to believe that an AI is going to fare much better?
These people aren't just helpful - they're often the difference between a system that works and months of experimentation.
Setting expectations
Make sure that the people who are pushing for AI know what to expect. It's not going to surpass human ability, it's not going to replace whole teams or years of accumulated knowledge.
The strength of AI is in automating and scaling tasks that are in that sweet spot where they're too nuanced or complex to write code to perform automatically but still simple enough a person would probably find them quite tedious. This is true even with the latest generative models, for messy real-world problems, you may find that AI will fail in weird and surprising ways if the problem space is not carefully constrained.
When companies set these realistic expectations upfront, their AI projects deliver results that genuinely make everyone's work more productive.
The two kinds of AI project
Actually, it's worth understanding that when we talk about AI projects, we're really talking about two different paradigms:
The LLM route: Building chatbots, document Q&A systems, automated writing tools. These need your company's knowledge base - all those FAQs, manuals, conversation histories that you've been accumulating.
The traditional ML route: Forecasting, anomaly detection, computer vision, recommendation systems. These need historical data with clear examples of what you're trying to predict or detect.
Both paths can lead to success, but they need different types of preparation. The companies that do well figure out which path they're on early and prepare accordingly.
The multiplier effect
What gets me excited about this: when companies get their data in order and approach AI thoughtfully, they don't just improve their own results. They create a multiplier effect that benefits everyone.
If every company started thinking about data collection and labelling now, it would be so much more efficient for everyone. The data would be there when needed. We could train models on the right information from day one. Projects would deliver results faster and more reliably.
Better preparation leads to more successful projects. More successful projects lead to more realistic expectations about what AI can actually do. And all of that creates a virtuous cycle where the entire ecosystem gets better at building genuinely useful AI systems.
Every company that methodically prepares their data and involves their domain experts makes it easier for the next company to understand what success looks like.
The opportunity right now
While everyone else is rushing to install the latest AI applications as quickly as possible, taking time to get your data house in order isn't just smart preparation - it's a competitive advantage.
The companies that invest in proper data preparation, involve their domain experts, and set clear expectations are building AI systems that actually deliver on their promise. They're the ones whose employees are genuinely excited about working with AI tools because those tools actually make their work better.
Your future self will thank you for doing this groundwork properly. More importantly, you'll be building systems that create real value rather than impressive demos that struggle in the real world.
And honestly? In a landscape where everyone's talking about AI but fewer are delivering consistent results, being one of the companies that gets the foundations right is a real opportunity.
Danny Wood is Lead AI Research Scientist at Fuzzy Labs, where he helps companies turn AI enthusiasm into systems that genuinely work. He believes the best AI projects start long before anyone mentions machine learning.
And finally...
Upcoming Events & Community Updates
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About Fuzzy Labs
We're Fuzzy Labs. A Manchester-rooted open-source MLOps consultancy, founded in 2019.
We help organisations build and productionise AI systems they genuinely own: maximising flexibility, security, and licence-free control. We work as an extension of your team, bringing deep expertise in open-source tooling to co-design systems that create real value in the real world.
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