AI Can Bake the Cake. But It Still Needs a Baker

How AI is transforming the daily work of data and functional analysts.

Have you ever noticed how some professions seem to change overnight? One day, success comes from mastering every step of the process. The next, it comes from knowing which steps deserve your attention and which not.

We often talk about generative AI as if it's creating an entirely new way of working. But it's really the latest chapter in a transformation that many professions have experienced before. A profession close to my heart offers surprisingly a lot of valuable lessons for the future of data and functional analysts.

Analysts have been spending too much time measuring flour.

Imagine walking into a traditional bakery thirty years ago. The baker's day began long before sunrise. Flour was weighed by hand, dough was kneaded manually and countless hours were spent on repetitive, labor-intensive tasks. Every loaf depended on hours of preparation before the real craft even began.

Sound familiar?

For many years, the day-to-day work of data and functional analysts looked surprisingly similar. Before they could focus on solving business problems, they spent countless hours documenting requirements, creating source-to-target mappings, summarizing workshops and writing user stories.

None of these activities are unimportant. They are the ingredients of a successful project. But they aren't where experienced analysts create the most value. Organizations hire experienced analysts for their ability to understand complex business problems, ask the right questions and design the right solution.

AI Becomes the Sous-Chef

Every great chef relies on a sous-chef, not to create the final dish, but to handle the preparation that makes the chef more effective.

AI is quickly taking on that role for analysts. Whether it's drafting user stories, summarizing workshops, generating SQL or producing documentation, AI takes care of much of the repetitive groundwork. Instead of starting with a blank page, analysts increasingly start with a well-prepared first draft, allowing them to focus their expertise where it matters most. This isn't just a future vision it's a shift we're already applying in our own work with our clients. The conversations don't become shorter, they become richer, because we can spend less time documenting and more time understanding, challenging and designing the right solution.

The real craft begins after the first draft

AI is becoming increasingly capable. It can analyze information, generate ideas, summarize conversations and even ask the right follow-up questions. So if AI can do all of this, where does human expertise still make the difference?

Let's go back to the bakery.

Imagine a customer walking into a bakery and saying: "I'd like something special for my daughter's birthday." An AI assistant could immediately start asking relevant questions: How old is she? Does she have any allergies? How many guests are coming? What flavors does she like? And that's valuable. But an experienced baker brings another layer of expertise. They notice the hesitation when the customer mentions the budget. They suggest a cake that will survive a hot summer afternoon. They recognize when the customer's request might not be the best solution and offer an alternative.

The difference is not about who can ask the most questions. It's about understanding the context behind the answers, challenging assumptions, and knowing what truly matters in a situation. The same principle applies to data and functional analysts. AI can generate possible solutions, draft requirements and suggest approaches but without someone who understands the business context, it is easy to end up in an endless loop of prompting, refining and validating

AI will happily generate ten plausible answers. The experienced analyst is the one who can recognize which answer actually fits the business need, which assumptions need to be challenged and where the solution needs to be adjusted.

That's not just about better quality, it is also about efficiency. Every unnecessary iteration costs time, attention and money. That's the first shift AI brings to the role of analysts: removing repetitive work and giving them more time to focus on where their expertise creates the most value. But the opportunity goes beyond making individual analysts more productive. The biggest transformation happens when organizations start capturing that expertise and turning it into a reusable capability.

The Recipe Becomes the Real Asset

Every successful bakery has recipes that are carefully refined over time. They capture years of experience, customer preferences, and countless small improvements. Anyone can buy the same flour or the same mixer. The recipe is what makes the bakery unique.

AI is creating a similar shift for data teams. The first step is using AI to accelerate individual tasks. The next step is capturing the domain expertise behind those tasks and making it reusable across the organization.

We believe this starts with practicing what we preach. That's why at Datashift, we're working with AI agents that embed our own methodologies, domain knowledge and best practices, so our consultants can focus more of their time on the conversations, decisions and expertise that create the most value for our clients. Just like a bakery's recipe is far more valuable than the mixer, an organization's competitive advantage won't come from using the same AI model as everyone else. It will come from the expertise it has embedded into the agents that support its people.

Great analysts, like great bakers, knows what good looks like

No matter how advanced bakery equipment becomes, the baker still tastes the bread before it reaches the customer.

Why? Because quality cannot be fully automated.

The same principle applies to AI-generated work. AI might generate an impressive requirements document, produce technically correct mappings and SQL. But correctness isn't enough. Experienced analysts recognize subtle issues that AI often misses: A requirement that solves the wrong business problem, data that is technically correct but commercially misleading, conflicting stakeholder expectations, hidden assumptions.

These judgments come from experience, context and conversations. Not from generic AI models. As AI takes over repetitive work, the role of the analyst moves up the value chain. Less time is spent creating artefacts, more time creating impact. Ironically, the more capable AI becomes, the more important uniquely human skills become like Curiosity. Critical thinking. Communication. Creativity. Empathy.

But the next evolution goes beyond individual productivity. The organizations that benefit most will be those that capture their best practices, domain knowledge and analytical expertise, turning them into reusable capabilities that support teams at scale. The future of analysis is not about replacing human expertise with AI. It is about amplifying it. Combining human judgment with AI capabilities to solve better problems, faster.

And just like every bakery still needs a skilled baker to create exceptional bread, every AI-powered organization will continue to need exceptional analysts. Only now, they'll have the best sous-chef they've ever worked with. And if you're wondering why I chose a bakery as the analogy… I may have grown up surrounded by flour, dough and very early mornings. As the daughter of a baker, I’ve seen firsthand that craftsmanship is never just about the tools, it’s about the expertise behind them.

It's also why, as consultants at Datashift, we don't see AI as something we simply advise on. We use it ourselves every day to work smarter, ask better questions and help our clients build capabilities that last. Contact us if this triggers you to work with or for Datashift.