What better way to celebrate Valentine’s Day than to share a little love, and debunk some myths, about AI?
After collaborating for several years with some of the largest Nordic companies working in the data, analytics, and AI spaces, one thing is clear: the AI learning journey is intense, humbling, and constantly evolving.
Here are some of the most common myths we still see and the honest truths we’ve learned instead.
💡 Myth 1: “Let’s start with Copilot for everyone.”
Adding GenAI on top of existing ways of working rarely scales.
You might see some efficiency gains from individuals using GenAI, but do you know how that time is actually used?
Without redesigned processes, those gains rarely translate into real outcomes, which means the added capacity often goes unused.
And when the value doesn’t materialise, it usually shows up as higher license costs rather than meaningful impact.
If Copilot licenses are your main AI initiative so far, it’s probably time to rethink both your strategy and your processes.
Instead, you should be asking:
What would our operations look like if we designed them with AI agents from the beginning and worked backwards from there?
💡 Myth 2: “We need a perfect data platform before we can start.”
A solid data platform is essential for long‑term analytics and ML, but many automation and agentic AI use cases don’t live inside the data platform.
What’s more, striving for perfection only delays value because a lot of early wins don’t depend on a fully mature data foundation.
You don’t need everything to be perfect to start; you need the right use case, tools, architecture, and focus.
💡 Myth 3: “Requirements aren’t that important anymore; the tech is so good.”
This perspective has always been risky, and with AI, the risk compounds.
Stronger technology magnifies unclear goals, resulting in faster mistakes rather than faster progress.
That’s why successful implementation still depends on redesigned processes and clear ownership. The tech can only perform as well as the direction it’s given.
💡 Myth 4: “We don’t need process or knowledge documentation; the LLM will figure it out.”
This is one of the biggest gaps we see. Business logic often lives in systems, people’s heads, and informal workarounds, and AI can’t learn what isn’t clearly expressed.
LLMs don’t replace structure; they rely on it.
If you want intelligent automation, you need to document the knowledge the model is supposed to use.
💡 Myth 5: “Jobs will disappear soon.”
Some junior tasks are already being automated, but senior roles are still very much needed.
At the same time, entirely new roles are emerging fast, especially in digital business and GenAI development.
The real question is:
Are you preparing your organization for this operational shift?
That’s it from us for today, but with how quickly AI is evolving, we’ll likely be back soon to explore some more myths. 😉
On a day that celebrates partnership, it feels like the perfect time to express our gratitude to the organizations who have entrusted us with supporting their AI journey and say thank you in advance to those we’ll work with in the future.
Happy Valentine’s Day ❤️
