AI adoption in the Nordics has moved fast. Organization-wide use of AI in production jumped from roughly 7% to 31% in a single year, according to Tieto’s 2026 Nordic AI Survey, and the share of organizations giving at least 40% of employees access to approved AI tools climbed from 37% to 56% over the same period, based on Deloitte’s State of AI in the Nordics report, as covered by Finnish AI Region. On paper, that looks like a region racing toward full automation.       

In practice, much of this AI use is not replacing everyday work. It is being added to the tools Nordic teams already rely on, especially Excel. Reports are still built in spreadsheets. Budgets are still modeled in spreadsheets. And the dashboard a department head checks on Monday morning is often still fed by a spreadsheet somewhere upstream. What has changed in 2026 is what happens inside that spreadsheet, and how much faster a team can move from raw numbers to a decision worth acting on.

Here is what that combination actually looks like across Nordic companies right now, and why the Excel skills underneath it matter more than the AI layer sitting on top.

Why Excel Is Still at the Center of Nordic Decision-Making

It would be easy to assume AI tools have started replacing spreadsheets in business teams. That is not really what is happening. Excel remains the place where procurement teams track supplier costs, finance builds monthly reports, and operations managers reconcile numbers before a meeting. AI has not removed that layer; it sits directly on top of it.

That distinction matters for how Nordic organizations are approaching AI. Deloitte’s research found that Nordic companies are leaning toward augmentation rather than automation, using AI to help employees do their existing work faster rather than replacing the work itself. Strong labor protections and a tradition of negotiating workplace change, rather than imposing it, both play a role here. The result is a slower, more deliberate rollout than in some other regions, but one that tends to fit neatly into tools teams already trust, Excel being the clearest example.

The Adoption Numbers Behind the Shift

A few figures explain why this year feels different from the last one. Tieto’s 2026 survey of IT decision-makers across Finland, Sweden, and Norway found that minimal AI use among employees dropped from 17% to 10%, while close to a third now use AI solutions extensively in their daily work. Almost no organizations report zero AI use anymore, according to the same survey.

At the same time, Deloitte’s State of AI in the Nordics 2026 report points to a gap worth paying attention to. Fifty-five percent of Nordic organizations feel well prepared on infrastructure, but only 14% feel equally prepared on talent. Strategic preparedness actually declined, from 61% last year to 43% this year. Put simply: the tools are arriving faster than the skills needed to use them well. That gap is exactly where Excel training becomes relevant again, not as a competing priority to AI adoption, but as the foundation that makes it work.

What Excel Plus AI Looks Like in Practice

Natural language formulas instead of memorized syntax

The most visible shift is how people build formulas. Instead of recalling the exact syntax for a nested IF or an XLOOKUP, employees describe what they need in plain language and get a working formula back, a capability Microsoft documents in its own Copilot training. A request like “add a column showing the difference between this month and last month’s sales” returns a usable result in seconds rather than a trip to a colleague or a forum search.

This lowers the barrier for less confident Excel users, but it does not remove the need to understand what the formula is actually doing. Someone still has to check whether the output makes sense.

Faster data cleaning before anything reaches a report

Messy exports are still the biggest time drain in most reporting workflows: inconsistent date formats, duplicate rows, blank cells where a number should be. AI tools inside Excel can now handle a good share of this on request, flagging duplicates, standardizing formatting, and filling gaps based on patterns in the surrounding data.

Teams that already understand what clean, structured data looks like get far more out of this than teams that do not. AI speeds up the cleaning step; it does not decide what “clean” means for a given report.

Spotting patterns and anomalies automatically

Where AI adds the most obvious value is in surfacing things a person might miss on a first pass: a supplier whose costs jumped without explanation, a region trailing its usual sales pattern, a spike that does not match seasonal expectations. Instead of scrolling through thousands of rows, a team can ask a direct question and get pointed toward the specific rows worth investigating, part of what Microsoft describes as advanced data analysis with Copilot in Excel.

This is where decisions genuinely get faster. The bottleneck was never really the decision itself; it was how long it took to notice the thing worth deciding on.

Turning PivotTables into a shorter loop

PivotTables have always been the fastest way to summarize large datasets in Excel, and that has not changed. What has changed is how quickly one gets built. AI tools can now suggest or generate a PivotTable structure based on a plain-language description of what someone wants to see, cutting out a step that used to take real PivotTable fluency to do well. Learnesy’s PivotTables course covers the grouping, filtering, and summarizing logic that still sits underneath this, AI-assisted or not.

Where Nordic Teams Are Actually Using Excel and AI

In practice, Excel and AI are showing up in ordinary business workflows, not only in advanced analytics teams.

For example, logistics and procurement teams can use AI-assisted Excel work to reconcile supplier data faster and flag cost changes that need review. Finance and FMCG reporting teams can use AI for first-pass checks during monthly reporting, while a human reviews the exceptions. Real estate and operations teams can bring scattered data, such as occupancy figures, maintenance logs, or tenant reports, into a cleaner view before a meeting.

None of this looks dramatic from the outside. It looks like the same reports being prepared faster, with fewer manual steps and fewer errors slipping through.

That is exactly the kind of change Nordic HR and L&D leaders tend to care about: practical, low-disruption, and easier to connect to business outcomes.

Why AI Doesn’t Replace the Need for Excel Skills

This part gets skipped in a lot of the AI adoption conversation. AI tools inside Excel are only as good as the person directing them. Someone who does not understand what a PivotTable is doing cannot judge whether an AI-generated one is correct, and someone unfamiliar with lookup functions will not catch it when an AI-suggested formula quietly returns the wrong result on an edge case.

Deloitte’s talent readiness figure, just 14% of Nordic organizations feeling prepared, reflects exactly this gap. Infrastructure is easy to buy. Judgment is not. Teams that already have a solid Excel foundation adapt to AI features quickly, because they recognize what good output looks like and what does not. Teams without that foundation tend to either over-trust AI-generated results or avoid the features altogether out of uncertainty, both of which slow decisions down instead of speeding them up.

What HR and L&D Teams Should Take From This Shift

For HR and L&D teams, the lesson is not that every employee needs advanced AI training. It is that AI adoption works better when employees already understand the tools and data they use every day.

That usually means building a stronger Excel foundation first: clean data habits, formulas, PivotTables, basic analysis, and the confidence to check AI-generated output before it reaches a report or decision.

The teams that move fastest with AI are not always the ones with the most tools. They are often the ones with the clearest baseline skills across the department.

How Learnesy Helps Nordic Teams Build This Foundation

Learnesy helps Nordic teams build the Excel foundation that makes AI tools more useful in everyday work.

The Excel Essentials course includes a dedicated chapter on using AI tools inside Excel, with lessons focused on real spreadsheet tasks rather than abstract AI theory. The goal is not only to help employees use AI faster, but to make sure they still understand the Excel logic behind the output.

For teams that want to explore how AI is changing data work more broadly, this article on AI and the future of data analysis is a useful next read.

For HR and L&D teams, the bigger challenge is often consistency. One confident Excel user is not enough if the rest of the department still works differently. Learnesy’s team and business plans support rollout across mixed-skill teams with short lessons in Swedish, Norwegian, and English, plus a Customer Success Manager and admin dashboard so progress is visible instead of assumed.

Summary: How Nordic Companies Combine Excel and AI for Faster Decisions in 2026

Nordic companies are not replacing Excel with AI. They are using AI to make everyday Excel workflows faster, from cleaning data and building formulas to spotting patterns that might otherwise take longer to find.

But the teams getting the most value from this shift are not relying on AI alone. They already have a solid Excel foundation. That means employees understand the data, know how to check the output, and can tell when an AI-generated answer needs a second look.

That is where Learnesy helps. Learnesy gives Nordic teams the Excel, data, and AI skills they need to use these tools with more confidence, so AI becomes a real shortcut instead of a source of quietly wrong answers.

Explore Learnesy for teams.