Nice AI pilot. Now let’s talk real business.

AI skills are business skills

AI isn’t just for a select few

AI has moved beyond the deeply technical. It is now for everyone; all you need is your phone to access the latest tech, and AI tools are becoming more intuitive as time goes. Designed with specific roles in mind, AI can now be experienced across entire organisations, and it would be a huge missed opportunity not to take advantage of that.

Organisations must pave the way

Expecting employees to magically deliver innovation is wishful thinking. It’s organisations’ responsibility to encourage proactivity on a cultural level and create space, supporting and investing in AI literacy. That’s how you will find new creative outlets and opportunities for collaboration, building momentum for future business impact.

Work smarter, not harder

No need to turn your company upside down to enable AI-powered collaboration – lasting results don’t happen overnight, after all. Instead, start to encourage experimentation with AI tools, offer introductory training sessions, and help identify where AI can be embedded in everyday tasks. In the end, AI skills are more about behaviour change than learning about new tools.

How can different teams use AI?

• Marketing teams: quickly generate campaign ideas tailored to different audiences,
• Finance teams: analyse spending patterns and forecast budgets more accurately,
• Operations: help with supply chain planning and predictive insights,
• Human Resources: personalise learning programs and enhance talent recruitment.
  

More about the topic in our research report: How GenAI is transforming Nordic work life

“From the AI roadmap to lasting impact”

The worst crime? Lack of vision

Forget theory and safe practices – discovery lies at the heart of experimentation. Through mistakes and successes, you’ll see how AI fits your business while building proof of its value. But how do you avoid getting lost in the hype? By having a strategy and roadmap as your guiding star. They point out the shift from isolated trials to a holistic approach, ensuring wise use of resources.

AI follows the business beat

AI can’t be random, and it can’t be randomly integrated. It needs to fit into the broader business vision, helping achieve strategic goals. But technology isn’t the be-all and end-all – you need talented and knowledgeable people who can make the right use of it. Support them in broadening their skills, reimagine core value streams, and evolve your organisation’s processes to strike gold.

Winging it means losing it

Everyone wants success, but few know how to get there. Things start to fall apart when the need for a specific plan arises – KPIs, objectives, and key results included. Those who wing it, find themselves submerged in utter chaos, as if driving through thick fog with no clear direction and limited visibility. You can find your way out only with a clear plan, supported by the right metrics.

Chase sustainability, not the hype

Following the Pareto Principle:

• Start where the impact can be greatest.
• Prioritise the parts responsible for the biggest wins and make the most of them.
• Always tie the focus point back to your broader business strategy – otherwise, you’re wasting your time and money on the less profitable things.
• Skip the flashy AI gimmicks in favour of future-proof, sustainable solutions.
  
  

Learn more and watch our Crash Course on data and AI strategy

Don’t waste money on dirty data

AI won’t turn garbage into gold

AI isn’t a miracle worker – you can’t feed it garbage and expect gold. And yet, 81% of companies pour money into AI, tripping over messy, incomplete or just plain wrong data. It’s like raising a kid on fairy tales, then tossing them into a corporate strategy meeting. Chaos, right? The same thing happens when your AI accesses questionable data.

Why it matters more than you think

Poor data isn’t just annoying – it’s expensive. Studies say it tanks productivity by 20% and cranks up costs by 30%. Now throw AI into the mix, and those problems get amplified; it’ll be really hard to pinpoint what caused the AI to go wrong if you don’t have your data in order. And let’s not even talk about the exec-team disconnect: while 90% of managers clearly see the data quality issues, only 17% of execs admit there’s a problem. Awkward.

The fix? Data first. Always.

Before you go all-in on the latest shiny AI model, make sure your data house is in order. Verify that you have the data to do the AI actions you want to do. Netflix didn’t change the game with fancy algorithms alone – they nailed the data side first. Their real edge? Clean, consistent, meaningful data that actually tells them something useful.

So, what can you do?

• Check your data story – if it’s inconsistent, your AI will be too.
• Define quality – decide what “good data” means: accurate, complete, relevant.
• Make it everyone’s job – teams thrive when data quality is a shared responsibility.
• And finally – don’t try to solve all problems at once. Decide on a use case, apply AI, test it, solve it, and then scale it. 

Learn more:

Deep Dive into How to get AI ready data with data management?

Crash Course on Solving data bottlenecks with GenAI

Are we ready to give AI agents the full autonomy they are built for?

Chatbots turned us humans into copy-paste bots…

…but there must be something better. The answer could lie in AI agents. Be careful, though. Not everything called an ‘agent’ actually acts like one. Real AI agents perform dedicated tasks in their assigned agent network, and they don’t need you constantly telling them what they should and shouldn’t do, and how.

True agents are a powerhouse

Agents can perform pre-defined workflows to deliver value through automation (process time and quality), and they can also solve open-ended business problems in new ways, thus creating new value in unexpected areas. Moreover, agentic systems can transform deeper levels of how we work: from workflows and business processes to business models and societal bonds.

High risk, high reward

Agents aren’t easy to build into production, nor are they necessarily cheap to operate. Agents need human oversight and governance, they need security and spend monitoring, and they need to be developed and tested at least as rigorously as any other software-based digital service. This is where you really need a trusted partner who knows tech and business broadly.

What does it mean for you?

• There’s more to AI than just chatbots, with many more possibilities to explore,
• AI agents have the potential to deeply disrupt and reorganise life as we know; but not all AI agents are the real deal.
• Is it expensive? Probably. Is integration difficult? Sure it is. But the potential return of investments can completely change the game. 

Read more: From automation to agents: What AI agents really are and why you should care

See also: Crash Course on GenAI for software development