AI in practice: Where technology actually creates value – and where it doesn’t
How AI can support analysis, decision-making and operational work
This article is about AI in practice – how the technology can genuinely support analysis, decision-making and day-to-day execution.
Artificial intelligence is no longer a future concept. For most organisations, it is already part of everyday work through analytics tools, CRM platforms, content production and decision support. Still, many experience that their investments in AI deliver limited real impact.
Not because the technology doesn’t work – but because it is often used the wrong way.
AI rarely fails because of technology
Most AI solutions today are technically solid. The models are powerful, data is available, and the tools are becoming increasingly user-friendly.
Several studies show that the challenge rarely lies in what the technology can do, but in how it is adopted and used in organisations (for example, OECDs work on AI adoption)
Typically, organisations have:
Advanced AI tools
The tools are powerful and flexible, but often lack clear ownership. When no one is responsible, optimisation becomes a vague “someone should probably do something” problem.
Structured and unstructured data
Data is collected, measured and reported continuously. However, the link between data, customer behaviour, churn, priorities and actual decisions is often weak.
An ambition to become more data-driven
The ambition is clear, but everyday operations move on quickly. When the threshold for change is too high, many processes remain unchanged.
Despite this, value fails to materialise. The issue is rarely the technology itself – but how it is applied in practice.
When AI becomes a side project
A common pattern is that AI is introduced as a limited initiative:
Pilot
tested without real ownership
Innovation project
isolated from daily operations
Experiment
owned by a single function
AI gets its own place in the organisation, instead of becoming part of existing workflows. The result is often solutions that look impressive in demos, but are never actually used in decision-making or daily execution.
Without clear ownership, prioritisation and anchoring, AI ends up standing on the sidelines of what truly matters.
AI creates impact when it supports existing work
AI creates value when it is used where decisions are already made – not as a separate layer next to the organisation.
The organisations that succeed with AI rarely start with advanced solutions or big ambitions. They start with everyday work: how people analyse, prioritise, evaluate and execute.
In practice, this means using AI to strengthen existing processes, not replace them. The technology contributes insight, structure and pace – but humans still own the decisions.
When AI is placed correctly, it reduces friction instead of creating new dependencies.
AI creates value when it:
- supports decisions rather than replacing them
- simplifies prioritisation instead of complicating processes
- improves flow in work that already exists
Successful organisations use AI where it naturally belongs – in analysis, planning, evaluation and execution. Not as an extra layer, but as an integrated part of everyday work.
AI in practice: Use cases that actually work
In practice, AI delivers the best results when use cases are concrete and clearly defined. The most successful examples are rarely large systems – but small improvements in everyday work.
Summarising and structuring insight
AI works well as support for collecting, sorting and clarifying information – whether it’s customer insight, internal notes or analysis. It creates better overview without making decisions on behalf of people.
Support for prioritisation and choice
By structuring alternatives, consequences and data, AI can contribute to better decision-making. The value lies in clarity and speed – not in replacing judgement.
Content variation and adaptation
AI is well suited for adapting messages across formats, channels and audiences. This reduces manual work while quality is maintained through clear guidelines.
Automating repetitive tasks
When tasks are predictable and rule-based, AI can increase efficiency. The key is not automation for its own sake, but freeing up time for work that requires human insight.
The common denominator is that AI is used to enhance human judgement – not replace it.
AI in practice: From tool to real value
The transition from technology to impact is based on a few simple, but demanding principles:
1. Start with a real problem
Not with technology, but with friction in everyday work – something that actually gets in the way of decisions or execution.
2. Use a simple solution
Choose something that can be adopted quickly, understood by many and adjusted over time – not necessarily the most advanced option.
3. Measure real change over time
Look for changes in behaviour, better flow or clearer priorities – not just output or activity.
Small steps often create more impact than large AI initiatives. When technology is used continuously, adjusted based on experience and linked to clear goals, value is created gradually – and sustainably.
Conclusion: From AI ambition to real impact
AI is no longer about what the technology can do. For most organisations, the real question is how it is used – in practice, in everyday work and in real decisions.
Those who succeed with AI don’t treat it as a side project or a magic solution. They start with real problems, work iteratively and build solutions that support existing workflows.
Over time, value is created – not through big promises, but through small, measurable improvements.
My focus is to help organisations do exactly this: move from tools to impact. Not by introducing more complexity, but by simplifying, structuring and clarifying where it creates the most value.
If you’re curious about how AI can be used more purposefully in your organisation – to support decisions, prioritisation and execution – I’m happy to have an obligation free conversation. You’ll also find more articles on the main page, Insights..
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