How companies can use AI effectively – and where they would be better off waiting (for now).

Artificial intelligence is currently on everyone's lips. Whether in headlines, meetings or strategy papers – hardly any other topic is currently being discussed as heatedly. Many companies are feeling the pressure to „do something with AI“However, not every application and use brings real added value. AI can automate processes, analyse data or support decision-making. However, it is not a panacea and certainly not a sure-fire success. The decisive factor is how it is used and where it really makes sense.

In this article, you will learn where AI is already creating added value today, where companies would be better off waiting (for now), and what it takes to use the technology's potential responsibly.

The most important information at a glance

Digitalisation must have a measurable impact. The central question is: Which steps will bring real progress, and which will remain mere actionism?

This is exactly where our Digitalisation Readiness Assessment comes in. We analyse processes, systems and data, assess the level of maturity and show where investments are really worthwhile. The end result is not a theoretical paper, but a clear roadmap that provides orientation and creates decision-making certainty.

AI can automate processes and support decision-making – but it is not a sure-fire success. It only works if data quality, objectives and responsibilities are clear.

Companies benefit particularly from AI in the following areas, where large amounts of data are processed or recurring tasks arise. This is the case, for example, in customer service, analysis, logistics or financial processes.

Missing or distorted data is the greatest risk. Without a clean foundation, AI models deliver inaccurate or incorrect results.

Not every process is immediately suitable for AI. If the benefits, data basis or data protection are unclear, it is better to wait and see.

AI is a tool, not an end in itself. Companies that take a strategic approach achieve more in the long term than those that simply follow the hype.

What artificial intelligence can do today, and where its limits lie

Artificial intelligence essentially describes systems that can recognise patterns, learn and independently derive suggestions or decisions. In everyday business life, this means that tasks that used to be performed manually can now be (partially) automated. And usually faster, more accurately and around the clock.

AI is therefore no longer a thing of the future. Many companies are already using it to simplify processes, make better use of data and improve customer experiences. But despite all the enthusiasm, not everything that is technically possible is also sensible.

How companies are already using AI today

Currently, AI is demonstrating its strengths in areas where large amounts of data are generated or recurring tasks need to be performed. Typical examples include:

  • Customer service: Chatbots and automatic response systems support teams in processing enquiries.
  • Marketing and sales: Analysis of customer data to personalise campaigns, pricing strategies or product recommendations.
  • Finance: Automated invoice verification and fraud detection.
  • Logistics and purchasing: Demand forecasting, route optimisation and inventory management.
  • Human resources: Support in recruiting or internal training.

These examples show: AI can make processes more efficient, prepare decisions and reduce the workload for employees. It helps to automate routines and free up time for value-adding activities. However, all of this requires the right foundations to be in place.

Limits of current technology

Despite impressive progress, AI has clear limitations: it can replicate linguistic connections very well, but it does not really understand them. It recognises patterns, but not meaning. Missing or distorted data can therefore quickly lead to incorrect results, which is often underestimated and can cause problems. 

Ethical and legal issues, such as those relating to data protection, copyright or traceability, also often remain unresolved. This can lead to uncertainty, for example when evaluating results, liability for errors or the use of sensitive data.

In addition, AI cannot think intuitively or take responsibility. Decisions must therefore always be reviewed and evaluated by humans.

That means: AI can do a lot – but only if it is properly embedded and used responsibly.

AI in practice: opportunities and limitations

Now that we have clarified what AI can achieve and where its limits lie, it is time to look at the practicalities: where does AI create real added value today, and when is it better to wait and see?

Where AI creates real added value today

AI unleashes its potential where processes are clearly defined, data is reliable and goals are measurable. Used correctly, it can relieve teams, use resources more efficiently and direct focus to what is essential.

  • Automate routine tasks: Whether it's invoice verification, order entry or email pre-qualification, AI can take over standardised processes involving many identical steps. This saves time and reduces errors.
  • Making better use of data: Companies collect large amounts of information every day. This information comes from sources such as ERP systems, online shops and production facilities. AI helps to make this data usable by recognising patterns, making predictions and thus enabling informed decisions to be made.
  • Improving customer experiences: Chatbots, intelligent search functions and personalised recommendations ensure smooth interactions. At the same time, employees gain time for tasks that require empathy and experience.

practical example:
A retail company uses AI to optimise inventory levels. The system recognises seasonal fluctuations and automatically adjusts order quantities. The result is lower storage costs, fewer bottlenecks and satisfied customers.

When companies should wait and see

However, not every process is immediately suitable for AI, and not every company is ready for it yet. Until the following fundamentals have been clarified, companies would be wise to wait a little longer before introducing AI tools.

  • If the database is missing: Without clean, reliable data, AI remains ineffective. Anyone who first needs to prepare data or network systems should tackle this step first.
  • If the benefit is unclear: „We are now doing this with AI as well.“ is not a goal. Only those who know the specific added value – such as time savings, quality improvements or cost reductions – can measure success.
  • When data protection and responsibility are unclear: Particular caution is required when dealing with sensitive information. Companies should know exactly where data is being processed and who has access to it.

Our advice: First understand, then automate. Because AI is not an end in itself, but a tool.

Requirements for meaningful AI use

Three things are necessary for AI to be useful: data, people and goals. They form the foundation for every successful application and determine whether AI really helps in everyday life or remains merely potential.

  1. Data as a foundation: Only those who know and integrate their data can use AI in a targeted manner. A networked IT landscape – for example, between ERP, CRM and LVS – is the basis for this.
  2. Involving employees: AI changes work processes. That is why it is crucial to get employees on board. This can be achieved through training, targeted dialogue and open communication, for example. When employees understand how AI supports them, both acceptance and quality increase.
  3. Set clear goals: Before a project starts, it should be clear: What problem needs to be solved? A clear purpose prevents actionism and makes success measurable in the end.
  4. Selecting suitable partnersIt is particularly helpful for small and medium-sized enterprises to involve experienced IT partners. They can assist in selecting technologies, reviewing data structures and securely integrating solutions.

Conclusion: Use AI with foresight rather than just for the sake of it

Artificial intelligence can make many things possible, but not everything at once. Forward-thinking companies therefore invest first in stable foundations: clean data, networked systems and employees who understand AI.

collana supports companies precisely in this endeavour: pragmatically, reliably and with a view to the future. Because we know that digitalisation is teamwork – and AI is part of that.

Learn more about how collana can support you with intelligent and practical AI solutions.

Our latest news