How do you know if your business actually needs an AI solution?

Not everyone needs it. Here are the criteria we use to assess this.

AI makes sense when most of the following apply:

The process is repeated – ten times a day, not ten times a year
Data on past cases is available, even if it is in poor condition
A decision has a measurable cost – time, errors, lost sales, downtime
The result result does not need to be perfectto be useful – 85 per cent accuracy, with human review, is often sufficient
Someone in the company experiences the problem every day and can describe it

AI is pointless when:

There are too few examples to learn anything from them
The problem is actually one of process, not technology – AI will simply automate the chaos
100 per cent accuracy is required without human review
No one owns the problem, and the project exists simply because ‘we have to do something with AI’
The existing tool can already do this, but nobody is using it

Off-the-shelf AI tools vs. custom development

	Off-the-shelf tools (SaaS)	Custom development
Time to deployment	days	4-8 weeks for MVP
Initial cost	low	from 5.000 €
Long-term cost	scales with users and volume	fixed development + infrastructure
Adaptation to your process	limited to what the tool provides	complete
Where the data is	with the provider	wherever you choose
Integration with ERP/MES/CRM	depends on the provider	planned from the beginning
Competitive advantage	none; competitors use the same tool	yours
If the provider raises prices or goes out of business	you move elsewhere, if possible	no dependency

Opt for a off-the-shelf tool if the process is standard (transcription, translations, general customer support), if the volume is small, or if you don’t yet know exactly what you need – a ready-made tool is the cheapest way to find out.

Opt for custom development if the process is your competitive advantage, if data must not leave your organisation, if you need deep integration with existing systems, or if the volume makes a SaaS subscription more expensive than development.

Often, the right answer is a combination: a ready-made model as the engine, with a bespoke layer built around it that understands your data and processes. That’s how we worked at Tehnoles – a commercial model for content generation, with bespoke logic for their catalogue and channels. The result: 80 per cent shorter content preparation time, 37 per cent higher CTR.

Which sectors benefit the most?

It is not so much about the sector as it is about whether the sector generates a large amount of structured data and involves costly, repeatable decisions. In practice, these are:

Manufacturing

Workforce planning, predictive maintenance, quality control.

At FTE Planning Tool, we reduced production line downtime by 35 per cent by optimising workforce planning.

Logistics and mobility

Route optimisation, demand forecasting, fleet management.

At E-Ture, we developed a smart e-bike locking system in collaboration with Domel.

Retail and e-commerce

Content creation, recommendation systems, stock forecasting.

At Tehnoles, this resulted in a 37 per cent increase in CTR.

Education and media

Processing and classification of large volumes of content.

At Videolectures.net, we carried out a content migration and website redesign, thereby ensuring a 25 per cent increase in content usage.

Data-intensive services

Analytics, procurement, finance.

At IK DataHub, we integrated system for analysis of millions of lines of data daily.

Where we’re sceptical: sectors with very few high-value transactions (where every transaction is unique) and companies that haven’t yet established basic reporting systems. If you don’t yet know how much this process costs you, AI won’t calculate it for you.