Bringing AI into operational business applications

Where is AI today and what should management teams think about to keep up to speed organisationally and technologically?

Luka Crnkovic-Friis, CEO at Peltarion.

We interview Luka Crnkovic-Friis, co-founder and CEO of AI platform provider Peltarion, about where AI is today, common misconceptions about the technology and what management teams should think about organisationally and technologically to keep up to speed.

He explains how the Peltarion platform is able to put the power of machine learning in the hands of non-experts to build operational AI systems that have been used for everything from cancer diagnosis and house price prediction to music categorisation and optimised pulp production. He also discusses Peltarion’s ambition to educate both managers and citizens in general about AI.

Where is AI today and what are the challenges?

During the industrial revolution, we automated physical power. Now we are automating intellectual power; ie we are in a cognitive revolution. This will likely affect sectors across the board. We have never seen such fast adoption of new technology as we are seeing now.

Challenges come from transformation and depend on where in the digitalisation process companies are. A data-enabled company (see picture below) has data at its core, around which it builds layers of first algorithms and then processes – encompassed by the people of the organisation. However, a more traditional company usually puts people at the core, tries to build a layer of culture and values and then tools around that, while data is an object that is distant from the thoughts of the organisation.

The first type of business, the “Googles” and “Amazons” of this world, should be able to utilise AI and machine learning (ML) quite easily, as data is at their core. For the more traditional type of company, it quickly becomes difficult to utilise data-driven processes. Most companies are somewhere in between these two extremes, and the closer to a traditional organisation that a company is, the more challenging it will be to succeed in an emerging AI/ML environment.

An understanding of data quality is naturally important, but data quality is usually quite misunderstood among management teams. When we meet board-level management, we usually hear that they have the best data in the industry. As we trickle down in the organisation, the promised data quickly becomes of lower quality, and then not current. Many times, it turns out that it is not even possible to get the data at all.

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