Christof Zogg speaking on artificial intelligence
6 min

Artificial intelligence in companies: ‘Business stakeholders can no longer avoid it’

Artificial intelligence offers companies great potential to increase their efficiency, develop more innovative products and make better strategic decisions – and thus gain a competitive advantage. Christof Zogg, Head of Business Transformation at Swisscom, explains in this interview why AI is important for companies’ success and concerns more than just their IT departments.

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How can AI support sustained company success?

Christof Zogg: This can be achieved mainly through the great potential of AI to drive efficiency gains, as enabled by the latest developments in machine learning (ML), a subcategory of AI, in particular. For example, the ever-improving language translation programs, the chatbots for the automated handling of simple customer queries and the so-called transformer models such as ChatGPT (forms of generative artificial intelligence) offer impressive performance in text analysis and synthesis. AI is already widely used as a software development tool: according to Microsoft, 58% of the software code checked into GitHub these days has been reviewed, commented on or generated by the integrated Copilot bot.

What specific steps must companies take to use AI to the benefit of their business?

The age of artificial intelligence has only just begun but there is already terrific momentum with the development of generative artificial intelligence. Companies now need to gain practical experience through specific, but not too broadly defined, use cases. Manual and expensive processes are usually good places to start when looking for suitable AI use cases. Most importantly, however, the business side should not just leave the topic of AI/ML to the IT department when it comes to implementation. AI is too important and its implications for all forms of knowledge work are too far-reaching for it to be neglected by business teams and management. AI and ML can be very important strategic tools – and strategy is ultimately a matter for management.

Can you give us examples that underscore the potential strategic importance of AI – particularly generative AI?

That’s what’s so exciting about AI: it can be used very universally. For example, in the digitisation of operating models, i.e. all processes needed to win clients, provide services and support existing customers. Specific examples of applications are text analysis (automated processing of invoices and claims reports) and text generation (automated creation of discharge reports and document summaries) as well as searchbots for HR topics or customer contracts that summarise results easily in impressive quality and natural language.

But it can also be used to gain a strategic knowledge advantage, usually based on training a prediction model to forecast factors such as product trends, order intake or infrastructure renewal needs as accurately as possible.

What do business stakeholders need to look out for when implementing AI in their company?

It’s important to distinguish here between using an existing ML model that a provider has already trained for its customers – and one that is useful for a specific company, industry or country. Only with the latter specialisation can you establish a lasting USP and thus an effective competitive advantage. As with all data applications, whether you want to create a dashboard or train an ML model, you first have to do your homework and build a reliable and automated data platform.

Have Swiss companies and business stakeholders already recognised the importance of AI?

According to a recent survey by Equinix, 80% of Swiss companies surveyed would like to use artificial intelligence. However, the majority of them doubt that their data and IT infrastructure is ready for this. So there’s still a lot to do. With new AI services such as ChatGPT and Google Gemini, however, business stakeholders have no choice anymore but to engage intensively with the topic of AI. After all, smart employees in their companies have been using these in their day-to-day work for some time now and are feeding the underlying ML models with business-critical data.

Which companies and industries are benefiting the most from AI? Are there sectors that are still making relatively little use of this potential?

In contrast to other trending IT topics such as the blockchain and the metaverse, AI/ML is universally applicable. From this perspective, all sectors have a lot of room for improvement. If I had to mention sectors where the benefits are particularly high, I would say pharmaceuticals, finance, health and education.

What applications of GenAI do you see most often in Swiss companies at the moment?

There are three areas of application for GenAI that are actually found in every company: analysis and redistribution of unstructured text (such as supplier invoices, damage logs and investigation reports), generation of text documents (including summaries and meeting minutes) and conversational AI in the form of chatbots and in-house, dialogue-based search engines.

When it comes to implementing AI, companies have to acquire the relevant data but also train the model and interpret the results. What are the biggest challenges?

Whether a specific use case can be successfully implemented depends essentially on two factors: do I have enough of the data needed and is it of the right quality? With a reasonable amount of effort, can I train a model to handle the desired classification task or make forecasts with sufficient accuracy and validity?

Where can business stakeholders find answers to these questions? Do they even need to know in detail how AI is used or can they obtain this expertise by, for example, employing data scientists or bringing on board external service providers such as Swisscom?

As with other key technologies – such as cloud computing – I believe that every successful business stakeholder in the future will need a solid understanding of AI/ML – if only to be able to ask the right questions and better gauge the benefits and costs. Whether this expertise is obtained internally by means of in-house data engineers and scientists or through external consultants depends on the company’s preference.

How broadly will artificial intelligence impact companies? Should it be seen as ‘just’ an extension of core business or does the entire organisation need to be aligned with it – essentially a transformation to a data-driven business?

As with all major technical disruptions, artificial intelligence will affect the entire organisation. In the end, it won’t be enough to simply establish a new team in some business unit. But companies have to take the first step somewhere on their journey into the age of artificial intelligence – and a dedicated team could be a possible starting point.

How can companies counter internal scepticism and rejection of AI?

So far, we’ve mainly talked about the promising new world of AI, but obviously this new technology category isn’t without its problems either. Quite the opposite: important tech visionaries like Elon Musk, Max Tegmark and Geoffrey Hinton are actually seriously worried about the future of mankind. Stakeholders in Swiss companies don’t have to go that far with their considerations. But as with most technology trends, there will be inflated expectations and costly exaggerations – all the more reason for stakeholders to really get to grips with the topic.

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Extended and updated version of the interview from July 2023.

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