How AI solves business challenges: Case studies from Autoklinikka and Otavamedia

The discussion around AI has long needed more concreteness and genuine use cases where technology is harnessed to solve real business challenges and create a better customer experience. In our latest webinar, we dived exactly into this theme. Led by our Innovation Director Sami Häkkinen and Chief Design Officer Katja Vakula, we gathered to unpack the latest AI solutions we have had the privilege to create with our customers.

The conversation delved into two highly interesting practical examples as Autoklinikka’s Heli Avikainen and Otavamedia’s Riku Österlund unpacked the recent AI innovations of their own organizations. Through these stories, a clear picture emerged of what the intelligent web means right now in the business field.

The intelligent web and the rise of machine experience

Just a couple of years ago, the focus was largely on large language models and content creation, whereas this spring, the conversation is dominated by multi-agent systems, autonomous AIs, and the intelligent web. This development is fundamentally changing the way we build digital services.

In online behavior, there is a strong shift towards a “zero-click” world, where more and more information searches end with a direct answer provided by a search engine or an AI bot, without a single click to actual websites. As a result, users arriving at company websites are even better informed than before. They expect to receive exceptionally in-depth, fast, and highly personalized content tailored specifically to their needs straight away.

Alongside the traditional customer experience, an entirely new concept has emerged: machine experience. In addition to humans, digital services are now read and interpreted by various AI agents. Content must be seamlessly discoverable and understandable by these autonomous bots, since neglecting the machine experience means invisibility in the era of the intelligent web. Companies must carefully balance their efforts to serve both target audiences with equal quality.

Case: Autoklinikka removes friction from windshield damage management

The first practical example of the webinar took us into the everyday life of motoring. Autoklinikka’s Marketing and Communications Director Heli Avikainen presented the AI Windshield Inspector launched earlier this year. The tool was born out of a desire to solve a very mundane pain point: when a rock hits a windshield, the driver often has to figure out if a simple repair is enough or if a more expensive full glass replacement is ahead.

“The idea for the Windshield Inspector started from our joint AI sprint. I feel it was really useful for us. It was great to get to spar with your experts on how AI could streamline our customer service process. We got a lot of good ideas from the sprint.” – Heli Avikainen, Autoklinikka

With the new AI feature, the customer only needs to take a picture of the damage with their smartphone and upload it to Autoklinikka’s service. The visual AI model purring in the background analyzes the mark in hundredths of a second, providing an immediate assessment of the situation. In the same view, the customer receives a price estimate and the opportunity to book an appointment directly at the nearest workshop. The technical implementation of the service is an elegant example of modern architecture, where a built-in component of the site communicates seamlessly with Google’s AI interface within the framework of limited and precisely defined guidelines.

In addition to facilitating the user experience, the tool has a significant ecological dimension. Autoklinikka emphasizes responsibility in its operations, and a fast AI assessment supports this goal perfectly. By directing the customer to have a small crack repaired immediately, the damage is prevented from expanding. Patching a windshield saves up to 40 kilos of waste compared to replacing the entire glass. The tool has indeed received an excellent reception from customers, and the accuracy of its assessments has been truly precise.

Case: Otavamedia tackles content overload with hyper-personalization

In the second expert talk, we dived deep into the transformation of the media industry led by Otavamedia’s Riku Österlund. Consumers’ media consumption habits have become faster-paced, as social media platforms have taught us fast, finger-scrolling content consumption. At the same time, a large media house has a massive amount of high-quality content to offer from under more than ten different brands.

Otavamedia tackled this challenge by developing the “For You” (Sinulle) web application. It is a hyper-personalized view, currently operating only on the Kotiliesi website, that gathers and recommends articles to the reader across all Otavamedia brand boundaries. The user interface is designed to be visually appealing and quick to browse. Under the hood runs very robust technology, as the recommendation engine is based on the same Google AI that YouTube uses in the background.

“Everything really started from a joint planning meeting. We approached the first steps with an open mind regarding the final outcome, focusing instead on exploring the changes in online and media consumer behavior. We pondered what new technologies could bring to our ability to better meet the consumer’s mindset. From these threads, the pieces fell into place and we ended up with this collaborative project.” – Riku Österlund, Otavamedia

The lessons shared by Österlund from the project were extremely valuable for all organizations utilizing AI. One of the most significant insights related to the importance of groundwork: utilizing new technology absolutely requires careful organization of your own data. The information must be consistent and structurally sound so that the AI can process it smartly and serve it seamlessly to the end-user.

In addition, Österlund emphasized the importance of a healthy culture of experimentation and tight scoping. Faced with endless possibilities, there is always the danger of drowning in tweaking details. The best results are achieved by taking the first version quickly to production, collecting genuine customer data, and further refining the service based on these lessons.

Keeping the focus firmly on business challenges

Behind the most successful projects is always a genuine desire to solve existing pain points in business or the customer journey. Both showcased case examples originated from needs-driven design, which new technology helped implement more cost-effectively and agilely than ever before.

Technology is now readier than ever, and the threshold for experimenting with it has lowered considerably. Now is the right time to open-mindedly map out your own organization’s processes and look for the spots where AI can genuinely streamline daily life and take the customer experience to a completely new level.

If you want to dive deeper into the topic and hear the entire discussion of the experts, you can watch the full webinar recording below: