At Rise of AI 2019 in Berlin, Reetu Kainulainen, CEO and co-founder of ultimate.ai, argues that AI should augment human customer service agents rather than replace them with chatbots. She traces customer service from 1960s call routing through the dot-com era, cloud-based contact centers and, later, instant-messaging channels, noting that 4 percent of OECD employees, an estimated 7 million people in Europe, now work in contact centers, at an average enterprise cost of over 50 million dollars a year with up to 300 percent annual staff turnover. She says many companies tried pure chatbot automation but found the technology too shallow to replace human conversation. Instead, ultimate.ai builds an AI backbone integrated with tools such as Salesforce, Zendesk and Genesys, using deep neural networks trained on historical chat and email data to give agents real-time recommended answers, with a feedback loop that improves accuracy as agents accept or modify suggestions. She describes handling complex languages like Finnish, using clustering to structure unlabeled conversation data, and reports customer results including one user whose agents now take 96 percent of answers from recommendations, and response times cut roughly in half.
Key takeaways
- Kainulainen argues standalone chatbots failed because they are not conversational enough and mask deeper structural problems in customer service.
- She says an average enterprise spends over 50 million dollars a year on customer service, with staff turnover as high as 300 percent annually.
- Finnish alone has around 2,500 inflected forms of a single word, which is why ultimate.ai uses deep learning rather than manual grammar rules.
- One customer's agents now take 96 percent of their answers directly from ultimate.ai's real-time recommendations.
- Kainulainen says agents in a test group that had recommendations removed reported feeling slower and more stressed.
Detailed notes
A short history of customer service technology
Kainulainen traces the evolution of customer service from 1965's intelligent call routing, which created the call center industry, through the dot-com boom's addition of email, the cloud era driven by companies like Salesforce, and the later arrival of instant messaging as a consumer channel. She notes that despite decades of adding new communication channels, the underlying way customer service is delivered has not changed: the same agents sitting at desks answering calls, emails and chats. She argues the next big shift will be AI-driven models that change how the people in these roles actually work.
The scale and cost of the problem
Kainulainen says customer service employs about 4 percent of the workforce in OECD countries, roughly 7 million people in Europe by her estimate, and that an average enterprise spends over 50 million dollars a year running its customer service department. She describes the operating environment as highly complex, with agents juggling calls, chats, email, tweets and WhatsApp messages alongside up to 50 different back-end systems, and calls the work highly labor-intensive, citing annual staff turnover as high as 300 percent. She argues this combination produces poor outcomes for consumers and brand damage for companies, and says many companies' attempt to solve it with standalone chatbots failed because the technology was not conversational enough and only masked a deeper structural problem.
Building an intelligent backbone: augmentation and feedback loops
Kainulainen describes ultimate.ai's approach as an intelligent backbone integrated into existing contact-center software such as Salesforce, Zendesk and Genesys. A deep neural network is trained on historical chat and email data and then gives agents real-time recommended answers as they handle multiple simultaneous conversations. A feedback loop captures whether agents accept or modify a recommendation and feeds that back into the model, improving accuracy without manually created training data. Once accuracy is high enough on simple, frequent cases, the system can move those cases to full automation, and because every message is classified, the aggregated data can also show a business its most common issues, agent performance patterns, and which conversations correlate with higher NPS scores or better sales conversion.
Three hard technical problems: language, labeled data and feedback
Kainulainen names three technical challenges. First, complex languages: Finnish, she says, has roughly 2,500 inflected forms for a single word compared with English's singular and plural, so the company avoids manual grammar rules and instead trains deep learning language models on public and customer-specific data to scale across languages. Second, labeled data: customer service databases hold millions or billions of unlabeled conversations, so the company uses clustering algorithms to semantically group similar questions and, especially, similar answers, which cluster more reliably than questions, to build initial training datasets. Third, the feedback loop itself, which she describes as the core of the company: agents validating or editing recommended answers continuously improves the model, which in turn produces better recommendations and more agent usage.
Customer results
Kainulainen presents data from an early customer with about 40 agents showing recommendation accuracy rising with usage, with one customer's agents recently taking 96 percent of their answers directly from the tool's recommendations. She cites an airline customer whose response times roughly halved within two months of deployment. She also reports that conversations handled per agent per hour rose after an initial learning curve, and that a test group whose recommendations were temporarily removed reported feeling slower and more stressed, which she compares to losing Google Maps or Spotify recommendations.
Q&A highlights
Asked how the algorithm learns, Kainulainen explains it is a supervised classifier: agents effectively annotate training data every time they select or edit a recommended answer, avoiding manual annotation. Asked how the company got early enterprise customers to test an imperfect product, she says it hired its first sales team in December, started by asking companies directly to share data under a data processing agreement, and built a first version from that data, favoring "show, don't tell." She lists over 30 enterprise customers across the Nordics, Germany, the UK and Romania, including Zalando and Telia (a further airline customer name is unclear in the transcript). Asked about onboarding speed, she says the initial data clustering step can be completed within a day, results from agent-facing recommendations are visible within the first 30 days, and the first automated cases can often go live within one to two months.
Quotes
Customer service is still between the two humans, and having a relationship with somebody to actually take care of you.
Reetu Kainulainen
The biggest innovation comes when you have these technologies and you understand how the process itself works.
Reetu Kainulainen
Questions answered in this talk
Why did standalone chatbots fail to fix customer service, according to Reetu Kainulainen?
According to Reetu Kainulainen at Rise of AI 2019, chatbots were not conversational enough and sat as a shallow layer on top of a contact center that was still struggling underneath, masking rather than solving the underlying problem.
How does ultimate.ai's AI improve customer service agents' work?
According to Reetu Kainulainen at Rise of AI 2019, ultimate.ai trains a deep neural network on historical chat and email data to give agents real-time recommended answers, with a feedback loop from agent acceptance or edits that continuously improves accuracy.
How much does customer service cost a typical large enterprise?
According to Reetu Kainulainen at Rise of AI 2019, an average enterprise spends over 50 million dollars a year running its customer service department, with some large companies spending significantly more.