When it comes to workplace bugbears, wasting time fruitlessly searching shared drives for a particular resource has to be up there. Yet would it not be easier to lighten the workload through an answer engine with a sprinkling of generative AI?
Machine learning software, by definition, is self-learning. As users ask more questions of an AI, and the AI provides answers, feedback loops are developed which help the product get stronger and the return on investment become greater.
“It’s really cool that a proper AI solution is self-learning,” Scott Litman, founder and chief operating officer of AI-powered answer engine Lucy, explains. “The AI is growing with them. If the AI misses, it’s a teachable moment, and [it] will be smarter tomorrow.”
With generative AI, the stakes are now so much higher. Generative AI is defined as algorithms which can be used to create new content, from text, to code, to audio. ChatGPT, from OpenAI, has understandably garnered a fleet of headlines because it appears to have opened up a world of possibility for content creation.
Yet it is not all plain sailing. For one, users have delighted in pointing out the fallibilities of ChatGPT, which is fine – it is always learning after all. But other users have spotted the software’s tendency to make up a response if it is unsure. “The smug confidence with which [the] AI asserts totally incorrect information is striking,” the writer Ted Gioia noted. “A con artist could not do better.”
Lucy’s job is not to make incorrect assertions, but to ‘liberate corporate knowledge’: put simply, get the right answer to the right person at the right time in seconds, regardless of where that answer lives. Much of this will primarily involve sifting through reams of PDFs, PowerPoints and Word documents and point to the most relevant detail, but this liberation can turn up insights in previously forgotten places, such as video training courses.
With the recent release of Lucy 4, the next generation of its platform, and Lucy Synopsis, there is a further push towards generative AI – but without the drawbacks. Lucy can not only point a user to an answer, but provide a unique two-to-three sentence summary which directly answers the question. Crucially, as Steve Frederickson, director of product management points out, Lucy’s summations are there solely to help the user, not offer a spurious alternative.
One of the key elements of Lucy 4, again involving the generative AI element, is expanded integration with Microsoft Teams and Slack, where users can mention Lucy in a chat. This reflects not just greater ease of use for employees, but a wider trend around search.
“One of the things we realised last year was that, along with the inefficiency of searching, people in some cases have given up on the idea of searching,” explains Litman. The result is that users are more likely to fire out a message on the chat apps than waste time on a frustrating scavenger hunt. “Which sometimes works – human intelligence is a great thing,” says Litman. “But if you’re the subject matter expert answering all the questions, you’re constantly being disrupted.”
“We come at it from our own perspective – we have a core value of experimentation,” adds Frederickson. “Lucy has always had the tenet of going above and beyond search. We hold ourselves to that higher standard.”
It is best to think of Lucy as like a new employee. No matter how glittering your recruit’s CV is, it will take time for a new starter to get used to the role, the systems, and the culture. But they will get better. Unlike human employees though, Lucy can hit the ground running. Frederickson notes that Lucy’s goal is ultimately to ‘give time back to the world’, and a more intuitive user interface and improved navigation help with this.
Enhanced collaboration is another important aspect of Lucy 4, and again relates to user behaviour. “What do users do once they’ve found the answer?” notes Frederickson. “Do they grab a quote? Do they share it with co-workers? Do they put it in their deck? What is the destination for this knowledge?” Annotating and adding context within the tool all help to retain the knowledge which has been liberated.
Ultimately, companies survive and thrive on their data literacy. While it is easy to be attracted to big, expansive projects and technologies, adding generative AI to a slick answer engine will help employees, continually improve ROI – and represents the next generation of knowledge management.
Find out more about Lucy 4 here.
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