Pattr gives researchers insight into Conversational AI
Leading technology research and consulting firm Gartner has featured Pattr in its latest paper.
The Conversational AI market is growing — and some firms will stand out from the crowd
Gartner estimates some 2,000 global firms specialising in Natural Language Technology (NLT), with many of those providing Conversational AI (CAI) platforms.
The paper listed 3 recommendations for CAI firms to compete:
- Businesses need to provide end-to-end conversational platforms rather than just Natural Language Processing (NLP) tools. For Pattr, this is where Team Inbox comes in, facilitating automation and humans working side by side. It’s also omnichannel for a consistent accessibility of the brand.
- They also need to be able to prove that they are measurably effective. Research for some of our clients shows an average of 28 hours saved per 1,000 conversations.
- Platforms need to provide end-to-end solutions across multiple use cases. Our transport clients started with personalised real-time alerts, and have expanded to feedback collection, conversational bots, lost items reporting, and more.
The paper notes until recently, few providers were offering automated intent and entity extraction — something Pattr is proud to have pioneered.
Differentiation in the market is critical, with solutions to help training, such as Pattr’s world-first conversation simulator for Lifeline, as well as supporting human-in-the-loop and low code/no code.
Customer service solutions have the highest client interest, such as our tripTWEETER product designed for Yarra Trams, where customers sign up for real time, personalised disruption alerts.
Gartner believes that the market will evolve towards end-to-end conversational platforms that orchestrate and integrate multimodally from front-to-back end.
That means that customers expect to engage with an organisation on the channel of their choosing, expecting the right support. Being single channel focused or sales only is no longer acceptable.
CAI vendors won’t differentiate between NLU capability but in use cases — for example, understanding common conversational flows for certain scenarios and building it into a platform.
Intent and entity functions will service high volumes, and extraction of knowledge and relationships will become increasingly important.
“Enterprise-grade refers to the platform supporting multiple use cases, multiple-orchestrated bots, no-code environments for multiple roles, and the ability to live inside of an established IT architecture.”
Four key competitive trends to look out for in a chatbot provider
- Prebuilt intents and flows to address multiple use cases. These will be easier to integrate into existing systems.
- Many brands will have an existing chatbot that was part of their early strategy — it’s expected that more complex CAI features will be able to integrate and leverage these.
- Low code/no code capabilities will be in demand as businesses look to design their conversations without significant IT input, meaning that they can design, build, write and rewrite conversation flows tailored to their brand, business logic, goals and customers.
- Integrated suites of Conversational AI products across voice and digital. Voice functions will go beyond converting speech to text — integration with systems and intent and entity extraction are predicted to be the next big advancements.
Would you like to know more about how conversational AI can be integrated into your organisation? Contact our sales team today.
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