The news
On October 9, 2024, Zendesk held its AI Summit in New York’s Chelsea Industrial. The AI Summit is an event mainly for customers to inform themselves about what is new at Zendesk but also to network with each other. The event featured an interesting lineup of customer and partner speakers, headlined by New York Times bestselling author and podcast host Kara Swisher.
My estimate is that there have been more than 250 customer representatives in attendance who not only could listen to the speakers but also get in-depth demos of Zendesk’s updated offerings, following real-life use cases.
True to its name, the event centered around the use of AI, in particular bots, to increase not only efficiency, but also customer- and employee satisfaction. CEO Tom Eggememeier opened the event with an emphasis that Zendesk’s AI is built to support humans by stating that it “is designed for humans”, and Zendesk’s service solution is built to strengthen the human – AI partnership.
Kara Swisher talked about the promise and peril of AI, giving the audience some food for thought on the day after Geoffrey E. Hinton, the godfather of machine learning turned AI warner got co-awarded the 2024 Nobel Prize in Physicsfor his “foundational discoveries and inventions that enable machine learning with artificial neural networks”. While Swisher sees the value that the use of AI can bring, she, too, warned about the hurdles that still need to be overcome, namely the concentration of power that the technology creates and its immense hunger for energy. The tie into the Zendesk story is that customer service is a prime candidate for the use of AI, which turns information to insight while customer service in general has quite some room for improvement – and this is smack in Zendesk’s territory.
Some of the news revealed is that the functionalities that got announced at Zendesk Relate earlier this year are now in general availability. Zendesk announced them as a series of innovations, including AI-powered agents for omnichannel support, enhanced agent copilot, powerful voice, and an agent builder. The company reiterates its vision of having 80% of all service interactions automated. To achieve this, the company also delivers improved agent autonomy and an enhanced agent co-pilot. It adds that it wants to automate up to 50% of voice interactions by partnering with poly.ai.
The bigger picture
There is a continuing need for enabling a good/great customer experience and to engage correspondingly. In customer service scenarios, this makes it crucial to get responses right, fast. At the same time, customer service is a profession that still suffers from high employee churn, with the corresponding cost of re-hiring, re-training and low employee morale. As Swisher said, there is scope for improvement and a good way to achieve this is to focus on creating a positive “human-AI relationship” that helps human agents do what they can do best by helping them with what an AI can do best, which is delivering knowledge and insights as opposed to have humans research it.
Looking at this from a customer angle, customers want faster and better service, i.e., higher quality of resolutions at a faster time to resolution.
Achieving this requires the right tools. These tools need to help agents focus on the right cases, give them help for their own improvement and, most of all, keep the mundane off their backs.
From businesses, this requires an outside-in mindset that is not solely focusing on efficiency and productivity as its outcomes, but on becoming “better” in a business sense by looking at employee- and customer outcomes.
My analysis and point of view
Zendesk tells a very compelling customer service story by combining autonomous agents, copilots and a sophisticated QA solution that helps steering a customer service organization into the right direction – namely outcomes for customers, which includes agent coaching as opposed to merely monitoring them. The latter does create a climate that a customer service organization’s most valuable assets – the agents – perceive as negative. Where the story is a bit difficult for me is where CX is used as a synonym for customer service, but this is a different topic.
The audience of the AI summit clearly shows that this story resonates. The demo-booths were well attended, and customers asked lots of questions about how to optimally leverage the capabilities. In fact, those customers, who I had the chance to briefly talk to, looked beyond the usual narrative of increasing efficiency. Instead, they looked at how to provide better internal or external service in an environment that needs to manage with limited resources and personnel. Having said this, not every customer is there yet. Many are still too focused on cost reduction by headcount reduction. This is still the current reality that also AWS General Manager of AI and ML Tia White spoke out, while maintaining a more optimistic outlook about refocusing employees on higher value tasks.
On the other hand, as also a recent Salesforce study shows (free download, requires registration), customer service professionals experience that customer expectations are continuously on the rise; customers ask for more: more service quality, more personal touch, and more speed of resolution. This combination puts customer service organizations between a rock and a hard place. And this is where helping customer service agents with automation and (generative) AI helps. As Zendesk CTO Adrian McDermott and SVP Product and Solution Marketing Lisa Kant emphasized, the adoption of AI is both, a marathon and a sprint. To support this, it needs both, fast time to value through short implementation cycles with well running “pre-trained” models, the continuous improvement of these models with clean data, as well as the careful selection and implementation of meaningful business scenarios plus the analytics to identify them.
The sprint part is the fast implementation and the continuous model improvement that come out of the box.
The marathon part is staying on focus by staying use case aligned and defining relevant KPIs that measure success.
The chosen vendor software must enable a feedback loop that helps establishing this.
In my eyes, Zendesk delivers this for customer service scenarios, although some aspects of it like the prediction of a response perception by the customer or the (semi-) automated improvement of knowledge bases, require complementing software. So, buyers who are in the market for a new omni channel customer service solution should have a good look at Zendesk.
Disclosure: Zendesk has paid for most of the travel and accommodation cost associated with my attending the AI Summit. This came with no obligation whatsoever. All thoughts and opinions expressed are solely mine.
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