At the moment, AI is the name of the game for customer experience tech vendors. From Salesforce to Dialpad to Genesys, it seems as if tech companies are trying their best to one-up each other with the most intelligent and automated CX solutions for their buyers.
Zendesk, of course, is another player in this race last month, adding Intelligent Triage and Smart Assist capabilities last month, designed to streamline the process of sorting customer support requests and offer more data on these requests.
Providing automation solutions makes sense in a context where companies are cutting back on tech budgets and struggling with declining consumer demand, as is the case currently due to inflation and a looming recession.
However, industry experts who spoke with CX Today think that Zendesk’s competitive differentiator lies not only in what it chooses to do with the trove of customer data it’s accumulated over the year, but also how it monetizes it.
In that respect, launching yet another product in an ever more crowded field might have been the wrong move for Zendesk.
As Zeus Kerravala, Founder and Principal Analyst at ZK Research, told CX Today in August:
If you look at every software company that became really big – such as a Salesforce, Oracle, or Microsoft – they all have one thing in common: they made that shift to platform. They allowed others to build products on top of theirs. They found a way to be more than just one product. Zendesk was working on that, but they couldn’t execute.
While the company has announced a raft of new features in recent months that potentially point the way at its platformization, it seems that the company’s focus is still very much on delivering a single-product experience.
Adrian McDermott, Chief Technology Officer, Zendesk, said during Zendesk’s recent launch: “With this technology, we’re delivering AI tools that can be set up in under a minute to streamline business operations without needing dedicated developers or an expensive implementation.
“Our approach is designed to take the burden off customer experience teams through pre-trained machine learning tools that do more, faster and with less guesswork involved”.
Whether this approach will work remains to be seen, but following the $95 million loss it declared in Q2 this year, the stakes are high.
