If data is truly the fuel for generative ai, and one of the keys to a successful implementation is access to meaningful data to run the business, it would seem that certain SaaS providers have a built-in advantage when it comes to data. Execution is another matter, but if the data is there, the models at least have something more meaningful to work with.
One of the first SaaS to adopt generative ai was ServiceNow, which has been able to leverage data from its own platform to help build more business-focused models.
For CIO Chris Bedi, it's about creating a hands-on experience that helps people do their jobs more efficiently. “I firmly believe that a model is only as good as the platform. If it's part of a big model, but not tied to an experience, or a workflow, what's the point? Bedi told TechCrunch.
Brent Leary, founder and principal analyst at CRM Essentials, says ServiceNow is making a deliberate effort to focus its ai on practical issues. “I think ServiceNow’s focus on building their own end-to-end generative ai platform gives them the ability to pivot their efforts toward creating, optimizing, and integrating workflows. This has the opportunity to impact processes that span multiple departments, areas, and platforms,” Leary said.
To achieve this, the company is embedding ai across all of its workflows. Bedi divides ServiceNow’s generative ai capabilities into three broad areas.
The first is to address requests more systematically. “When someone asks for something, we call them a requester. It can be a customer, a supplier or an employee. How can we help you get a response faster?”
The second part involves helping agents do their jobs better, regardless of their focus. “You could be an HR agent, an IT agent, a customer service agent, somebody is doing something, helping them do repetitive tasks faster, or transferring it completely to the machine, and we’re seeing productivity gains there as well,” he said.
The final step is finding ways to accelerate innovation. Bedi believes this could lead to a new level of automation, such as converting text to code, text to automated workflow, or even multimodal work to allow users to do things like take a photo of a diagram or a brainstorming session on a whiteboard and turn that photo into a workflow.
Take a broad approach
“ServiceNow is pursuing a unique ai strategy that combines build, buy and partner,” said Holger Mueller, an analyst at Constellation Research. He says the company needs such a diverse strategy for several reasons.
“First, ServiceNow customers have a wide range of ai partnerships and they want ServiceNow to leverage and coexist with them,” he said. Among these alliances are Nvidia and Microsoft, among others. “Then you need to develop your own ai automation, as customers also expect out-of-the-box ai experiences,” he said. Finally, combine internal development with acquisition to develop the platform.
At the same time, the company has customers with varying degrees of ai readiness and needs to offer a range of solutions that encompass those capabilities, said Jeremy Barnes, vice president of ai products at ServiceNow, who joined the company following the acquisition of his previous company, Element ai. “I would say that the largest and fastest-growing companies have, for the most part, achieved the organizational changes necessary to implement digital transformation,” he said.
But those who are not so advanced are trying to combine their own solutions with the help of ISVs and MSPs to catch up and take advantage of ai.
Financial analyst Arjun Bhatia of William Blair sees new ai capabilities as something customers are willing to pay for. “While it is still early days, ServiceNow highlighted strong demand trends for its new Pro-Plus SKUs as enterprises look for ways to invest in next-generation ai,” he wrote. in a report published in May. What's more, the company has seen relatively few price pullbacks, which could indicate that they see value.
Moving at the speed of customers
IDC analyst Stephen Elliot says the company has been investing in ai, generative ai and related talent for more than five years, and customers are seeing results from that effort.
“Customers I have spoken to who are using Now help They say early results look very positive with business returns related to ticket deflection, knowledge base summarization, and improved customer experiences with virtual agents. Cost and team productivity are the core themes for business value realization,” Elliot told TechCrunch.
Bedi says he thinks about ai in two ways: one is more near-term, and the other looks toward the future, when ai can be more capable and have deeper advancements within businesses. “The way we define mode one is really about incremental improvements to existing ways of working,” he said. He sees businesses using current ai technology to improve the way they move and organize work.
But the really interesting part will be in the future, when you can look at a process and come up with a completely new way of working, powered by ai. “Mode two would be to say, if we started with a blank sheet of paper, what work would go to machines, what work would be left, and what interesting work could we get humans to still do?” he said.
Bedi has also tried to leverage internal artificial intelligence for its own employees. The company has created an artificial intelligence platform called ai Control Tower to help provide a unified experience to developers building apps in-house. “The idea is to give engineers the freedom to choose the model they want and not have to do all the extra work of managing what is required, but do it differently, according to their choice,” he said.
And from an IT management perspective, they manage models like any other IT object. “So a model in production is an asset, and an asset needs to have cyber posture and operational resilience – we need to know that it’s working when it’s needed. And we’re measuring the effectiveness of the models and their adoption.”
For Barnes, that fits with the overall approach the company is taking to get customers more ai-centric. “We’re really moving from core use cases of generative ai to reimagining every part of how work gets done,” he said. “It also includes the ability to tackle higher-level types of tasks, using better tools to understand what’s happening with ai and how ai and humans can contribute to getting work done together.”
