If data is the true fuel of generative AI, and one of the keys to successful implementation is access to data that is meaningful to run the business, it appears that some SaaS vendors have a built-in advantage when it comes to data. Implementation is another matter, but if the data is there, the models at least have something more important to work with.
ServiceNow was one of the first companies to adopt generative AI in the SaaS space, leveraging the data in its own platform to help build more business-centric models.
For CIO Chris Bedi, it’s all about building a hands-on experience that helps people get work done more efficiently. “I’m a big believer that the model is only as good as the platform. If it’s part of a great model, but it’s not tied to the experience, and it’s not tied to the workflow, what’s the point?” Bedi told TechCrunch.
Brent Leary, founder and principal analyst at CRM Essentials, says ServiceNow makes a deliberate effort to focus its AI on practical matters. “I believe ServiceNow’s focus on building their own integrated generative AI platform gives them the ability to direct their efforts toward workflow creation, optimization, and integration. This has the opportunity to impact operations that span multiple departments/regions and platforms,” Leary said.
To achieve this, the company is integrating AI into all of its workflows. Bedi breaks down ServiceNow’s generative AI capabilities into three broad areas.
The first is to handle requests more systematically. “When someone asks for something, we call them a requester. It could be a customer, a supplier, an employee. How do you help them get a faster answer?”
The second part involves helping agents do their jobs better, regardless of their focus. “You can be an HR agent, an IT agent, a customer service agent — someone who does something — helping them do repetitive tasks faster, or completely moving them to a machine, and we’re seeing productivity gains there as well,” he said.
The final part is finding ways to accelerate innovation. Bedi believes this could provide a whole new level of automation like text-to-code, text-to-workflow automation, or even multimedia workflows that allow users to do things like take a picture of a chart or a brainstorming session on a whiteboard, and turn that picture into a workflow.
take a broad approach
“ServiceNow is implementing 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 diversified strategy for several reasons.
“First and foremost, ServiceNow customers have a wide range of AI partnerships, and they want ServiceNow to leverage them and coexist with them,” he said. These partnerships include the likes of Nvidia, Microsoft, and others. “Then you need to build your own AI automation, as customers also expect out-of-the-box AI experiences,” he said. Finally, he’s combining internal development with acquisitions to build the platform.
At the same time, the company has customers with varying degrees of AI readiness, and needs to provide a range of solutions that go beyond those capabilities, says Jeremy Barnes, vice president of AI products at ServiceNow, who came to the company via the acquisition of his previous company, Element AI. “I would say that the largest, fastest-growing companies, for the most part, have made the organizational changes necessary to implement digital transformation,” he says.
But for those who haven’t made it that far, they’re trying to combine their own solutions with the help of ISVs and managed service providers (MSPs) to help them accelerate their AI adoption.
Financial analyst Arjun Bhatia of William Blair sees the new AI capabilities as something customers are willing to pay for. “While it’s still early days, ServiceNow has highlighted strong demand trends for its new Pro-Plus SKUs as companies look for ways to invest in general AI,” he wrote. In a report This article was published in May. Furthermore, the company has faced little resistance regarding pricing, which may indicate that it sees value in this area.
Move at the speed of customers
IDC analyst Stephen Elliott says the company has been investing in AI, generative AI, and related talent for more than five years, and clients are seeing results from that effort.
“The customers I talked to are using Now help “The initial results look very positive with business revenues around ticket conversion, knowledge base summarization, and improved customer experiences with virtual agents,” Elliott told TechCrunch. “Cost and team productivity are the two primary business value drivers.”
Bedi says he thinks about AI in two ways: one is short-term, and the other looks to the future when AI will be more capable and make deeper inroads into companies. “The way we define Mode One is really about making incremental improvements to existing ways of working,” he said. He believes that companies are using current artificial intelligence technology to improve the way they move and organize their work.
But it will be really interesting in the future when you can look at a process and come up with a whole new way of working that is based on AI. “The second mode will be, if we start with a blank sheet of paper, what work will go to machines, what work will stay, and what interesting work can we get humans to do?” he said.
Bedi has also looked to leverage AI within the company for the benefit of his employees. The company has built an artificial intelligence platform called AI Control Tower to help provide a unified experience for developers who build applications in-house. “The whole idea is to give engineers the freedom to choose the model they want, and not have to do all the extra work to manage everything that is required of them differently, based on their choice,” he said.
Furthermore, from an IT management perspective, they manage models like any other IT object. “So a model in production is an asset, and the asset has to have a cyber posture, operational resilience to it; we have to make sure it works when it needs to work. We measure the effectiveness of the models and the adoption of the models.”
For Barnes, this fits into the company’s broader approach to getting customers to focus more on AI. “We’re really moving from the core use cases of generative AI to reimagining every part of how work gets done,” he said. “That also includes being able 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.”