If data is truly what drives generative AI, and one of the keys to successful implementation is access to data that is meaningful to business operations, it would seem that certain SaaS vendors have an inherent advantage when it comes to data. Execution is another matter, but with the data, your models can at least process more meaningful things.
One SaaS company that was an early proponent of generative AI was ServiceNow, which has been leveraging data from its platform to help build more business-centric models.
For CIO Chris Bedi, it's about building actionable experiences that help people do their jobs more efficiently. “I firmly believe that a model is only as good as the platform. Even if it's part of a great model, if it's not tied to an experience and workflow, what good is it?” Bedi told TechCrunch.
Brent Leary, founder and principal analyst at CRM Essentials, said ServiceNow is making an intentional effort to focus AI on practical problems. “I think ServiceNow's focus on building its own full-stack generative AI platform allows them to focus on creating, optimizing and integrating workflows that can impact processes across multiple departments/areas and platforms,” Leary said.
To achieve this, the company is embedding AI into every workflow. Bedi categorizes ServiceNow's generative AI capabilities into three broad areas:
The first is to process requests more systematically: “When someone is requesting something, we call that person a requester. It could be a customer, a supplier or an employee. How do we help them get an answer faster?”
The second element is helping agents do their jobs better, regardless of their specialty. “Whether it's an HR agent, an IT agent, a customer service agent, someone is doing something. By helping them do repetitive tasks faster or shifting them completely to machines, you also see productivity gains,” he said.
The final piece is finding ways to accelerate innovation. Bedi believes this will enable whole new levels of automation, from text to code, text to automated workflows, and even working multi-modally, allowing users to take photos of diagrams or whiteboard brainstorming sessions and turn those photos into workflows.
Take a broad approach
“ServiceNow has a unique AI strategy in place that combines build, buy and partner,” says Holger Mueller, an analyst at Constellation Research. There are several reasons why the company needs such a diverse strategy, he says.
“First, ServiceNow customers have a wide range of AI partnerships and want ServiceNow to leverage and coexist with them,” he said. Those partnerships include Nvidia and Microsoft, among others. “Second, customers also expect an out-of-the-box AI experience, so we need to build our own AI automation,” he said. Finally, the company will build out its platform through a combination of in-house development and acquisitions.
At the same time, the company has customers with different degrees of AI readiness, and it needs to offer a range of solutions to cover those capabilities, said Jeremy Barnes, vice president of AI products, who joined ServiceNow through the acquisition of his previous company, Element AI. “I would say that the largest and fastest-growing companies are, in most cases, the ones that are successful in making the organizational changes necessary to implement digital transformation,” he said.
But companies that are less advanced are cobbling together their own solutions with the help of ISVs and MSPs to take advantage of AI.
William Blair financial analyst Arjun Bhatia believes the new AI capabilities are something customers are willing to pay for: “Though it's still early days, ServiceNow highlighted strong demand for its new Pro-Plus SKUs as businesses look for ways to invest in the AI generation,” he wrote in a May report. Additionally, the company has seen relatively little resistance on pricing, which may indicate it sees value.
Move at the speed of your customers
IDC analyst Stephen Elliott said the company has been investing in AI, generative AI and related talent for more than five years, and customers are seeing the results of its efforts.
“In talking to customers who are using Now Assist, we're seeing very positive early business benefits, including ticket reduction, knowledge base summarization and improved customer experience with virtual agents. Cost and team productivity are central themes in business value realization,” Elliott told TechCrunch.
Bedi says he thinks about AI in two ways: one in the near term and one looking to the future as AI becomes more capable and more deeply ingrained within the enterprise. “The way we define Mode 1 is as incremental improvements to the way we work,” he says. He sees companies using current AI technologies to improve how work is done and organized.
But where it gets really interesting is in the future when we look at processes and come up with entirely new, AI-driven ways of working. “Mode 2 is, if you were to start with a blank slate, which tasks would you leave to machines, which tasks would you leave, and what are the interesting tasks that you can leave to humans,” he said.
Bedi is also looking to leverage AI internally for his own employees. The company has built an AI platform called AI Control Tower to provide a unified experience for developers building 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 all the things that have to work differently depending on the choice,” he said.
Moreover, from an IT management perspective, they manage the models like any other IT object: “So a model in production is an asset, and the asset needs cyber posture and operational resilience. We need to make sure it's running when we need it to. And we're measuring the effectiveness of the model and the adoption of the model.”
For Burns, this fits into the holistic approach the company is taking to move its customers toward an AI-first mindset. “We're going from our core use cases of generative AI to rethinking every part of how we get work done,” he says. “That includes the ability to tackle more advanced types of tasks with better tools to understand what's going on with the AI and how AI and humans can work together to help get the job done.”