If data really is the fuel for productive AI, and one of the keys to a successful implementation is access to data that makes sense for running the business, it seems that some SaaS vendors have a built-in data advantage. Execution is another matter, but if the data is there, the models at least have something more substantial to work with.
One of the earliest SaaS adopters in genetic AI was ServiceNow, which was able to leverage the data on its own platform to help build business-centric models.
For CIO Chris Bedi, it’s all about creating a hands-on experience that helps people do their jobs more effectively. “I firmly believe that a model is only as good as the platform. If it’s part of a great model, but it’s not connected to an experience, it’s not connected to 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 believe ServiceNow’s focus on building their own full-stack AI platform enables them to focus their efforts on workflow creation, optimization and integration. This has the opportunity to impact processes that cross multiple departments/regions and platforms,” said Leary.
To achieve this, the company is integrating artificial intelligence into all of its workflows. Bedi divides ServiceNow’s AI production capabilities into three broad areas.
The first is the more systematic treatment of requests. “When someone asks for something, we call him a requester. This can be a customer, it can be a supplier, it can be an employee. How do you help them get an answer faster?”
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 is doing something — helping them do repetitive tasks faster or moving it completely to the machine, and we’re also seeing productivity gains.” he said.
The final piece is finding ways to accelerate innovation. Bedi believes this could bring a whole new level of automation, such as 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 in the table and converting that image into a workflow.
Taking a broad approach
“ServiceNow is implementing a unique AI strategy that is a combination of build, buy and collaborate,” said Holger Mueller, analyst at Constellation Research. He says the company needs such a different strategy for a number of reasons.
“First of all, ServiceNow customers have a wide range of AI partnerships and want ServiceNow to leverage and co-exist with them,” he said. These partnerships include companies such as Nvidia and Microsoft, among others. “Then it needs to build its own AI automation, as customers also expect out-of-the-box AI experiences,” he said. Finally, it combines internal development with acquisition to create 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 cross those capabilities, says Jeremy Barnes, VP of AI product at ServiceNow, who came to the company through his previous company’s acquisition , Element AI. “I would say that the largest and fastest growing companies have, for the most part, nailed the organizational changes needed to implement digital transformation,” he said.
But for those who are not that far, they are trying to put together their own solutions with the help of ISVs and MSPs to help them take advantage of AI.
Financial analyst Arjun Bhatia from William Blair sees new AI capabilities as something customers are willing to pay for. “While it’s still early days, ServiceNow highlighted strong demand trends for the new Pro-Plus SKUs as businesses look for ways to invest in the AI generation,” he wrote. in a report published in May. Additionally, the company has seen relatively little push in pricing, which could indicate they see value.
Moving at the speed of customers
IDC analyst Stephen Elliot says the company has been investing in AI, genetic AI and related talent for more than five years, and customers are seeing results from that effort.
“Customers I’ve talked to use Now Help let’s say the early results look very positive with business returns around ticket diversion, knowledge base summarization and improved customer experiences with virtual agents. Cost and team productivity are the key issues of realizing business value,” Elliot told TechCrunch.
Bedi says he thinks about AI in two ways: One is more short-term, and the other is looking ahead to when AI can be more capable and make deeper inroads into companies. “The way we define function one is really about incremental improvements to existing ways of working,” he said. He sees companies using current AI technology to improve the way they move and organize their work.
But where it will get really interesting is in the future when you can look at a process and come up with a whole new way of working based on AI. “The second function would be to say, if we started with a blank sheet of paper, what work would go to the machines and what work would stay, and what interesting work could we still make man do?” he said.
Bedi also tried to exploit internal AI for his own employees. And the company has built an AI platform called AI Control Tower to help provide a unified experience for developers building apps in-house. “The whole idea gives engineers the freedom to choose whatever model they want and not have to do all the extra work of managing what’s required in a different way, based on their choice,” he said.
Furthermore, from an IT management perspective, models are managed like any other IT object. “So a model in production is an asset, and an asset has to have a cyber place on it, operational resilience on it. we need to know it runs when it needs to run. And we measure the effectiveness of the models and the adoption of the models.”
For Barnes, this fits with the company’s overall approach to pushing customers to focus more on AI. “We’re really going from the basic use cases for genetic AI to rethinking every part of how work is done,” he said. “It also includes being able to tackle the kinds of higher-level tasks, using better tools to understand what’s going on with AI and how AI and humans can help get the job done together.”
