ServiceNow’s generative AI solutions are taking advantage of the data on its own platform | TechCrunch

If data is indeed the fuel for generative AI, and one of the keys to successful implementation is access to meaningful data to run a business, it appears that certain SaaS vendors have a built-in advantage with regard to data. Execution is another matter, but if the data is present, the model at least has something more meaningful to work with.

One of the early SaaS followers of generative AI has been ServiceNow, which has been able to leverage the data in its own platform to help create more business-focused models.

For CIO Chris Bedi, it’s all about creating a practical experience that helps people work more efficiently. “I strongly believe that a model is only as good as its platform. If it’s part of a great model, but it’s not connected to an experience, not connected to a workflow, then what’s the point of that?” 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 use cases. “I think ServiceNow’s focus on building its own full-stack generative AI platform gives them the ability to target their efforts on workflow creation, optimization, and integration. This gives them the opportunity to impact processes that cross across multiple departments/areas and platforms,” ​​Leary said.

To achieve this, the company is building AI into all of its workflows. Bedi divides ServiceNow’s generative AI capabilities into three broad areas.

The first way is to deal with requests in a more systematic way. “When someone asks for something, we call them a requester. That could be a customer, a supplier, an employee. How do you help them get an answer faster?”

The other part is helping agents do their jobs better, no matter what their focus is on. “You could be an HR agent, an IT agent, a customer service agent — someone doing something — helping them do repetitive types of tasks faster, or moving it completely to the machine, and we’re seeing productivity gains there as well,” he said.

The last part is to find ways to accelerate innovation. Bedi believes this could lead to a new level of automation such as text to code, text to automated workflows or even working in a multimodal way where users can take a picture of a diagram or a whiteboard brainstorming session and turn that picture into a workflow.

Taking a Broader Approach

“ServiceNow is implementing a unique AI strategy that is a mix of building, buying, and partnering,” said Constellation Research analyst Holger Mueller. He says the company needs such a diverse strategy for a few reasons.

He said, “First, ServiceNow’s customers have a wide range of AI partnerships, and they want ServiceNow to leverage them and work closely with them.” These partnerships include companies like Nvidia and Microsoft. He added, “Then it needs to build its own AI automation because customers also expect an out-of-the-box AI experience.” Finally, it combines in-house development with acquisitions to build the platform.

At the same time, the company has customers with varying levels of readiness for AI, and it needs to provide a range of solutions that span those capabilities, said Jeremy Barnes, vice president of AI product at ServiceNow, who came to the company through an acquisition. His previous company, Element AI”I would say that the largest and fastest-growing companies have, for the most part, completed the organizational changes needed to implement digital transformation,” he said.

But those who haven’t yet moved ahead in this field try to combine their own solutions with the help of ISVs and MSPs, so that they are able to take advantage of AI.

William Blair Financial analyst Arjun Bhatia believes the new AI capabilities are 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 enterprises explore ways to invest in generational AI,” he wrote. In a report published in May. Furthermore, the company has seen relatively little resistance on pricing, which could indicate that they see the value.

Moving 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 customers are seeing the results of that effort.

“The customers I’ve talked to are using it Help Now “Early results are looking very positive, with business returns around ticket deflection, knowledge base summaries, and improved customer experience with virtual agents. Cost and team productivity are the key business value realization themes,” Elliott told TechCrunch.

Bedi says he thinks about AI in two ways: one as it is short-term and the other as it looks to the future when AI can become more capable and make deeper inroads inside companies. “The way we define Mode One is it’s really about incremental improvements to existing ways of working,” he said. He believes companies are using existing AI technology to improve the way they organize and carry out their work.

But it will be really interesting in the future when you can look at a process and come up with a completely new, AI-powered way of doing things. “Mode two would say if we start with a blank sheet of paper, what work will go to machines and what work will be left out, and what interesting work can we have humans do?” he said.

Bedi has also tried to leverage in-house AI for his employees. And the company has built an AI platform called AI Control Tower to help provide a unified experience for developers building in-house applications. “The whole idea is to give engineers the freedom to choose any model they like, and not have to do extra work to do different things based on their choice,” he said.

Also, from an IT management perspective, they are managing the model like any other IT object. “So a model in production is an asset, and an asset must have cyber currency, operational resilience; we need to know it is running when it needs to run. And we are measuring the efficacy of the model and the adoption of the model.”

For Barnes, this fits with the company’s overall approach, which is adopting a vision for customers to become more AI-centric. “We’re really going to reimagine every part of how work is done by moving beyond the core use cases for generative AI,” he said. “That also includes the ability to tackle higher level tasks, using better tools to understand what’s happening with AI, and how AI and humans can contribute to working together.”