Published on November 16, 2023, 5:13 pm

As generative AI capabilities become more prevalent in the IT industry, measuring their impact and effectiveness is crucial. ServiceNow CIO Chris Bedi encourages other CIOs to do the same and emphasizes the value of implementing generative AI features on a single platform.

While ServiceNow was not the first to announce its generative AI capabilities, it was among the early adopters who made these capabilities available to all customers. The Vancouver release of ServiceNow’s Now Platform introduced Now Assist, which utilizes generative AI to power interactive chatbots for IT Service Management, Customer Service Management, and HR Service Delivery. These chatbots come with new text creation and summarization features tailored for different enterprise activities. Now Assist enables businesses to automate ticket triaging, automate responses, and improve agent productivity.

The addition of Now Assist to ServiceNow’s core platform could potentially disrupt third-party software developers who had built AI-powered automations for ServiceNow’s ticketing system. However, according to Bedi, these developers’ traction was quite limited. He also highlights that other vendors are introducing AI functionality previously provided by third-party developers in order to offer a more integrated approach on a single platform.

ServiceNow not only sells the Now Platform but also uses it internally. As customer zero, Bedi had an advantage in adopting the new generative AI features ahead of others. The company has leveraged these capabilities to enhance user experience, improve agent productivity, and accelerate digital transformation initiatives. Generative AI enables self-service access to problem-solving tools by understanding human intent better and providing relevant information from knowledge base articles.

When it comes to enhancing “agent” productivity, Bedi refers broadly to HR staff, IT service desk operatives, customer service agents, and sales staff who can benefit from generative AI’s ability to find answers within vast amounts of documentation. By indexing go-to-market content and product documentation using one large language model (LLM), ServiceNow aims to make its sales representatives highly knowledgeable from day one.

Generative AI also plays a crucial role in accelerating digital transformation efforts by offering text-to-code and text-to-flow capabilities. The latter involves building complex processes using low-code development platforms, with the initial flow created by a business analyst and technical teams filling in the remaining details. These tools bridge the gap between business teams and technical teams, bringing them closer together.

Contrary to concerns that generative AI might replace jobs, Bedi believes that it will allow companies to achieve more with their existing workforce and even improve the performance of software engineers. The focus is on optimizing productivity rather than reducing headcount.

Based on his experience with implementing generative AI within ServiceNow, Bedi offers practical advice for CIOs looking to maximize the value of such applications. He emphasizes the importance of measurement across four key categories: sentiment, adoption, coverage, and business impact.

Sentiment measures employees’ willingness to incorporate generative AI into their workflow. After one month of use, 58% of IT agents reported that these tools were helping them be more productive.

Adoption goes beyond sentiment and focuses on whether employees are consistently using generative AI tools as part of their everyday work. It’s important to integrate these tools seamlessly into existing workflows for sustained adoption rates.

Coverage refers to how well generative AI can assist in different tasks. For example, it can effectively condense texts for case summarization but may have limitations when used for search queries. Defining thresholds and confidence levels helps determine where generative AI can truly make an impact.

Lastly, measuring business impact through standard metrics like speed, productivity, customer Net Promoter Score (NPS), and employee NPS provides a clear understanding of how generative AI influences overall performance.

In conclusion, Chris Bedi’s experience with implementing generative AI within ServiceNow highlights the value of adopting this technology on a single platform. By measuring its impact across various metrics, CIOs can effectively harness the power of generative AI to enhance productivity, improve user experiences, and drive digital transformation within their organizations.


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