AI in action: How InsightFinder AI and Robin AI transform IT and legal workflows at major organizations

Technical.ly is a free news resource thanks to financial supporters like Comcast NBCUniversal LIFT Labs, a Technical.ly client, which sponsored this article. It was reviewed before publication. Learn more about LIFT Labs here.

Helen Gu, founder of InsightFinder AI, envisions AI becoming enterprises’ version of the Wizard of Oz. 

“It should be non-intrusive working behind the scenes to empower efficiency,” Gu said. To that end, InsightFinder AI integrates its real-time machine learning algorithms into clients’ IT systems. 

The North Carolina-based company is far from the only startup leveraging artificial intelligence to support its customers. Robin AI, headquartered in London, has used AI to supplement lawyers’ workload since its founding in 2019.

Both companies prioritize best practices for enterprise adoption, which involves integrating new technologies into large organizations’ existing operations, ensuring effective use through planning, training, change management and ongoing support. 

As part of the Comcast NBCUniversal LIFT Labs Accelerator program’s cohort in the spring of 2024, InsightFinder AI and Robin AI explored automated technology’s potential at large-scale businesses. The program connects strategically relevant startups from around the world with Comcast to explore partnerships that impact the ways they work and the products and experiences they deliver to their customers and employees.

“There’s been a real shift in the market, from big enterprises wanting to sort of learn and increase their awareness of AI to now moving to this period of adoption and implementation,” said Lauren Watson, Robin AI’s head of product marketing.

Since its conception, InsightFinder AI has set out to tackle one of the most pressing issues in IT management: the efficiency and reliability of IT systems. The company’s mission is to empower clients, many of whom are Fortune 500 companies, with full visibility into their IT infrastructures, Gu said. This year the company is expanding its focus to include monitoring and management of large-scale AI models.

At the core of the company’s technology is its Unified Intelligence Engine (UIE), leveraging unsupervised machine learning algorithms to automatically fit various customer environments. After identifying potential issues, InsightFinder AI sends real-time notifications to clients’ IT operators through their preferred channels, such as Slack or email, helping to reduce mean time to detection and resolution (MTTD and MTTR) as well as preventing outages that could lead to substantial operational breakdowns.

InsightFinder AI aims to create a more reliable IT ecosystem, allowing operators to focus on impactful tasks rather than constant troubleshooting, Gu said. 

“InsightFinder AI is here to reduce inefficiencies, and even better, prevent them from occurring in the first place,” she added. 

Similarly, Robin AI is working to modernize the legal tech industry by eliminating mundane, repetitive tasks for lawyers, particularly those in the commercial sector. The start-up fundraised $51 million in 2024,  including a $26 million Series B round announced in January 2024 and $25 million secured outside of traditional funding rounds. In November 2024, Robin AI announced that a group of customers and existing investors helped raise the latter sum, including PayPal Ventures and Cambridge University. Other customers of Robin AI include Pepsico, PwC, Yum! Brands, AlbaCore Capital Group and BlueEarth Capital.

Robin AI streamlines the process of managing contracts, a time-consuming yet critical aspect of any business. Managing them efficiently can save enterprises significant amounts of time and money, according to Watson.

“There’s a real opportunity to save not just hundreds of thousands or millions of dollars, but hundreds of millions of dollars through being able to accelerate deals, through getting contracts signed and negotiated faster, through being able to spot opportunities where productivity can be enhanced in the legal team,” she added. 

In line with this mission, Robin AI launched its Reports Builder tool in September 2024. This tool, which is built on advanced AI and natural language processing, has significant use cases for large enterprises, Watson said. 

Imagine an attorney needing to check ESG provisions or ensure compliance with specific data protection regulations. Reports Builder enables users to extract and synthesize key information from thousands of documents without manually reading each one. Its functionality extends to M&A transactions, for which traditionally reviewing hundreds of thousands of agreements could cost millions in external counsel fees. However, with Robin AI’s technology, lawyers can efficiently manage these tasks.

“This is about humans empowered by AI. Lawyers have unique skill sets, and we’re developing tools that enable them to be more successful and productive,” Watson said. “This can help lawyers to operate at the top of their license and not do the routine legal work that they didn’t spend years studying for.”

Robin AI and InisghtFinder’s human impact

For Robin AI, enterprise adoption is not just about putting money back in the pockets of their large clients. It’s about saving time so their users have more hours in the day for their lives outside of work, Watson said.

She added that she found legal teams can be overworked and under-resourced, but Robin AI’s tools create space for a better work-life balance. 

“Our tools enhance productivity, but if we look at what that actually impacts, it means you can go home and have dinner with your kids instead of being in the office all the time,” she said.

Gu, similarly, thinks often about the overwhelming workload of IT teams, many of whom perform mission-critical tasks for their organizations.

Her hope for InsightFinder AI is that it creates a robust, automatic system that can prevent sporadic issues, creating more space in IT teams’ lives for high-value projects and rest outside of business hours. 

“Our ideal goal is that we also want our operators to have a life, right? They don’t need to do firefighting 24/7 during holidays or during late nights. We want to give time back to people,” Gu said. 

Using real-life experience to inform enterprise adoption

Both Gu and Watson apply lessons learned from past experiences in their day-to-day roles. 

Prior to Gu’s journey as a startup founder, she worked on IBM TJ Watson Research Center’s distributed stream processing system, which is a technology that handles high-volume data streams across various end-users and applications in real time. It eventually became the IBM InfoSphere Stream product. 

This early-career experience at IBM inspired Gu’s research on machine learning for distributed system reliability, which is the foundation of InsightFinder AI.

Gu was also a visiting scientist at Google in 2015, helping the tech giant evaluate the patented unsupervised behavior learning algorithm she co-invented. Google licensed it because of its accuracy on real system failure data, and Gu was able to deploy her technology on a massive, cloud-based scale, preparing her for InsightFinder AI’s launch in 2016. 

“The AI engine can constantly learn and become smarter. Our AI platform is organically growing, and we aggregate a lot of knowledge to help people extract insights out of massive amounts of data,” Gu said, “It becomes more powerful to learn how to operate IT systems and AI models and also improves efficiency.”

With prior experience as a lawyer and legal tech professional, Watson relies on her empathy when helping organizations facing massive change management issues. Watson understands that law is a particularly risk-averse industry, so lawyers adapting to unfamiliar tools is an uphill battle. She said it’s all about making the technology approachable. For example, Robin AI built an add-in for Microsoft Word that enables lawyers to work alongside AI, rather than having to learn how to use a different application. 

This reduces barriers and enables them to benefit from AI without dramatically changing their processes, Watson said. 

Gu’s and Watson’s pragmatic approaches to ideation and incremental tech deployment show that enterprise adoption doesn’t mean overhauling all of an organization’s systems in one day. 

Most successful enterprise adoption tactics are predicated on a step-by-step approach to showcase all that AI can accomplish, Watson said.

“Success is a combination of making the change management piece as straightforward as possible,” Watson added. “And that’s through delivering simple, intuitive tools that employees are happy to use because they’re easily integrated into their day-to-day workflow.”