As document-intensive enterprises search methods to scale AI with out disrupting core operations, world danger and claims administration firm Sedgwick is integrating new AI instruments into its legacy programs to course of huge volumes of case documentation.
The Memphis, Tenn.-based firm, which employs roughly 30,000 folks globally, not too long ago expanded its “Sidekick+” platform — launched in 2023 to assist claims examiners summarize and analyze paperwork — with new agentic capabilities designed to assist claims workflows and discover related info extra rapidly.
Sedgwick developed the proprietary Sidekick instruments utilizing OpenAI GPT-4 know-how as a part of a broader technique to modernize and scale AI capabilities over time, whereas persevering with to depend on present claims infrastructure.
“Consider Sidekick because the wrapper that sits round our massive language fashions that offers info to … our shoppers, our examiners, adjusters,” stated Sean Safieh, the corporate’s CIO of world platforms and digital options.
Safieh spoke with InformationWeek about how Sedgwick built-in generative AI and agentic AI capabilities into present claims workflows whereas avoiding disruptions to its core programs and for shoppers.
The constraint: The claims workflow problem
Claims examiners and adjusters at Sedgwick typically should sift by means of 1000’s of pages of medical information and different documentation whereas working beneath time-sensitive circumstances.
For Safieh and his workforce, the problem was discovering methods to assist workers course of massive volumes of knowledge extra effectively whereas sustaining the velocity and consistency shoppers anticipate from claims operations.
“How will we assist the examiner of their on a regular basis job with instruments to provide them info at their fingertips, as an alternative of them looking out and searching [and] studying documentation?” Safieh defined.
As a result of client-facing applied sciences additionally fall beneath Safieh’s duties at Sedgwick, he and his workforce needed to take into account how the AI instruments would have an effect on the claimant expertise.
Sean Safieh, CIO, Sedgwick
The choice level: Discovering the proper AI use case
Given Sedgwick’s scale, the corporate makes use of a lot of applied sciences, together with optical character recognition (OCR), robotic course of automation (RPA) and a claims platform with automation sources to assist the claims administration course of, Safieh stated. OCR permits RPA bots to see and scan paperwork.
With the rise of AI, the corporate sought particular use instances for the know-how:
How may AI shorten the period of time {that a} declare is open? How may it present adjusters and examiners with real-time info to assist their decision-making?
To drive quicker claims decision and ship the proper info to customers, Sidekick is designed to simplify AI prompting for examiners and adjusters by dealing with the prompting course of within the background.
“We give them a button that permits them to simply get the knowledge that they want, and we deal with all the prompting within the background,” Safieh stated.
What modified, what stayed the identical: AI layered into present programs
Whereas Sidekick was designed to streamline claims workflows, Safieh and his workforce nonetheless needed to take care of the architectural realities of constructing these instruments and integrating them into Sedgwick’s present ecosystem.
Key architectural priorities included:
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Integrating AI into present providers and APIs.
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Avoiding disruption to legacy claims platforms.
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Monitoring system observability and efficiency.
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Sustaining human oversight in claims selections.
Sedgwick’s present providers and APIs simplified the combination course of.
“At Sedgwick, we have already got an ecosystem constructed on providers and APIs,” Safieh defined. “So, whether or not it was getting knowledge from the claims platform or pushing knowledge — a word, a diary or info … [it] was truly comparatively straightforward as a result of we had a basis of providers already out there to the generative AI, or Sidekick.”
Sidekick doesn’t display scrape like an RPA software. As an alternative, the platform sits in entrance of the corporate’s massive language fashions (LLMs) to provide a easy, front-end consumer expertise.
On the again finish, Safieh and his workforce should rigorously monitor efficiency to make sure that Sidekick can question legacy programs with out breaking them.
“What we would have liked to do is ensure that the answer labored and we weren’t overtaxing what you’ll be able to name our legacy programs or claims platforms,” Safieh stated. “Observability monitoring and efficiency monitoring had been issues that we put in place very early on.”
The Sidekick instruments are designed to assist examiners and adjusters do their jobs extra effectively inside Sedgwick’s present decision-making framework.
Sedgwick has an consumption course of, dubbed good.ly, that determines the validity of consumer requests and documentation, resembling a photograph. If a declare meets particular consumer necessities, resembling the kind and quantity, it will likely be despatched for automated processing. If it doesn’t meet these standards, it’s despatched to a human.
The objective is to provide examiners extra time to work together with claimants and make their selections.
“There’s all the time a human within the loop for decision-making once we’re taking a look at claims processing, and that can proceed as we go ahead,” Safieh stated.
Sidekick reduces the time it takes for examiners and adjusters to sift by means of documentation. It offers a abstract in a minute or two for a doc that might take 10 to fifteen minutes to learn, based on Safieh.
Sidekick additionally amplified Sedgwick’s capacity to audit claims and supply insights.
“Usually … you audit a subset of claims, and also you say, ‘Based mostly on this subset and this demographic, we consider we’re doing the proper factor,'” Safieh stated. “Now, with these instruments, we’re auditing each declare.” This consists of guaranteeing that the corporate’s selections and the knowledge offered are acceptable.
Whereas Sidekick automates extra of the claims administration course of, it has not altered the corporate’s tradition, based on Safieh. “The consensus or tradition … of serving to our claimants of their time of want nonetheless stays and is deeply rooted,” he stated.
The friction: Constructing belief in AI outputs
Safieh stated he knew he and his workforce must construct belief with the individuals who can be utilizing the Sidekick instruments internally. Sedgwick approached constructing that belief by operating pilots after which parallel testing.
By means of pilots, the workforce works to make sure the proper prompts are in place and that the software offers the proper output. Then, by means of parallel testing, the output an examiner or adjuster offers is in contrast with the output from Sidekick.
“In one thing like a doc summarization, we’re seeing 98% to 99% accuracy from what an examiner or adjuster would output [compared] to … Sidekick,” Safieh stated.
Sedgwick constructed a workforce to facilitate conversations throughout its operations and completely different enterprise groups.
“We’ve got to know what the enterprise case is. We’ve got to know what the end result is,” Safieh stated. “We constructed [a] workforce that is ready to have these conversations in the proper method to deal with these use instances.”
The price of getting it proper: The guardrails behind the AI rollout
The corporate’s very first GenAI use case with Sidekick took roughly three months to implement.
Sedgwick was already an Azure Cloud consumer, enabling it to rapidly and securely deploy the OpenAI mannequin in its surroundings, based on Safieh.
He described establishing the structure to assist safety, efficiency and entry as probably the most time-consuming a part of the method.
“What might have taken just a little bit extra time than perhaps anticipated was ensuring that, one, we’re getting one of the best outcomes out of the mannequin, and two, how will we deploy this appropriately throughout the org and educate customers learn how to use it,” he stated.
Making certain that using a software like that is proper requires recognition of what may go incorrect and placing needed guardrails in place. The corporate handles a large quantity of delicate knowledge throughout the claims administration course of and should be certain that Sidekick is not going to compromise knowledge safety.
Key guardrails included:
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Preserving delicate claims knowledge inside Sedgwick’s ecosystem.
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Stopping firm knowledge from coaching their-party LLMs.
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Utilizing anonymized knowledge for inner mannequin coaching.
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Managing hallucination danger by means of prompting controls and clear knowledge units.
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Sustaining provenance and audibility claims for claims selections.
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Architecting the platform to cut back vendor lock-in.
The corporate ensures that its knowledge shouldn’t be saved wherever exterior of its ecosystem. It isn’t used to coach third-party LLMs, and for coaching inner LLMs, the corporate makes use of anonymized knowledge, Safieh defined.
The fashions behind Sidekick additionally want controls for accuracy.
An AI resolution may wrestle to interpret a doc, resembling a messy handwritten word, throughout consumption. AI programs are incentivized to supply solutions to prompts, a lot in order that these solutions could also be inaccurate and even hallucinated.
“The way in which we have designed the answer is that if it isn’t capable of interpret info, it would outline that [inability] as a part of the output,” Safieh defined.
He and his workforce have labored to cut back the danger of hallucinations, first by guaranteeing the Sidekick instruments have entry to solely clear knowledge units and second by managing prompting.
“Sidekick actually manages what we’re asking and the way we’re asking it in order that now we have repeatable responses or repeatable units of information that scale back the general probability of an AI hallucination,” he stated.
Provenance is one other key space the corporate has to get proper. If a declare determination is challenged, Sedgwick should be capable to present how AI was used within the decision-making course of.
“The Sidekick resolution shops not solely the knowledge that is being offered but in addition the abstract that is output,” Safieh stated. “What we will do at any time is have a look at the 2 items of knowledge that had been offered to the examiner — what did the precise doc present, what did the abstract present — and what was the advice that the examiner made.”
Safieh, like different CIOs, is cognizant of the vendor lock-in danger within the AI house. With the know-how shifting so rapidly, the power to vary distributors is interesting. Sedgwick opted to architect its Sidekick options with that flexibility in thoughts.
Sedgwick’s proprietary platforms fulfill the have to be nimble, based on Safieh.
“We personal them. We personal the roadmap. We personal the design. We personal the structure,” he stated. “After we have a look at these options and architect them, we ensure that they’re versatile sufficient to both change fashions or leverage a number of fashions in order that we do not get caught in a vendor lock-in.”
Up to now, Safieh hasn’t run into any sudden prices associated to Sidekick, however he stated he’s conscious this might change as extra AI options come on-line and the price of compute climbs.
“How is the market going to take a look at monetizing these options, and what’s going to we have to do from there?” Safieh requested.
Making ready to scale AI brokers: Inquiries to reply
Safieh stated the Sidekick resolution has a stable basis that Sedgwick can use to roll out extra AI capabilities.
“From a scalability perspective, as we introduce an increasing number of brokers, what we have to do is proceed to ensure now we have observability in place, efficiency monitoring in place, and that knowledge safety is in place,” he stated.
Scaling AI additionally raises broader architectural questions on orchestration and workflow design, Safieh added:
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“How do we wish the providers to be orchestrated, particularly with agentic AI?”
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“Will we follow providers, transfer to extra mannequin context protocol(MCP)?”
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“And the way will we string alongside sequential steps within the course of with the inspiration of providers?”
The tradeoff: Selections have to be made rapidly
Sidekick has taught Safieh that discovering worth with AI is completely different from different know-how initiatives. As a CIO, he has much less time to get there. It’s all about effectivity.
“Many occasions with different initiatives, they’re constructed and executed otherwise, whether or not it is a several-month undertaking, multi-year initiatives, the place there is a spend allotted to it. You have got time to make sure selections all through the lifecycle of that undertaking to sort things,” he defined.
“With this kind of know-how, you decide in a short time that it really works or it does not work, and you progress on.”
What different CIOs may get incorrect: Do not chase each new AI software
Safieh stated that in his conversations with different CIOs, he hears loads of pleasure in regards to the many new instruments out there out there. He suggested his friends to take a measured method reasonably than chasing the most recent and biggest providing. Ripping out and changing instruments too typically truly slows an enterprise’s velocity, he cautioned.
“If in case you have a software that’s working, that meets your wants, keep it up so long as you’ll be able to as a result of it would proceed to evolve and alter. If it does not meet your wants, then change,” he stated.
The one factor he would do otherwise: Construct belief early on
Wanting again, Safieh stated the one factor he would do otherwise is give attention to consumer adoption earlier within the course of.
“The sooner you may get folks on board and utilizing the options and trusting them, the higher adoption you are going to have, the higher scalability you are going to have as a result of everybody’s on board,” he stated.
