It’s a bright Wednesday morning in Philadelphia. Mark, after picking up his favorite cold brew from Starbucks, has just entered his office, where he has been working as an underwriting assistant for the past five years. As usual, he has a full queue.

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But today looks different. Out of the 25 cases assigned, about 15 have already been pre-screened by artificial intelligence (AI). Routine checks are complete. Low-risk applications are identified. And complex, high-value cases that require human experience and nuance are highlighted under the priority section. For Mark, this means less time spent on repetitive tasks such as checking basic details and reentering them across systems, and more time focused on decisions that demand experience, human intelligence, awareness, and technical expertise.

This shift reflects a broader change being felt across the insurance industry.

How AI is transforming insurance underwriting

AI is reshaping underwriting by automating manual validation and low-risk cases assessments. It reviews cases in real-time, around the clock. Unlike the human mind, which needs rest, AI keeps working even beyond working hours and during holidays, including Thanksgiving, Christmas, and New Year’s Eve. It can validate basic details, cross-check applicants’ medical histories, perform risk scoring, and do much more. Where does the human figure into this picture? They need to review AI-generated outputs. Using their unique industrial expertise and skills and insights, they can either approve/reject the proposal or make fixes where AI has faltered. AI systems can continuously learn from this feedback, improving accuracy in forthcoming applications.

But how many insurance companies have truly adopted AI in their underwriting workflows? If so, are they able to realize enterprise value with this transition? Let’s explore underwriting before and after AI, how businesses can benefit by transitioning to AI-driven processes, and more in the further sections.

Before the introduction of AI into insurance workflows and systems, skilled underwriters spent much of their day on repetitive, time-consuming tasks such as manual data entry, document review, and handling low-risk cases. This led to exhaustion and delayed decision-making, affecting both operational goals and customer expectations. Because the latter had to wait from days to even weeks before a policy could be issued in their name.

Verifying documents, checking and updating data across multiple legacy systems and tools such as ERPs and CRMs, applying static rules, and manually calculating risk scores was tiring, especially when the workload was heavy. There was little effective prioritization in place, and even when guidelines existed, they were largely inconsistent and paper based. No preference was provided for complex cases. It was simply a volume game. A game that must be completed successfully every day. And this cycle continued for years.

Many insurers still continue to operate this way even today. About 74% of insurance companies still rely on old tech. However, the information needed to assess risk has grown more complex today. Evidence, financial reports, and third-party data come in different formats, requiring multiple systems to interpret them. This can consume more time for underwriters and create a deeper structural bottleneck that is difficult to break. The underwriting process has to go digital-first, where technology accelerates human judgment instead of replacing it. And AI is a frontrunner technology in helping insurance businesses make this transition.

From volume to value – How AI reshapes the underwriter’s workload

Modern AI models can help:

Ingest structured (spreadsheets, relational databases, CSV files) and unstructured data (images, videos, emails) from multiple sources. It can help standardize and contextualize data, offering insurers real-time insights.Automate intake and validate submissions to reduce administrative friction and shorten timelines. This allows underwriters to focus on complex high-value cases that demand human judgment and practical expertise.Score risk based on historical outcomes, not just fixed rules. AI can analyze metrics, including customer profiles, past policies, and claims behavior, allowing underwriters to review files faster and, most importantly, with increased accuracy.AI can provide feedback loops for continuous learning. By linking underwriting decisions with claims outcomes, insurers can improve product accuracy and pricing. For example, telematics data from vehicles lets insurers reward responsible drivers with lower premiums.

This is the magic of AI. It can co-exist along with your existing processes/workflows and data systems.

Where does this leave humans?

AI doesn’t make your employees redundant. Instead, it augments them to achieve greater milestones in significantly shorter timelines thanks to its powerful algorithms. Humans can read between the lines, understand context, and respond to subtle emotional shifts more proactively than any AI model can. Complex cases, portfolio strategy, and client relationship management are not AI’s scope of work. This is where experienced underwriters would always be favored. AI’s contribution is in handling repetitive work and surfacing actionable insights that are difficult for a human to miss, especially when they’re drowning in paperwork. In short, AI augments humans, helping them make informed, consistent, and faster decisions, improving operational efficiency and customer satisfaction.

AI and automation aren’t just buzzwords or optional tools. Instead, integrating AI is a must to speed up underwriting decisions. Some insurers have already realized its potential and are aggressively adopting AI to automate mundane, repetitive underwriting tasks. It helps them reduce operational costs, increase productivity, and boost customer satisfaction.

That’s why the global AI in insurance market is expected to account for USD 63.27 billion by 2032, up from a mere USD 6.44 billion in 2024.

The next decade of underwriting will be defined by augmentation, not replacement. Insurers that harness AI responsibly and securely will free capacity, reduce friction, and help underwriting experts make more informed and sound decisions at unmatched speed and scale.The outcome: Faster underwriting, better quality, higher ROI, increased job satisfaction, and improved customer experiences.