Modernizing underwriting has become a clear priority across P&C insurance especially as teams evaluate insurance underwriting software that can support faster, data-driven decisions. Leaders tell us the pressure to shorten time to quote continues to intensify, while underwriters are being asked to work with more data—data that needs to be timely, accurate, and usable at the point of decision.
Across many organizations, workforce dynamics are also shifting. Experienced underwriting and operations teams are managing increasing volume, while carriers are looking to attract new talent with a more modern, technology-enabled environment.
We also see this combination shaping how modernization decisions move forward. Industry research shows that while insurance has moved quickly to adopt AI underwriting and automation, only 7% of carriers have successfully brought those initiatives to scale, with most still operating in pilot stages.
Even with that momentum, the path forward can feel less certain than the goal itself.
Leaders are aligned on what modernization should deliver—faster quoting, stronger risk selection, and better use of data across the underwriting process. The challenge is identifying a path that delivers measurable results early, builds internal alignment, and fits within existing workflows.
That is where many teams are taking a more practical approach. Rather than committing to large-scale transformation upfront, they are starting with focused, high-impact improvements that prove value quickly and create a clear foundation for impact over time.
Why the big-bang approach keeps failing
Large-scale transformation sounds right in theory. In practice, most underwriting teams are balancing active initiatives, limited bandwidth, and the need to maintain day-to-day performance.
Large-scale technology initiatives in insurance are rarely quick or contained. Once a major effort is underway or recently completed, underwriting leaders and IT teams are often handling a full workload. By the time one initiative wraps, attention shifts to stabilizing systems, supporting users, and maintaining performance. Introducing another large deployment at that point requires careful consideration.
Historically, the path forward has felt binary. Continue operating within legacy systems, or adopt more flexible underwriting software solutions. That second option often introduces significant coordination across systems, workflows, and teams, creating a level of disruption that is difficult to absorb alongside ongoing operations.
We also see that complexity increases when multiple stakeholders are involved. Advancing an underwriting automation initiative requires alignment across underwriting, operations, distribution, IT, and executive leadership. Each group is working toward a different outcome. Underwriting is focused on risk clarity, operations on reducing manual effort, distribution on speed to quote, IT on system stability, and leadership on measurable return.
Bringing those priorities together within a single, large-scale initiative can slow progress. Progress often depends on coordinating across multiple teams, systems, and timelines.
The third way: modular scalability
Leaders tell us the path forward rarely comes down to a single decision. It comes down to where to start and how to build from there.
That is where a modular approach starts to take shape. Instead of treating modernization as a single, all-or-nothing initiative, it breaks the underwriting ecosystem into focused areas that can be improved over time using more flexible underwriting software.
Start with the most immediate constraint
We often see teams begin with a single, high-friction area. For many, that is the manual effort required to process submission data. Focusing on one module first, such as automated ingestion, creates a clear starting point and delivers early value that teams can build from.
Build around how your teams already work
A modular approach fits into existing workflows rather than forcing them to change all at once. Teams can introduce targeted improvements while continuing to use the tools and processes that already support their underwriting strategy.
Create space for adoption over time
Introducing change in stages allows teams to adjust as they go. Underwriters can build confidence in the data and the workflow, while leadership gains visibility into what is working before expanding further.
Start now, iterate, and grow confidently: what this looks like in practice
Getting started is often the hardest part. A focused approach that starts now, builds through iteration, and expands over time makes that first step more manageable—especially when teams begin with high-impact areas like AI data ingestion, insurance data extraction, or submission triage.
When a project feels too broad, momentum can slow during planning. We often see teams move forward more effectively when they focus on a specific, high-friction area first. That initial step creates a clear starting point and builds confidence through real results.
In many cases, that starting point sits at the entry to the submission process. Whether it is digitizing complex loss runs or extracting data from a specific line of business, improving how data enters the workflow creates a foundation for everything that follows.
A focused initial scope tends to deliver three outcomes:
Speed to value
What it looks like: Early improvements show up quickly in day-to-day workflows
Why it matters: Early results provide tangible performance data and ROI, reinforcing confidence and building momentum for continued investment
Refining in the real world
What it looks like: Teams focus on a specific line of business to improve a defined workflow
Why it matters: That experience becomes a repeatable standard, making it easier to expand into other areas with consistency
Earning adoption
What it looks like: Underwriters experience immediate relief through streamlined data and clearer inputs
Why it matters: That shift builds trust in the workflow, turning early users into advocates who support broader adoption
Why stakeholder alignment follows results, instead of roadmaps
Across underwriting teams, alignment tends to build through experience, not planning. It takes shape as teams begin to see results within actual workflows.
That pattern shows up across the industry. McKinsey’s latest global survey found that most organizations are still in the early stages of scaling AI automation in underwriting, with many yet to embed it into core workflows or realize enterprise-level value.
We also see that early, tangible outcomes create momentum. When a focused use case delivers value for a specific team, it gives others across the organization a clear reference point. That visibility helps connect priorities across underwriting, IT, operations, and leadership.
As teams begin working with improved data and workflows, feedback becomes part of the process. Underwriters share how data is returned and used in decision-making, creating opportunities to refine and improve the experience over time.
That cycle of results, feedback, and refinement helps alignment develop naturally, allowing organizations to move forward with greater clarity and consistency.
What the numbers look like
We often see this approach deliver measurable results early, with the ability to scale over time.
A mid-sized P&C carrier facing rising submission volume and increasing pressure for faster quoting focused first on automating loss run ingestion for Workers’ Compensation. That initial step created a clear foundation before expanding further.
- Within 3 months: loss run summaries processed 24x faster
- Within 6 months: 75% reduction in rating preparation time
- Within 12 months: 54% time savings across submission intake
In another case, a Workers’ Compensation and Enterprise Risk carrier implemented AI-powered ingestion to automate submission intake and triage across complex documents.
- 90% reduction in intake and triage time, from days to hours
- 2x growth in book of business within two years
- Expansion into additional lines of business, including auto fleet
The cost of waiting
Momentum is already building across underwriting teams. The difference often comes down to how quickly that momentum turns into results that can scale.
As expectations around speed to quote and data-driven underwriting continue to rise, delays can make it harder to keep pace with both market demand and internal priorities.
We also see that progress accelerates when teams focus on a defined starting point and build from there. An approach that starts now, builds through iteration, and expands over time allows organizations to generate measurable value early and carry that momentum forward. The key is choosing the right place to begin.
The path forward does not need to start with a full transformation. It starts with a clear first step and a way to build from it.
If you are evaluating insurance underwriting software and looking for a practical place to begin, our one-page guide outlines how underwriting teams are starting now and growing confidently with focused, measurable progress.
For a deeper look at how insurance leaders are sequencing change, reducing risk, and building alignment across the organization, explore the full roadmap behind modern insurance underwriting software and solutions.