For more than a decade, insurers have been examining how technology is changing underwriting.
Yet one challenge has remained remarkably persistent: underwriters spend too much time doing work that is not actually underwriting.
Across the industry, professionals have traditionally devoted a significant portion of their working day to activities such as collecting information, checking documents, entering data, coordinating administrative tasks, and navigating multiple systems.
Recent research suggests that this is beginning to change.
The improvement may have been gradual so far, but the expectations surrounding artificial intelligence and automation are anything but incremental.
For the first time, many insurance executives appear to believe that technology could fundamentally reshape how underwriting is performed—and do so at a much faster pace than previous waves of innovation.
The Difference Between Another Technology Wave and a Real Shift
Insurance has experienced its share of technological revolutions.
Knowledge-management systems promised easier access to information. The Internet of Things introduced new sources of real-time data. Advanced analytics gave insurers increasingly sophisticated ways to identify patterns and assess risk.
Each became part of the broader insurance technology landscape.
But none completely redefined the underwriter’s role.
AI may be different.
The combination of generative AI, automation, advanced data ingestion, natural-language processing, and increasingly intelligent decision-support tools has the potential to address one of underwriting’s most persistent problems: the amount of time spent assembling and processing information instead of applying expertise to risk.
That distinction is important.
The goal is not simply to make existing underwriting faster.
It is to rethink what the underwriter should actually be doing.
Automation Could Change the Equation
Recent executive research points toward a significant reduction in the amount of time underwriters may spend on non-core activities as AI and automation mature.
Across different insurance segments, executives increasingly expect these technologies to have a meaningful impact on underwriting.
The change is already underway.
Over the past several years, insurers have experimented with AI in areas such as data collection, information synthesis, risk analysis, and underwriting support.
Not every experiment has delivered the expected results. But the broader direction is becoming clearer: AI is increasingly being viewed as a practical tool for removing friction from underwriting rather than simply an experimental technology.
Several workforce expectations illustrate the scale of the change:
- 81% of surveyed underwriting executives expect AI and generative AI to create new roles to a large or very large extent.
- 65% believe their workforce will require additional skills as AI becomes more deeply integrated into underwriting.
- 42% expect they may need access to external talent pools to fully capture the technology’s potential.
These figures point to an important conclusion.
The AI transformation of underwriting is not only a technology story.
It is a workforce story.
The Rise of the AI-Augmented Underwriter
The underwriter of the future is unlikely to be replaced by a machine.
Instead, the role may increasingly become a collaboration between human expertise and machine capabilities.
AI can already support many activities that traditionally consume substantial amounts of underwriting time.
Natural-language systems can interpret requests from customers and brokers, identify relevant information, and route inquiries toward appropriate workflows.
Automated data ingestion can collect and organize information from multiple sources.
Pattern-recognition models can identify relationships and anomalies that might otherwise require significant manual investigation.
Decision-support tools can help assess straightforward cases, while automated workflows can coordinate multiple steps within a single process.
The result is a potential shift in the division of labor.
Machines handle more of the information-heavy work. Humans spend more time on judgment, relationships, exceptions, and complex risk decisions.
That does not make underwriting less important.
It makes the human contribution different.
From Data Collectors to Risk Decision-Makers
Consider how much of an underwriter’s expertise can be buried beneath administrative work.
A professional may have years of experience assessing complex risks, yet much of the working day can still be consumed by finding documents, reconciling information, entering data, requesting missing details, and moving between systems.
AI has the potential to absorb more of these activities.
Instead of beginning every assessment with a blank screen and a collection of fragmented information, an underwriter could increasingly begin with an AI-generated view of the risk, supported by relevant data, identified patterns, and suggested next steps.
The human then becomes the critical layer of judgment.
They can challenge assumptions, investigate unusual circumstances, apply contextual knowledge, communicate with brokers or customers, and make decisions where automated systems are less reliable.
This is not the disappearance of underwriting.
It is the reinvention of underwriting work.
Three Priorities for an AI-Enabled Underwriting Future
Technology alone will not deliver this transformation.
Insurers will need to rethink strategy, talent, workflows, and organizational culture at the same time.
1. Build an AI-Led Strategy
AI initiatives should not exist as disconnected experiments.
Insurers need a clear strategy for how AI will operate within their broader technology environment, supported by a strong digital foundation.
As AI systems become increasingly agentic, the opportunity becomes even broader.
Instead of simply using AI to answer questions or summarize information, underwriters may eventually be able to delegate individual workflow tasks to specialized AI agents.
An agent could gather information, another could organize documents, another could compare relevant risk factors, and another could prepare a preliminary assessment.
The underwriter remains responsible for the overall decision while AI coordinates more of the surrounding work.
2. Reimagine Talent and Workflow
Introducing AI without redesigning the underlying workflow can limit its value.
Insurers should consider how work should be divided between people and machines, which skills will become more important, and where human expertise will deliver the greatest value.
A skills-based approach can help organizations identify emerging capabilities, retrain existing employees, and prepare teams for new responsibilities.
At the same time, AI adoption needs to be connected to broader process redesign.
Simply adding an AI tool to an inefficient workflow does not create an efficient workflow.
The process itself may need to change.
Responsible AI principles should also be embedded throughout this transition, particularly when automated systems influence important underwriting decisions.
3. Create a Culture of Experimentation
AI is developing too quickly for organizations to rely entirely on traditional top-down innovation models.
Employees working closest to underwriting processes often have the clearest understanding of where technology could remove unnecessary effort.
Giving teams room to experiment can reveal valuable use cases that may not emerge from a centralized technology strategy.
The objective is not uncontrolled experimentation.
It is structured curiosity: allowing employees to test new capabilities while maintaining appropriate safeguards around core decisions, data, security, and risk.
The organizations that learn fastest may be those that create enough freedom to experiment without losing control of the decisions that matter most.
The Underwriter Is Not Disappearing
Technology has repeatedly changed the tools underwriters use.
AI may change the work itself.
But that does not mean human expertise becomes less valuable.
In a more automated environment, underwriters may spend less time collecting information and more time interpreting it. Less time navigating administrative processes and more time evaluating complex risks. Less time performing repetitive tasks and more time exercising judgment.
The central question is therefore not:
“Will AI replace the underwriter?”
A more useful question is:
“What could an underwriter accomplish if AI handled more of the work surrounding the decision?”
That question opens a much broader vision for the future.
From Automation to Augmentation
The next chapter of underwriting is unlikely to be defined by technology alone.
It will be defined by how effectively insurers combine human judgment, intelligent automation, data, and increasingly capable AI systems.
Previous technology waves changed individual parts of underwriting.
The current generation has the potential to connect those parts into something much more integrated.
If insurers build the right digital foundations, rethink workflows, invest in new skills, and encourage responsible experimentation, AI could help move underwriting away from administrative complexity and toward what it does best: understanding risk and making informed decisions.
The future underwriter may not be less human.
They may simply have a much more capable machine working beside them.
