First published in FAnews, May 2026.
Something fundamental has shifted in the economics of business, and the speed of the shift is catching industries off guard.
Artificial intelligence is not simply a feature upgrade. It changes what things cost, how quickly they happen, and who is able to compete. For industries built on information, process and human judgment — insurance among them — this moment represents a structural inflection point.
The cost structure revolution
Every major technology shift rewrites the cost curve. The internet collapsed distribution costs. Cloud computing removed the need for large infrastructure investments. AI is producing a similar shift — but this time across the entire value chain at once.
The results are not incremental. Contact centres using AI report operational cost reductions of roughly 30%. Underwriting decisions that once took weeks can now be completed in hours, with some insurers reporting processing times improving by as much as 90%. Even complex claims resolution has fallen from around 30 days to under eight in certain cases.
When cost curves move like this, they don’t reward the organisations with the best technology. They reward the organisations whose operating model is built around the new cost structure. That distinction is the theme of everything that follows.
New competitors, different architecture
The competitors that will threaten established insurers and brokers are unlikely to come from familiar rivals. They emerge from unexpected places, built by teams that operate at speeds traditional organisations rarely anticipate.
In 2024, Corgi Insurance was founded in San Francisco. Within a year, it secured full regulatory authority to operate as an AI-native, full-stack carrier, raised over $100 million, and exceeded $40 million in annual recurring revenue.
These entrants carry no legacy weight: no expensive technology stacks to maintain, no organisational structures designed for a different era. Their cost structures are fundamentally, structurally lower.
It is worth pausing on what has changed here. The barriers that historically protected incumbents — capital, distribution, regulatory scale — used to include technology. Building an insurance platform took years and tens of millions. It no longer does. Technology has moved from the incumbent’s moat to the entrant’s weapon.
The trap of augmentation
Technological disruption follows a familiar pattern: incumbents hesitate to disrupt their own revenue streams. Kodak, Blockbuster and Nokia all saw the new era coming, adopted pieces of it, and were still overtaken by faster entrants.
The trap many businesses are falling into today is augmentation — adding AI to existing processes to make them slightly faster or slightly cheaper. A chatbot in front of the same call centre. A summarisation tool on top of the same claims file. These improvements feel like progress, but they preserve the underlying process — and therefore the underlying economics.
There is a related failure mode: pilot purgatory. Organisations run proof-of-concept after proof-of-concept, each one “successful”, none of them ever changing how the business actually operates. Activity substitutes for adaptation.
Defending an increasingly costly way of operating in an AI-driven world is no longer viable. Systems and platforms must be rethought and rebuilt from the ground up — questioning years of established practice to align with the economics of tomorrow.
The path forward
Adaptation starts with an honest assessment: which parts of the business exist only because the old economics justified them?
For insurers, it means rethinking the underwriting pipeline. AI can ingest submissions and produce assessments within minutes. The underwriter’s role shifts from data processing to judgment — focusing on the complex risks that genuinely require human insight, rather than re-keying the ones that don’t.
For brokers, the shift is equally significant. Information gathering and market navigation are increasingly automated. The brokers who thrive will move decisively up the value chain, into risk advisory and complex problem solving — the work clients actually pay for.
For everyone, data architecture becomes critical. AI is only as good as the information it can reach, and it depends on clean, structured, accessible data. An organisation whose knowledge lives in email threads and PDF attachments has an AI strategy in name only.
There is also a workforce dimension that makes this urgent rather than optional. In the USA alone, roughly half of the insurance workforce is expected to retire by 2036, leaving an estimated 400,000 positions unfilled. That expertise is walking out the door faster than it can be replaced — and AI is becoming a vital way to retain capability as experienced talent leaves.
The three-year window
Industry analysis points to the next three years as the decisive window. Organisations that redesign workflows and modernise their data structures now will build advantages that compound; those that wait will be competing against cost structures they cannot match.
Rebuilding “from the ground up” does not mean a five-year transformation programme — that would be the old economics again. It means picking one workflow, rebuilding it AI-native end-to-end, measuring the delta, and letting the result make the case for the next one. Speed of iteration is the strategy.
The choice is simple: adapt now, or get sidelined.
Ready to rebuild a workflow rather than augment it? Our launch playbook covers how to take that first AI-native process live — and get users to actually adopt it.