A Case study: AI IN CLAIMS
FROM TRADITIONAL
INSURER TO
SMART INSURER.
How a traditional legal insurance company deployed AI as a co-pilot for its legal professionals and demonstrated 100 minutes of time savings per claim in seven weeks.
Client
Sector
Legal Insurance
Approach
Financial Services
MVP Timeline
Strategy → PoC → MVP
4 months
30%
Target reduction in average claim handling time
7
WEEKS
Proof of Value
The Challenge:
A well-established legal insurance company was under pressure: claim volumes grew year over year, digital-native competitors entered the market with lower operational costs, and talent was increasingly scarce. The combination made a different approach unavoidable.
The organisation needed to become more efficient without sacrificing the high customer satisfaction that had been its trademark for years. The answer: AI as a co-pilot for legal professionals. Not as a replacement, but as an accelerator.
FOUR PHASES.
Allynce worked through a circular model of four sequential phases. Each phase delivers a concrete outcome that unlocks the next. Not a linear trajectory, but a system with a permanent feedback loop back to the strategic starting points.
01
Strategy
Strategic alignment on market dynamics, competitive advantage and talent scarcity. Setting the North Star for AI investment and prioritising strategic domains.
Outcome: Align strategy & goals to prioritize strategic domains for AI investment.
02
AMBITION
Six high-impact use cases defined. Business case built for the 30% reduction ambition, covering investment for build and run phases, data and AI consumption.
Outcome: Define use cases, create the business case for high impact AI value.
03
validation
7-week Proof of Concept to validate technical feasibility, user desirability and economic viability, before committing capital at scale.
Outcome: Validate use cases and build the plan for MVP Development
04
mvp & scale
Production-ready AI system launched for the Employment Law domain. Roadmap built for scaling to all 40+ legal specialisms across the organisation.
Outcome: Define use cases, create the business case for high impact AI value
RUNNING THROUGH
EVERY PHASE.
01
Responsible AI & Governance
Ethics, data privacy, bias mitigation and accountability, defined in Phase 1, enforced in every phase without exception.
02
Adoption, workflow redesign, training and cultural alignment, tracked from Phase 2, running continuously through and beyond MVP launch.
03
Feedback Loop
Quarterly review cycle linking Phase 4 outcomes directly back to the North Star KPIs from Phase 1. Circular, not linear.
Change Management
KEEP THE HUMAN IN THE LOOP. USE AI AS CO-PILOT, NOT AS THE DECISION-MAKER. RESPONSIBLE AI IS NOT A CHECKBOX, IT IS THE FOUNDATION OF THE ENTIRE APPROACH.
Allynce • AI & Automation
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