Insurance Part 2: Subordinate Strategies
02.16.2026
The “AI Adoption” Series: Where We Are
In Part 1, we defined the Business Strategy: the specific financial outcomes (e.g., reducing the combined ratio, improving risk selection) that AI must achieve.
Now, we move to the execution layer. A business strategy without an engine is just a document. To drive that strategy, you need Subordinate Strategies—specific, aligned plans for your Technology (IT), Talent (HR), and Operations.
These are not separate initiatives. They are derivative strategies that exist solely to support the business outcome defined in Part 1.
The Industry Barrier: Spending Without Strategy
The insurance industry is currently cash-rich but capability-constrained. The appetite for AI is massive, but the infrastructure—both human and technical—is often unable to digest it.
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IT Spending is surging: Global insurance IT spending is projected to reach $291 billion over the next 12 months, according to HG Insights.
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The Talent Gap is widening: The U.S. Chamber of Commerce predicts that 400,000 positions in the insurance industry will remain unfilled over the coming decade (RGA).
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The Execution Reality: Despite high spending, Accenture reports that while 90% of insurance executives plan to increase AI investment in 2026, 25% cite skilled talent shortages as the primary barrier to value.
The Strategic Imperative:
You cannot simply “buy AI.” You must build the subordinate strategies that allow AI to function.
1. The Technology Strategy: Enablement Over Maintenance
For decades, insurance IT strategies were defensive: maintain the mainframe, patch the policy administration system (PAS), and ensure uptime. The AI-ready IT strategy must shift from maintenance to enablement.
The Core Challenge:
AI cannot read data trapped in a siloed, 30-year-old mainframe. If your Business Strategy is “Real-time Risk Pricing,” your IT strategy cannot be “Batch Processing Nightly.”
The Subordinate Strategy Template:
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Outcome: “IT will provide an API-first layer that exposes legacy data to modern AI tools in real-time.”
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The Shift: Stop trying to replace the core systems (a 5-year, high-risk nightmare). Instead, invest in the “wrapper” layer—APIs and microservices that allow the AI to “talk” to the core system without disturbing it.
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Key Metric: Reduction in “time-to-data” (how long it takes a model to access a new data field).
2. The Talent Strategy: The “Actuary 2.0”
You cannot hire your way out of the AI talent gap. With 400,000 unfilled roles and tech giants competing for data scientists, insurance carriers will lose the bidding war for pure technical talent.
The Core Challenge:
A data scientist who doesn’t understand Incurred But Not Reported (IBNR) reserves is dangerous. An underwriter who fears AI is an obstacle.
The Subordinate Strategy Template:
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Outcome: “HR will upskill the existing workforce to be AI-literate, rather than replacing them with AI-experts.”
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The Shift: Create the “Actuary 2.0” and “Underwriter 2.0.” These are insurance experts who are trained to audit AI outputs, not build AI models.
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Tactical Move: Instead of hiring five expensive AI PhDs, hire one to lead the strategy and use the remaining budget to train 50 underwriters on how to prompt, query, and validate the AI tools you are building.
3. The Operations Strategy: Workflow Redesign
The most common mistake in Operations is “paving the cow path”—using AI to do a bad process faster.
The Core Challenge:
If your current submission process requires a broker to email a PDF, which a human then re-types into a system, adding an AI to “read the PDF” is a low-value play.
The Subordinate Strategy Template:
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Outcome: “Operations will redesign workflows to eliminate the need for the PDF entirely.”
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The Shift: Move from Task Automation (using OCR to read a form) to Process Re-engineering (creating a digital intake portal that feeds data directly to the risk engine).
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Governance Note: This is where governance begins. The Operations strategy must define who owns the decision when the AI flags a risk. Is it the machine’s fault, or the underwriter who approved the machine’s recommendation?
The Direction: Convergence
We are moving toward a convergence of these three strategies.
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Current State: IT builds tools, HR hires people, Ops runs processes. They rarely talk until something breaks.
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Future State: A “Product Team” model where an Underwriter (Ops), a Data Engineer (IT), and a Change Manager (HR) work on a single business outcome (e.g., “Automate Small Commercial Quotes”).
Next Step: The Fuel for the Engine
You now have the Business Strategy (The Destination) and the Subordinate Strategies (The Engine). But the engine requires fuel.
In Insurance Part 3, we will discuss The Data Foundation. We will look at how to move from the buzzword of “Big Data” to the practical reality of hygiene, lineage, and accessibility.
Salvatore Magnone is a father, veteran, and a co-founder, a repeat offender in the best way in fact, and a long-time collaborator at DOOR3. Sal builds successful, multinational, technology companies and runs obstacle courses. He teaches business and military strategy at the university level and directly to entrepreneurs and military leaders.
