Most major enterprise technologies mature alongside financial methods for pricing, transferring, benchmarking, and accruing/reserving against risk. 𝐀𝐈 𝐢𝐬 𝐩𝐫𝐨𝐠𝐫𝐞𝐬𝐬𝐢𝐧𝐠 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐥𝐲.

𝐂𝐲𝐛𝐞𝐫 𝐫𝐢𝐬𝐤 did not remain a purely technical concern. It became an insurance market, with premiums, limits, exclusions, loss histories, and underwriting benchmarks. Munich Re[1] expects the global cyber insurance market to reach $16.3 billion in 2025, while NAIC[2] reported nearly $15 billion in global cyber premium written in 2024.

𝐂𝐫𝐞𝐝𝐢𝐭 𝐫𝐢𝐬𝐤 matured through scoring and pricing infrastructure. FICO[3] states that its scores are used by the top 90 U.S. lenders, making credit scoring a core layer of consumer finance risk assessment.

𝐓𝐫𝐞𝐚𝐬𝐮𝐫𝐲 𝐑𝐢𝐬𝐤 matured through Value-at-Risk, stress testing, counterparty limits, and liquidity frameworks. 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐫𝐢𝐬𝐤 became capitalized and benchmarked through Basel[4] frameworks designed to improve comparability and risk sensitivity.

𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐀𝐈 adoption 𝐢𝐬 𝐬𝐜𝐚𝐥𝐢𝐧𝐠 𝐛𝐞𝐟𝐨𝐫𝐞 equivalent 𝐟𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐪𝐮𝐚𝐧𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧. McKinsey[5] reported that 78% of organizations were using AI in at least one business function in its 2025 survey, up from 55% two years earlier; its later 2025 survey[6] also found that 23% of respondents were already scaling agentic AI systems somewhere in the enterprise, with another 39% experimenting.

That is the cart before the horse problem. AI is being deployed, embedded, automated, and funded before many enterprises can answer one basic financial question:

𝐖𝐡𝐚𝐭 𝐢𝐬 𝐭𝐡𝐞 𝐩𝐫𝐢𝐜𝐞𝐝 𝐝𝐨𝐰𝐧𝐬𝐢𝐝𝐞 𝐨𝐟 𝐭𝐡𝐢𝐬 𝐀𝐈-𝐞𝐧𝐚𝐛𝐥𝐞𝐝 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧, 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰, 𝐦𝐨𝐝𝐞𝐥, 𝐨𝐫 𝐯𝐞𝐧𝐝𝐨𝐫 𝐝𝐞𝐩𝐞𝐧𝐝𝐞𝐧𝐜𝐲?

Deployment is moving first. AI risk exposure is MIA.

References:
1. https://lnkd.in/g-KJGGyF
2. https://lnkd.in/gzWFSHKk
3. https://lnkd.in/gdhF-dwU
4. https://lnkd.in/gP9Tb6sG
5. https://lnkd.in/gqRAd-m3
6. https://lnkd.in/eVhaKx-C