Analysts are evaluating companies' AI implementation not just on technology, but across six business functions: personalization, customer acquisition, product innovation, labor productivity, supply chain, and inventory management. The assessment also considers breadth, depth, and proprietary initiatives to differentiate leaders.

Analysts created a method to evaluate corporate AI adoption across six key areas: personalization, customer acquisition, product innovation, labor productivity, supply chain, and inventory management. Companies are then ranked on the breadth, depth, and proprietary nature of their AI initiatives.

Don't evaluate your team's AI readiness as a standalone capability. True AI strategy requires a deep understanding of customer problems and unique value. Without strong core product competencies, AI adoption is merely tactical, not strategic.

For an incumbent, mission-critical company, AI presents a significant opportunity. By leveraging their proprietary data to build AI tools, they can enhance their product, improve margins, and further solidify their market leadership, making them more attractive credit risks.

While tracking business outcomes is vital, the most predictive KPI for successful AI transformation is an "AI Fluency Score." This tracks team members' participation in activities like training and tool usage. This leading indicator of adoption is directly correlated with downstream business results.

Instead of a top-down AI strategy, Brookfield encourages its 500 portfolio companies to experiment independently. The key is a structured process for sharing all outcomes. A successful application in one business can be rapidly deployed elsewhere, while failures prevent 499 other companies from making the same mistake.

To make AI adoption tangible, Zapier built rubrics defining "AI fluency" for different roles and seniority levels. By making these skills a measurable part of performance reviews and rewards, you create clear incentives for employees to invest their time in developing them, as behavior follows what gets measured.

When evaluating software loans, Blackstone moves beyond financials to product underwriting. Its investment committee uses a specific scorecard to assess a company's risk of AI disruption, how embedded its product is in workflows, and how its technology stacks up, demonstrating a structured approach to modern threats.

While many firms are just now reacting to AI's impact, major credit investors like KKR have been actively underwriting AI-driven business model risk for nearly six years. This proactive, long-term approach to assessing technological disruption is a core part of their due diligence process, not a recent development.