Healthcare payers create networks, define rules, set prices, and process payments—tasks that artificial intelligence (AI) can execute well, with the right direction. The conventional wisdom is that AI-driven automation can increase efficiency, while lowering administrative and medical costs. That is true enough, but like most conventional wisdom, it’s also obvious.
Let’s go a little deeper: How can AI change how work is done? And how can payers do more for their members?
At a typical payer, McKinsey estimates that 65% to 80% of jobs are transaction-oriented, such as claims processing; all or almost all of these could be fully automated. Another 10% to 25% of jobs are knowledge-oriented, such as pricing actuaries and medical management clinicians. The remaining jobs, including sales, are relationship oriented. For these, the automation potential is less, but generative AI (gen AI) can boost productivity in both knowledge and relationship oriented work by as much as 50% by handling tasks such as looking up information, cleaning and collating data, taking notes, and writing proposals.
Another way to break down the payer workforce is to look at roles. “Do-ers” are individual contributors who perform the necessary day-to-day tasks. “Deciders” are executives and managers who have the authority to set strategy, allocate resources, provide approvals, and ensure sound governance. The rest, about 10% to 15%, are “interpreters.” People in these roles, including project coordinators and many middle managers above the direct supervisory layer, transmit information from the deciders to the doers or among the deciders. Agents could replace many of these interpreter roles; the same is true for the doers.
