How Finance Pros Should Manage Data Context and Token Forecasting

AI fatigue in finance stems from the relentless pace of model releases and the pressure to constantly adopt new features. Instead of chasing benchmarks, finance professionals should prioritize building robust data context layers—such as data dictionaries and clear KPI definitions—to enable effective AI-driven decision-making. Token usage, once viewed as an experimental expense, now requires a structured budgeting approach that balances input costs with tangible output value. Rather than incentivizing raw token consumption, organizations must focus on productivity and strategic alignment. Establishing a clear source of truth and leveraging existing systems of record allows for more efficient forecasting and analysis. Ultimately, the goal remains to provide better, faster business decisions; AI serves as a tool to enhance these outcomes rather than an end in itself.