Human teams naturally focus on top-performing products and major retailers due to limited bandwidth. AI agents can manage the entire catalog and all retail channels, capturing significant revenue and efficiency gains from the often-neglected "long tail."

DBS quantifies AI impact not by cost savings, but by the incremental revenue generated from AI-driven customer "nudges." Using rigorous A/B testing, they track the lift from these interactions, reframing AI's value proposition from an efficiency tool to a revenue growth engine, targeting over a billion dollars.

The primary benefit of using AI for revenue planning isn't just build speed. It's the ability to regenerate a complex, multi-tab model with thousands of formulas in minutes in response to feedback or methodology changes—a task that would previously take days of manual work.

Adopting AI hasn't changed core business metrics like growth or retention. Its true value is in operational efficiency, allowing teams to analyze data more deeply. AI provides the ability to explore 'second and third level questions' and investigate previously inaccessible KPIs, improving the *how* without altering the *what*.

AI doesn't replace analysts in revenue planning; it changes their focus. By automating tedious formula creation and data pulls, it allows them to concentrate on higher-value activities like running sophisticated scenarios, incorporating new business context, and exploring deeper data insights.

The sheer number of variables in a consumption model—individual customer seasonality, new bookings, timing, and rep forecasts—creates a level of complexity that is nearly impossible for humans to manage effectively. AI is becoming essential to aggregate and analyze this data to produce a reliable forecast.

A killer app for AI in IT is automating tedious but critical tasks. For example, investigating why daily cloud spend deviates by more than 5%. This simple-sounding query requires complex data analysis across multiple services—a perfect, high-value problem for an AI agent to solve.

The most significant value from AI is not in automating existing tasks, but in performing work that was previously too costly or complex for an organization to attempt. This creates entirely new capabilities, like analyzing every single purchase order for hidden patterns, thereby unlocking new enterprise value.

AI can move from diagnosis to prescription. After identifying an underperforming metric (e.g., low close rate in a city), it can generate a specific action plan, frame suggestions by effort and impact, and even calculate the projected revenue impact of reaching the performance benchmark.