Using AI in Consumption Pricing Models

In this short segment of the Revenue Builders Podcast, John McMahon and John Kaplan are joined by Devavret Shah, an MIT professor, data expert, and CEO of Ikigai Labs, to explore how AI is revolutionizing consumption-based pricing models and forecasting. Devavret delves into the challenges of predicting demand, the role of AI in empowering sales teams and CFOs, and how artificial intelligence can enhance trust and accuracy in revenue prediction. This engaging discussion also highlights how businesses can leverage AI to enable seamless decision-making and gain a competitive edge.

KEY TAKEAWAYS

[00:00:28] Understanding Consumption-Based Pricing Models: Consumption models focus on token-based API calls, offering a modern alternative to traditional SaaS pricing.
[00:01:58] Data as a Predictive Framework: Viewing data as a massive queue allows organizations to forecast compute volumes and future revenues with more accuracy.
[00:03:23] Challenges in Forecasting: Smooth macro-forecasting often clashes with micro-forecasting, such as predicting localized or choppy demand.
[00:04:14]AI's Role in Cohort Analysis: Leveraging AI to analyze sales reps and channels as cohorts improves prediction accuracy and fosters trust within organizations.
[00:05:16] Aggregating Complex Parameters: AI simplifies the aggregation of historical, seasonal, and booking data to deliver actionable insights for consumption forecasting.

QUOTES

[00:01:58] "Each forecasted data point is like a prediction query—it shows the volume of compute you're doing."
[00:03:23] "It's like forecasting smooth water versus forecasting when umbrellas are purchased—two very different problems."
[00:04:36] "AI helps organizations work with more trust, rather than more finger-pointing."
[00:05:39] "I almost feel like it's not possible without AI to effectively forecast the consumption business."

Listen to the full conversation with Devavret Shah through the link below.

https://revenue-builders.simplecast.com/episodes/cutting-through-the-noise-understanding-ai-through-history-and-practical-application

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