From Intelligence to Action: The 3 Stages of AI Transformation in Marketing

GUEST AUTHOR: Deepinder Singh Dhingra, Founder & CEO of RevSure

Over the past decade, the marketing technology landscape has borne witness to an explosion of solutions designed to help professional marketers analyze and optimize their efforts. While this turned a once nebulous art into a far more quantitative science, virtually all of this technology is focused on point solutions for various stages of the buyer’s journey. It remains challenging for marketers to tease out definitive, actionable insights on end-to-end marketing spend.

The diversity of channels and complexity of campaigns in marketing have long made this problem intractable. Data is in silos, naming conventions and other identifiers frequently differ from one source to another, and the sheer volume of data cannot be normalized and analyzed at scale without data scientists, time, and budget. End-to-end marketing intelligence was just too impractical and expensive to really achieve at scale. 

Like everything else these days, however, AI is now demolishing these limitations. LLMs and causal reasoning make it possible to look into billions of data points across the funnel to normalize data, understand ROI, and stitch together unique marketing journeys at a granular level. My company, Revsure AI, deploys agentic AI in marketing workflows that derives insights from past campaigns and makes accurate predictions about future efforts—by audience, segment, message, content, and call to action. This allows marketers to realign their marketing mix, campaign expenditure, and go to market strategy with far more effectiveness than they ever could before. 

While use of this technology is at its earliest stages today, within 5-10 years, every marketing organization will have AI to do this inexpensively and autonomously.

To arrive at that destination, we must first pass through three stages of AI development:
Stage 1. System of intelligence:—AI offers insights, predictions, and recommendations that help humans to make better choices.

Stage 2. System of decisions: AI proposes the decisions it believes to be correct, and humans accept or reject the proposals, based on nuanced influencers and experience. 

Stage 3. System of action: Having improved and learned from human feedback in the previous stage, AI makes all the decisions autonomously. Humans remain on the loop to monitor the AI, but rarely need to intervene. 

Companies like Revsure are already operating at stage 2, on the way to stage 3. The transformation from intelligence to action across knowledge industries will fully manifest when humans witness two characteristics from AI:

1. Reliability—To be comfortable loosening our grip on the steering wheel, humans need to observe consistently accurate and reliable results from AI. This takes time. 

2. Reasoning—AI must also reveal causal reasoning on the suggestions it makes—to “open the kitchen and show how the soup is made,” so to speak. This will assuage fears of a “ghost in the machine” that could make wrong or even malignant decisions.

Worldwide, $1T is spent on marketing—and yet the “net magic number” that defines SaaS marketing results is diminishing. The way we manage marketing today will seem positively archaic in the span of 5-10 years—like using MapQuest or TripTiks, instead of GPS. Ultimately, it will be unacceptable to wait until the end of the quarter, or even the end of the campaign to analyze results and adjust course, according to the right parameters and cadence.