Is Your AI Strategy Missing Reinforcement Learning’s Power?
Most companies see AI as a productivity multiplier by making existing processes faster and cheaper. However, they’re missing AI’s bigger opportunity: business optimization engines that don’t just execute better, but learn to continuously improve decision-making, getting smarter with every interaction.
Listen to “How Reinforcement Learning Drives Compound Business Optimization?” Deep Dive
Seventy years before Meta trained AI to optimize ads, B.F. Skinner, the father of behaviorism, trained pigeons to guide missiles using the same principle: learning through trial, error and reward. His “operant conditioning” became the foundation for everything from classroom management to workplace incentives. Today, that same concept, now called reinforcement learning (RL), is revolutionizing how AI systems optimize business outcomes.
“If Facebook (Meta) can use RL to train its ad products to perform better, what’s the potential for other businesses and sectors to improve aspects of their business by using RL to capitalize on dynamic decision-making?”
How Meta’s Reinforcement Learning Transformed Its Digital Advertising Performance
A new study reveals how Meta’s text generation AI model, dubbed AdLlama, was being used to automate how brands and ad agencies can optimize CTRs and the overall ad performance. The large language model (LLM) was trained on RL with performance feedback (RLPF). As described in the study, RL is “a post-training method that uses historical ad performance data as a reward signal.” The study found that “In a large-scale 10-week A/B test on Facebook spanning nearly 35,000 advertisers and 640,000 ad variations, AdLlama improved click-through rates by 6.7% compared to a supervised imitation model. This represents a substantial improvement in advertiser return on investment on Facebook.”
To break down how Meta trained its LLM on RL: “Facebook did two basic things. It trained a performance reward model on a historical archive of ad data on Meta’s platform, which let it directly tie text to different click-through rates. It then used this reward model to train its LLM via RL finetuning to generate text with higher click-through rate.”
I wondered: if Facebook (Meta) can use RL to train its ad products to perform better, what’s the potential for other businesses and sectors to improve aspects of their business by using RL to capitalize on dynamic decision-making?
The “Productivity vs. Optimization” AI Strategy
As companies begin to evaluate their AI solutions, they need to first ask a more strategic question: are they looking to improve overall productivity (faster, cheaper, more efficient) or are they looking to continuously optimize (smarter, adaptive, self-improving) by getting smarter with every decision versus increasing productivity alone?
What’s so intriguing about Meta’s AdLlama platform is how it can automatically and continuously optimize the best ads to improve revenue and performance. So how can other industries apply RL to their business so that it not only improves outcomes, but can serve as a financial optimization lever?
For example, take healthcare: National overuse of hospital emergency rooms results in $38 billion in annual wasteful spending, with an estimated 56% of ER visits being avoidable. Instead of static AI that simply identifies high-risk patients, an RL system could continuously learn which intervention strategies work best for different patient profiles, thereby optimizing not just the message, but the timing, channel and follow-up sequence based on real patient responses. Each patient interaction teaches the system to make smarter decisions about the next patient. In turn, this reduces unnecessary ER visits and brings down costs for healthcare systems.
Here’s the key difference between productivity AI, which gives you fixed improvements today, and RL, which gives you improvements that compound by learning from every interaction to get smarter tomorrow.
This aligns with AdLlama and how other industries can use RL to show the direct impact it can have on revenue and outcomes, creating optimization engines that don’t just execute better, but learn to make fundamentally smarter decisions over time.
Retail e-commerce is already embracing RL through dynamic pricing. For example, last year, when I was purchasing tickets for a Pearl Jam concert through Ticketmaster, every time I clicked on a seat to see the cost, the price kept going up.
This approach was originally brought to market through Uber’s “surge pricing,” which increases fares during periods of high demand or low driver availability, incentivizing more drivers to take requests and ensuring service availability. Other retailers, such as Wendy’s, implemented a dynamic pricing strategy, which was met with displeasure by customers who saw the change as a way to be charged more at peak times.
What makes these systems powerful isn’t just that they adjust prices; rather, it’s that they learn from every transaction. Each customer’s response teaches the algorithm to make smarter pricing decisions, creating the compound learning advantage that separates RL from traditional pricing models.
This report by Nvidia reveals an opportunity: retailers are “embracing AI technologies, with 42 percent of surveyed retailers already using AI and an additional 34 percent assessing or piloting AI initiatives. Interestingly, 14 percent are unaware of AI technologies relevant to their business needs.” Most are probably still thinking about productivity rather than optimization. These companies can learn from competitors using AI to better understand how they are improving productivity or creating dynamic environments that can easily respond to changes in market conditions or as a way to test and learn customer preferences.
Why Companies Struggle to Shift from AI Productivity to Optimization: The Hidden Barriers
As companies continue to address their workforce readiness gaps, they should understand the AI path they wish to pursue. Is it one that is focused on achieving results that are faster, cheaper and more efficient? Or is it one that is more strategic and financially viable by virtue of adopting AI solutions that are smarter, adaptive and constantly self-improving? I don’t think there’s a wrong answer, but when implementing technology that can iterate outcomes on the fly, nothing should be off the table.
I’m a big believer in looking back at history as well as outside of your industry to learn how other industries solved their most challenging problems. Every business leader should be asking this question: how can Meta’s AdLlama concept be applied to my business, and what would the potential outcomes be? Despite these clear opportunities, most companies remain stuck in productivity mode.
“The biggest barrier isn’t technical; it’s mindset. Most companies are stuck in productivity mode, thinking that because RL requires a fundamental shift from “deploy and measure” to “deploy and learn continuously.”
The biggest barrier isn’t technical; it’s mindset. Most companies are stuck in productivity mode, thinking that because RL requires a fundamental shift from “deploy and measure” to “deploy and learn continuously,” implementing it will demand different skills, longer time horizons and tolerance for the initial uncertainty that most organizations struggle with.
However, the companies that make this shift—like Meta did with AdLlama— will have the same advantage that B.F. Skinner had with his pigeons: a system that doesn’t just perform tasks, but gets smarter with every iteration. The question isn’t whether your competitors will adopt reinforcement learning optimization; it’s whether you’ll be ready when they do.
Questions about your AI strategy?
Email me here. As always, thank you for reading and/or listening.
Photo by Sneha Cecil on Unsplash
