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From Mice to Machines: The Dilemma That Still Defines Us to Shape Product Strategy

Venkatesh Rajamani
(India)
July 30, 2025
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Years ago, scientists conducted a fascinating behavioral experiment with mice. Placed in a maze with multiple paths leading to food, each route had varying rewards—some consistent, others unpredictable. Over time, some mice took the “safe” path for a guaranteed snack. Others ventured into uncertainty, testing new routes. What fascinated researchers wasn’t which mouse got more food, but how each one made decisions in the face of uncertainty.

Now, fast forward to today’s Product Managers navigating the maze of user needs, feature releases, and business KPIs. We are no longer mice—but in the age of AI and relentless change, we face a similar question every day: Should we stick with what we know works—or take a risk on something new that might work even better?

This is known in decision science as the exploration vs. exploitation dilemma. And with machine learning, data overload, and rapidly shifting markets, the cost of choosing wrong—or not choosing at all—is higher than ever.
Bandit algorithms—once reserved for high-stakes applications in AI—now offer product leaders a way to make smarter, adaptive decisions without relying on guesswork. In this blog, I’ll share five powerful ways Product Managers can apply these algorithms to navigate uncertainty, optimize learning, and thrive in today’s digital maze.
 
Because in a world where algorithms learn from every click, shouldn’t our product strategies do the same?
In the contemporary digital marketplace, the role of a Product Manager comes with great responsibility as they need to make critical decisions on a daily basis—each decision with little to no data and in an ever-evolving environment. The pressure to select a feature, message, channel, design, or even a combination of all is at an all time high. But what if there exists a far smarter approach to adaptively test and learn?

This is precisely the point where product management can benefit from Bandit Algorithms, which are based on machine learning and decision science. Following the foundational principles laid out by Lattimore and Szepesvári, Bandit Algorithms provide a structured, balanced, and data driven approach to experimentation and performance.

tryBusinessAgility believes that with the proper tools and technologies, an organization can equip the PMs with the essential tools and adapt to changing environments, proving to be critical by Bandit Algorithms equipping wit five decisive ways product leaders can use them to embrace uncertainty.

1. Accept Dilemma of Exploitation and Exploration

With every product manager, the following options exist:
  • Exploration: Implement new features, pricing models or markets.
  • Exploitation: Focus on what has already proven successful.

Unlike static A/B tests, bandit algorithms let you iteratively adjust your model as new data arrives. This ensures that you maximize value without missing out on breakthrough opportunities.

At tryBusinessAgility, we teach PMs to frame product hypotheses through this lens. It centers around intelligent risk-taking, not random risk; it focuses on structured discovery.

2. Move on from A/B Testing

Most A/B tests are time-consuming and user-split based. Techniques like Upper Confidence Bound (UCB) and Thompson Sampling can use all information available to optimize features in real time.

UCB focuses on options with great potential but low certainty.
Thompson Sampling blends probability with past performance to sample the most promising variants.
These methods enhance user traffic allocation toward winning features while still exploring other options. These improvements result in faster insights and better ROI while reducing user loss.

3. Achieve Hyper-Personalization Through Contextual Bandits

User behavior is not universal, so modern applications from content feeds to pricing engines use contextual bandits.

Decisions are made on user attributes like device type and location. Instead of testing features on everyone, users are optimized on each segment or even down to the individual level.

So I recommend PMs to construct context-adaptive experiments because customization isn’t merely a catchphrase; it’s a tactical approach.

4. Keep Changing Markets Agile Using Non-Stationary Models

User preferences evolve, and markets shift. What worked yesterday doesn’t always work today.

Bandit algorithms can accommodate non-stationarity, which is environments that change over time. Your experiments can forget outdated data and account for new realities using decay functions or windowed learning.

This supports Agile principles of reacting to change over following a set plan.

5. Instill a Culture of Learning at All Levels

Finally, the true power of Bandit Algorithms isn’t in the math—it’s in the mindset.

These models foster:
  • Testing without fear of failure.
  • Learning from every interaction
  • Aligning decisions with long-term goals, not shallow, short-term wins

Concluding Thoughts

Bandit Algorithms provide more than optimization; they give a methodical approach to agility. With precision and clarity, they become a tactical asset for today’s Product Managers navigating through multilayered complexities.Reach out if you are prepared to go past rigid choices and step into your product’s path of dynamic learning. Our workshops and courses are crafted to equip learners with the skills necessary to become adaptive leaders.

Our focus is not simply product creation; rather, our goal is the construction of intelligent, learning-oriented ecosystems.
Explore our executive Education at www.trybusinessAgility.com

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