AI Product Management Lessons from the Trenches

Navigating AI Product Management in B2B: What have we learned?

We are pleased to feature Shwetha’s in this blog, where she shares her insight on AI product Management, and what it takes to be successful, in our fast-changing world driven by breath-taking AI innovations.  

Shwetha is a trailblazer and breakthrough thinker who has learned valuable lessons by being in the trenches.  Her experience allows her to drive a clear vision that defines the path needed to create better AI-powered products for security, risk and compliance professional. She’s pivotal member of 4CRisk’s leadership team, driving product vision and delivering award-winning products intersecting AI, risk management & compliance.  4CRisk’s products have been recognized by 4 global organizations: 2024 Banking Tech Finalist in the Tech of the Future: Risk & Compliance category, FinTech Global’s 2024 AI FinTech 100  ‘one of the World’s Most Innovative AI technology Companies in Financial Services, 2024  Winner Artificial Intelligence Excellence Award by the Business Intelligence Group, and also nominated by RegTech Insights A-Team as the leading 2024 Regulatory Intelligence Product.

Now, over to Shwetha….

Navigating AI Product Management in B2B: Lessons from the Trenches

AI product management is both remarkably similar to traditional product management and yet uniquely different. As the product head for AI-driven solutions at 4CRisk, I’ve come to realize that managing AI products, especially in the B2B space, requires a distinct mindset that balances technical knowledge, customer insights, and an ever-evolving understanding of AI’s capabilities. Here are some of my key learnings:

1. Data Is Everything

Data isn't just the fuel; it’s the foundation. As an AI product manager, you must become intimately familiar with the data your model is seeing. You’re the closest link to your customers and the domain, so it's crucial to ensure the right data governance processes are in place. Be vigilant about how data is labeled, because your product’s success depends on the quality and relevance of this data.

2. Embrace Subjectivity

AI outputs can often be interpreted differently by different customers. Subjectivity is inherent, and no single model will satisfy all viewpoints. It’s critical to build flexibility into your solutions—creating room for feedback loops that adjust the model to suit various interpretations without losing its core purpose.

3. The Enterprise Data Challenge

When dealing with enterprise-level data, the stakes are even higher. One of the challenges is that our models are typically trained on generic data, not customer-specific datasets. This means that while the model might be robust, it might not fully align with a customer’s unique data landscape. Finding ways to bridge that gap is an ongoing challenge.

4. Continuous Training: Keep the Model in Shape

Consistent and frequent model training is non-negotiable. The key to keeping AI products relevant is by building strong feedback loops from users. But here’s the catch: you have to carefully manage and filter out user biases that can creep into the model, ensuring that feedback improves the product without distorting it.

5. The “In-Between” Role

As an AI product manager, you’re in this unique position where you’re neither a data scientist nor a software engineer—but you need to understand both. You’ll need to get your hands dirty with technical concepts and model logic to avoid costly missteps. It’s a constant learning curve, but it’s essential for making informed decisions and driving the product forward.

6. Understand the Techniques Driving the Model

It’s crucial to grasp the underlying techniques your models rely on. Dive into the algorithms, familiarize yourself with the data processing techniques, and learn how AI models function. This knowledge will enable you to make better judgments about your product’s performance and, more importantly, build confidence within your team and among stakeholders.

7. Know Where AI Works—and Where It Doesn’t

AI can be a game-changer, but it’s not a magic bullet. Knowing when AI adds real value and when a traditional solution might work better is pivotal to the success of any use case. Be strategic about how and where you apply AI to ensure you’re solving the right problems.

8. Manage Expectations Relentlessly

Finally, the most important part of AI product management: setting the right expectations. Be crystal clear with customers about what the AI can and cannot do. AI is powerful, but it has limitations, and managing those boundaries upfront is essential for long-term trust and success.

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- Shwetha Shantharam, AVP Product Head, 4CRIsk.ai

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About 4CRisk.ai Products: Our AI products use language models specifically trained for risk, compliance and regulatory domains to automate manual, effort-intensive tasks of risk and compliance professionals, providing results in minutes rather than days; up to 50 times faster than manual methods.  
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