By Sunil Koparde, HEXstream project manager

AI is no longer just a buzzword—it has become a core part of modern products and processes. 

Yet for many people, AI still means ChatGPT or conversational tools. In reality, AI has been around for years, quietly powering products and processes through recommendation engines, predictive analytics, smart chatbots, and automation. Developers have long been embedding intelligence into their code, and product teams have consistently pushed for smarter, faster, and more adaptive systems.

A product manager’s job isn’t to build AI models or fine-tune algorithms, but is to ensure that when AI is introduced into a product, it solves a real problem, delivers business value, and integrates seamlessly into the user experience. Similarly, managing AI projects differs from traditional software initiatives—processes, people, risks and even success metrics are evolving.

1.       Start with “Why AI?” not  “Where can we add AI?”

The first temptation many teams face is to add AI just to appear innovative. That’s the wrong starting point. A product manager’s first responsibility is clarity: What specific user or business problem does AI solve better than existing methods?

AI should enhance capability, not complexity. For example:

·       Predicting demand trends faster than manual analysis

·        Personalizing recommendations to improve user engagement

·       Automating repetitive support queries to save time

The “why” defines everything else—the data needed, the architecture, the success metrics, and how users interact with the feature.

2.       Understand the shift: From logic to learning

Traditional software development is based on logic. Developers write clear rules: If X happens, do Y. AI, in contrast, learns from data, which means outcomes are probabilistic, not deterministic. This shift has two major implications for product managers: First, you manage model behavior, not fixed rules. Secondly, you must account for data quality and change over time.

As product managers, we must adjust our mindset: success isn’t about completing requirements—it’s about training, monitoring, and improving performance and accuracy over time.

3.      New team dynamics and roles

AI projects introduce new stakeholders. Besides engineering, design and QA, you’ll now coordinate with:

·       Data scientists who develop and train models

·       Machine-learning engineers who operationalize the models

·       Data engineers who build and maintain data pipelines

·       Legal and compliance teams who review ethics, bias and privacy risks

A product manager’s job becomes one of translation—ensuring everyone understands why the AI exists and what success looks like. They will need to connect business intent with technical design in clear, measurable terms.

4.      Data is the foundation

In AI, data is not an afterthought—it is the product. Without quality data, even the best models fail. And from a product manager’s viewpoint, this introduces new responsibilities:

·       Assess data availability early. Do we have enough relevant, high-quality data to train the model?

·       Plan for data collection. If not, how will we gather it—ethically and legally?

·       Monitor for bias. Does the dataset represent diverse user groups fairly?

·       Evaluate privacy. Are we respecting regulations and user consent?

Preparing data takes longer than building the model itself. Planning for that time is part of smart product management.

5.      Defining the right success metrics

AI metrics differ from traditional KPIs. You need two perspectives:

·       Model-performance metrics (accuracy, precision, recall)

·       User or business outcomes (engagement, satisfaction, ROI)

For example, a chatbot might have 95% intent-recognition accuracy, but if users still prefer to call support, the model’s success doesn’t translate into real product value. A product manager must define metrics that measure impact, not just intelligence.

6.      Managing uncertainty and iteration

AI development is inherently experimental. Models improve over time with data, so product managers must embrace iteration. Here’s what that means in practice:

·       Launch in limited scope or beta

·       Observe real-world performance

·       Collect new data

·       Retrain or fine-tune models regularly

It’s similar to agile delivery—but instead of just releasing code updates, you’re releasing and retraining intelligence.

7.      Ethics, fairness and transparency

AI introduces ethical considerations that go beyond functionality. A product manager needs to ask:

·       Can users trust the system’s decisions?

·       Are results fair across different demographics?

·       Are users aware when AI is making choices for them?

Transparency matters. For example, in the utility industry, if an AI system assigns a particular crew to a job, users deserve to understand why.

8.      Cost, ROI and sustainability

AI systems can be expensive tobuild and maintain. Training models, storing large datasets, and running inference at scale all add operational costs. As such, product managers must evaluate costs to develop and deploy. They must predict expected business return (ROI) and gauge ongoing maintenance efforts.

Sometimes, a simple automation or rule-based approach can achieve the same result much faster and cheaper. The product manager’s role is to challenge assumptions and choose the solution that maximizes impact...not the one that sounds most sophisticated.

Product manager’s evolving role

Managing AI projects redefines the productmanager’s role in several ways:

·       You think beyond features to systems that learn

·       You measure not just delivery, but accuracy and trust

·       You collaborate across a broader, more technical ecosystem

·       You become a bridge between data and decision-making

In the AI era, product managers evolve from feature managers to value architects. They understand that adding AI into products is not just about technology—it’s about direction, discipline and responsibility. Increasingly, a product manager’s job is to:

·       Identify where AI truly adds value

·       Ensure ethical and transparent implementation

·       Manage uncertainty through iteration

·       Balance cost, capability and ROI

·       Keep users at the heart of every decision

AI can transform products—this fact becomes clearer each day. But that transformation is optimized when it is guided by thoughtful, informed product management.

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