Integrating AI for product people: The playbook

Implementing AI can feel overwhelming — full of hype, uncertainty, and pressure to “do something” with AI.

During my mentoring sessions over the past year I’ve often been asked:

“Where do I start?”

“How do I make sure AI delivers real value for my customers and my business?”

This guide is here to help product people find answers to those questions. You’ll find practical steps, explanations, and a checklist you can use along the way.

Before jumping in, it’s critical to make sure that your organization is truly ready to invest in AI meaningfully.

Answer these questions:

If you answered “yes” to all of the above, you’re in a good place to continue. If not, these should be addressed first before embarking on your AI journey.

AI should always start with your users and should serve your existing goals, not create new ones in isolation. Your product roadmap and priorities should be already well-defined by this point, so your focus here is to dig deeper into your priority areas and understand which customer jobs and workflows within them are ripe for improvement.

Look out for areas where you have momentum, potential for big impact and pain-points:

With your focus areas identified, the next step is to assess which tasks within those workflows are genuinely suitable for AI.

Look for tasks that are repetitive, too complex for basic automation, and follow clear, predictable patterns. Good data availability is crucial here, as if your data is incomplete or messy, even the best AI models will struggle.

Check if tasks align with typical AI strengths like classification, forecasting, answering user queries, summarizing content, translating, or generating text. Also, consider whether personalization at scale could meaningfully improve the customer experience at key touchpoints.

💡 Tip: Don’t try to force AI where it doesn’t fit. Without clear patterns and quality data, AI can create more problems than it solves.

🎯 Goal: Finish with a clear, focused shortlist of tasks well-suited for AI — ready for pilot selection.

With your shortlist of AI-friendly tasks in hand, it’s time to pick a pilot project. The goal here is to choose something achievable, with clear value, and minimal complexity.

Look for tasks with clear outcomes and limited dependencies on other systems. Aim for projects where even a 10–15% improvement will make a real difference, whether that’s efficiency, customer experience, or internal operations.

Clear goals turn AI experimentation into measurable progress. Without them, it’s too easy to get lost in the noise of experimentation.

Use the SMART framework to define success:

AI isn’t a one-time project. Treat it as an evolving capability that improves over time, helping your company reach AI maturity. Here are a few more essentials to set you up for long-term success:

Communicate benefits in a simple language:

Assume you’ll start with a beta group for your AI rollout. **Do not forget when presenting the business case, avoid technical jargon. Instead, focus on how AI will improve operations on clear, relatable terms.

Address compliance and governance:

Proactively engage legal and governance teams early in the process. Address concerns related to compliance, data privacy, and risk management. Framing AI as a solution that mitigates risks and delivers measurable value will help in gaining the necessary approvals.

Gather data as you go

Gather information incrementally as your model runs in production. While remaining compliant with privacy regulations, collect additional input-output examples from real-world use. These examples will be a crucial resource for examining user interactions, identifying challenges, and ultimately creating more refined versions of your application.

Good data is the foundation of good AI based improvement. Poor data management leads to biased outputs, hallucinations, and ultimately, bad product decisions. More importantly, it risks user trust, impacts key metrics, and slows down your AI progress.

Think of your data as your AI engine’s fuel. If the fuel is low-quality or contaminated, performance suffers. Follow these four principles to build strong foundations.

AI models are only as good as the data they are trained on. If the training data does not accurately reflect the diversity of real environments, and use cases, the model will struggle to generalize effectively.

Example: if a recommendation algorithm for an e-commerce platform is trained primarily on data from casual female shoppers, it may fail to serve male sports customers effectively, leading to poor user engagement and lost revenue.

To avoid this issue, ensure that your dataset is diverse and covers different demographics, users’ behaviors, parameters and edge cases.

If certain parts of your user base are underrepresented in your dataset (e.g. gender, browsing history, purchase history, or other behavioral parameters), you can balance it out through data augmentation or synthetic data generation.

Data augmentation

Data augmentation increases the size and diversity of your dataset by generating new data points from existing ones. This can be done by making controlled changes to your current data or by using deep learning models to generate realistic variations.

It’s a simple but effective way to give your AI more varied examples to learn from, especially when collecting new real-world data is slow or limited.

Synthetic data

Synthetic data is artificially created using computer algorithms. It’s especially useful when real-world data is scarce, sensitive, or difficult (or expensive) to collect.

Synthetic data can be generated using:

One big advantage of synthetic data is that it avoids many regulatory constraints associated with real data. Plus, synthetic datasets are typically pre-labeled, saving you time and effort on manual data labelling.

However, synthetic data isn’t perfect. There are some important challenges to be aware of:

Common examples of synthetic data include:

If your task involves supervised learning, ensure that your data is properly labeled with the correct target variables. Proper data labelling is essential for reducing bias in AI models.

Example: imagine you're building an AI model for customer support, and your labelling process incorrectly tags refund requests as general inquiries. The model will then fail to prioritize these cases correctly, frustrating customers and increasing response times.

A diverse and representative set of labeled data helps prevent skewed predictions that might otherwise arise from biased training inputs. To achieve this, teams can leverage a combination of automated labelling tools and human-in-the-loop validation. This would ensure both efficiency and accuracy.

Maintaining high-quality labels demands regular QA procedures, including random sample reviews and validation techniques. Labelling is an iterative process, so continuous feedback and refinement are necessary to enhance label accuracy and consistency over time.

AI models learn by recognizing patterns in historical data. But if your data is outdated or no longer reflects real-world conditions, your model will keep making decisions based on a past that no longer exists.

Historical data is essential because it gives your AI the depth it needs to understand long-term trends. However, historical data alone isn’t enough. You also need to make sure the data you’re using stays relevant to your current market environment and user behaviors.

Example: Your fraud detection model is trained mostly on patterns from a year ago, it might miss new scam techniques that have emerged more recently. Fraudsters continuously evolve their strategies, and if your AI isn’t learning from fresh data, fraudulent transactions could slip through undetected.

To avoid this, monitor both your data and your model performance regularly. Look out for data drift, where shifts in behavior quietly erode your model’s accuracy over time. Refresh your data frequently to ensure your AI stays aligned with the realities of your customers and market.

💡 Tip: The minimum dataset size required to train an AI model will vary depending on factors like your desired accuracy, the complexity of your use case, and the type of model you’re using.

The widespread integration of AI across industries has significantly increased concerns around data privacy and security. AI systems rely on vast amounts of data for training, much of which can be sensitive or personal. This raises important legal and ethical questions around consent, usage, and data protection.

Regulations such as the General Data Protection Regulation (GDPR), CCPA, and other region-specific laws set strict rules on how personal data must be collected, processed, and stored to safeguard individual privacy. Yet, even with these frameworks in place, AI systems remain vulnerable to data breaches and security risks.

To reduce these risks and build trust, it’s essential to follow some key practices:

Choosing the right vendor is critical to the success of your AI initiatives. A good vendor can guide you through AI complexities and provide the necessary model and support.

Work closely with your legal and compliance teams from the start. Create a clear checklist that covers data privacy, user consent, encryption standards, and security requirements, so that every vendor is properly vetted.

As you evaluate potential vendors, focus on a few essential areas.

Expertise

Implementation

Scalability

Support

It’s always a good idea to consider multiple vendors before implementation. This way, if issues arise during testing, you have alternatives. Alternatives give you flexibility if early tests fall short, costs increase, or results are not delivered as expected.

💡 Tip: The plan for applying models to specific use cases should also anticipate future improvements, both in the models themselves and in the data they rely on. Make sure to define a clear timeline for iterative updates, including how frequently models will be refined and enhanced.

Once you have narrowed down your vendor shortlist, build a clear picture of both the initial investment and ongoing operational costs.

Focus on these key areas:

Computational costs

AI models, especially those running in the cloud or at scale, require significant compute power. Costs can quickly add up as you process larger volumes of data or move into production environments. Estimating these costs accurately from the start is essential.

Team effort

In my experience, having in-house expertise makes all the difference when implementing AI. External consultants can offer valuable skills, but they typically lack the deep understanding of your product and priorities. Having team members who intimately understand both your product vision and the technical nuances ensures that any issues, questions, or potential roadblocks can be promptly addressed without the typical delays that come from relying on an external partner.

Successfully integrating AI into your product starts with careful, deliberate planning. It is not about following trends or adopting technology for its own sake. The real value of AI comes from solving meaningful customer problems, improving critical workflows, and identifying clear business opportunities. Whether it is reducing costs, increasing efficiency, or unlocking entirely new experiences, the impact of AI depends on how well it is aligned with your goals.

Robust data management practices are essential to make this possible. Your data must be representative, accurately labeled, historically relevant, and secure. Choosing the right vendor is just as important. Look for AI vendors who are aligned with your organizational needs and can ensure scalability, compliance, and ongoing support.

Finally, remember that effective AI integration is inherently cross-functional, demanding close collaboration between product managers, data scientists, engineers, and legal experts. By thoughtfully addressing these areas, product teams can harness AI as both a tool for incremental improvements and a transformative driver of innovation, customer satisfaction, and long-term competitive advantage.