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.

Table of contents

• How to recognize and evaluate AI opportunities
• The four data principles for AI success
• Choosing the right vendor and estimating costs

How to recognize and evaluate AI opportunities in your product

Step 0: Check that you’re ready to start

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

Answer these questions:

• [ ] You believe technology should serve real user needs, not be used for its own sake.
• [ ] Your company has resources available:
  • Budget for at least one AI engineer or data scientist.
  • Infrastructure (cloud compute, storage, etc.) to support AI development.
• [ ] Your product roadmap and priorities are clear and well-defined.

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.

Step 1: Review customer jobs and spot opportunities

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:

• [ ] Clearly list out core customer workflows within your priority areas.
• [ ] Identify specific friction points: manual, repetitive steps or frequent complaints.
• [ ] Highlight key touchpoints ideal for personalization.
• [ ] Prioritize workflows clearly based on: frequency, customer impact and business value.

Step 2: Evaluate shortlisted workflows for AI suitability

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.

• [ ] Confirm data availability and quality for each potential task.
• [ ] Map tasks to AI strengths (classification, forecasting, etc.).
• [ ] Flag opportunities for personalization at scale.
• [ ] Prioritize tasks based on impact and ease of implementation.

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

Step 3: Choose a high-impact pilot

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.

• [ ] Shortlist tasks with minimal dependencies on other teams or systems.
• [ ] Prioritize projects where even small gains will create noticeable impact.
• [ ] Ensure the pilot aligns directly with your business objectives and vision.
• [ ] Define what success looks like before you start (e.g., speed, accuracy, cost saving).
• [ ] Scope the project tightly to keep it manageable and focused.

Step 4: Set clear, measurable goals

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:

• Specific: What exactly are you trying to achieve? (e.g., "Reduce cart abandonment by 15%").
• Measurable: How will you know if you’re successful? (Set KPIs to track progress).
• Achievable: Are your goals realistic? (Based on current capabilities)
• Relevant: Do they tie back to broader business goals? (Align AI goals with overall business objectives or North Star Metric).
• Time-bound: When will you measure progress? (e.g., "Achieve a 10% reduction in support costs within 6 months").
• [ ] Define specific objectives for your pilot (e.g., “reduce customer response time by 20%”).
• [ ] Set measurable KPIs to track progress throughout the pilot.
• [ ] Validate that your goals are realistic based on resources and data.
• [ ] Align objectives with overall business priorities and metrics.
• [ ] Establish clear timelines for milestones and final evaluation.

Other recommendations

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.

The 4 principles of data requirements in AI

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.

1. Data must be representative

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:

• Statistical methods
• Generative models like GANs (Generative Adversarial Networks)
• Simulation techniques that create realistic datasets based on known patterns

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:

• While synthetic data can reduce bias if designed well, its accuracy heavily depends on the quality of the original patterns it’s based on.
• Synthetic data requires careful verification. You’ll need to compare model results with real-world, human-annotated data to ensure reliability. This additional step can extend project timelines.

Common examples of synthetic data include:

• Customer records: Artificial customer profiles with attributes like name, age, address, or purchase history.
• Transaction logs: Generated financial transactions or sales data.
• Healthcare records: Synthetic patient data, including diagnoses, treatments, and outcomes.
• etc

2. Data must be well-labeled and contextual

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.

3. Data should be historical and remain relevant

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.

4. Data should be private & secure

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:

• Review GDPR, CCPA, and other relevant privacy regulations to ensure your data collection practices are fully compliant.
• Obtain explicit consent from users before collecting their data, and make sure they understand how their information will be used.
• Apply the principle of data minimization by collecting only the data necessary for your AI models to function effectively.
• Anonymize user data wherever possible, removing identifiable details such as names, email addresses, or user IDs to protect privacy.

Selecting the right vendor, model & estimating costs

Selecting the right vendor

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

• [ ] Vendor has relevant experience in our industry and use cases
• [ ] Vendor has provided case studies of successful implementations
• [ ] Vendor has proven ability to manage large-scale AI projects

Implementation

• [ ] Solution integrates easily with our current technology stack
• [ ] Implementation process is clear and straightforward

Scalability

• [ ] Solution can handle increasing data volumes
• [ ] Platform is flexible enough for future applications, such as adding algorithms
• [ ] Vendor offers infrastructure resources for large-scale deployments

Support

• [ ] Vendor provides ongoing support for our team
• [ ] Vendor has clear processes for maintaining and updating the system
• [ ] Vendor offers defined customer service during and after implementation

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.

Costs

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.

• [ ] Compute costs per unit of work (for example, per document or transaction) are clearly estimated
• [ ] Forecast includes how compute costs will scale as data volumes grow
• [ ] Hidden costs such as infrastructure maintenance, data cleaning, monitoring, and retraining are factored in

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.

• [ ] Internal team effort and expertise are included in cost planning
• [ ] Plan priorities building internal capability over external dependency
• [ ] External consultants are only used selectively to fill short-term gaps

Conclusion

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.