Bridging the Gap: Guiding Stakeholders into the Age of AI

Bridging the gap: Guiding stakeholders into the age of AI

Transitioning to the AI era isn’t always easy—especially in more traditional, old-fashioned industries.

People are often reluctant to change or may believe that “if something works, why bother fixing it?” But the product lifecycle tells another story. At some point, every product will need a drastic revamp, a re-introduction to the market, or a complete phase-out.

Let me walk you through my journey, and then we’ll dive into the practical aspects—which are, honestly, much more interesting!

Who Am I? 👋

Hi folks! I'm Danai Aristeridou—a Product girl who landed (somewhat organically) in AI product management from a data science (DS) background.

I still remember my early days in a hands-on DS role. Stakeholders struggled to understand what we were doing. There was a lack of trust and a need for control.

We had to educate them, build understanding step-by-step, and iteratively move toward our goals. It wasn’t always smooth, but we embraced the process and learned a lot along the way.

📖My AI Product Cheatsheet

1. Start Simple and Iterate

We began with solutions that were interpretable and self-explanatory. Logistic regression was our go-to in the beginning. Gradually, we moved from traditional statistical approaches to more robust models—like tree-based algorithms—to improve outcomes.

This step-by-step approach made it easier for stakeholders to understand and adapt. By introducing new concepts incrementally, we helped “train the muscle” and bridge the gap between the two worlds: business and AI.

2. Build Trust

As I transitioned into the product space, I realized that keeping stakeholders in the loop is the most important part of the job.

They don’t need to understand why a random forest or neural net is technically superior. What they do need is to trust you and feel confident in the decisions being made.

How to Build That Trust:

Provide frequent updates — Let them know where you stand.
Speak their language — Share updates in terms that matter to them.
- (I struggled with this a lot!) They might not know what AUC or recall means—so use real-life examples.
Share the results — Numbers matter.
Invite them to model/sprint reviews.
Facilitate brainstorming sessions — Involve them in feature ideation.

3. Facilitate Tailor-Made Workshops

One of my favorite techniques is gathering both the technical team and stakeholders in a room and gamifying a real use case.

Here’s How to Do It:

Choose a familiar problem from your customers’ world.
- Example: If you're working with an airline, try predicting how many passengers might miss their flight.
Simplify the problem.
- Create 3–5 fictional personas with different traits.
- Ask stakeholders to guess who might miss the flight and why.
- Use tools like ChatGPT to generate realistic fake profiles.
Promote collaboration.
- Form mixed groups of clients and tech team members.
- Encourage open brainstorming and sharing of ideas.
Explain how AI can help.
- Use a simple model (like a decision tree) to demonstrate value.
- Emphasize that manual methods can't scale well with data volume.
Go deeper.
- Share the basics of model building:
  Data collection → Feature engineering → Training → Tuning → Evaluation → Deployment → Monitoring → Iteration
- Tie everything back to the original use case to make it relatable.

Common Misconceptions to Address

"The model learns on its own."

Nope. Some clients assume the model self-updates or evolves magically over time. It’s essential to explain that AI needs continuous human input to retrain, recalibrate, and remain relevant.

Closing Remarks

Human input is essential.
Good models don’t appear from nowhere. Data quality and feedback loops are critical. We must bust the myth of “garbage in, magic out.”
AI won't steal all the jobs.
A common fear is that AI will replace humans. But really, it can help free up time from tedious tasks and shift focus toward more strategic, high-impact work.

✨ Final Thoughts

That’s it for today! I hope these techniques help you speak the same language as your customers and stakeholders—and make AI more accessible in your organization.

Disclaimer: Images created with ChatGPT 💓

No Idea What to Build Next? Let Customer Complaints Talk

Feeling stuck? Not sure what you should work on next? Is it time to build your product roadmap, but you only have a few eggs in one basket? Ok, I got you. We are product people, and our job is simple (but not easy): solve customer problems . The real question is—how do you reliably identify those problems? There’s one place you should always start. Customer Support.🙌 They know your customers better than anyone else. They talk to them every single day. They hear the frustrations, the confusion, the anger, and the unmet expectations. Go sit with them. Talk to them. And most importantly, get access to their support tickets or complaints database. That’s the Holy Grail .💥 There’s nothing more valuable than a well-maintained complaints database. From Manual Pain to AI Superpower Back in the day—before large language models were widely available—you had to manually go through this data. Teams relied on N...

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