AI is Not a Priority... Until It Becomes a Priority and Then Again Not

How to Navigate the Ebb and Flow 🌊 of AI’s Importance in Organizations

#beyondAI

“Don't build until you have clear stakeholder buy-in” — this is what I always preach. But I also know that it's more difficult than it sounds. Getting that buy-in requires more than just initial enthusiasm; it demands ongoing commitment, clear communication, and sometimes, a bit of persuasion. Ensuring that everyone is not just on board at the start, but stays on board throughout the journey, can be a real challenge in the fast-paced, pressure-filled environments we often find ourselves in.

As an AI Product Manager in a large organization, your primary role is to help colleagues from various business areas tackle their day-to-day challenges with AI solutions. However, it's essential to understand that not every problem requires AI, and not every problem that could be solved with AI is worth pursuing. This distinction may not be immediately clear to your colleagues, who are experts in their respective domains but may not be familiar with product management and AI. They simply need a solution to their problem, regardless of the technology used.

They can articulate their challenges (sometimes), but it's your responsibility to dig deeper and ask the right questions: Is this problem worth solving? And if it is, does it truly require an AI solution? I often ask the latter question first to determine whether my team and I can support or if there's a better-suited team for the problem. But this is more organization-specific than a general rule.

Anyway.

Once you've determined that the problem is worth addressing and that AI is the right tool, the next crucial step for me is always to secure a commitment from my stakeholders.

And this is what this issue is about.

Happy reading 🛋️

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P.S. If you’ve found my posts valuable, consider supporting my work. While I’m not accepting payments right now, you can help by sharing, liking, and commenting here or on my LinkedIn posts. This helps me reach more people on this journey, and your feedback is invaluable for improving the content. Thank you for being part of this community ❤️.

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Commitment: More Than Just a Verbal Agreement

But I'm not talking about a simple verbal agreement. Make sure they are fully on board for the journey ahead and ready to collaborate closely with you. Without this commitment, even the best AI solution will struggle to deliver value—or worse, never reach the level required to become an AI solution. As you might have already read in my "The Path to AI Product" article, only an AI solution can evolve into an AI product.

So, ask them directly and be transparent about why you're asking:

• Are you ready to embark on this journey with me?

• Will you be by my side in bad times and in good times?

• If yes, let’s make it official.

I know how this sounds.

And that's exactly what it is. You're building a product. It has a lifecycle and doesn't just end with a deadline, like a project. So yes, it’s like a marriage. You'll be on that journey for quite a while, and going it alone is, to put it mildly, not much fun.

But this isn’t just about the working level. Have you ever heard the saying, "You're not just marrying the one you love; you're marrying the entire family"? It means that when you enter into a relationship, you're not just building a connection with your partner, but also with their family, including their dynamics, traditions, and sometimes, challenges. This saying is often used to emphasize the importance of considering the broader context and relationships that come with a commitment, not just the individual person. The same principle applies here. You must also ensure you have management buy-in. The leadership within the business areas needs to be fully aware that you’re stepping in to address their teams' challenges with AI. Whether it's your manager aligning with their manager or you handling it directly, this alignment needs to happen promptly and clearly.

Things might be easier if you're in an organizational setup where their manager is also your manager, but that isn't common in most companies. Perhaps a data mesh organization could address these challenges, but until that's the norm, we need other approaches.

📙 What is a data mesh organization?

A data mesh organization is a way of managing data that spreads the responsibility for data across different teams rather than having one central team in charge. This approach is inspired by ideas from software development, where teams are organized by specific areas of expertise or "domains." Instead of focusing on a single data storage system, data mesh emphasizes making data easily accessible and usable across the organization. Each team manages its own data, while a central platform team ensures that the data can be shared and used consistently across all teams, reducing confusion and data silos.

One of the best ways to gauge genuine interest and commitment from the start is to involve them in the business case formulation phase. Many AI use cases require more domain-specific knowledge to create a solid business case than an AI Product Manager might possess. This is where you can see if they’re truly willing to engage and contribute meaningfully. Their involvement at this stage is crucial, not just for gathering the necessary insights but also for building their understanding and investment in the project. Moreover, engaging them in the business case phase can serve as a strong motivator. It allows the business stakeholders to see firsthand the potential gains or drawbacks of the proposed AI solution. This early involvement helps to align expectations, build a sense of ownership, and ultimately increase the likelihood of long-term commitment and success for the initiative.

Resilience in the Face of Pressure: Strategies and Realities

But regardless of which tactic you implement to secure stakeholder buy-in, you must be aware that businesses are constantly under pressure. If they encounter significant hurdles that threaten their primary objectives, they will quickly lose interest in optimization efforts. When that happens, you and your AI ambitions will be dropped like a hot potato.

In such situations, your AI capabilities and promises may become the least of their concerns. They shift into survival mode. Focused on ensuring their processes continue to function. And no blame there—that’s their job, after all. They own processes that are proven to bring in revenue, so it’s clear they will lose interest in any other initiatives once things aren’t working as intended.

This is the harsh reality of building AI products within organizations. It’s important to remember that.

As an AI Product Manager, you’re mainly focused on optimizing existing processes and workflows. Rarely do you get the chance to create entirely new business models, especially when building internal products. Yes, I know. Occasionally, internal products can evolve and open doors to external markets—like some well-known products that started as internal tools (e.g., Amazon Web Services, Slack, and Gmail). But first, they excelled in the internal “market”. It will always be the same: As long as everything is running smoothly and no one is under immediate pressure, your AI initiatives might be a priority, and will have the chance to excel internally. However, be prepared for that priority to shift quickly when the business faces critical challenges.

So, what's the strategy when this happens?

This is one of those situations where I say the core ability of a Product Manager is to solve problems, no matter the type of problem. You’re not an AI Product Manager if you can’t solve problems. A bit provocative, but the truth.

Now, if you ask me to give you one piece of advice, it would be these three 😉

1. Deliver Quick Wins

When you're working on an AI initiative, especially in a high-pressure environment, delivering quick wins can be your best strategy. Quick wins are smaller, tangible successes that show immediate value to your stakeholders. They don’t have to be the full solution, but they should demonstrate that you’re making progress and that your AI solution can deliver real results.

Example: Let’s say you’re working on a project to optimize customer service response times using AI. Instead of waiting until you’ve built a full-fledged AI chatbot, start by implementing a simpler solution—like an AI-powered FAQ that can answer the most common customer queries. This not only improves response times right away but also shows your stakeholders that AI can indeed make a difference. As they see the immediate benefits, they’re more likely to stay engaged and supportive of the larger initiative. Regardless of the pressure they are facing, since they understand, that the earlier the solution is live the quicker they will not need to deal with that kind of issues anymore.

2. Become a Domain Expert on Your Own. Quickly.

As an AI Product Manager, you need to become a domain expert as quickly as possible. While you may not have the depth of knowledge that your business colleagues have, you need to understand enough to speak their language, ask the right questions, and identify where AI can make the most impact. The faster you can get up to speed, the better positioned you’ll be to drive the initiative forward. Especially at times when you can not reach out to the stakeholders, due to tough business times.

Example: Imagine you’re tasked with implementing AI in the supply chain department, an area you’re not familiar with. Start by immersing yourself in their world—read up on supply chain management, attend their team meetings, and even shadow some of their processes. The more you understand their pain points and terminology, the better you’ll be able to tailor your AI solutions to meet their specific needs. This also builds trust, as your colleagues see that you’re genuinely invested in understanding and solving their challenges.

3. Find Other Potential Stakeholders

Sometimes, despite your best efforts, the initial stakeholders you’re working with may lose interest or prioritize other initiatives. When this happens, it’s crucial to have other potential stakeholders in your back pocket. These could be other departments or teams that could also benefit from your AI solution. By broadening your network, you increase the chances that your project will survive even if the original stakeholders back out.

Example: Let’s say you’ve been working with the HR department on an AI project to streamline recruitment processes, but midway through, they shift focus due to other pressing concerns. In this case, you could approach the training and development team with a similar AI solution—perhaps one that identifies skills gaps and recommends personalized training programs. By identifying and engaging other stakeholders who could benefit from your AI solution, you keep the momentum going and demonstrate the broader value of your work.

The Reality of Risk in AI Product Development

These are just my best tactics to handle losing stakeholder buy-in. They’ve worked quite often, but not always. At some point, even the best tactics won’t work. Sometimes, you won’t be able to deliver quickly or become a domain “expert” in time. Or you simply won’t find any other interested stakeholders. Other times, you might build the AI system, but it won’t evolve into a full AI solution because the stakeholders are no longer willing to integrate it into their systems—for various reasons.

Yes, the risk remains, and it’s something you need to be aware of.

Not every AI endeavor will be successful. One reason is that the company may struggle to handle a hot potato. But another reason is simply because you want to build a product.

If building products were risk-free, well… you know the rest. But I will continue applying these tactics until I find better ones.

I know there are some highly experienced AI Product Managers reading this newsletter, and I’d love to hear your thoughts and strategies in this regard.

Until then.

JBK 🕊️

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P.S. If you’ve found my posts valuable, consider supporting my work. While I’m not accepting payments right now, you can help by sharing, liking, and commenting here or on my LinkedIn posts. This helps me reach more people on this journey, and your feedback is invaluable for improving the content. Thank you for being part of this community ❤️.

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