The Risky Business of AI: A TPM's Take on Navigating Uncertainty

In the evolving world of AI, TPMs must spearhead risk discovery and mitigation. From dependency risks to ethical dilemmas, learn how we can prepare for the unknown while staying agile.

Navigating Risks In High-Stakes Projects

Picture this: a high-stakes meeting where the air is thick with anticipation. The product launch is just weeks away, and the team is buzzing with excitement. But lurking in the background is a looming risk that could send everything crashing down. It’s moments like these that define us as Technical Program Managers (TPMs). Today, I want to share my thoughts on the critical role we play in risk discovery and mitigation, particularly as we dive deeper into the realm of Artificial Intelligence.

Let’s start by unpacking the various dimensions of risk we face. Dependency risks can be particularly insidious. A few sprints ago, our team adopted a shiny new AI library that promised to enhance our product’s capabilities. But as I dug deeper, I discovered that this library relied on another third-party service, which, unbeknownst to us, was experiencing stability issues. This dependency risk could have derailed our timeline, but I was able to devise a proactive playbook to assess and mitigate potential impacts. We established regular check-ins with the library maintainers and built contingency plans that included fallback options.

Then there’s schedule risk. As our projects grow in complexity, it’s all too easy to overlook how one team’s delays can cascade through others. I remember a project where the AI team was waiting on data from the backend team, who were themselves lagging due to an unexpected server issue. Instead of waiting until the last minute to address this, we created a shared visibility tool that highlighted critical path dependencies. This allowed us to proactively escalate the issue and reallocate resources, ensuring the impact on our timeline was minimized.

Now, let’s chat about technical debt. It’s like that one pesky weed in a garden that, if left unchecked, can take over the entire plot. In my experience, the faster we move, the more tempting it is to ignore technical debt for another day. But as we integrate AI solutions, the implications of that debt can become amplified. I’ve learned that establishing a culture that values code quality and regular refactoring is essential. I often encourage my junior PMs to champion ‘tech debt sprints,’ where the focus is solely on addressing accumulated debt. This not only improves our codebase but also boosts morale—after all, who doesn’t like cleaning up a messy environment?

Another crucial area is AI ethics risks. With great power comes great responsibility. As we develop AI models, we must be vigilant about bias and data privacy. I recall a tense moment when our model’s predictions began to reflect societal biases present in our training data. It was a wake-up call. We paused our deployment, gathered a cross-functional team, and established an ethics review process before proceeding. This kind of proactive risk assessment ensures we don’t just build great technology but also use it responsibly.

Incident preparedness is yet another layer in our risk management toolkit. I vividly remember a day when one of our AI systems started generating erroneous outputs due to a faulty data feed. The

Swift Action Enhances Preparedness And Resilience

team rallied, leveraging our incident response playbook to quickly diagnose the issue, implement a fix, and communicate transparently with stakeholders. This real-time escalation not only minimized damage but also reinforced the importance of our preparedness strategies. After the incident, we held a retrospective, which led to the creation of a more robust monitoring system to catch similar issues before they escalate.

As we equip our junior PMs with the tools to navigate these challenges, I often remind them of the balance between proactive strategies and adaptable responses. It’s not just about having a great plan on paper; it’s about cultivating a mindset that embraces uncertainty and values communication. For instance, I encourage them to foster relationships with cross-functional teams—these connections are invaluable when we need to escalate risks in real time.

In closing, the world of AI and risk management is complex and ever-evolving. As TPMs, we hold the reins in steering our teams through this landscape. Our ability to identify, mitigate, and respond to risks—whether they be dependency, schedule, technical debt, ethical, or incident-related—will define our success. The more we embrace proactive playbooks and remain agile in our response to real-time challenges, the better we will navigate the unpredictable waters of AI development. Let’s continue to learn, adapt, and lead with integrity as we shape the future together.