Reflections from the Future: Crafting Processes in the Age of AI

Looking back from a future where AI is seamlessly integrated into tech processes, I reflect on the vital importance of effective incident management, SLO/SLA hygiene, and the balance between governance and speed in TPM. Here’s how we navigated the challenges.

Reflecting On AI’S Transformative Journey

As I sit in my office, gazing through the panoramic window that overlooks the bustling cityscape, I can’t help but feel a mix of nostalgia and relief. It’s hard to believe we’ve come this far in our journey with AI, especially considering the challenges we faced just a few years ago. Back then, the landscape was riddled with confusion, with processes that felt more like shackles than frameworks. Today, I want to share some of those lessons learned, particularly around the processes that shaped our successes and failures.

One of the most pivotal moments in my career occurred during an incident management meeting. A critical outage had just occurred, sending ripples of panic through our teams. As we gathered to dissect the issue, I remembered the old school ways of assigning blame like it was a team sport—fingers pointed, defensiveness trending, and a palpable tension in the air. But we had recently adopted a blameless postmortem culture, and I was determined to steer the conversation in that direction.

“Let’s focus on what happened, not who made mistakes,” I urged. As we shared our findings, a transformation unfolded. Each team member spoke candidly about their contributions, and rather than a blame game, we fostered a sense of shared responsibility and learning. This approach not only mitigated the anxiety around failures but also created a safe space for innovation. In the world of AI, where experimentation is key, this became a cornerstone of our culture.

Speaking of culture, let’s talk about SLOs and SLAs—those seemingly dry metrics that can either be an anchor or a sail. I recall the time when we had a flurry of discussions around setting our Service Level Objectives. Initially, we fell into the anti-pattern of bureaucracy, with endless meetings and document revisions that led us nowhere. But then we shifted to a more lightweight, data-informed approach. We started by looking at user behavior and feedback rather than just internal expectations. This shift was revolutionary; we aligned our SLOs with what actually mattered to our users. It wasn’t just about uptime anymore; it was about user satisfaction and experience. Our SLAs became living documents that evolved with our understanding, rather than static agreements that felt more like shackles.

As I reflect on our release trains, I chuckle at the memory of our early attempts, which resembled a clunky machine with too many gears and not enough oil. We had quality gates that felt like hurdles rather than checkpoints. Yet, through trial and error, we learned to adapt. We introduced a streamlined release process, making quality gates collaborative checkpoints rather than bottlenecks. It was crucial to maintain speed without sacrificing quality, especially in a field where AI developments could shift overnight. The key was to provide teams with the data and insights they needed to make informed decisions on the go.

This idea of adaptability was further emphasized during our design and PRD review rituals. Initially, we had a rigid structure that stifled creativity. But as we became more comfortable with AI’s fluidity, we learned the value of making these reviews more dynamic. By incorporating real-time feedback and iterative design principles, we created an environment where ideas could flourish. It was less about adhering to a checklist and more about fostering innovation—a breath of fresh air in our process.

However, it wasn’t all smooth sailing. The allure of cargo-cult practices crept into our workflows, tempting us to replicate successful processes without understanding their context. I remember a team that attempted to mimic a competitor’s incident response strategy, believing that it would yield the same results. It didn’t take long for them to realize that their unique challenges required a tailored approach. We learned that processes must be adaptable, informed by data, and grounded in our specific circumstances. Just because something works for one team doesn’t mean it’s the right fit for another.

As I look back, I’m grateful for the lessons learned. The balance between governance and speed remains a delicate dance. We’ve come to appreciate that governance doesn’t have to stifle innovation; it can actually enhance it when done right. The processes we’ve established are now flexible enough to adapt to the rapid changes in AI while ensuring we maintain quality and accountability.

So here I am, reflecting on the past from a future that once seemed far-fetched. The processes we’ve cultivated have shaped our success, and while we still face challenges, they no longer feel insurmountable. The journey has taught me that processes are not just about adherence but about creating a culture that thrives on learning, adaptability, and collaboration.

Embracing Setbacks For AI Innovation

And as we continue to navigate the ever-evolving world of AI, I remain committed to fostering an environment where each setback is a stepping stone towards greater innovation.

In this fast-paced world, remember: it’s not about perfecting the process; it’s about ensuring that the process serves us, not the other way around.