Proactiveness

July 22, 2024

I've been reflecting on a fundamental aspect of teamwork that's become even more crucial in the age of AI: the difference between mere execution and owning the outcome. When we collaborate with colleagues or hire new team members, we might not always articulate it, but we value those who can autonomously tackle problems. These are the people who don't just wait for a detailed plan to execute; they dive deep, ask the right questions, make informed assumptions, and take responsibility for delivering results.

In my earlier days at a company where I served as VP of Product, I noticed a stark contrast during interviews. Some candidates, when presented with a problem, would hesitate. They'd say things like, "I don't have enough data to solve this," or "This isn't a real-world scenario." Their reluctance stemmed from a need for complete information before taking any action. On the other hand, the standout candidates approached the same problems differently. They'd identify the missing pieces of information, make reasonable assumptions based on their general knowledge, and proceed to outline a solution. They'd say, "Based on what I know, here's how I would tackle this, but I'm open to any additional information you might have." These were the individuals who, once on the team, could be relied upon to navigate uncertainty and drive projects forward.

This distinction is becoming even more significant as we integrate AI into our workflows. With the rise of advanced tools and automation, execution is increasingly handled by machines. The real value now lies in the ability to think critically, adapt, and own the results. It's about constantly evaluating what needs to be done, seeking feedback, and refining the approach—all while leveraging AI as a collaborative partner.

In designing our AI assistant at Momentum, we're embracing this philosophy. Our assistant isn't just a passive tool waiting for exhaustive input before it acts. Instead, it engages proactively. When you present it with a goal or a problem, it doesn't bombard you with endless requests for information. It starts by making informed assumptions where necessary, based on the data it has. It then offers a plan, shares its reasoning, and transparently communicates any assumptions it made along the way.

For instance, suppose you're embarking on a new project but haven't fleshed out all the details. Our assistant might say, "Based on your objective to launch a marketing campaign, I've outlined a preliminary plan. I've assumed your target audience is the 18-25 demographic and that you'd prefer digital channels over traditional media. Here's how we can proceed, but let me know if any of these assumptions need adjustment." This approach accelerates the planning phase, allowing you to see actionable steps quickly. You can then refine the plan by correcting any assumptions and adding more specifics.

This design philosophy reduces the barriers to starting a project. Instead of getting bogged down in gathering every piece of information upfront—a process that can be daunting and delay progress—the assistant helps you move forward. It's akin to collaborating with a team member who takes initiative, thinks ahead, and values your input to refine their approach.

Consider the alternative: an assistant that refuses to proceed without complete information. It might respond to your project idea with, "I can't generate a plan without knowing every detail about your target market, budget, timeline, and resources." This not only stalls progress but can also be frustrating, mirroring the experience of working with someone who lacks initiative.

In the rapidly changing landscape of technology and business, agility is paramount. Companies that adapt quickly to new information and changing circumstances outperform those that are slow to adjust. A study by Harvard Business Review found that organizations with agile practices increased their success rate of projects by 28%. This agility comes from a mindset of moving forward with the best information available, making assumptions when necessary, and being prepared to adjust as new information emerges.

Our goal with Momentum's assistant is to foster this kind of agility. By designing an AI that owns the outcome—not just the execution—we empower users to achieve their goals more efficiently. The assistant becomes a true collaborator, one that doesn't shy away from uncertainty but navigates it confidently, just like the best team members do.

In an era where tools and technology handle much of the execution, the human role shifts towards strategic thinking and decision-making. But even as AI takes on more responsibilities, the principles that make for effective collaboration remain the same. We value initiative, adaptability, and a willingness to proceed thoughtfully in the face of incomplete information.

By embedding these qualities into our AI assistants, we not only enhance their utility but also create a more seamless integration between human and machine. The assistant doesn't just follow orders; it thinks alongside you, anticipates needs, and helps chart the path forward.

In the end, whether we're talking about human colleagues or AI partners, the ability to own the outcome—to take responsibility for both the plan and its execution—is what drives success. As we continue to develop and refine AI technologies, keeping this principle at the forefront ensures that these tools truly augment our capabilities and help us reach new heights of productivity and innovation.