Like most leaders, I make decisions based on a mix of hard data, qualitative input, and gut instinct—intuition that’s been shaped by years of experience and human judgment. That approach works, but it takes time. And time is our most precious resource… and if you are feeling anything like I do these days, I need to do more with less and at greater speeds for strategic outcomes.
Technology, especially AI, is often touted for its efficiency gains. But where I find it creates the most value is in accelerating effective decision-making. The ability to gather, synthesize, and analyze information quickly allows me to focus more energy on the human elements of decision-making—intuition, judgment, critical thinking—and helps me act more confidently. And, yes, with greater speed. I have found that for AI to be a collaborative partner, I must teach it my contextualized language. It needs to be given my strategic thoughts, strategies, and business scenarios to partner with me in my decision-making processes. It’s been about a year of experimenting with it to have a contextualized collaborator. This thought leader capability is outstanding for strategic thinking and associative scenario planning across industry and technology (looking for fusion impacts).
Using Technology to Accelerate Decision Velocity
My goal is to use AI for maximum impact, in my organization and for my customers. But my first AI initiative was simply experimenting on myself. To understand what’s possible, and what I can ask of my team, I needed to work with the technology directly. I discovered that to change how I worked with AI, I had to change. Becoming AI-first doesn’t just happen; you have to reach for the technology intentionally, until it becomes automatic.
That bottom-up learning and individual commitment was critical to pushing through my traditional ways of working and integrating AI to create an Agent that had the right amount of contextualization/personalization. At Stellix, we prioritize “internal innovation”—the idea that to help our customers apply emerging technologies, we must first apply them ourselves. AI may have given “internal innovation” a deeper meaning: taking responsibility as individuals for experimenting with new technology before we come together to create organizational change. As individuals, and as a team, these have been our most successful experiments to date:
Making meetings more focused and productive
Sharing and reviewing updates isn’t exactly the best use of people’s time, and yet historically, that was a primary focus of some of our meetings. With the help of technology, we’re now using meeting time more effectively.
Instead of 40 people spending an hour looking at slides, each team records a short video sharing their updates in advance. Everyone watches those recordings independently—at double speed if they want—so by the time we meet, we can focus on what we do best together: solving problems and making decisions.
AI tools add value by automatically generating notes and summaries from each recording—helpful recaps that facilitate moving forward to the next steps. Over time, the summaries become a rich dataset we can analyze for patterns and insights that might otherwise get lost in conversation.
Staying current with industry trends
As CEO, part of my job is staying on top of trends across life sciences, manufacturing, and technology, which entails consuming an enormous amount of content every day.
AI has become my filter. It distills research, reports, and articles into concise summaries, allowing me to grasp the key points in minutes. Sometimes, the highlights are enough for me to stay informed and continue moving forward. At other times, I may delve deeper. Either way, the summaries help me invest my time more wisely.
Recognizing patterns across the business
Some of Stellix’s customer engagements are so large and complex that there could be 20 different people working on the account at any given time. Getting a complete picture of an engagement used to involve chasing updates, pulling reports, and stitching together insights from multiple conversations.
That approach was sufficient years ago, when the scale was much smaller—when there was less data to work with, when Stellix was a leaner company, and everyone was in the office full-time. Fast-forward to today: the volume of data we work with is enormous, and our organization has expanded and evolved to using a hybrid model. The old way of doing things is no longer sustainable.
The good news is that now, I can type a customer’s name into one of our internal AI-driven tools and instantly generate an executive summary of what’s happening, essentially getting thousands of data points distilled into one concise view. I have access to internal activity, performance trends, and even external signals like company news or market shifts.
From there, I can quickly identify opportunities or potential issues and direct specific questions to the relevant team members.
Validating (or challenging) assumptions
As a CEO, I have the authority to make big decisions, and it’s a responsibility that I don’t take lightly. I often utilize AI early in the decision-making process to challenge my own assumptions and refine my thinking before presenting ideas to the team.
I’ll share the context behind a hypothesis, ask for an interpretation, and see how the AI’s analysis aligns or conflicts with mine. Sometimes, I even ask it to call out blind spots or offer alternative viewpoints I might have missed. That way, when it’s time to collaborate, I’m coming to the table with a clearer, more well-rounded perspective.
Enabling Successful Technology Adoption
It’s important to note that AI and other technologies aren’t a silver bullet for making smarter decisions. The technology is only useful when the fundamentals are in place.
Good decisions depend on having accurate and reliable information. We’ve all heard countless times that AI tools are only as effective as the data feeding them. That means ensuring that data is clean and reliable (and contextualized to your language), and that agents and systems actually communicate with one another. If I can’t see customer data across multiple projects—or if that data is incomplete—I can’t get a full picture of the relationship. When data is clean and connected, insights become more useful and far easier to act on.
Still, for AI to create leverage, people have to use it—and use it consistently. If you’re asking your team to submit AI-generated summaries or reports, give them a standardized template and a clear process. Those shared frameworks create efficiency gains everywhere, from helping people absorb new information quickly to reducing the time it takes to complete a task.
My background is in engineering, where mastery has long been the pathway to success. AI has upended some of that; now, creativity and curiosity (also traits that many engineers possess) are replacing mastery in some instances as the most important traits for adapting in this new environment. (Case in point: you can actually ask your AI tool how it can help you with your work!) Technology always changes how people work, and getting comfortable with that shift isn’t easy.
The most effective organizations recognize that change management is key to successful technology adoption. To mitigate pushback or hesitation from employees, make it clear that the tools you’re implementing are there to help them use their time more wisely, not replace them. Assure them that while AI can accelerate decision-making, their human brains remain the most valuable piece of the puzzle.
The Human Advantage
The beauty of agentic AI is that you can use plain language to interact with it—and even prompt the model to tell you how to use it most effectively. The barrier to entry is significantly lower. However, the outputs of AI still largely depend on the kind of mastery our team has cultivated over time. As immense as AI’s promise is, it still can’t replicate human intuition—the kind of instinct that comes from years of experience and a sense of what’s right for your business and your customers.
Maybe one day, technology will make the decisions for us. For now, the best results still come from combining human judgment with machine intelligence.
The more we can bridge those two worlds, the better—and faster—we’ll be able to make the decisions that matter. My AI thought leader agent has a seat at our table. What agents are at your table?
