The Hidden Supply Chain Data Problem: An interview with Cameron Jensen

Shaped by a master’s in psychology, a decade of service in the US Army, plus hands-on experience solving complex data challenges, Cameron Jensen brings a multidisciplinary perspective when working with supply chain and IT leaders.

In this Q&A, Cameron shares how those experiences translate into modern supply chain consulting as well as where he is seeing AI deliver real value and expose some foundational data gaps.

Cameron, you started your career in the US Army. How has that impacted your experience working in supply chain?

There are three principles I learned while serving in the military that have shaped how I work with clients at Spinnaker SCA.

The first is commander’s intent which refers to the idea that no plan survives contact with the enemy. Instead of focusing on perfecting plans, you define the outcome, the context and the “why.” That way, when conditions change in the real world—and they always do—teams can adjust and still achieve the objective.

That’s especially relevant in supply chain, where ambiguity is constant. When things shift, the question becomes: Are we still aligned on the outcome? And what do we need to change to get there?

The second principle is to conduct after-actions reviews where you create space for honest, ego-free conversations about what went wrong and how to improve. Most organizations struggle with that level of candor, but it’s essential for progress.

The third is learning to own something and be accountable for it without having actual control. As a consultant, you often don’t own the business outcome—but you’re still accountable for improving it. Learning how to operate in that space came directly from my military experience.

If you weren’t in supply chain consulting, what would you be doing instead?

I’d likely be doing something more related to psychology or organizational development.

My background includes geospatial intelligence and a master’s in psychology, and I’ve always been interested in helping people better understand themselves—whether it’s cultural dynamics in an organization or individual blind spots.

I also think that as AI becomes more embedded in companies and supply chain processes, the human element is only going to matter more. Technology doesn’t replace humans—it amplifies the value they are creating.

What’s something people outside the industry often overlook about supply chain and data?

I think the sheer scale of impact is often overlooked. There aren’t many careers where your work directly influences whether essential goods make it to market—or whether people have access to what they need day to day.

During COVID, for example, we were solving problems the world never saw. But without those solutions, materials wouldn’t have arrived and products wouldn’t have made it to shelves.

For better or worse, supply chain is nearly invisible when it works—but when it fails, the consequences are immediate and widespread.

Supply chain technology is at an inflection point with the commercialization of AI. Where do you see the biggest opportunity for supply chain leaders?

The biggest opportunity is pairing AI with upskilled talent. When done right, you can take work that used to require entire teams of people, apply AI-assisted solutions, and enable a smaller group of people to move faster and focus more time on data analysis and decision-making instead of data preparation.

But the goal shouldn’t be to reduce teams—it should be to expand what those teams can solve. Most organizations have a backlog of problems they’ve never had time to address. Now AI is creating an opportunity to finally tackle them.

What’s the biggest challenge related to AI that companies must address?

It comes down to data quality and integration. Every technology is only as good as the data feeding it—and in most technology ecosystems, there’s a hidden layer of effort happening behind the scenes to clean and validate data.

Executives often don’t see it. They get clean outputs and assume systems are ready for AI. But the reality on the ground is different.

This gap can’t be addressed by simply adding AI. AI doesn’t fix the problem—it mainly accelerates timelines, producing inaccurate outputs faster and with more confidence.

That’s why organizations should first focus on data quality standards, end-to-end data integration and transparency into how data is prepared.

There’s concern that AI is eliminating entry-level roles. What advice would you give to those early in their supply chain career?

Be someone people trust. Technology will always change. The tools you learn today won’t be the same ones you use tomorrow. But what doesn’t change is the need for people who can operate in ambiguity and deliver results.

If you want to stand out, start by learning how to use AI tools. Build a strong understanding of the fundamentals behind them and be sure to focus on outcomes, not just tasks.

There’s a difference between saying, “I ran the analysis,” and saying, “Here’s the outcome we were trying to achieve—and the impact we delivered.” That’s what organizations value. And that’s what builds trust.

AI is reshaping supply chain—but success isn’t just about adopting new tools. It’s about strengthening the fundamentals that make those tools effective like data quality, aligned teams, and improved decision-making.

Cameron’s perspective reinforces a simple reality: The organizations that get this foundation right won’t just move faster—they’ll make better decisions with greater confidence.

Want more insights from the people shaping AI and supply chain technology? Keep an eye on our Employee Spotlight series—from Team Spinnaker SCA.