Interview: Apache’s Blueprint for Scalable, People-Centric Innovation

Digital transformation in oil and gas is no longer about deploying tools. It’s about enabling people.

At Apache Corporation, this shift is being led by disciplined governance, outcome-driven roadmaps and a relentless focus on clean, consistent data. Under the guidance of Lisa Cruz, Director of IT Corporate Applications, Apache has built a repeatable model that delivers measurable efficiency gains, strengthens compliance and prepares the workforce for an AI-enabled future. She shares the lessons behind Apache’s transformation and the people-centric principles that have become part of the organization’s DNA. 

Lily Mae Pacey, Industry Analyst: Can you describe your role as director of IT corporate applications at Apache Corporation and how you guide IT strategy and governance across your global operations?  

Lisa Cruz: As Director of IT Corporate Applications, I am responsible for the application portfolio that supports our corporate functions. In an upstream oil and gas company, you have operations, and then you have all the back-office functions that keep those operations running. That’s the part of the business my team supports.  

Our IT strategy is grounded in Apache’s business strategy. We’re not an IT company. We’re here to enable the business. Apache has four key pillars: remain committed to oil and gas, deliver top operational performance, maintain financial discipline and build and grow a high-quality portfolio. We anchor our IT decisions and investments directly to those pillars. 

My role is to ensure the business has the tools it needs. Tools that support better decision-making, cost transparency, effective planning and strong governance. All that helps us stay disciplined and focused on delivering value across the portfolio.  

Lily Mae Pacey, Industry Analyst: Apache accelerated cost savings and exceeded production guidance in 2025.  How do long-term technology and data roadmaps help enable operational discipline across the organization?  

Lisa Cruz: When we build multi‑year roadmaps, we always start with the outcomes, not the technology. Operational discipline comes from being clear about the problems we’re solving and the value we’re trying to deliver. Before considering tools or new technologies, we ensure alignment on the specific problem or opportunity, whether it be productivity, safety, decision speed, data quality or risk reduction. We start by defining the outcome. 

You do this with the business, not for the business. IT leaders have to be visible listeners, not just solution providers.

Execution today is highly iterative, so the roadmap must stay flexible. As we deliver value, we use real data to assess whether we’re achieving the expected outcomes. If the business needs to change, we pivot with them. That approach keeps us aligned on long‑term objectives while still allowing us to respond quickly to what the business needs. We’re building the underlying infrastructure to support the future, but we stay nimble enough to adjust along the way.

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Lily Mae Pacey: How does Apache balance the need for quick wins with longer-term digital transformation goals, especially when planning multi-year technology investments? 

Lisa Cruz: You still must build the underlying infrastructure. But the balance comes from how you design your pilots. We focus on quick wins through short, well-defined pilots, but we also design them with scale in mind. You demonstrate value early, build trust and make sure those pilots aren’t treated as throwaway efforts.

The work becomes the foundation you build, and you gain momentum as you hit each milestone. That way, you remain aligned with the long-term architecture and operating model, so you can move quickly without creating technical debt. The business sees early success and clear progress toward the outcomes and the infrastructure can scale if those outcomes are met. 

Lily Mae Pacey: Your case study session stresses the importance of clear governance models. At Apache, what governance structures or operating models have proven most effective for aligning IT, operations and business units?  

Lisa Cruz: We have what we call cross-functional steering committees that bring together both IT and business leaders, and both groups stay actively engaged throughout the entire delivery process, right from the start. It’s not just about approvals or periodic checkpoints. For us, strong governance means shared accountability, clear ownership and timely decision-making. 

This approach is also change management 101, because adoption is a huge part of success, right? When stakeholders are aligned from the beginning, there are no surprises at delivery. We like to say that we’re doing this with the business, not to the business and that really sums up our philosophy. 

Lily Mae Pacey: Can you share a specific moment where governance prevented a major issue or accelerated delivery? 

Lisa Cruz: Honestly, there are so many examples now because it’s almost become part of our fabric.  It’s just how we operate today. These cross-functional steering committees have become second nature. I can also say that because I was here in various roles before we had this model and then after we implemented it. The difference in adoption is huge when you have that cross-functional alignment. 

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In hindsight, it seems obvious. But when you get everyone around the table from the beginning, agreeing on outcomes, weighing in on decisions and shaping the direction together, everything just gets easier. It removes the surprises, the misalignment and the last-minute friction. 

So, for Apache, it’s really become part of our DNA rather than something we point to with one or two isolated examples. Its framework has made the biggest impact, especially when it comes to adoption. 

Lily Mae Pacey: Which KPIs are most valuable for measuring the success of Apache’s connected worker initiatives and ensuring continuous improvement?  Do you have any metrics to share from current projects? 

Lisa Cruz: The most meaningful measures are always tied directly to the outcomes you define right at the start. That might include safety metrics, efficiency gains, or the big one we’re focused on right now, reductions in task completion time. Are the things we’re implementing helping people get their jobs done faster? 

We also look at improvements in data accuracy and overall data quality, as well as reductions in operational risk. Those are tangible indicators that the work is delivering value. 

On the flip side, the adoption of metrics matters just as much. Are people following the processes? Are we consistent in how we measure that adherence? Because if adoption isn’t there, then the initiative won’t be successful. You can implement the technology perfectly, but if no one uses it, it doesn’t count as a win. 

Lily Mae Pacey: Why is high-quality, clean and consistent data so critical for Apache’s AI vision? What data-related pitfalls has Apache encountered when data wasn’t clean or consistent? 

Lisa Cruz: High-quality, clean and consistent data is critical because AI is an amplifier. It amplifies everything, including your data quality. The old saying “bad data in, bad data out” is basically the foundation of AI. These models are incredibly confident in whatever you feed them, so if the underlying data isn’t clean or consistently defined, AI will very confidently produce inaccurate results. It will do that at scale if you haven’t set the right guardrails from the beginning. 

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One of the biggest pitfalls we’ve seen is fragmented data ownership and inconsistent definitions across the organization. If one group defines a data element one way and another group interprets it differently, the AI won’t magically reconcile that. Even worse, the people creating the data may think it means one thing, while the people using it believe it means something else entirely. When those definitions aren’t aligned, the outputs are likely to be inaccurate.  

As Apache evolves the workforce from digital to AI-enabled, what role should IT leadership play in driving workforce engagement and adoption?  

Lisa Cruz: You do this with the business, not for the business. IT leaders have to be visible listeners, not just solution providers. That’s how you build trust and drive adoption. 

AI is an amplifier. If the underlying data isn’t clean or consistently defined, AI will very confidently produce inaccurate results - at scale.

When employees see their feedback reflected in the solution, there’s this natural sense of ownership. They feel like they have skin in the game because they can see you’re hearing them and acting on what matters. 

One of my core beliefs is that if the technology you bring in creates friction, it’s a non‑starter. People simply won’t use it. It shouldn’t add steps. It should make life easier. 

A great example is our maintenance application rollout. We went directly into the field, had hands‑on engagement and supported users where they worked. That mattered, especially because we were working with a portion of the workforce that had limited exposure to smartphones.  Some still had flip phones. That alone was the first hurdle. 

But when you engage directly, listen to concerns and respond where you can, it eases that anxiety. It’s not just about listening. It’s about acting on the things you can change. Some constraints are just the reality, especially with off‑the‑shelf solutions where customization is limited. But even then, you can guide people through the change, so it feels less overwhelming and a lot less sticky.  

Lily Mae Pacey: Looking ahead, where do you see the next wave of ROI coming from as Apache scales digitally and moves deeper into AI? 

A lot of our ROI comes from increased efficiency and reduced operational risk. Some of those benefits translate into straightforward ROI calculations, while others fall into what we’d consider soft ROI. Our plant maintenance initiative, the one that really kicked off this whole process, is a great example. It helps ensure we maintain regulatory compliance at certain well sites. Maintaining compliance helps us avoid potentially significant financial penalties. 

If the technology you bring in creates friction, it’s a non‑starter. People simply won’t use it. It shouldn’t add steps - it should make life easier. 

In that sense, the ROI isn’t just about time savings. It’s equally about risk avoidance and ensuring we can continue operating safely and without interruption. When we look at the value of these efforts, it’s really a blend of tangible efficiency gains and the very real financial impact of staying compliant and reducing operational risk. 

Lily Mae Pacey: How do you really balance technology innovation with people-centric execution in a modern work environment? What advice do you have for other senior leaders trying to do the same? 

Lisa Cruz: One of the largest lessons from our journey is the importance of focus and discipline, especially when you’re working under a tight deadline. As teams start to see what’s possible, it’s human nature to say, “Oh, but what about this?” That’s where scope expansion creeps in. It’s tempting, but you must be intentional about managing it. 

For us, it goes back to listening. You capture every enhancement idea, and you’re transparent about what can be acted on now versus what needs to be prioritized later. That balance lets innovation continue without compromising the timeline or the outcomes you committed to at the beginning. 

It really comes down to disciplined scope control. Stay focused on delivering what you set out to do, while still encouraging continuous improvement.  It’s less about saying no and more about prioritizing the right initiatives at the right time. That’s how you meet deadlines without shutting down good ideas.  

Lily Mae Pacey: You recently participated in the Connected Worker in Energy Summit. What prompted you to get involved and why do you think forums like this are important for advancing AI adoption in oil and gas?  

I really place a higher value on peer‑to‑peer insights rather than vendor narratives. That’s where the real ROI stories come from. It’s lived experiences, not the sales pitch. Hearing people who are doing the work, who’ve tried things, who’ve stumbled and learned, that’s what’s meaningful. 

The Connected Worker Summit Series creates space for exactly that: sharing real experiences. For me, it’s about contributing just as much as I’m learning, being transparent about what’s working for us, what isn’t and why. 

I’m genuinely looking forward to hearing how others are approaching workforce connectivity as we all navigate this AI world together. It’s not really “future‑focused” anymore, it’s here, it’s now and it’s a hot topic. The more we can learn from each other, the better positioned we are to move forward with confidence. 

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