Driving Forward, Not Looking Back: Reorienting the Industrial Base with AI

Artificial intelligence is often framed as a tool for efficiency - faster processes, lower costs, improved output. But in an organic industrial base under pressure, its true value lies elsewhere. AI is not just about doing things better. It is about changing how leaders see, decide, and act.

The opportunity is not incremental. It is structural.

AI’s impact in the industrial base can be understood through a simple framework: how organizations measure performance, how they incentivize behavior, and how they translate data into decision advantage. At its core, this framework describes a shift from a system that looks backward to one that enables leaders to see and act ahead of events.

The result is an enterprise built on lagging indicators - optimized for reporting, not for decision advantage.

Artificial intelligence narrows that gap. By identifying patterns in workforce output, bottlenecks, and excess capacity, AI enables a more dynamic understanding of performance. Leaders can move beyond static reports and begin to see their organizations in motion - where friction is emerging, where capacity is underutilized, and where intervention will have the greatest effect.

This is not about surveillance. It is about clarity.

The first element of the framework – measurement - evolves from static reporting to real-time visibility. The second – understanding - emerges as those measurements are contextualized and translated into insight. Together, they allow leaders to align resources more effectively: shifting talent, reinforcing teams, and addressing inefficiencies as they emerge rather than after the fact.

AI shifts the organization’s orientation - from backward-looking assessment to forward-looking anticipation.

Focus becomes a command capability. Instead of reacting to lagging indicators, organizations can act on leading ones.

This is not theoretical. At Corpus Christi Army Depot, these principles are already being put into practice. A team of artisans and analysts recently took first place in an Army-wide artificial intelligence competition by developing tools that provide real-time visibility into program performance - highlighting emerging risks, identifying inefficiencies, and enabling earlier intervention by leaders. In practice, this includes automated updates pushed directly to program leaders that flag program health and emerging areas of concern before they become systemic issues. The value is not simply in the technology itself, but in how it changes the way leaders understand and act on the data in front of them.

The challenge in the industrial base has never been the absence of data. It is the gap between data and understanding. AI accelerates the movement from information to insight by organizing and contextualizing large volumes of data across systems that were never designed to speak to one another. It does not replace judgment - but it sharpens it.

Better understanding leads to better decisions made earlier and with greater confidence. In an environment defined by constrained resources and growing operational demands, that advantage compounds quickly.

Improved understanding alone does not produce change. If measurement evolves but incentives remain static, organizations will continue to behave as they always have.

This is particularly true in large government systems, where incentives are often broadly distributed but insufficiently differentiated. High performers and low performers are frequently treated the same, and outcomes are not always tied clearly to rewards. In that environment, better data risks becoming just another layer of reporting rather than a driver of action.

The third element of the framework – incentives - is where insight becomes impact.

Artificial intelligence creates an opportunity to better align incentives with outcomes by identifying top performers, high-impact roles, and the conditions that produce success. Leaders can reinforce the behaviors that matter most - whether throughput, quality, or innovation - not through broad guidance, but through targeted action.

The shift is not simply about rewarding performance. It is about shaping it.

Leaders can use these insights to direct recognition, allocate opportunities, and design interventions that reinforce desired outcomes across the organization. Over time, this creates a feedback loop: behaviors that drive results are recognized, repeated, and scaled.

For an organic industrial base that has often struggled to align performance with results, this may be one of the most consequential applications of artificial intelligence. Technology alone will not change organizational behavior - but it can illuminate the path. Incentives determine whether leaders follow it.

What gets incentivized gets repeated.

The application of artificial intelligence is not without risk, and those risks must be addressed directly.

First, trust is essential. AI is only as reliable as the data behind it. If leaders cannot explain how insights are generated - or if outputs consistently conflict with operator experience - confidence will erode quickly. In organizations built on technical expertise and institutional knowledge, credibility cannot be assumed. It must be earned.

Second, there is a risk of overreach. Not every decision should be data-driven, and not every aspect of performance can be captured in a model. Leaders must resist the temptation to treat AI as a substitute for judgment rather than a complement to it.

Third, implementation matters. Integrating AI into legacy systems and processes is not trivial. Without careful execution, organizations risk creating parallel systems that add complexity instead of reducing it.

These risks are real, but they are manageable. More importantly, they do not outweigh the cost of inaction. The industrial base is already under strain - from declining workloads in legacy systems, rising costs, and increasing demands for readiness. In that environment, failing to improve how decisions are made carries its own risk.

The framework remains instructive here. Poor measurement produces flawed understanding. Misaligned incentives undermine even the best insights. And without trust, none of it endures.

The question is not whether to adopt artificial intelligence, but how to do so in a way that reorients the industrial base - from one that explains the past to one that anticipates and shapes the future.

COL Kevin Consedine is the commander of Corpus Christi Army Depot with 25 years of active-duty service in Army Aviation.

The opionions expressed in this article are the author's own and not of the U.S. Army or Department of War/Department of Defense.