In recent years, the artificial intelligence (AI) landscape has shifted from quiet curiosity to relentless noise. Conference taglines, vendor solicitations, and slide decks all seem to begin with the same question: What can AI do for you? And too often the answer comes in the form of a catalog of hundreds of “use cases,” neatly packaged, context-free, and ready to be plugged in to any organization which accepts that transformation can begin with a menu. 1898 & Co., part of Burns & McDonnell, takes the opposite view: AI is not a destination but a powerful tool to be used in solutioning for particular types of problems. The first question is not what the client would like to order, but what problems they seek to solve. The right approach to the challenge, and the appropriate toolbox for the job, are developed from there.

This mindset is emblematic of how we approach client needs and engineering, data, and now AI, alike. Technology should never be a destination. AI itself is not the deliverable. It is a tool, but one of many, that helps us deliver meaningful, measurable outcomes. When applied correctly AI can be transformative, while when applied indiscriminately it may well represent yet another expensive experiment destined to never reach production. The work begins long before a model is selected or an algorithm vetted, developed, or tuned. It’s crucial to start by understanding the business challenge at hand. That means working directly with domain technical specialists in generation, transmission, manufacturing, or any other environment where operational decisions matter. It’s imperative to define the problem, the constraints, the desired outcomes, and the conditions in which a solution must work. From there, the reality of the client’s data and systems landscape needs to be assessed: what information exists, where it is stored, and how it can be transformed, connected, or augmented. Gaps and obstacles need to be identified to determine how to move forward. It’s then that it is time to reach for the technological toolbelt. Sometimes the optimal answer is AI. Other times, it is advanced analytics, automation, or machine learning. In most cases, it is a combination, all orchestrated to solve a problem rather than to showcase a technology. Solutions need to be architected to scale responsibly, improving operational reliability rather than compromising it. Piloting is done not to “demo” but to de-risk: To solve the core problem in a controlled environment, creating clarity rather than hype. This approach may seem straightforward, but it is what differentiates successful AI programs from stalled ones. AI is a means to an end; it is not an end in itself.