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.
Starting with the Problem, Not the Platform
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.
The Search for Use Cases
Last year a client came to us with a familiar request: Provide us with a list of AI use cases. Several large consulting firms had already pitched compendiums of hundreds of possibilities, described in abstract terms and packaged for maximum excitement. We, of course, had such a list as well. As the dialogue continued, however, it increasingly became clear that a list was bringing us no closer to the client’s ends.
No client, after all, needs hundreds of solutions. What they need are tangible, practical answers to real business and operational challenges.
Once we got beyond the high-level solicitation and engaged in conversations with operators, asset managers, engineers, and data teams across the organization, it became evident that the real opportunities were hiding behind day-to-day operational pain points. None of the stakeholders on the ground asked for AI. They asked for help resolving issues that had become so entrenched they were assumed to be permanent. Inconsistent data, inaccessible documents, duplicate records, assets without traceability, and information that took hours or days to locate. All common problems with new solutions courtesy of emergent and AI technologies. As such, the path forward became obvious. While AI was no longer the goal, for this slate of challenges it proved the most effective tool in the toolbox for a series of data extraction, organization, and remediation challenges.
This problem-first focus ultimately produced a highly targeted pilot that addressed one of the organization’s core operational bottlenecks: generating clean, complete, trustworthy asset data for their generation fleet. And the proof of concept wasn’t merely a throw-away technology demonstrated, it solved the problem, delivering validated asset hierarchies far faster than the client believed possible. Within months, that pilot grew into a $1.3 million implementation, accelerating the maturity of the client’s asset data environment and improving the reliability of their operations and maintenance (O&M) generation activities in the process. What they expected would take years to accomplish—if it could in fact feasibly be done—instead was accomplished in a matter of months.
As so often happens when real problems are solved, the project revealed new opportunities where AI could meaningfully reduce effort, mitigate risk, and finally address challenges that had been considered too costly, too complicated, or where data quality was deemed too poor to tackle. Bills of materials (BoMs), attributes, and work order automation were all, for the first time, on the table for the client, and for us to apply AI tools to deliver. Success proliferated not because we pushed technology but because we followed value.
The 1898 & Co. Approach
AI, for us, is never the starting line. It is never the product. It is a mechanism for solving problems that matter—problems tied to safety, reliability, compliance, productivity, and cost.
Our clients do not need another slide deck full of possibilities. They need solutions grounded in business logic, engineered for operational reality, validated by domain experts, and designed to scale responsibly.
We approach AI the same way we approach engineering: by defining the problem, understanding the system, selecting the right tools, and proving value in controlled increments. The results speak for themselves, as AI becomes a capability rather than an experiment; an asset, not a trend.
At 1898 & Co., we will continue to build AI this way: Problem-first, outcome-driven, domain-aligned. We’re not helping clients apply AI, we’re solving problems with the new and advanced tools increasingly populating our technological environment. More and more, we have the right tools to optimally solve an ever-broadening array of problems.
—Chris Wiles is an AI solutions architect at 1898 & Co., part of Burns & McDonnell, specializing in applying AI to solve complex operational challenges across energy and infrastructure.
