Key Takeaways:
- The artificial intelligence (AI) landscape has shifted from curiosity to noise, with many companies focusing on the wrong question: "What can AI do for you?"
- AI should be viewed as a powerful tool to solve specific problems, rather than a destination or a catalog of "use cases"
- The right approach to AI is to start with the problem, not the platform, and to work directly with domain technical specialists to understand the business challenge
- AI is a means to an end, not an end in itself, and should be applied in a way that is grounded in business logic, engineered for operational reality, and validated by domain experts
- A problem-first approach to AI can lead to tangible, practical solutions to real business and operational challenges, and can help to identify new opportunities for AI to reduce effort, mitigate risk, and address challenges that were previously considered too costly or complicated
Introduction to the AI Landscape
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. However, this approach is misguided, as it focuses on the technology rather than the problem it is intended to solve.
Starting with the Problem, Not the Platform
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. 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.
Understanding the Business Challenge
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.
Reaching for the Technological Toolbelt
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.
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.
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.
Conclusion
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. By focusing on the problem, not the platform, and by working directly with domain technical specialists, we can deliver meaningful, measurable outcomes and help our clients achieve their goals.