Why AI and User Experience (UX) Must Evolve Together
As artificial intelligence (AI) expands what is technically possible, utilities may be entering a new phase of digital transformation. The importance of usability may increase as data systems become more complex. Success may therefore depend not only on whether a system works, but also on whether people can understand it, trust it and adopt it. This is where user experience (UX) processes can become central.
For years, UX was treated as a finishing layer applied after technical decisions were made, but that approach may no longer be sustainable. How solutions are structured, communicated and introduced to users can help determine whether innovation delivers value or encounters resistance as systems become more data-intensive and AI-driven. As organizations implement AI into their operations, the conversation should shift from how fast systems can be built to how effectively users can align with them.
AI System Complexity
Workflows in utilities and other asset-intensive organizations have always been complex. Spreadsheets, models and analytical tools encode years of domain knowledge. Beyond mere calculations, they represent reasoning patterns and institutional knowledge.
AI systems are entering that ecosystem. Large language models can interpret structured data, generate code, analyze logic and even propose interface layouts. What once required hours of manual analysis can now happen more quickly. While this acceleration is powerful, it also introduces risk.
When technology advances quickly, the human layer can become compressed. Teams may move from data to prototype to production faster. Without a strong process guiding that progression, clarity can erode. Users might receive systems that are technically impressive but cognitively overwhelming. The challenge is ensuring users can still interpret and trust increasingly sophisticated systems.
UX as Strategic Infrastructure
UX should not be framed as an aesthetic layer. A rigorous UX process clarifies how users think about their work. The process surfaces mental models, such as how a planner thinks about a feeder, that are often invisible in requirement documents and tests assumptions before they become embedded in code.
In utilities and other asset-intensive contexts, this is particularly important. Many systems originate in spreadsheets that users deeply understand. Rows and columns provide transparency, and formulas create traceability, leading to users having the tools needed to follow logic from input to output.
When that logic is translated into modern applications or AI-enhanced tools, the structure changes. For example, when a load forecasting model moves from Excel into an AI-enabled application, users often lose visibility into intermediate calculations that previously helped them validate and trust results. Information might be grouped differently, calculations might be abstracted, and automation might replace manual adjustments.
Without intentional design, this shift can feel like a loss of control rather than an improvement. Through a thoughtful UX process, that transformation respects familiarity while introducing efficiency. It is the mechanism through which complexity becomes usable.
AI’s Role in Design Workflows
AI is also beginning to influence how systems are built. AI tools can analyze spreadsheets and identify dependencies. For example, an AI tool might scan a complex outage planning workbook and quickly map how switching steps, crew constraints and safety checks are interrelated, work that could traditionally take hours for an engineer to trace manually. They can generate summaries of complex logic and propose interface structures based on data relationships. In early design phases, this capability can reduce the manual burden of interpretation.
Instead of manually decoding complex spreadsheets and underlying logic, teams can surface structural insights more quickly. That shift frees UX designers to focus on higher-order questions:
- What needs to remain visible for users to trust the system?
- Which interactions simplify workflows without obscuring logic?
- Where does automation enhance experience and where does it introduce opacity?
In this sense, AI can act as a catalyst for stronger UX practice rather than a shortcut around it. However, there is an important distinction. Automation that prioritizes speed alone could replicate functionality without improving understanding. True value can emerge when AI is integrated into a disciplined design process that tests, validates and refines outputs with real stakeholders. The presence of AI does not eliminate the need for UX rigor; it increases it.
As shown in Figure 1, AI can accelerate the transition from spreadsheet analysis to prototype development, enabling teams to spend more time validating workflows with users rather than manually decoding logic.
Speed Versus Understanding
AI can raise expectations for speed. As systems can be generated more quickly, stakeholders often expect faster iteration cycles. However, faster output does not eliminate the time required to build understanding.
Technical accuracy alone is not enough to generate user trust. Research on AI-assisted decision-making has found that system transparency plays a critical role in user trust and perceived reliability (Sullivan & Weger, 2025). Trust can emerge from transparency, continuity and alignment with established workflows; without transparency, users may disengage from technically accurate systems. If AI accelerates development without sufficient attention to experience, organizations risk scaling confusion rather than scaling impact. For instance, a predictive maintenance model might generate accurate failure alerts, but if field crews cannot see what factors drove the prediction or how to act on it, the tool is likely to be ignored despite its technical accuracy.
A strong UX process can act as a stabilizing force that ensures rapid generation is matched by deliberate evaluation, transforming AI outputs into structured conversations instead of unquestioned deliverables. In complex utility and asset-intensive environments, this balance can influence whether digital initiatives succeed or stall.
Partnership Between AI and UX
Utilities should recognize that AI and UX are inseparable disciplines whose futures must advance together. AI brings analytical power, pattern recognition and generative capability. UX brings empathy, structure and a framework for human-centered decision-making. When combined thoughtfully, they create a design environment in which insight surfaces faster and alignment happens earlier.
The future of digital projects in utilities and other asset-intensive industries could depend on this partnership. As systems become more intelligent, the responsibility to design intelligible experiences inherently increases. As automation reduces manual effort, the importance of intentional structure naturally grows.
Utilities beginning this transition should start with workflows users already understand, preserve visibility into underlying logic, and validate AI-supported interfaces with operational stakeholders before scaling. The success of AI initiatives can depend both on model sophistication and whether users can interpret and act on the outputs confidently.
Innovation does not always fail because of insufficient technology. It can also fail when people cannot see how it fits into their work. UX processes can provide that bridge, and AI has the potential to strengthen it. Organizations that recognize this relationship can build faster systems that people are ready to understand, trust and use.
References
Sullivan, V., & Weger, K. (2025). Transparency and explainability in AI-assisted decision making: Effects on trust, perceived reliability, confidence, and ease of understanding. Proceedings of the Human Factors and Ergonomics Society
Annual Meeting, 69(1), 1827–1833. https://doi.org/10.1177/10711813251369473
About the Author
Kiran Chandra is a software developer specializing in UI/UX design at 1898 & Co., part of Burns & McDonnell. She focuses on creating intuitive, user-centered experiences that support business goals and improve how complex systems are understood and used. She works closely with clients to gather business requirements, translate user needs into design solutions, and support user acceptance testing to achieve successful outcomes.
