The core challenge was not simply showing more information. Sales teams already had too much information. The opportunity was to help them recognize what mattered, why it mattered, and what action to take next.
AI supported the product vision, but it also played an important role in the design process itself. It helped accelerate early wireframing, summarize competitive patterns, explore use cases, and validate whether the product could create useful signals from real-world industry activity.
AI was used in both the product concept and the research process. To understand the competitive landscape, I used AI to create a feature-overlap model across major CRM and sales intelligence products, then grouped those features by depth, maturity, and common usage patterns. From there, we identified an industry median journey — the baseline experience users would likely expect before Intenti.co could credibly differentiate.
That research helped frame the UX around a sharper opportunity: not another CRM, feed, or generic intent-data layer, but a focused experience that connects industry signals to account relevance and timely sales action. The result is a modern UX story about relevance, timing, trust, and helping users move from scattered industry news to useful intervention points.
I led the UX strategy and product design exploration, shaping the early experience around how salespeople discover, evaluate, and act on industry signals. My work included product framing, competitive research, workflow mapping, wireframing, feature prioritization, validation planning, and interface design. I also used AI as part of the UX process to accelerate research synthesis, generate early design directions, and pressure-test whether the concept could produce useful sales insights from real industry news.
The first step was defining the difference between generic news aggregation and actionable sales intelligence. Salespeople did not need another feed. They needed a filtered view of client-relevant activity. That meant the experience had to move beyond headlines and help users understand the connection between an outside event and a potential business need. The first step was defining the difference between generic news aggregation and actionable sales intelligence. To work at its best, the software combines AI-harvested content and scoring to help users understand the connection between an outside event and a potential business need.
We explored questions like:
AI was used to speed up competitive research across sales intelligence, account monitoring, news aggregation, CRM enrichment, and intent-data platforms. This helped identify common product patterns, including:
AI was not treated as a magic layer added at the end. It was used throughout the UX process to work faster, test assumptions, and improve the quality of early decisions.
Wireframing
AI helped accelerate early wireframe exploration by generating alternate dashboard structures, signal-card layouts, prioritization models, and user flows. These were not used as final designs, but they helped move quickly through low-fidelity options and compare different ways of organizing complex information.
This allowed more time to focus on judgment-heavy UX questions, such as hierarchy, trust, explainability, and user control.
Competitive research
AI was also used to summarize competitive patterns and identify common product conventions across sales intelligence tools. This helped separate table-stakes functionality from areas where Intentico could differentiate.
Validation
AI was used as one of the validation methods by testing whether real industry news could be converted into useful sales signals. Sample articles and market events were analyzed to see whether the system could extract relevant entities, identify possible business implications, and suggest reasonable sales actions.
This helped validate the core product premise before over-investing in interface detail.
Each signal card included:
Explainability over automation
The product needed to show its reasoning. Instead of simply recommending action, the interface explained why a signal was relevant. This helped preserve user trust and kept the salesperson in control.
Prioritized signals over raw feeds
A simple news feed would have recreated the user’s existing problem. The design emphasized ranking, filtering, and relevance so that users could focus on the most useful opportunities first.
Action-oriented summaries
Summaries were written to support sales judgment. The product did not just say what happened. It connected the event to a possible business issue, buyer need, or timing opportunity.
Confidence and source visibility
Because AI-generated interpretation can be wrong or incomplete, the experience included source links and confidence indicators. This gave users a way to verify the signal before acting.
© 2026 Jon Gifford