Most AI marketing stories still start with the tool. Behr’s ChatHUE case is more useful because it starts with hesitation. Paint shoppers do not only need more inspiration. They often need enough confidence to choose one color and move forward.
Brand Innovators published an August 17 interview with Behr global CMO Andy Lopez that frames the problem clearly: color selection is one of the hardest parts of the journey. Lopez described ChatHUE as an AI-powered color selection agent created to help people narrow choices and feel confident. Google Cloud’s earlier release said ChatHUE combines Behr’s color data with Google Cloud capabilities including Gemini, Vertex AI, BigQuery and Cloud Run.
The decision problem behind the AI
Color choice is not a generic engagement moment. It is a bottleneck. A consumer can collect inspiration, save rooms, watch creator videos and still delay the project because the final choice feels risky. The wrong shade is visible, emotional and costly to reverse. That makes confidence a business metric.
For marketers, this is the important distinction. AI is not valuable because it talks. It is valuable when it reduces a specific decision cost: too many options, weak visualization, lack of product knowledge, uncertainty about fit or fear of regret.
Why proprietary product knowledge matters
A generic assistant can suggest colors. A useful brand assistant should understand the product system: color families, undertones, room context, finish, adjacent products and the practical language shoppers use. That is why Behr’s proprietary color data matters as much as the model layer.
The same principle applies outside paint. A beauty brand can help match shade and routine. A B2B software company can help choose implementation paths. A furniture retailer can help resolve room constraints. The assistant becomes useful when it has domain knowledge that a normal search box does not carry.
A decision-confidence model
- Find the moment where the customer hesitates before purchase or project start.
- Define the evidence needed to move forward: comparison, visualization, compatibility, cost, availability or peer proof.
- Feed the assistant with product knowledge the brand uniquely owns.
- Connect the recommendation to the next action: sample, store visit, cart, consultation or saved project.
- Measure confidence and completion, not only time spent with the tool.
How to measure the assistant
Engagement is not enough. A shopper can chat for ten minutes and leave more confused. Better signals include fewer abandoned selections, more saved palettes, higher sample orders, lower return or repaint risk, more store visits from assisted sessions and stronger conversion among users who reached a confident recommendation.
Qualitative feedback matters too. If users say the assistant felt generic, the issue may be product knowledge. If they trust the recommendation but do not act, the issue may be retail connection, price, availability or a missing next step.
The CMO takeaway
The lesson from ChatHUE is not that every brand needs a branded chatbot. The lesson is that AI should be placed where a decision is commercially valuable and emotionally difficult. Paint color is one such moment. Many categories have their own version.
Start with the hesitation, then design the assistant around evidence, confidence and action. That is the difference between AI as a press release and AI as a piece of the customer journey.
