Form vs. Flow: A B2B AI Study

Problem and Research Question

B2B order forms are efficient for routine requests. But when buyers have dietary constraints, budget limits, and uncertain guest counts, forms collect whatever users type without helping them think through complexity. I wanted to test whether conversational AI could do something forms structurally cannot: guide users through complexity in real time.

Research question: What kinds of ordering tasks actually benefit from conversational guidance, and under what conditions does an AI chatbot outperform a static form for B2B catering intake?

Method

I structured the project as a Design Science Research study mapped onto a Double Diamond flow, running from literature review and platform benchmarking through iterated prototype development to a 12-participant mixed-methods evaluation.

• Discover and Define: Reviewed literature across conversational AI design, chatbot usability measurement, and B2B digital lead generation. Benchmarked three leading conversational platforms and translated findings into six concrete design decisions for the prototype.
• Develop and Deliver: Designed two instrumented prototypes for the same catering inquiry task: an AI chatbot (Alex) built in Voiceflow with Claude Sonnet, iterated from V1 to V10, and a static JotForm baseline. Ran a within-subjects mixed-methods study with 12 participants. Quantitative measures included qualified lead rate, a 7-dimensional order accuracy rubric, and the B2B-Bot Usability Scale-15. Qualitative measures included 3 open-ended questions per participant.

Findings

Forms won on simple tasks.
For routine, low-complexity orders, participants preferred the form. It felt faster, and they stayed in control. Conversational guidance added overhead where none was needed.

Chat won on complex constraints.
When orders involved multiple requirements, dietary restrictions, budget caps, and uncertain guest counts, the chatbot’s proactive checking caught omissions that forms missed entirely. The chatbot performed better on budget compliance (100% vs. 90%) and date/time accuracy (62.5% vs. 50.0%).

Trust followed reliability, not novelty.
The chatbot’s B2B-BUS-15 Trust and Privacy factor scored near-neutral. Qualitative feedback showed this reflected two-session crashes rather than skepticism about AI. Operational reliability is a precondition for trust, not a separate dimension.