AI Interaction Patterns for Salesforce

AI Interaction Patterns for Salesforce

Tags
User ResearchEnterprise AI StrategyUX Design
Date
2025
Salesforce is exploring how emerging AI interaction patterns reshape user behaviour inside enterprise tools. I led competitive analysis, survey design, user interviews, and usability testing to surface new design opportunities around Learnability and Control. (This was mixed-methods research conducted for Salesforce, not on their platform.)

My Role

Our team included five UXR students. My contributions were:

  • Led the competitive analysis of enterprise AI tools, identifying design patterns across Polymet, Cursor, Airtable, Zendesk & Copilot.
  • Designed and analysed the survey on workplace AI use.
  • Conducted user interviews with enterprise professionals (PMs, designers, IT admins, support reps).
  • Facilitated usability tests using Polymet.ai to observe traceable feedback, onboarding gaps, and version control expectations.
  • Synthesised findings across methods to shape Salesforce’s next-gen design guidance around learnability, control, trust, and onboarding.

The Problem Space

Across domains, enterprise AI tools face high barriers of adoption due to…

LOW LEARNABILITY Description.

LIMITED USER CONTROL Description.

POOR RELIABILITY Description.

SOLUTION PROMPT

How might we design enterprise AI tools that improve learnability and user control without overwhelming the workflow?

I made two design decisions to answer this HMW statement:

  1. Layered, contextual onboarding that introduces AI capabilities gradually where users need them.
  2. Transparent, traceable AI actions with easy override and version control.

KEY DECISION #1

Decision Summary

ARTEFACT SHOWING WHAT IT LOOKED LIKE BEFORE

PROBLEM description of the problem with this interface

SOLUTION description of what was specifically improved

ARTEFACT SHOWING THE RESULT AFTER IMPROVEMENTS

KEY DECISION #2

Decision Summary

ARTEFACT…

PROBLEM description

SOLUTION description

ARTEFACT…

The Outcomes

1) RESULT

description

2) RESULT

description

3) RESULT

description

Want to know more?

If you have any questions about this project, reach out and let’s set up a call.

  • Email me at: edwardfraser@berkeley.edu
  • Connect on LinkedIn: linkedin.com/in/edward-fraser/

Where to next?

🏠

RETURN TO HOMEPAGE

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READ “ENTERPRISE AI” CASE STUDY