AI Archetype Update & Creation

quick facts

METHODS & SKILLS
Grounded Theory Ethnographic Research
One-on-one Interviews
Data Analysis
Data Synthesis
Low Fidelity Diagramming
High Fidelity Diagramming

TOOLS
Keynote
PowerPoint
Photoshop
Illustrator
Slack
Box
Box Notes
Previous Team Research

DELIVERABLES
Iterated Diagrams
Weekly Status Reports
Final Report
Finalized Workflow

 

 

the quest

With the increasing adoption of artificial intelligence (AI) in enterprise environments as a means to introduce new services and enhance insights from company data, the availability of AI resources has also increased. In order to maintain a competitive edge, companies that provide AI products must look closely at how those products are being used, who is using them, and how they can be improved to increase efficiency, integration with existing environments, and identifying key areas for growth.

In order to accomplish this, I with investigating the overall process involved in implementing AI products in an enterprise environment with a particular focus on the following:

  • Cross-Company workflows for Developers; Data Scientists; IT Admins/Data Engineers

  • Collaboration (both inside and outside a system) to understand how people work together

  • Research insights on key behaviors, success factors, things to avoid, etc.
     

the journey

My approach to this project was:
1. Stakeholder interviews with UX researchers, UX designers, business analysts, developers, and business partners across a variety of teams in order to gather data and insights on current and past user research, what was known about AI implementation workflows, and team collaboration throughout the implementation process.
2. One on one interviews with internal subject matter experts to ascertain insights on the various technologies and their users at each stage of the AI implementation workflows.
3. Analysis and synthesis of interviews, previous research and data, AI implementation workflows, and user communication.
5. Iterative lo-fi and hi-fi diagrams of company wide AI implementation workflows and the players involved at each stage.
6. Consultation with internal subject matter experts to verify workflow.
7. Identify areas of where more research is needed and communicate those back to the team.

Collaboration End to End Overview copy.jpg

 

the destination

The efforts for this project inspired the following results:

  1. I was able to identify the major players involved at various stages of Data & AI implementation across an organization.

  2. Key moments of collaboration were pinpointed and allow for avenues of further research and product development. 

  3. User pain points and workflow weaknesses were identified, illustrating areas for improvement and highlighting the need for the development of new processes.