Diagnosing Pain Points to Define a Path for AI

How we helped Data Culture define AI use cases and build a prioritized implementation roadmap in 6 weeks for one of their client’s modernized Data & AI modernization efforts:

Outcomes

Generated dozens of AI and modernized data use cases over 6 weeks

Defined
5 evidence-backed priority AI use cases
to be implemented

Surfaced 5-6 additional ideas for expansion beyond original scope for project

Generated notable buy-in from financial services client for ongoing modernization

Context

Data Culture is a boutique data consultancy that sought our help to support a data modernization project they were engaged with for a legacy brokerage in the financial services industry.

This client of theirs was facing a lack of a long-term data & AI strategy, and no concrete plan to understand their current state capabilities.

Objectives

Defining 4-5 AI use cases based on client’s current state and industry context

Prioritizing existing client pain points and needs around pre-existing efforts

Building roadmap to facilitate transition to implementation phase for project

Client Perspective

“Superposition helped us come up with a repeatable way to cut through complexity with this particular client, so we could align across all their different teams and focus on what really mattered for making this project succesful!”

A picture of one of our clients, Brittany Bafandeh (CEO of Data Culture), who we helped create a series of workshops for as part of AI Use Case Prioritization Project!

Brittany Bafandeh, CEO - Data Culture

How We Did It

  • Completed 1-on-1 discovery sessions with 6 different key leaders to determine pain points

    • Compiled an initial list of use cases per leader and defined potential obstacles to educate prioritization process

    • Built and socialized a structure for an upcoming prioritization workshop after 4 weeks

Phase 1: Discovery & Education

A showcase of a 1:1 workshop as part of our AI Use Case Discovery workshops!

Phase 2: Prioritization Workshop

  • Completed 4-hour workshop (with participation from all 6 leaders) to understand the impact/effort of potential use cases

    • Defined cross-workstream priorities based on workshop activities

    • Broke down priority use cases to generate starting points for implementation efforts

A detailed workshop titled 'Use Case Prioritization Workshop' outlining a two-day process. Day 1 focuses on impact and effort mapping with sections for use case recap, impact vs effort chart, and perspectives. Day 2 covers use case prioritization, priority use case breakdown, and outputs.

Phase 3: Implementation Prep Outputs

  • Used outputs from sessions and workshops to generate 3 outputs for Data Culture’s implementation team:

    • Full list of modernized data use cases (incl. AI ideation)

    • Prioritized list of Use Cases

    • Tactical roadmap of business value per use case to implement

A screenshot of a spreadsheet titled 'Prioritization Workshop Output,' containing columns for priority, stakeholder, use case, bucket, impact, effort, priority reasoning, V1 idea, V1 inputs, short-term success factors, next step business owner, and notes. The sheet lists multiple entries with details like 'Name,' 'Use Case,' 'High' impact, 'Simple' effort, and various input details.

Want to see more?

A detailed project presentation slide for a case study titled "Superposition Case Study - Use Case Definition & Prioritization." It includes sections about client context, objectives, phases of discovery and education sessions, prioritization workshop, and implementation prep outputs, with various charts, diagrams, and a video thumbnail. There are additional sidebar sections featuring a quote from Brittany Bafandeh, a testimonial, and a promotional video about the project.
Check out our Case Study Breakdown!
Pixel art image of a person in glasses and a jacket tending to a campfire in a forest with a background of trees and night sky, with the words "Light the Data Fire" and the logo "superposition" to link to a detailed recap of our work for Data Culture
Read our Recap!