Nexmine

About the project

Company

McKinsey & Company
August 2023 - September 2023

Contribution

Design Specialist

Team

Engagement Manager
Data Scientists
Front & Backend Dev Resources
McKinsey Leadership

The challenge
A major generative AI use case that McKinsey had been exploring with various clients is a smart maintenance advisor that is informed by client data. This tool would serve as a copilot for maintenance workers and technicians across mining sites, aiding in equipment upkeep.

The key questions were: How could we harness available data to create a conversational AI chat interface that addresses maintenance workers' real pain points? How might this tool simplify daily tasks in ways traditional analytics couldn't?
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While McKinsey had already developed some technical aspects from previous client projects, our mission was threefold: Determine practical applications for the tool, anticipate potential user queries and ensure we had robust data to support these queries.

Additionally, we needed to craft a streamlined user experience that would impress a board of directors. Our goal was to transform raw technical capability into a solution that could revolutionize maintenance workflows, making the complex simple and the mundane efficient.

Our initial requirements included the following:
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Help diagnose a problem
Help outline a maintenance procedure
Identify and order necessary tools and parts
Identify potential safety hazards
Facilitate communication with shift supervisors
Help update a maintenance log and reports
potential safety hazards of a maintenance issue

We drafted an initial storyline to illustrate the original vision we discussed with the client in early meetings.

My roles

I led all design efforts on the project including research, facilitating ideation, journey mapping, design and prototyping.

Project impact

Our proof of concept was presented to the board of directors and it was unanimously approved to be built. The AI tool went into development in Q4 of 2023.

Research Overview

Personas

Our client identified three distinct user personas for the AI tool. However, our primary focus centered on Greg, the maintenance worker, who would be the tool's principal user. This strategic emphasis allowed us to tailor the product's features and interface to address the specific needs and challenges faced by front line maintenance staff in their daily operations.

Research Methodology

I aligned on a qualitative approach to uncover the voice of the user that would inform a current state journey along with identified pain points and potential future state concepts.

After some internal ideation and the creation of a future state journey map, our second round of research would consist of validation and refinement of the journey map.

Round 1

9 Qualitative interviews with maintenance workers

Round 2

6 Qualitative interviews with maintenance workers from the same group, focused on journey map validation

Questions we addressed

What are you doing during your workday?
What are your major pain points?
What technology are you currently using to do your job?
What operational and logistical tasks might be helped by a tool like this? How?
If you had a magic wand, what would you create to make your job easier?

Analysis

Some themes emerged during our research and we identified four opportunity areas worth exploring:
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Routine maintenance
Is a specific function of a piece of equipment working appropriately?
General issue diagnosis
The equipment is broken, what is the appropriate course of action?
Visual diagnosis
The equipment is broken and something looks out of the ordinary, what am I looking at?
Repair logistics
I need parts and tools to fix this equipment, how do I get those and how do I document everything?

Pain Point Themes

Tests are run to determine if equipment is indeed broken, this can be time consuming and not always effective.
Even though manuals and work history is digitized, looking for the right information can be arduous and time consuming.
It’s common to not know which maintenance bays are open and available, it takes calling around to figure it out. Sometimes unclear where to send the broken equipment and repair gets delayed.
The process of ordering parts is very manual, we either go personally or send someone to go in person
It’s easy to forget to update the fleet management system which causes problems downstream
No central knowledge base, if someone leaves the company process is lost

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Journey Mapping & Ideation

I put together a fidelity current state journey map in Miro based off of research insights that we would eventually use as ideation session stimulus.

I facilitated a ideation session with our internal team that consisted of one engagement manager, three data scientists and two developers.

Drawing from our research insights and internal ideation sessions, I crafted a future state journey map. This map illustrates a typical scenario at a mine site, showcasing how our AI assistant would integrate into daily operations. Each activity in the journey is purposefully linked to one of the key themes that emerged from our research, ensuring a direct connection between user needs and the proposed AI solution.

The top portion of the journey map has five swim lanes that represent the location of each touchpoint along the timeline.
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The day starts at the maintenance bay and bounces between the operations center, the app that we are building, the mine pit and the warehouse that stores the needed tools and parts.
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In the bottom two swim lanes, we identified potential future state concepts as well as each pain point we we would address.

To ensure accuracy and realism, we validated our journey map with an internal McKinsey & Company expert with extensive knowledge and experience in the mining industry. This expert provided invaluable insights, refining the order of touchpoints and offering precise terminology for various elements. For instance, we learned that what we had been calling "tow trucks" are actually referred to as "floats" in mining operations. This collaborative refinement process significantly enhanced the authenticity and relevance of our scenario, grounding our AI solution in the practical realities of the mining environment.
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Our second round of user research was solely focused on the validation of this artifact.

Design

My research findings revealed distinct usage patterns for the virtual assistant tool across different roles. Maintenance workers and supervisors would primarily interact with the AI via tablets and mobile devices, reflecting their need for on-the-go access in the field. In contrast, operations personnel would predominantly utilize the tool on desktop computers, aligning with their typically office-based responsibilities. This insight into device preferences across user groups was crucial in shaping our approach to interface design and functionality, ensuring optimal user experience for each role.

Core Features

Task-centered dashboard
Spanish language support
Documents section
Admin settings
AI chat interface leveraging the ChatGPT API
Suggested prompts
Chat history
Ability to export chat manuscripts
Image recognition
In-line safety guidelines
In line work orders

I developed a functional prototype in Figma, designed to showcase both the client's originally requested features and additional capabilities inspired by our user research insights. This prototype served as a visual representation of the tool's potential.

Subsequently, we created a coded version using dummy data, which acted as a compelling proof of concept. This tangible demonstration was instrumental in convincing the board of directors to green-light the project. Our presentation to the board was successful, resulting in their approval and the commencement of the tool's development in December 2023.