Redesigning Requirements Gathering for
Smarter Product Development
AI-powered plugin redefines requirements gathering by connecting new requirement to past projects, minimizing errors, and reusing proven components
AI Plugin
Design + Research
Enterprise Tools
2 Months
Role
Product Designer
Client
Hyster-Yale
Team
2 UX Researchers
1 Business Analyst
2 Product Designers
1 Subject Matter Expert
Challenge
Inaccurate requirements and repetitive validation, verification, and testing (RVV) processes lead to project delays, inefficiencies, and high failure rates, consuming 80% of product development time.
Solution
An AI-powered plugin streamlines requirements gathering by matching current projects with past data, reusing components, and reducing redundancy, potentially saving 10,400 hours/year and cutting development time in half.
Hyster-Yale's Strategic Goals
01
Grow Through Global Market Expansion
02
Integrate AI In The Current Product Development Process
We identified the inefficiencies by analyzing industry data, common challenges in the product development process, and the major challenges Hyster-Yale faces.
37%
of all organizations reported inaccurate requirements as the primary reason for project failure
80%
of the time accounts for validation and verification in the overall time spent in product development
6 Gate Process serves as the backbone of product development
A structured framework to ensure disciplined project execution ensuring progression from ideation to launch. Through our analysis, we identified that the majority of time is wasted in the Requirement Definition, and Verification & Testing gates, primarily due to inaccurate requirements, redundancy, and repetitive processes.
For Whom?
We then pooled our users into three major user groups and saw who are the people involved in the process. Becoming the backbone of the ecosystem.
Cost
Project Manager
Sets project goals, timelines, and manages resources, ensuring the AI solution is effectively utilized within the development process.
Technical
Design Engineer
Translating requirements into innovative and manufacturable designs through conceptualization, creation of technical documents.
Logistics
Operations Manager
Oversees workflow implementation, manages data quality, and ensures smooth execution across all global locations.
Few Key Insights That Informed My Design Decisions
01
Repetitive cycle during requirements gathering, revisiting points without progress.
02
Misunderstandings and missed requirements frequently lead to project delays or derailments
03
Repeated challenges across projects indicate a failure to leverage past experiences
04
Inefficiencies in requirements gathering processes hinder productivity and innovation
How might we
cumulate fragmented intelligence to streamline the information hunt
and reduce human errors?
The Big Idea
Our solution should enable stakeholders to match current projects with past ones during requirement gathering, reducing errors in verification and reusing base components like resource documents in validation.
Using AI as plugin we are able to integrate our concept very easily into existing systems without the need to change the current behavior
Introducing Reqyr.ai
A plugin that provides real-time information on past relevant projects while defining your project.
Pro Match
AI matches current requirements to past projects to leverage the wealth of knowledge and experience accumulated within the organization.
Detailed Analysis
Conduct a thorough analysis of past projects and current requirements. This will provide a data-driven foundation to streamline processes and reduce inefficiencies.
Highlights Relevant Information
Dashboard that surfaces key insights and relevant data from past projects during requirements gathering. This ensures stakeholders can make informed decisions, avoid redundancy.
How are we cutting product development time?
Potential
This can potentially cut down time spent in RVV by 10,400 hours/years in a sample 100-person team.
Improve Decisions
Enable project managers to swiftly evaluate project similarities and reducing design rework.
Continuous improvement
Gather, analyze and learn from vast amounts of data to identify opportunities for optimization for stakeholders without revisiting points.
Managing Risks
By identifying potential risks early in the project lifecycle, by allocating resources to critical requirements.
How Did We Get Here?
User Interviews
Co-Design Sessions
Product Benchmarking
We conducted a series of stakeholder interviews with key clients to gain an understanding of their challenges and objectives. This provided valuable insights into their pain points, expectations, and vision for the solution.
Kickoff meeting with the client teams from HysterYale where we received the prompt.
With the problem defined, we were able to set clear research goals and desired outcomes for our research phase. We started by looking at the current process of Product Development.
Companies need to explore where they are generating sufficient data to extract patterns that could be used to support operational decisions.
AI In What Form?
Sketching
Crazy 8s
User Enactment
User Feedback
After multiple brainstorming sessions, we realized intelligence can be a very broad term to optimize these processes. We explored different forms of this intelligence like AI as plugin, software, guide, or even a chat bot.
As we tested these concepts with some people, we identified limitations with these ideas. AI as software: effort, time and cost. AI as a chat bot: might create a lot of back-and-forth
We explored different forms of this intelligence like AI as plugin, software, guide, or even a chat bot.
Final pitch presentation to the client at Kelloggs Design Challenge (Northwestern University Chicago, April 2024)
This is just a part of the story…
Reach out for more on this project!