Inspiration
ValuePilot came from challenges I experienced at two very different organizations. At a boutique wealth management firm, I helped implement AI solutions but discovered that employee adoption was often harder than building the technology itself. Later, at EY, I encountered challenges surrounding AI token consumption, cost visibility, and optimization. That made me wonder if large organizations face these challenges, how can smaller businesses manage AI investments without dedicated technology teams?
What it does
ValuePilot helps small and midsize businesses understand whether their AI investments are actually delivering value. Designed for companies with 20–499 employees, the platform brings together AI adoption, employee productivity, tool usage, and cost data into a single dashboard. It helps managers identify what's working, where resources are being wasted, and how AI translates into estimated financial returns.
How we built it
As a finance student with limited coding experience, I used multiple AI tools to turn my idea into an interactive web prototype. ChatGPT helped with project management, research, and strategy; Claude with presentation design; Codex and Claude Code with software development; Grok with the demonstration video; and Kearney Velocity with idea exploration and research. GitHub helped me manage the codebase and coordinate development.
Challenges we ran into
My biggest challenge was translating a business concept into a functional software prototype without a traditional engineering background. I also spent considerable time researching competitors and determining how ValuePilot could differentiate itself. Rather than building another enterprise-focused AI monitoring tool, I chose to focus on making AI adoption and ROI measurement accessible to smaller businesses.
Accomplishments that we're proud of
I'm most proud of building an interactive software prototype independently during the hackathon, despite having limited coding experience. I also developed a business model around a problem I'd personally encountered and learned to coordinate multiple AI tools across research, design, and development.
What we learned
Building AI technology is only part of the challenge. Getting people to adopt it and demonstrate measurable value is what matters. I also learned that combining business knowledge with AI-assisted development can make software creation far more accessible to people without traditional programming backgrounds.
What's next for ValuePilot
My next steps are to validate ValuePilot (if I decide to go through with its creation) with small and midsize businesses, build integrations with AI platforms such as Microsoft Copilot. Ultimately, I want ValuePilot to help businesses move beyond simply investing in AI toward understanding exactly what they're getting from it.
Built With
- ai-assisted
- chatgpt
- claude
- code
- codex
- development
- generative
- github
- grok
- kearney
- openai
- velocity
