Deion Wells Ross

AI Solutions — from problem to adoption

I turn operational problems into AI-enabled workflows that change how work gets done.

I work at the intersection of business and technology — identifying where work breaks down, designing AI-enabled workflows to fix it, integrating the technical solution, and helping people actually adopt it. Not an introduction to AI. A change in how the work happens.

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Selected work

Selected Projects

A few representative engagements — the problem, what was built, and the outcome for the business.

01

Placeholder Project One

  • Workflow automation
  • LLM extraction
  • Internal APIs
Problem
A mid-size operations team spent hours each week manually reconciling data across disconnected systems, creating delays and error-prone handoffs.
Solution
Designed an AI-enabled workflow that extracts, validates, and routes the data automatically, with a human review step for exceptions only.
My contribution
Led discovery, mapped the current-state process, designed the target workflow, built the integration, and ran the adoption rollout with the team.
Business outcome
Placeholder outcome — e.g. reduced cycle time from days to hours and eliminated a recurring category of manual errors.

02

Placeholder Project Two

  • RAG
  • Vector search
  • Embedded UI integration
Problem
Customer-facing staff lacked fast access to institutional knowledge, so answers were inconsistent and onboarding was slow.
Solution
Built a retrieval-backed assistant grounded in vetted internal documentation, embedded directly in the tools the team already used.
My contribution
Defined the use case and success metrics, curated the knowledge base, directed the build, and coached the team through adoption.
Business outcome
Placeholder outcome — e.g. cut average research time per inquiry and improved answer consistency across the team.

03

Placeholder Project Three

  • Opportunity assessment
  • Process mapping
  • Roadmapping
Problem
Leadership wanted to adopt AI but had no framework for deciding where it would actually create value.
Solution
Ran a structured opportunity assessment, scored candidate workflows on impact and feasibility, and delivered a sequenced roadmap.
My contribution
Facilitated stakeholder interviews, built the scoring model, and produced the prioritized implementation plan.
Business outcome
Placeholder outcome — e.g. a prioritized portfolio of initiatives with a clear first project underway within the quarter.

Background

About

My work spans AI implementation leadership, forward-deployed engineering, management consulting, workflow automation, and customer adoption. The through-line: I sit between the business problem and the technical solution, and I stay involved until the solution is actually part of how the team works.

In practice that means running discovery to find where work breaks down, designing AI-enabled workflows around those problems, building or integrating the technical pieces, and coaching people through the change. I care as much about adoption as about the build — a solution nobody uses solved nothing.

I hold an MBA from Nebraska Wesleyan University, which grounds the technical work in how organizations actually make decisions, allocate resources, and measure value.

Method

How I Built This

This site is itself an example of the work. It was built by directing an AI coding agent through a defined process — the same way I approach client solutions.

  1. 01

    Defined the audience and objective

    Named the visitors this site is for — hiring leaders, recruiters, potential clients, collaborators — and the single job it needs to do: show that I can translate a business problem into an AI-enabled solution.

  2. 02

    Translated the brief into architecture

    Wrote a structured brief — purpose, audience, scope, tone, constraints — and turned it into an information architecture and a phased build plan before any code was written.

  3. 03

    Directed iterative AI-assisted development

    Built the site by directing Claude Code as a coding agent: specifying each section, reviewing the output, correcting course, and iterating in passes rather than one prompt.

  4. 04

    Reviewed and refined the code

    Read the generated code, tightened structure and semantics, kept the dependency footprint minimal, and made deliberate calls on what to keep, cut, or rework.

  5. 05

    Tested and deployed

    Checked responsiveness, links, accessibility, and performance, then deployed on Vercel with continuous deployment from the main branch.

Contact

Contact

If you're working on something where AI could change how the work gets done, I'd like to hear about it.

Résumé Download résumé (PDF)