Product · Platforms · Applied AIAtlanta, GA

Edward J. Laurent, PhD

Edward J. Laurent, PhD

Detect. Diagnose. Design. Adopt.

I design systems that make work faster and more effective, and I find the problems worth solving in the first place. I work across enterprise technology, AI products and operations, usually getting teams that do not report to each other to agree on one plan.

Download the resume Review selected work
20+Years turning complex systems into repeatable workResearch · Enterprise · AI product
1,000+High-level enterprise escalations resolvedGoTo Foods · 2023 to present
2,300Platform users delivered to productionGriffin Groups · 2012 to 2017
780+Citations across 25+ peer-reviewed publicationsh-index 10

Approach

Same four steps, whether it is a restaurant POS or an AI product.

I run the same sequence in every environment. The problem people report is usually a symptom, not the cause. Find what keeps going wrong, find out why, then build something that lets the team handle it without me.

01

Detect

Notice what keeps happening. One bad ticket is noise. The same ticket forty times is a system working exactly as built. The same look forward finds opportunities: work worth automating, gaps nobody has named yet.

02

Diagnose

Find the real cause. What breaks, why it breaks, who owns the decision, and where information gets lost between teams.

03

Design

Build the fix. The product, the workflow, the roles, and what is supposed to happen when something goes wrong.

04

Adopt

Hand it off. Write it down, give it an owner, and make sure it still works after I stop touching it.

Selected systems and products

Products, platforms and rollouts I have shipped.

The industries are different. The job is the same: find the problem, build the system, hand it over.

2025 to present WalelAI LLC AI product · Independent venture

WorkOutput

A generative AI decision-support product that helps professionals capture, evaluate and improve consequential decisions over time. Shipped first to the GPT Store, then rebuilt on Claude to sustain state and governance across long-running workflows. Currently in private alpha, with a closed beta for prospective subscribers next.

Problem it solves: AI output accumulates without resolving into a decision. Multi-agent routing and governance gates force structure before output, so extended work converges instead of restarting.

Beta testing enquiries: info@workoutput.com

Private alpha
2023 to present GoTo Foods Enterprise systems

Retail technology administration across seven brands

Administer a centralized POS and online ordering database serving seven national and international brands for a $6B franchisor with thousands of franchised locations. I administer the POS ecosystem across three of those brands on Toast, Aloha, Qu Beyond and Olo, and support the rest. Team lead for Schlotzsky's, prior administrator for Jamba. Menu architecture and system strategy are coordinated with marketing, brand, app and loyalty teams, holding data integrity where their requirements conflict.

Operational evidence: 50+ store builds, 100+ menu builds and 1,000+ escalations resolved involving pricing logic, data integrity and third-party integrations. 20+ knowledge base articles reduced repeat escalations across all brands.

Current role
2008 to 2013 Metaweb, then Google · US-NABCI Schemas and standards

Structured knowledge and public standards

I spent five years testing Freebase, the open structured knowledge base Google acquired in 2010, and designing its graph database type and property models under paid contract. In parallel, project lead on national recommendations for standardizing avian sampling grids, adopted by the North American Bird Conservation Initiative.

Why it matters: Freebase supplied both the schema architecture and the seed data for the Google Knowledge Graph. The sampling standard is still shared across federal, state and NGO programs.

Adopted at scale
2023 to present GoTo Foods AI enablement

AI enablement for a 20-person retail IT department

Asked by the Senior Director of Product to establish an AI knowledge baseline for the 20-person retail IT department. Authored the internal curriculum covering prompt engineering standards and AI decision hygiene, and now serve as the team's first call for automation ideas and build guidance.

How it works: subject matter experts surface automation opportunities, Claude and Claude Code are applied against them, the operational requirements that come back get refined, and the cost and benefit case goes to leadership in terms the business acts on.

In production
2025 to present WalelAI LLC AI education · Independent venture

AI with Love, judgment-first AI education

A four-month, twelve-session applied AI program for professionals learning to use AI responsibly, covering prompting, workflow automation, verification protocols and human decision rights. Enrolling now, with the first class on 17 August 2026.

Co-created with Jessica Valor.

System contribution: curriculum architecture, applied exercises, responsible-use boundaries and translation of technical concepts into plain language for nontechnical learners.

Enrolment enquiries: ed@walelai.com · Programme page

Enrolling now
2012 to 2017 Connecting Conservation Enterprise platform

Griffin Groups

B2B collaboration and knowledge platform delivered from concept to production for 2,300 users across federal agencies, NGOs and universities. Release planning and dependency tracking across 800+ Jira tickets and 30+ sprints, with 30+ platform plugins scoped and delivered.

The constraint: no participating organization reported to any other. Adoption came from translating competing requirements into one shared roadmap.

2,300 users
2017 to 2023 Arden's Garden Operational systems

Toast POS across 16 stores in a single day

Single-day cutover with no downtime across a retail footprint. Dependencies mapped across hardware procurement, configuration, staff training and go-live sequencing. Followed by Microsoft Dynamics ERP configuration across thousands of SKUs and vendor relationships.

What held: sequencing by dependency rather than by store. Nothing waited on a decision that had not been made.

Delivered
2000 to 2005 Michigan State University Research software · Modeling

HABIClass, wildlife distribution modeling

Doctoral work across a 400,000 hectare forested region of Michigan's Upper Peninsula. Bird counts, habitat measurements, forest inventory and satellite imagery had to resolve into one predictive model. Collaborated with programmers on a standalone C++ program that extrapolated the field data through spectral associations with Landsat 7 ETM+ imagery. The method, Habitat Analysis By Iterative CLASSification (HABIClass), was published in Remote Sensing of Environment.

The design goal: predict where a species occurs without first deciding what its habitat looks like. Conventional models route through a human-drawn land cover map and inherit its bias. HABIClass classified raw spectral data against known occurrence locations instead. Published 2005.

Published method
2005 to 2008 North Carolina State University Database and schema design

LitCentral

A structured database of bird habitat associations, built for the National Gap Analysis Program. It converted published prose, scientific and popular, into records that could be mapped. Data entry forms held the structure. Controlled lexicons held the language. Every record carried its citation.

The constraint: habitat gets described in whatever words the author chose. Unstandardized, the literature will not aggregate. Uncited, the map will not survive review. The schema had to solve both at once.

Delivered
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About

One career. Different operating environments.

I began as a conservation scientist, where the work required finding patterns in complex systems, testing explanations and making decisions from incomplete evidence. That record includes a PhD, more than 25 peer-reviewed publications and a NASA fellowship.

I carried the same operating model into enterprise software, product delivery, retail technology and applied AI, and into my independent venture, WalelAI LLC. The environments changed. The core work did not.

I detect what is happening, diagnose why, design the system that should exist and drive adoption so the result survives the person who built it.

Product leadership · Enterprise platforms · AI enablement · Operational design · Decision systems

Current focus

Putting AI where it changes a decision, not where it looks good.

I am not trying to put AI into every process. I look for the places where better structure and better evidence actually change what someone decides.

Enterprise

Getting a team to actually use AI

Training, finding the right use cases, designing the workflow, and writing the business case, all inside real constraints.

Product

Decision systems that improve over time

Products that remember the context, show their assumptions, and connect a decision to what actually happened next.

Leadership

Cross-functional delivery without formal authority

Getting product, operations, technology and subject matter experts aligned when none of them report to me.

Published record

The papers that still shape how I work.

My complete record of peer-reviewed papers, reports and standards sits on Google Scholar and ResearchGate. The work below was selected for what it still contributes, not for how often it has been cited.

2011 · Springer

The role of assumptions in predictions of habitat availability and quality

Laurent, E.J., C.A. Drew, W.E. Thogmartin. In Predictive Species and Habitat Modeling in Landscape Ecology, pp. 71 to 90.

A framework for stating what a model assumes before anyone acts on its output. The same discipline every recommendation on this page runs on.

Publisher · Paywalled
2013 · US-NABCI

Standard Sampling Grids for Avian Monitoring Programs

US-NABCI Monitoring Subcommittee. Project lead: E.J. Laurent. Recommendations to US-NABCI.

A shared measurement standard adopted across federal agencies, state programs and NGO partners. It outlived the working group that produced it.

Free PDF

Contact

Email is the fastest path.

LinkedIn works. The resume covers the record.

LinkedIn Resume