Adé IjidakinroAI Enablement · Program Management · Learning & Workflow Design
New York · Open to remote opportunities
I’m a program manager with experience in learning technology, product design, and enterprise IT. At Ehoro Village, I develop AI-supported workflows, support team adoption, and use learner data to guide improvements. My work combines practical implementation, clear documentation, and human review.
01 Featured case study
The clearest example of what I do: take a process that already exists, build the AI-supported version, train the team and document it, then measure the result.
02 Experience
Program management came first. AI made it faster; it didn't replace it.
Ehoro Village
AI-supported course production, team training, onboarding design and documentation, cross-functional enablement workshops for 20+ internal champions, and a Python/Streamlit review tool that cut manual triage 40–60%. I use learner data to guide improvements.
University of Michigan · BRAID
Research with 100+ stakeholders; a redesign that lifted new sign-ups 40% in four weeks. Case study →
Procter & Gamble
Coordinated 11 regional project managers on a $20M+ global initiative; drove adoption of a program-management framework across 10+ teams with training and documentation; P&G CEO Award for program impact.
University of Michigan · Multi-Ethnic Student Affairs
Nearly four years of heritage months, seminars and workshops, including anti-racism workshops for classes of 40+.
03 Lanes
Pick the lane you're hiring for. The proof is the same work, seen from a different side.
I help teams adopt AI workflows they keep using: the workflow itself, the training and onboarding, the documentation, and the review step. Change management, not just tools.
For AI enablement, AI adoption, change management, technical education and customer education roles.
I run cross-functional programs: scope, decision rules, review gates and stakeholder management, documented so anyone can follow what was decided and why.
For program manager, enablement program manager and technical program roles.
I design learning and onboarding people can follow: instructional design, needs analysis, action mapping, facilitator guides and job aids, and the workflow behind them.
For learning experience design, curriculum, and workflow or process design roles.
I vibe-code small AI tools and run AI agents across Claude, ChatGPT, Gemini and DeepSeek like a program: each agent owns a lane, handoffs are written down, and a usage meter stops runs before the limit.
For AI generalist, AI operations and internal tooling roles.
04 Work
Learning design and independent builds that show the technical side. Open any row.
I designed, facilitated and evaluated a 75-minute workshop built on one framework: Frame → Generate → Audit → Log. In a two-person pilot, both participants caught more planted flaws with the audit checklist than without it. Evaluated at Kirkpatrick levels 1–3; level 4 wasn't measurable at pilot size.
Built for faculty. An instructor pastes an assignment and rubric; three agents attempt it the way students commonly use AI, and an advisor agent writes a vulnerability report with specific revisions. Status: functional prototype.
Cleans mixed-method feedback, turns comments into signals and ranks churn risk by segment, with the reasons shown. It scrubs personal details and labels results as directional when there are fewer than 200 responses. Status: MVP.
Waveform slicing, a step sequencer and a live looper in the browser. No install, no signup.
05 The lab
Outside client work, I run small teams of AI models on real builds. In plain terms: one model decides, one plans, others build and test, and I approve. It runs on the habits I bring to teams: decision logs, review gates, written handoffs and a budget.
An octopus keeps about two-thirds of its neurons in its arms. Each arm works on its own; the head sets the intent. That's how I run AI agents.One owner of intent. Many arms that think. A written rule for when they disagree.
A script reads the real usage meter and stops each run before the limit. Each platform works its own lane, with one commit per finished ticket.
Five agents coordinating through claims and heartbeats used 98% of a 5-hour usage window in one night.
Reports lead with failures and attach evidence. I check the work, not the summary.
The planning agent's status reports understated problems three separate times.
A check-first gate asks “already written or already ruled?” before anything reaches the decision-maker, and rulings cite their source.
A design ruling made from memory instead of the written source had to be withdrawn.
The queue asks me first, and a ping budget caps interruptions at three a week.
Agents drafted all night while the work waited on five answers only I could give.
Best at careful judgment and clean merges, so it held the rulings and the only merge key. It also ruled from memory once, and the ruling had to be withdrawn.
Rule nowCite the source before ruling. One model merges; the others propose.
A tireless planner and runner. Its status reports understated problems three times, and a five-agent run burned 98% of a 5-hour window in one night.
Rule nowEvidence over status. One session per lane, and every run stops on the meter.
A strong second opinion. It ran the smoke tests on finished pieces, and handled research and longer documents in long solo runs.
Rule nowDon't let the builder test its own work. Give testing to a different model.
Fast and cheap for breadth: market scans, source checks and research briefs. It ran a full shift until it hit the meter line.
Rule nowSpend expensive models on judgment and cheap ones on breadth. Research arrives as a brief; another agent commits it.
06 About
“I design like sushi: few high-quality ingredients, applied in a way that makes sense.”
I came to AI through learning design and program management, and I still work that way. AI speeds up the scripting, the code and the busywork; people make the calls. The method on this page grew out of a playbook I wrote across 24+ AI build sessions.
Fun fact: I make beats, and I'm allergic to carrots.
07 Contact
I’m open to AI enablement, program management, and workflow improvement roles in New York or remote.
My first 90 days in an enablement role
Roles I’m open to