ResearchWhat makes an institution ready for AI?
My co-authored Rutgers research examines governance, workforce, data, and infrastructure as foundations for responsible AI in public services.
Explore the research →AI transformation · Platform development · Capability building
Ojobo Agbo Eje
I help leaders and teams turn AI ambition into useful value—identifying AI use cases, redesigning work, developing capability, and building solutions that produce ROI for you.
Work with me
Different ways to make AI useful
Whether you need to decide what to change, make something work, develop your team, or convene a conversation, the starting point is the problem in front of you.
01
Transform
Find the right problem and use case, redesign the work around it, and plan for adoption.
Explore transformation →02Build
Move from an important question to a usable data product, tool, or platform.
Explore building →03Train
Build AI literacy, practical judgment, and confidence through tailored learning.
Explore training →04Speak
Bring a grounded perspective on AI, work, and human capability to your audience.
Explore speaking →Proof in practice
From a public-interest question to a live platform
I conceived and led CHEDVI, a Camden health-equity data platform that lets visitors explore neighborhood patterns through maps, profiles, rankings, and an interactive dashboard.
See the CHEDVI case study →
Evidence of the work
Research, publication, and public conversation
My work connects rigorous inquiry with practical communication for leaders, teams, and communities.
Rutgers · AI readiness
State AI Preparedness Project
Co-authored research and a 2025 AAAS presentation on state-level AI readiness.
See the research →Algorithms · 2026Explainable AI in Healthcare
A peer-reviewed systematic review of 76 studies and the challenges of clinical adoption.
Read about the paper →Book & speakingMaking AI understandable
Author of an introductory AI book and a speaker on ethical AI at the Rutgers–Camden Business Symposium.
Explore speaking →Latest writing
Ideas for making AI useful
Long-form explanations and practical questions for leaders, teams, and people navigating AI-driven change.
AI maturity
The Five Stages of AI Maturity (And Why Almost Everyone Is Stuck at the Third One)
Most organizations think they are doing an AI transformation. They are actually adopting AI, and the difference determines everything.
Read article →
Organizational transformation
New Wine, Old Wine Skins; AI Transformation in Broken Systems
Why AI Transformation Efforts Fail, and how you can make it scale in your organization.
Read article →AI investment
History Predicted It Already. AI Investment Won't Produce Immediate ROI
Understanding why AI transformation moves at the speed of human change, not technological capability
Read article →The AI transformation framework
Start with the work. Then decide what AI should do.
A practical sequence for finding real value: define the problem, understand the process, specify the use case, develop skills, choose the technology, and create a culture that can sustain the change.
Explore the framework →For decision-makers
Where should we begin?
Three practical routes into the questions leaders ask when moving from interest in AI to meaningful change.
The problem
What are we trying to improve?
Frame the outcome before choosing technology.
Explore the framework →
The use case
Where can AI actually help?
Connect a specific application to a real process.
Examine use cases →The build
What needs to be built?
Move from analysis to a working platform.
View technical work →Selected works
Platforms, analysis, and applied ideas
My work includes developing technical solutions, not just advising on what others should build.
Public-interest platform
Camden Health Equity Data Visualization Initiative
A live health-equity platform I conceived and led, built to make neighborhood patterns easier to explore.
View project →
Measurement system
State AI Readiness Index
A framework for understanding the foundations of meaningful AI adoption.
Explore work →Applied research
Explainable AI in Healthcare
Transparency, trust, and implementation in high-stakes contexts.
Explore research →Research & perspectives
The deeper questions behind adoption
Research and practice meet in questions of readiness, equity, learning, and institutional change.
ResearchWorking thesisRedesign work before automating it
Automation can make the wrong process faster. Transformation asks what should change, how, and for whom.
Read the framework →Speaking & events
Join the conversation
Speaking engagements and events where you can find me next.
A working thesis
“Most organizations are adopting AI without transforming. The work begins with the problem—not the tool.”
Signals
What I am watching
Shorter observations on the technologies, skills, and institutions shaping the next phase of AI.
Work
Human judgment
What should remain a human decision when capability is delegated to AI?
Capability
AI literacy
How do teams gain the confidence to use AI critically, not just frequently?
Systems
Platform thinking
When should an idea become a reusable product rather than another pilot?
Concepts
The ideas that underpin my work
These are the questions I return to across strategy, research, and building.
01Problem before platform
What are we trying to solve, improve, or make possible?
02Capability before scale
Do people and institutions understand, trust, and sustain the change?
About
Builder, researcher, transformation partner
My path from consulting and business intelligence to data science, Rutgers research, and live platform development informs how I help organizations adopt AI and redesign work.
More about me →