I've spent my career bouncing between technology, strategy, operations, data, consulting, and the occasional problem that doesn't fit neatly into any of those categories.
These days, I'm particularly interested in artificial intelligence and what happens when it collides with real organizations and real work.
Not the demo. The Tuesday morning version.
I've worked in business intelligence, consulting, public health, organizational strategy, education technology, product development, and AI.
On paper, that's either an unusually broad career or evidence that someone wasn't supervising me closely enough.
But there is a through line.
I like complicated systems. I like figuring out why they behave the way they do. Where information gets stuck. Where processes become rituals. Where technology helps. Where it absolutely doesn't.
Then I like building something better.
AI has radically changed what a small team—or sometimes one sufficiently caffeinated person—can build.
My work focuses on applying AI to actual problems: software development, product design, analytics, automation, curriculum, organizational workflows, and new kinds of human-computer interaction.
I'm less interested in asking, “How can we use AI?”
I'm more interested in asking, “What can we do now that wasn't practical before?”
Those are very different questions.
Organizations generally aren't suffering from a shortage of data. They're drowning in it.
I've led business intelligence work focused on turning fragmented organizational data into systems leaders can actually use: performance measurement, executive dashboards, strategic-plan reporting, program inventories, usage analytics, and decision-support tools.
The goal isn't a prettier dashboard. It's reducing the distance between knowing something and doing something about it.
Some problems don't need software. They need someone willing to map the mess.
I've worked on organizational strategy, operating models, executive decision-making, process improvement, funding alignment, performance management, and large cross-functional initiatives.
Operations is where strategy discovers gravity.
A beautiful plan eventually has to survive budgets, meetings, organizational politics, procurement, staffing, deadlines, and humans.
I like that part.
Education technology is an unusually good laboratory for product development.
The users are busy. The systems are complicated. The constraints are real. And a feature that looked brilliant in a product meeting can meet a classroom and die before lunch.
My work in EdTech combines AI, software development, analytics, curriculum tools, and product strategy.
The classroom doesn't care about your roadmap. It cares whether the thing works.
Consulting taught me something I've carried into almost everything I've done since:
The problem someone brings you is rarely the entire problem.
Good consulting starts by resisting the urge to immediately solve what was written in the email.
Understand the system first. Then decide what needs fixing.
Jason Matthew Howes