Maryland’s AI Workforce Needs Product Thinkers, Not Just Coders
Matthew Olugbemi | Product Leadership · AI · Digital Transformation | August 2026.
Maryland is investing in AI skills, education and public-sector adoption. The next challenge is developing people who can turn technical capability into useful, trusted products.
By Matthew Olugbemi
Maryland is preparing for an AI economy, but one question deserves more attention:
Are we preparing enough people to turn AI capability into products that people can actually use, trust and benefit from?
Coders matter. So do data scientists, cybersecurity analysts, cloud engineers and machine-learning researchers.
But technology does not become valuable simply because someone can build it.
AI becomes useful when somebody understands the problem being solved, knows who the user is, designs the right workflow, tests assumptions, manages risk, defines what success means and keeps improving the product after it reaches real people.
That is product thinking.
And Maryland needs more of it.
Maryland is already building AI momentum
Maryland has taken meaningful steps toward preparing for greater AI adoption.
The state’s AI strategy emphasizes responsible and productive use of artificial intelligence, stronger governance, experimentation, workforce capability and the challenge of moving successful AI proofs of concept into production environments. Maryland’s AI Strategy
Maryland has also committed funding to prepare workers, employers and community organizations for AI and other emerging technologies. At the university level, the University of Maryland has introduced new undergraduate AI pathways, including study that considers AI’s relationship with people, policy and society.
These are important developments.
But technical education alone will not solve the adoption problem.
Organizations also need people who can connect technology to real human and business needs.
The difficult part often begins after the demo
Across my work in banking, digital marketplaces, enterprise technology, data and AI-enabled products, I have repeatedly seen the same pattern.
Creating an impressive demonstration is rarely the hardest part.
The harder questions arrive afterwards.
Who exactly will use this?
What problem are we solving for them?
Where does the product fit into their existing workflow?
What information should the AI be allowed to access?
What happens when it gives the wrong answer?
When should a person take over?
How do we know the solution has actually improved the outcome?
These questions determine whether an AI experiment becomes a product or simply remains an interesting prototype.
Consider an AI assistant designed to help Maryland residents navigate public services.
Engineering matters, but engineering alone does not make the product successful.
Someone must determine which questions the system may answer, how it should explain uncertainty, what happens when information is incomplete, how language accessibility is addressed, when the conversation should be escalated to a human and how residents’ information is protected.
A healthcare AI product raises additional questions about clinical workflow, privacy, safety and accountability.
A cybersecurity product must fit the way analysts review alerts, investigate threats, document evidence and escalate incidents.
These are product decisions as much as technical decisions.
Product development is a team sport
One of the misconceptions we should remove from conversations about technology careers is the idea that building technology means becoming a programmer.
Programming is one part of a much larger system.
A modern product might bring together:
- Software engineers who build the technology
- Data scientists who find patterns and develop models
- AI engineers who create intelligent capabilities
- Cybersecurity specialists who protect systems and data
- UX designers who make products understandable and usable
- Quality engineers who test whether the solution works
- Business analysts who clarify processes and requirements
- Cloud engineers who provide the infrastructure
- Domain experts who understand the environment in which the technology will operate
- Product managers who connect user needs, technology, business outcomes, and delivery priorities
The strength of the product comes from how these disciplines work together.
That matters for Maryland’s workforce strategy.
STEM students should see how the pieces connect
Product development can make STEM education more tangible for young people.
Imagine a student learning algebra.
The student may understand the lesson but still struggle to see how algebra connects with a future career.
Now give that student a challenge:
Design a product that helps students know when their school bus will arrive.
Suddenly, STEM becomes practical.
Mathematics and statistics can help estimate arrival times.
GPS technology provides location information.
Software engineering connects the systems.
Data science can identify traffic patterns.
A designer determines how information should appear on a phone.
A cybersecurity specialist asks who should have access to student location information.
A product manager asks which features students and parents actually need.
A quality engineer tests what happens when GPS data is unavailable.
One problem has introduced the student to several possible careers.
More importantly, the student begins to understand that technology is a team sport.
AI literacy should mean more than using AI tools
AI literacy is increasingly associated with knowing how to use generative AI tools or write effective prompts.
Those skills have value.
But genuine AI readiness should go further.
A person working around AI should increasingly be able to ask:
- Is this a problem worth solving with AI?
- Who will be affected?
- Is the underlying data reliable?
- What happens when the system is wrong?
- Could the result be biased?
- What decisions should remain with humans?
- How should the product be tested?
- What evidence would prove that the technology has created value?
These are useful capabilities even for people who never become software engineers.
A teacher may need to evaluate an AI learning product.
A case manager may need to understand when automation should return a decision to a human.
A hospital administrator may need to determine whether an AI workflow improves patient care.
A small-business owner may need to decide whether an AI product actually saves time or merely creates another subscription expense.
An operations professional may need to describe a business process clearly enough for engineers to automate it safely.
This is where product thinking becomes a workforce capability.
Teach people how products actually get built
A practical approach to AI and STEM education would combine technical skills with the product-development process.
Students and workers should learn to ask:
What problem are we solving?
Who experiences the problem?
How is it handled today?
What would a better experience look like?
What is the smallest useful solution we could test?
What data and technology would we need?
What risks must we manage?
How will we know whether the solution worked?
This introduces learners to problem discovery, user research, design thinking, prototyping, Agile development, data quality, cybersecurity, responsible AI, testing and measurement.
Coding remains important.
But now coding has context.
From STEM learning to product creation
This is also why I believe product development deserves greater exposure within STEM programs.
When students understand how real products are created, they begin to see more possibilities for themselves.
The student who enjoys mathematics may discover data science.
The student who loves drawing may discover UX design.
The student interested in computers may discover cybersecurity.
The student who enjoys solving problems and bringing people together may discover product management.
The student interested in starting a business may discover technology entrepreneurship.
The objective should not be to push every young person toward one technology occupation.
It should be to expose them to the range of ways their abilities can contribute to solving meaningful problems.
Maryland has an opportunity
Maryland already has many of the ingredients needed to lead this conversation: major universities, federal agencies, healthcare institutions, cybersecurity companies, technology startups, public-sector innovation and a diverse student population.
The state also has real problems worth solving.
Transportation.
Healthcare access.
Education.
Small-business productivity.
Public services.
Cybersecurity.
Environmental management.
Workforce development.
AI gives Maryland another set of tools for addressing these challenges.
But tools alone will not determine the outcome.
People will.
Maryland’s future AI workforce will still need excellent programmers, engineers and technical researchers.
It will also need people capable of standing between technology and the real world and asking:
Are we solving the right problem?
Are we building the right product?
Can people trust it?
And did it actually make something better?
That is product thinking.
And it deserves a place alongside coding, AI, data science and cybersecurity in how Maryland prepares its future workforce.
About the Author
Matthew Olugbemi is a product leader, author, educator and global mentor working across product management, artificial intelligence, digital transformation and technology-enabled business growth.
His professional experience spans banking, digital marketplaces, enterprise technology and product development, including work associated with organizations such as PwC, Gumtree and Avo by Nedbank. His work and international impact have also been profiled by Vanguard News. Vanguard News
Matthew was named to the ADPList100 — 2026 Most Influential Mentors in the Product category, recognizing his contribution to mentoring and developing professionals across international markets. ADPList
He is the author of The Product Manager’s Playbook: Mastering the Foundations, Strategy, and Go-to-Market Success and The Strategic Product Leader: Innovation, Measuring Success, and Leading Teams to the Future, both available through Amazon. LinkedIn
Matthew also created the Alison diploma course Diploma in Product Management: Agile, Analytics, and AI. Alison currently reports more than 2,400 learners enrolled globally, with the curriculum covering product management, Agile, design thinking, analytics, AI, prioritization and responsible AI considerations. Alison
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