The Future Is Open for Business
I’ve spent much of my career helping organizations use technology to solve difficult problems. Microsoft, GE Digital, Torc Robotics, and now ZynSource AI.
I’ve witnessed some remarkable changes, but I don’t believe I’ve ever seen the economics of building software change this quickly.
And that raises an interesting question.
What happens when a business student with a good idea can build something that once required an entire software development team?
That’s one of the questions I’ll explore when I return to Virginia Tech to speak with Juniors and Seniors at the Pamplin College of Business. My presentation is titled The Future Is Open for Business.
I want students to think beyond the headlines about AI replacing jobs. That’s an important discussion, but something much bigger is happening. AI is changing who can innovate, how businesses compete, and what it means to have valuable expertise.
What We’re Building Today
Through ZynSource AI, I’m seeing these changes firsthand.
We’ve developed an AI-assisted software engineering environment supported by a roughly 700-page development playbook and 22 specialized AI development agents, which we call development oracles.
These agents assist with software architecture, development, testing, quality, and deployment. The objective isn’t simply to write code faster. It’s to make sophisticated application development more accessible while maintaining the discipline required to build reliable systems.
We’re applying this approach to problems in mining, healthcare, and sales.
In mining, we’re developing systems that bring together equipment information, operational data, and sensor readings to identify potential problems. But a mining engineer must determine whether an unusual reading represents a genuine danger or simply a sensor problem.
In healthcare, AI can help analyze hospital capacity, staffing, and transfer requests. But healthcare professionals understand the clinical risks and operational realities that determine whether a recommendation makes sense.
In sales, we can build sophisticated territory management and customer analysis tools. But experienced sales leaders understand the relationships, market dynamics, and human factors that make those tools valuable.
Technology can help build the application. The subject-matter expert makes it useful.
AI can help write the code. Someone still has to know what good looks like.
That’s a significant change in how businesses can approach innovation.
For decades, organizations hired people who understood technology and taught them about the business. We’re entering a period when the reverse is becoming increasingly practical: people who understand the business can use AI to help build the technology.
That doesn’t eliminate the need for experienced software engineers, cybersecurity, testing, or governance. But it changes the relationship between the business expert and the technology itself.
I’ve Seen This Movie Before. But Something Is Different.
At Microsoft, I watched software development platforms and cloud computing change how organizations built and deployed applications.
Before the cloud, businesses often needed substantial investments in servers, infrastructure, and technical personnel. Cloud computing made those resources available as services, lowering barriers for smaller companies and entrepreneurs.
At GE Digital, I saw how industrial organizations were collecting enormous quantities of information from machinery, manufacturing processes, and operational systems.
But data alone didn’t solve problems. An engineer or operator had to understand what the information meant and how it should influence decisions.
At Torc Robotics, where I served as Chief Transformation Officer, we faced another challenge. Autonomous trucks must operate safely in a physical environment where mistakes can have serious consequences.
A convincing demonstration isn’t enough. Engineering discipline, validation, reliability, and operational expertise matter enormously.
Each experience taught me the same lesson: technology creates possibilities, but people with the right expertise turn those possibilities into results.
What’s different today is the speed and declining cost of creating the technology itself.
And I think we’re only beginning to understand the implications.
What Does This Mean for a Business Student?
If I were graduating from Virginia Tech today with a degree in finance, accounting, marketing, management, or business information technology, I’d be both excited and concerned.
Excited because the opportunities to innovate are expanding.
A finance graduate might build a specialized forecasting application. A marketing graduate could develop tools to better understand customer behavior. A supply chain graduate might create a system that identifies inventory problems before they become expensive disruptions.
An entrepreneur who understands a particular industry could test a business idea without first raising substantial capital to hire a software development organization.
That’s a remarkable opportunity.
But there’s another side of this conversation that deserves far more attention.
If AI performs the entry-level work that traditionally taught people their professions, how will they gain the experience necessary to become tomorrow’s experts?
Consider a young accountant who develops judgment by working through financial statements, or a new consultant who learns by researching industries, interviewing customers, and preparing analyses.
What happens when AI completes much of that work in minutes?
The productivity gains could be substantial. But we risk eliminating experiences that teach people to recognize mistakes, challenge assumptions, and make sound decisions.
Universities and employers need to take that problem seriously.
Producing an answer isn’t the same as understanding whether it’s correct. And understanding whether it’s correct isn’t always the same as knowing what to do with it.
Those distinctions matter.
Virginia Has an Opportunity, Too.
This transformation also has important implications beyond individual careers.
AI depends on significant investments in computing infrastructure, electricity, data centers, and communications networks.
Here in Virginia, we’re debating the costs and benefits of that infrastructure. Those conversations are necessary. Communities deserve clear answers about electricity, water, land use, and long-term economic returns.
But I’d argue we’re missing part of the opportunity if the conversation stops there.
Building data centers is one thing. Building an economy that uses those computing capabilities to develop new businesses, improve existing industries, and commercialize research is another.
Virginia Tech, our research universities, entrepreneurs, manufacturers, and regional businesses have an opportunity to connect these investments to something much larger.
I’d like Virginia to be known not only for hosting technology infrastructure, but for what we create with it.
My Challenge to the Next Generation of Hokies
When I speak with these students, I won’t tell them that everyone needs to become a software engineer. Nor will I suggest their business education is becoming less relevant.
Quite the opposite.
I want them to find a real problem worth solving.
Maybe it’s in a family business, a local manufacturer, a hospital, a nonprofit, or an organization where they’ve interned.
Talk to the people doing the work. Understand their frustrations. Learn how the business operates, what decisions matter, and how it measures success.
Then explore what AI might make possible.
Build something. Test it. Find out where it fails. Get an experienced person to challenge your assumptions. Improve it.
And be prepared to explain the business value you created, not simply which AI tools you used.
I’ve watched technology evolve for decades. Every major transition brought extraordinary opportunities, difficult disruptions, and predictions that didn’t turn out as expected.
I don’t pretend to know exactly what the workforce will look like five or ten years from now.
But I believe the advantage will increasingly belong to people who combine technological fluency with genuine business expertise, sound judgment, and the ability to solve meaningful problems.
That’s what I hope these 500 students take away from our conversation.
And I’ll leave them with the same question I’d ask any experienced business leader or entrepreneur:
What problem do you understand well enough to solve?
Because for the first time in my career, the technology required to build the solution may be the easiest part.
The future is open for business.
#VirginiaTech #PamplinCollegeOfBusiness #ArtificialIntelligence #FutureOfWork #Innovation #Entrepreneurship #ZynSource
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