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Trend Watch: The Next AI Divide Isn’t Access. It’s Whether You Can Deploy AI Safely.

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AI tools are easier to get than ever. The harder question is how can use them without creating new risk.

The AI conversation has shifted, and most people haven’t caught up to where it actually is.

A year ago, the question was access. Could your company afford the tools? Could your team figure them out? Could you get past the learning curve fast enough to matter? That question is fading. Tools are broadly available. Many are free or nearly free at entry level. The barrier to entry has dropped to almost nothing.

But “free to start” is not the same as “cheap to run.” At enterprise scale, the economics are different and getting harder. Compute costs have not followed the trajectory that most AI marketing implies. Energy costs, as we covered in last month’s Trend Watch, are climbing. The large AI companies themselves are not yet generating profits proportional to their infrastructure spend, which means the current pricing environment is unlikely to last. When the subsidy phase ends, the companies that deployed carelessly will feel it twice: once in rising costs, and once in the operational debt they accumulated when the tools were cheap.

What is GenAI? Generative AI refers to AI systems that create new content, such as text, images, code, audio, or video, based on patterns learned from large amounts of data. In business settings, GenAI is often used for drafting, summarizing, research support, content creation, and workflow assistance. The risk is that fluent output can look authoritative even when it is incomplete, wrong, or unsafe, which is why human review still matters.

That would be enough to think about. But there’s a second shift happening at the same time. And it’s arguably much more consequential.

The market is moving past basic chat use and into systems that can retrieve information, use tools, act across software, and make limited decisions on their own. This is the shift from AI as a writing assistant to AI as a semi-autonomous operator. Gartner says the growth of agentic AI applications and Model Context Protocol (MCP) is creating new avenues for cyberattacks and other exploitations, especially when those AI systems access sensitive data, ingest untrusted content, and communicate externally – all in the same workflow. Open Worldwide Application Security Project‘s (OWASP) latest guidance reinforces the point by highlighting prompt injection, excessive agency, and unsafe handling of tool output as leading risks in agentic applications.

The AI story is shifting from “Can it help?” to “What exactly can it touch, what can it do, and who is accountable when it gets something wrong?” That’s a different management problem. And it’s arriving at the same time the cost assumptions are about to change.

Why Does It Matter?

The real divide is becoming operational maturity. Two companies can buy the same AI tool. One gets speed. The other gets new exposure.

This isn’t theoretical. Reuters reports that banks in Asia and Australia are already revisiting their AI deployment protocols because frontier models could increase the speed and scale of cyberattacks. Gartner predicts that by 2028, a quarter of enterprise GenAI applications will experience at least five minor security incidents per year, driven in part by immature security practices around agentic systems. The pattern is clear: the organizations that moved first on AI are now moving first on governance, because they’ve seen what ungoverned deployment actually looks like.

For OMSDC’s audiences, the implications split three ways.

A small firm can adopt AI quickly. That’s genuinely good. But a small firm can also expose customer data, internal files, or API credentials quickly if the tool is poorly configured. For a company with no IT department and thin margins, a data exposure event isn’t a learning experience. It’s an existential one.

A larger minority business may already be connecting AI to client service, documentation, proposals, and internal workflows. The question isn’t whether the tools work. It’s whether those connections have clear boundaries, review points, and someone accountable for what the system does when no one is watching.

A corporate member will increasingly evaluate suppliers through this lens. A vendor that uses AI carelessly can become a security or compliance liability. A vendor that uses it with discipline becomes more responsive, more consistent, and easier to trust. That difference will show up in execution long before it shows up in a questionnaire.

The point for every reader is the same: access alone won’t create advantage. Controls, review processes, and permission design will. And deploying well isn’t just a security question anymore. With costs poised to rise, it’s also an economics question. The firms that build disciplined, efficient AI workflows now will be the ones that can afford to keep running them later.

Where Do AI Agents Fit In?

If you’ve been following the AI conversation this year, you’ve probably heard the word “agent” more than any other term. It’s where productivity and risk start to converge, and the distinction matters more than most coverage suggests.

A standard AI chat tool generates text in response to a question. You ask, it answers. An agent does something fundamentally different. It can call tools, access files, browse the web, send messages, update databases, or orchestrate multiple steps in sequence. That’s genuinely useful. It’s also a genuinely different risk profile.

What is an AI agent? An AI agent is a system that does more than answer a prompt. It can plan, take actions, call tools, interact with external systems, and carry work forward across multiple steps. NIST describes AI agents as systems capable of autonomous actions such as writing and debugging code, managing calendars and email, and handling other emerging tasks. That added usefulness is exactly why control, permissions, and monitoring become more important.

Think of it this way. A chatbot that drafts a bad email wastes your time. An agent that sends a bad email wastes your client’s trust. A chatbot that hallucinates a number gives you wrong information. An agent that hallucinates a number and enters it into your accounting system gives you a wrong record that looks like a real one. The failure modes change when the system can act, not just speak.

OWASP describes “excessive agency” as a real vulnerability when an LLM has too much ability to trigger actions in response to manipulated or ambiguous inputs. Gartner warns that ordinary use can produce security failures when agents both touch sensitive data and consume untrusted inputs. Axios’ recent cybersecurity roundtable made the same point from an access-control angle: organizations are not yet managing agents with the same discipline they would apply to any other privileged workload that can read, write, and execute across systems.

Most business leaders hear “agent” and think convenience. Instead, they should hear “permissions,” “scope,” and “review.” A workflow that can take action isn’t just a better chatbot. It’s closer to a junior intern who works fast, never sleeps, and has no judgment about when to stop and ask. Deploying agents aren’t just about productivity. It needs to be a governance and cybersecurity discussion.

What does “excessive agency” mean? Excessive agency is the security risk that appears when an AI system has too much authority, too many connected functions, or too much autonomy for the job it is supposed to do. OWASP defines it as a vulnerability that enables damaging actions when an LLM-based system can call functions or interact with other systems in response to ambiguous, manipulated, or otherwise faulty outputs. The business version is straightforward: an AI assistant that should only retrieve information should not also be able to delete records, send messages, or trigger transactions without tighter controls.

Why System Prompts and Surface Guardrails Are Not Enough

Many teams still treat AI safety as a prompt-writing problem. Write better instructions. Add guardrails to the system prompt. Tell the model what not to do. Hope it listens.

System prompts and better instructions are good. But relying on hope is increasingly insufficient, as the gap between what prompts can control and what systems can do is widening.

OWASP’s 2025 guidance confirms that prompt injection remains a core vulnerability, and that techniques like retrieval-augmented generation (RAG) and fine-tuning don’t fully solve it. NVIDIA’s AI red-team work adds another layer: new semantic and multimodal prompt-injection techniques can bypass existing guardrails entirely, which is why output controls, layered defenses, and behavioral analysis matter more than relying on a single control surface.

What is prompt injection? Prompt injection is a security vulnerability in which a model is manipulated by crafted inputs that alter its behavior in unintended ways. OWASP notes that this can happen directly through a user prompt or indirectly through content the model reads from a website, file, email, or another outside source. The practical implication is simple: if an AI system can read untrusted content and also access tools or sensitive data, the model may be pushed into doing something it was never meant to do.

Why system prompts are not enough.

A system prompt is the instruction layer developers use to shape how a model should behave. It matters, but it is not a complete security strategy. OWASP notes that prompt injection can still succeed even when guardrails exist in prompts, and that techniques like RAG and fine-tuning do not fully eliminate that risk. Once a system can retrieve, call tools, or take action, controls need to live in permissions, architecture, approval flows, and output checks, not just in the prompt itself.

Here’s an analogy that may help. Telling an employee “don’t share confidential information” is important. But if that employee has unrestricted access to every file in the company, an unlocked door to the server room, and the ability to email anyone in your client’s organization, the instruction isn’t the problem. The architecture is. The same logic applies to AI systems. The issue isn’t that prompts don’t matter. It’s that prompts are not a sufficient control plane once the model can retrieve, execute, and communicate. At that point, permissions, sandboxing, output validation, and human approval gates matter more than any instruction you can write.

A Cautionary Example: When Easy Setup Meets Deep Access

The spread of consumer-friendly, self-hosted agent tools illustrates where the gap between “easy to try” and “safe to run” becomes dangerous.

McAfee’s recent guidance describes tools like OpenClaw as self-hosted agents with deep system access, and warns that poor configuration can expose passwords, API keys, and private data. The same guidance cites reports of exposed installations and malicious plug-ins targeting credentials and financial information. TechRadar is making a broader argument about “shadow AI,” agentic tools that slip into business environments without IT oversight, proper configuration, or anyone asking whether the convenience is worth the exposure.

OpenClaw is worth naming not because it defines the whole market, but because it illustrates a pattern that will repeat. The tools that are easiest to install are often the ones with the broadest default access. The distance between curiosity and exposure is shrinking. And for a small business that treats a self-hosted agent as a casual productivity tool rather than a privileged system with deep reach, the consequences can arrive faster than the benefits.

This isn’t a reason to avoid experimentation. It’s a reason to treat experiments like experiments: sandboxed, monitored, and kept away from your primary business systems until you understand what the tool can touch.

What is MCP? MCP, or Model Context Protocol, is an open protocol designed to standardize how AI applications connect to external tools and data sources. Anthropic describes it as a standardized way to connect AI models to different systems, similar to how USB-C standardizes how devices connect to peripherals. In practice, MCP can make AI systems more useful by giving them access to files, apps, and business tools. It can also increase risk if those connections are too broad or poorly governed.

Where Humans Fit

The strongest AI deployments don’t remove human review. They move it to the places where judgment, approval, and accountability matter most.

That distinction is worth being specific about, because “keep a human in the loop” has become one of those phrases that sounds reassuring without actually telling anyone where to stand.

Human review should sit at permission design: deciding what the system can and cannot access before it’s turned on. It should sit at approval for external actions: anything that sends, publishes, submits, or commits on behalf of the business. It should sit at output review for sensitive or regulated workflows, where a polished-sounding wrong answer can create legal, financial, or reputational damage. And it should sit at monitoring: watching for failures, exceptions, drift, and the slow degradation of quality that’s easy to miss when every output looks professionally formatted.

There’s one more review point that most teams skip, and it may be the most important: periodically asking whether the tool is actually saving time.

One of the easiest AI mistakes is assuming that automation creates efficiency by default. In practice, some systems create rework, monitoring burden, debugging time, and workflow complexity that outweigh the productivity gain. A tool that drafts a document in ten seconds but requires thirty minutes of review, correction, and reformatting didn’t save twenty minutes. It moved the work and added a quality-control problem. That calculus is especially relevant for smaller firms, where every hour of overhead falls on the same few people.

A useful question to keep asking: if an AI workflow takes six steps to supervise, document, and correct, did you automate the work or just relocate it?

What Should You Do?

For smaller businesses (under $1M):

Stay with low-risk use cases first. Drafting, summarizing, internal organization, proposal support, and repeatable content are safer starting points than tools with deep file, inbox, or financial-system access. These are also the use cases most likely to survive a price increase, because the compute they require is modest.

Avoid self-hosted agents on your primary business systems unless you understand the security model. Tools like OpenClaw are better treated as sandbox experiments, not casual installs.

Ask one discipline question before adopting any new tool: is this reducing real work, or is it adding supervision overhead that I’ll be paying for in time, attention, and eventually in subscription costs?

For larger businesses:

Start drawing a clear line between AI use and AI governance. Standardize which tools are approved, where sensitive data can and cannot go, and what actions require human approval before the system executes.

Treat workflows involving customer data, contract data, or financial data as higher-risk categories. Gartner’s warning about “no-go zones” when agents combine sensitive data, untrusted content, and external communication is a useful frame for deciding where the boundaries should sit.

Begin thinking about cost resilience. The tools that are cheap today may not be cheap in eighteen months. Building workflows that depend on underpriced compute is a form of technical debt. The firms that deploy efficiently now, using the right-sized tool for the right-sized task, will be better positioned when pricing reflects actual costs.

For corporate members:

Update supplier and internal evaluation questions. Don’t just ask whether a vendor uses AI. Ask how it’s governed, what review points exist, what systems it can access, and what happens when something goes wrong. The quality of those answers will tell you more about operational maturity than any capabilities deck.

Treat AI agents as privileged workloads, not casual productivity tools. Axios’ security roundtable used that frame well: if an agent can read, write, and execute across systems, it deserves the same access controls and monitoring you’d apply to any other system with that level of reach.

Build internal policy around access, outputs, logging, and approval workflows, not just acceptable-use language. Acceptable-use policies tell people what not to do. Governance architecture makes it harder to do the wrong thing by default.

The Takeaway

The first AI divide was access. That divide is closing fast.

The next one is operational, and it has two faces. The first is security: who can deploy these tools with discipline, clear permissions, and real human review? The second is economics: who can deploy them efficiently enough that the workflows still make sense when the current pricing environment changes?

As AI systems gain the ability to retrieve, connect, and act, both questions start to matter more than whether a company has the tools at all. Reuters, Gartner, and OWASP are all pointing in the same direction: the spread of AI is becoming a security and governance story as much as a productivity story. And last month’s infrastructure analysis still applies. Software is getting more capable. The physical and financial systems underneath it are getting more constrained.

As quality differences between commercial and even open source AI models shrinks, disciplined deployment will become the biggest differentiator.

Thirty-Five Years of Proving What’s Possible: How UNICON’s Jane Lee Turned a Two-Year Leave Into a Legacy

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Pei-Chen Jane Lee came to Columbus from Taiwan for graduate school, built a career at AT&T Bell Laboratories, and launched UNICON International in 1990. What started as a one-placement-at-a-time IT staffing firm has grown into a full-service provider of workforce staffing, IT solutions, and managed services, with Fortune 500 client relationships spanning more than two decades.

For more than three decades, UNICON International, Inc. (UNICON) has quietly built a reputation as one of Ohio’s most trusted workforce and IT services firms. The company has served clients like American Honda Motor Co.  (Honda) and American Electric Power (AEP) for over two decades, won multiple OMSDC awards, and evolved from a specialized IT staffing firm into a full-service provider of workforce staffing. IT solutions, and managed services.

Behind that growth is founder Pei-Chen Jane Lee (Jane), an immigrant from Taiwan. She came to Columbus Ohio for graduate school with nothing but a dream and began her career at AT&T Bell Laboratories.  On the first day of her first job, her assigned mentor told her he couldn’t understand her English. He had been one of the people who interviewed and hired her for the job. “I was crushed,” Jane recalls. She stopped asking him questions after that. But by the end of that first year, the same mentor asked Jane to review his performance write-up before he submitted it. “I was shocked,” she says. “But at the same time, I felt flattered.”

There was also the co-worker who told her, “If you don’t like it here, go back to where you came from.” and later remarked, “You Asians are going to take over AT&T.” Jane looked around her work location. There were fewer than a dozen Asian Americans there. “Not until the day I become President of AT&T,” She replied calmly.

Those moments didn’t break Jane. Instead, they set her trajectory. “Very early on in my career, I set my goal,” Jane says. “I really wanted to prove to people that we, as minorities and women, can do an equally good job, if not better.”

She’s spent her entire career doing exactly that.

Five years after joining AT&T Bell Laboratories, Jane became the first Asian woman promoted to Technical Manager at her location. She managed large-scale real-time telecom projects and was eventually handed supplier management responsibilities on top of her technical leadership role. That dual assignment gave her something most founders don’t get before launching a business: an operator’s view of what a good supplier looks like from the buyer’s chair. She saw what clients needed and what suppliers often failed to deliver. And she started thinking she could do it better.

When AT&T Bell Laboratories later offered employees a special leave-of-absence program, two years with insurance and a held position, Jane initially thought she’d take six months off. She had two young children. The pace of corporate life had become relentless. But once she started exploring the idea of starting her own company, the possibilities took over. She used the full two years of leave. And she never went back.

In 1990, Jane officially launched UNICON as a lean IT staffing firm, built by technical engineers and managers from Bell Labs. They knew how to recruit technical talent because they were technical talent. The early model was deliberately low-risk: IT staffing, one placement at a time.

Then clients started asking for more. Project-based work came first. Then managed services. Then professional services, a category Jane initially resisted because it sat outside UNICON’s core IT identity. But the logic was hard to argue with. “We use recruiters internally. We use administrative staff. We know how to find those people,” she recalls. The expansion into professional services proved to be a success.

UNICON’s client relationships are long-term. Honda has been a client for more than twenty years. AEP, the same. Contracts like those are renewed year after year because UNICON keeps earning the work. In a services business where switching costs are low and competitors are always calling, two-decade relationships are the strongest proof of performance a company can offer.

The recognition and awards followed. Over the years, Jane and UNICON have received numerous honors, including five or six from the Ohio Minority Supplier Development Council (OMSDC) alone. Yet for Jane, the awards themselves were never the most meaningful part of the journey. In fact, when George Sims, OMSDC’s President and CEO, encouraged her to apply for MBE of the Year, her first instinct was to step aside. “We’ve won too many awards,” she told him. “I want to give the opportunity to other newer MBEs.” Sims looked at her like she was kidding. She applied. She won. Again.

But what shaped Jane’s thinking about OMSDC wasn’t the trophies. It was a program called the Center of Excellence, chaired at the time by Abigail Kofete of Cardinal Health, now OMSDC Board Chair. The program paired corporate members with MBE suppliers in a year-long cohort that met monthly, rotating through corporate sites. UNICON was the only firm sponsored simultaneously by two Fortune 500 corporate members – Honda and Nationwide Insurance.

“That program was one of the best I’ve ever attended,” Jane says. The value went far beyond networking. It provided firsthand exposure to how major corporations actually operate: their procurement processes, priorities, expectations and internal language. Meetings were held at the sites of various participating corporate members, where executives often spoke directly with the MBEs. The result was the kind of buyer intelligence that no brochure or networking event can replicate.

That understanding aligned naturally with Jane’s vision for UNICON: to be the “Best Business Partner for Success” for both clients and associates. Over the years, that philosophy has guided UNICON’s operations and culture. “The extremely positive feedback from our clients, associates and even job candidates made all of our hard work worthwhile,” Jane says.

UNICON’s next chapter is shaped by two forces pulling in different directions. The first is Artificial Intelligence (AI). Jane finds AI both exciting and unsettling. Her VP recently ran an experiment: after UNICON’s team finished a proposal the traditional way, he fed the same requirements into ChatGPT. The AI-generated version was, in her words, “equally good, if not better.”

That raises a question she thinks the entire services industry will have to answer: how do you differentiate a great supplier from someone who’s simply good at prompting?

The other force is legacy. Jane is direct about it. “I’m not getting any younger,” she says. “Before I become history, I hope I can contribute to making some differences.” That ambition extends beyond UNICON itself. She wants to help reshape how MBEs support one another, how corporate members create real opportunities instead of symbolic inclusion, and how organizations like OMSDC can advocate more effectively, especially as the policy landscape shifts.

She’s particularly clear-eyed about the gap between access and outcomes. OMSDC helped open doors by creating introductions and connections, she says. But turning those introductions into sustainable business relationships has always been the harder challenge. Too often, large corporations default to the assumption that bigger suppliers are safer and better suppliers. For MBEs, and for the organizations supporting them, the challenge is building enough proof, enough trust, and enough track records that buyers stop defaulting and start investing in capable local talent.

That’s what thirty-five years of showing up looks like. Not a single breakthrough moment, but a compounding record of delivering the results – over and over again that makes the next conversation easier than the last.

Jane is clear about what comes next. “How can we work together, between MBEs and corporations and organizations like OMSDC, to give every MBE the opportunity to prove themselves?” It’s not a rhetorical question. She’s been answering it for thirty-five years.

Jane’s legacy is clear. Showing up, doing the work, and leaving the door a little wider for whoever walks through it next.

How Can You Support OMSDC in Growing Ohio’s Economy?

If you’re an Ohio-based, minority-owned business ready to compete for larger contracts, or a buyer building a resilient, local supply base, connect with OMSDC. Start with certification, sourcing introductions, and practical guidance on the changing landscape: certification@ohiomsdc.org.

Have a success story you want to share? Email us at marketing@ohiomsdc.org or submit our story form.

Growth Under Pressure: Practical Operating Strategies for Minority-Owned Firms in a Tight Economy

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Diverse business leaders and minority-owned firm executives reviewing financial reports and operational strategy during a corporate meeting, representing collaboration, financial planning, supply chain resilience, and growth strategies in a tight economy for minority-owned businesses.

In a tight economy, the phone rings less often. Proposals go out, but purchase orders take longer to come back, and the checks that used to arrive in 30 days now arrive in 55. Our new white paper, by Jamie Van Doren, Growth Under Pressure, examines what that slowdown actually looks like at the firm level — and what minority-owned firms can do about it. Used well, the playbook helps lean firms protect cash, win confidence faster, and build demand through the relationships they already have.

But the advantage only shows up when the response is operational, not rhetorical. The white paper highlights a few critical truths: pressure moves through time before it shows up in survival metrics, and the most consequential variables are days to decision, days to invoice, and days to payment. Minority-owned firms often face stacked exposure rather than a single disadvantage — smaller revenue bases, weaker financing outcomes, and a higher trust burden when entering new buyer systems. And relationships only become an economic asset when they’re structured. The takeaway is simple — the firms that grow under pressure run a pressure plan rather than a hope plan, not by selling harder but by converting faster. Download our white paper and learn how.

Download our Free White Paper

Trend Watch: AI Is Getting Cheaper to Run. Infrastructure Is On Shaky Ground.

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New efficiency breakthroughs could lower AI costs for everyone, but energy strain, grid limits, and war-driven supply disruptions will shape who benefits first.

by Jamie R. Van Doren

What’s Changed?

Last week, Google Research published a compression algorithm called TurboQuant that does something genuinely useful: it shrinks the working memory AI models need to operate — by roughly six times — without degrading performance. On Nvidia’s H100 chips, a version of the technique delivered up to an eightfold speedup in a key processing step. Memory chipmakers noticed. Within hours of the announcement, shares of Samsung, SK Hynix, and Micron all dropped, as traders recalculated just how much physical hardware the AI industry will actually need.

The internet, naturally, compared it to the fictional compression algorithm from HBO’s Silicon Valley. Fair enough. But the business implications are real.

TurboQuant is not an isolated event. It sits inside a broader pattern: AI models are getting smaller, faster, and cheaper to operate. Techniques like quantization, distillation, and compression have been steadily reducing the computing resources needed to run useful AI. What once required racks of specialized hardware is beginning to run on leaner setups: smaller cloud instances, edge devices, even laptops.

Google released TurboQuant under an open research framework, and community developers had already begun porting it to consumer-grade hardware within a day of the announcement. An official open-source release is expected in the second quarter of this year.

Full Credit Google Research

This is the efficiency side of the equation. And for smaller firms, it’s the side worth paying attention to.

But there’s also another side.

Why Does It Matter?

AI infrastructure is under strain. Not eventually. Right now. The U.S. Department of Energy projects AI energy demand will double or triple within the next few years, potentially reaching 12% of total national electricity consumption by 2028. The country’s largest grid operator, PJM Interconnection, which serves over 65 million people across 13 states, has warned it could be six gigawatts short of reliability requirements by 2027. For everyday Americans, that means being told not to run your air conditioner on the hottest day of the year. And if enough people ignore that request, it means rolling blackouts so the whole grid doesn’t go down.

Retail electricity prices have already risen more than 40% since 2019. Some of that is weather, regulation, and fuel costs. But data center demand is an accelerating factor, and utilities from Virginia to Ohio to Texas are scrambling to keep up.

Large tech companies are responding by locking in long-term energy contracts, investing directly in power generation, and competing for grid capacity in ways that smaller firms simply cannot replicate. Meta recently committed up to $27 billion in a single deal for dedicated compute infrastructure. Google, Microsoft, and Amazon are collectively planning hundreds of billions in data center capital expenditure through the end of this year alone.

There’s a less obvious ripple, too. Iranian strikes on Qatari gas infrastructure have knocked out roughly a third of global helium production — a gas most people associate with party balloons but that chipmakers need to manufacture the semiconductors AI runs on. Without helium, you can’t etch the chips that power data centers. Software can get more efficient, but it still needs hardware, and that hardware supply chain just got more fragile. Cheaper algorithms don’t help much if you can’t build the machines to run them on.

So, here’s the tension. AI is getting cheaper to run, yes. But the infrastructure that supports it is getting more expensive and much more constrained. Energy costs are climbing. Grid capacity is tightening. And now, war-driven supply chain disruptions are threatening the materials needed to build the hardware itself. Efficiency improvements like TurboQuant help at the software layer, but software runs on chips, chips run on power, and both of those supply chains just got more complicated. The bottleneck isn’t the algorithm anymore. It’s, well… everything else.

For MBEs, this creates a two-sided opening.

The opportunity: If useful AI tools require less memory and less compute, the cost of adoption drops. You don’t need a massive technology budget to use AI well. You don’t need to build anything from scratch. The tools that help you write faster proposals, run quicker competitive analysis, automate reporting, and tighten forecasting are getting better and cheaper at the same time. That lowers the barrier to entry in a meaningful way.

The risk: Infrastructure advantages compound. Companies that can secure compute capacity, negotiate energy contracts, and invest ahead of demand will operate with structural advantages that have nothing to do with intelligence or effort. If energy costs keep climbing and grid access remains uneven, operational resilience becomes a competitive differentiator, not just a nice-to-have.

It’s worth repeating on point in particular: recent war-driven energy shocks are a reminder that technology growth doesn’t happen in a vacuum. As oil, power, and materials markets tighten, operational resilience matters just as much as innovation. The AI economy depends on the physical economy, and the physical economy is under pressure from several directions at once.

What Should You Do?

For MBEs: focus on disciplined adoption, not ambition.

The advantage right now isn’t in building custom AI. It’s in applying existing tools to real bottlenecks. The companies that benefit won’t necessarily be the ones experimenting casually. They’ll be the ones building repeatable workflows with clear outputs and measurable time savings.

Where are you spending hours on work that a well-configured AI tool could cut by a third? Proposal drafting, compliance documentation, market research, internal reporting? These aren’t glamorous use cases, but they’re the ones that actually change how a small company operates day to day.

The right question isn’t “Are we using AI?” It’s “Can we point to specific outcomes that improved because of it?” If the answer is vague, the implementation isn’t working yet.

For corporate members: start looking at efficiency as a signal.

A supplier using AI well may not look different on a capabilities slide. But they’ll be more responsive. Their documentation will be cleaner. Their turnaround will be faster. Their communication will be more consistent. These are observable differences, and they’re worth weighting in supplier evaluation.

As AI becomes more accessible, the gap won’t be between companies that use it and those that don’t. It will be between companies that use it with discipline and those that treat it as a novelty. That distinction will show up in execution long before it shows up in an RFP/RFQ response.

OMSDC’s Upcoming AI Workshop Series

The Ohio Minority Supplier Development Council (OMSDC) is developing a practical AI series for MBEs and others. We want to hear what formats and topics would actually be useful. Take the short survey at the link below. Complete it and you can download the Competitor Analysis AI Workflow one-pager, a step-by-step guide with prompts and instructions you can put to work immediately.

Take the survey and download the workflow

Questions Worth Asking

For MBEs:

  • Where are you spending time that could be reduced by 30–50% with the right tools? And have you actually tested that?
  • Are you building repeatable AI workflows, or relying on ad hoc experimentation?
  • If energy and compute costs rise, does your operating model stay viable?

For corporate members:

  • Are your suppliers becoming measurably more efficient over time, or staying flat?
  • Are you evaluating responsiveness and operational clarity, or just price and scale?
  • How do you identify partners who are quietly improving their operations through technology?

For both:

  • If AI becomes cheaper and more accessible, what differentiates you?
  • If infrastructure becomes more constrained, how do you stay flexible?

The Takeaway

The efficiency race in AI is real. And it favors smaller, disciplined adopters more than most people realize. But it’s happening against a backdrop of physical constraints, energy, grid capacity, supply chain friction, that won’t resolve quickly. The companies that navigate both sides of that equation, getting leaner on the software side while staying resilient on the infrastructure side, are the ones best positioned for what comes next. The most advanced AI won’t necessarily be the differentiator. The most disciplined use of it will be.

From One-Man Shop to Multi-Million Pipeline: How Unified Building Technologies Turns Complex Jobs Into Turnkey Wins—and Community Impact

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by Jamie R. Van Doren

Unified Building Technologies launched in December 2023. By Q1 2025, revenue moved from about $60,000 in 2024 to roughly $600,000, with year-end pacing near $850,000 and $6 million in projects in the near pipeline. Unified Building Technologies became an OMSDC-certified MBE in April 2025 and immediately sponsored ConnectingOHIO.

The water in Kingman, Arizona, came out at ninety-five degrees, but the plants needed sixty-eight. A horticulture facility had the wells, the RO system, and the ambition—just not the cooling. When John Ramos stepped in, he didn’t just reach for a catalog SKU. He designed a solution.

That instinct to craft solutions, not just sales, has been shaped over time. Ramos started in HVAC at Stark State, earning an associate’s degree and the mentorship of two professors who pushed him to master both the technical and business sides of the field. Wholesale distribution followed at Robertson Heating Supply, where he learned how manufacturers think and why projects stall when design and lead times fall out of sync. Those lessons would matter later, when speed and availability became the difference between a fix and a failure.

Wadsworth Solutions drew him closer to the applied side—specification work, engineer relationships, and the kind of open-ended problems that reward initiative. In that “wild west,” as he puts it, Ramos saw a recurring need: owners needed one accountable person to design, procure, coordinate, and deliver. Meeting that need required a new structure and real capital.

With backing from Lion’s Share Company, a legacy Ohio organization, Unified Building Technologies took shape in December 2023. The partnership gave the young firm the scaffolding new contractors often lack—legal, bonding and insurance support, and working capital—while keeping Ramos’ design-build vision at the center. The result is a business that collapses seams. Unified Building Technologies folds engineering, procurement, and delivery under one roof so owners can move from problem to plan without losing time to handoffs.

The numbers tell part of the story. Unified Building Technologies recorded about $60,000 in 2024, then opened 2025 with roughly $600,000 in Q1. Their largest job this year landed around $400,000, and year-end should close near $850,000. Next year could reset the curve. 2026 projects, including an Arizona design-build chiller opportunity, will net them around $6 million, with more even opportunities forming behind it.

For now, scale is intentional and lean and Unified Building Technologies is exploring when and how to expand next. Ramos keeps engineering direction close, sketches solutions before sending them to licensed engineers for the stamp, and maintains direct lines to a few manufacturers for availability intelligence. Subs and partner “muscle” are brought in based on schedule and qualifications. He’s pursuing HVAC and hydronics licensure to bring commissioning and startup in-house. Acquisition is on the table for 2026, once recurring work can support a crew and a management layer. In other words, “crawl, walk, run”—not as a slogan, but as an operating system.

Certification fit that plan. Unified Building Technologies became an OMSDC-certified MBE in April 2025 and, on the same rhythm, stepped in as a ConnectingOHIO sponsor. The point wasn’t a piece of paper to hang on the wall. It was a key to more opportunities. In construction, you often compete with century-old legacy companies. Certification opens doors on the public side and signals credibility. It also plugs you into a network that shares the same goal: building a more resilient Ohio economy. Shortly after, Unified Building Technologies added City of Cleveland and CMHA certifications and won an initial CMHA job around $28,000—an entry made easier by validation and visibility.

The Kingman, AZ job shows how Ramos and Unified Building Technologies pulls these threads together. The owner needed to pull ninety-five-degree water down to sixty-eight on a tight clock. Ramos mapped a chiller strategy that could be built from available equipment and delivered on schedule. He coordinated design, lined up gear, managed subs, and drove commissioning. The fix looks straightforward in hindsight. It rarely does at the start.

ConnectingOHIO 2025 and 2026 extended the same logic to building business relationships. When Ramos decided to attend and sponsor ConnectingOHIO (Columbus, August 4 – 6, 2025) he didn’t expect a one-conversation contract. He went into the room with a plan and a purpose. He accomplished more than expected – leaving with a list of follow ups: primes, city contacts, healthcare systems who might be able to open the right doors at the right time. The challenge now is focus. A small firm can’t chase everything, so Ramos is picking lanes where design-build speed and procurement savvy create an edge. The callback is clear: the work of managing a $400,000 job looks a lot like managing a $4 million job. Coordination scales before headcount does.

Young multiracial people having fun stacking hands outdoor – Diversity and joyful lifestyle concept – Soft focus on african hand

Community impact is built into Ramos’ business model. He serves on four local boards, including United Way’s Community Impact Council, and helps steward a CDC that builds affordable housing for seniors. As Unified Building Technologies grows, that capital—and the jobs that follow—circulates locally. Dollars retained in the region multiply. Dollars that leave drain resources, as community capital flows elsewhere. Ramos, like many who advocate for more minority business investment, recognizes that supplier diversity isn’t charity. It’s how you keep the flywheel turning at home.

Policy shifts are changing the ground under every MBE. Ramos is tracking the moves on disadvantage standards and the downstream changes across cities and state reciprocity. What helps most, he says, is clear guidance and practical tools—especially if owners must document disadvantage narratives under new rules, like the Department of Transportation’s changes to Disadvantaged Business Enterprise definitions. Continued advocacy matters too, not only with government but with private buyers who still care about equity and need qualified suppliers to perform. The throughline for Unified Building Technologies’ remains: success depends on clarity, speed, and the right partnerships.

What should other MBEs take from Unified Building Technologies’ path? You don’t need a large payroll to start winning larger scopes. You need a clear value proposition, partners willing to share risk and support you, and a network (like OMSDC’s) that opens rooms you can’t open alone. Certification and event aren’t paperwork and parties. They’re how you gain credibility, build relationships, hear about a bid opportunity—and sometimes how you just get your foot in the door.

Ramos wrote a sponsorship check for ConnectingOHIO the moment his OMSDC certificate arrived because he sees the loop. “This community helps me grow, and I want my growth to help the community,” he says. “Let’s grow each other.”

How Can You Support OMSDC in Growing Ohio’s Economy?

If you’re an Ohio-based, minority-owned business ready to compete for larger contracts—or a buyer building a resilient, local supply base—connect with OMSDC. Start with certification, sourcing introductions, and practical guidance on the changing landscape: certification@ohiomsdc.org.

Have a success tory you want to share? Email us at marketing@ohiomsdc.org or submit our story form.