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AXI: Capitalize on the AI Hype in 2026

By George K. Mehok | February 11, 2026

The Question Everyone Is Asking 

In 2025, we were bombarded with news about AI—its promise, its risks, and its global implications. I expect 2026 will bring more of the same. The question many leaders are asking is simple: Is AI real, or is it just hype? 

I can honestly say it’s real. It’s big. Really big. 

Some recent thoughts from former Google CEO Eric Schmidt:

 

And this is not a pitch to sell an AI product or service. My perspective is grounded in experience—specifically, the quantifiable business results of AI-enabled automation deployments I’ve been involved with over the past few years across several core industries. 

Why AI Is Different 

AI is more powerful than any technology that has come before it. There are very few historical comparisons that hold up. Maybe the written word comes closest. What is clear is that AI is acting as a catalyst for a wave of ancillary technologies that are rapidly emerging—self-driving vehicles, humanoid robots, reusable rockets, unmanned military systems, and more. 

Lessons from the Internet Era 

Thirty years ago, business leaders were dealing with another highly hyped technology: the Internet. Unprecedented levels of venture capital and institutional investment fueled the Internet boom. Many companies without viable business models collapsed during the late-1990s bubble. But a small number not only survived—they thrived. Amazon, eBay, and Google went on to define the next era of business. 

Then came the practical application phase. Businesses learned how to use the Internet to sell products through ecommerce, transact with vendors digitally, distribute content at scale, and facilitate payments. Over time, B2B SaaS emerged as the dominant enterprise software model—Salesforce, NetSuite, Office 365, Dropbox. Eventually, every business, regardless of size or industry, became dependent on internet-based, connected technologies. 

Mobility Accelerated Everything 

Mobility accelerated that transformation. The introduction of 4G and 5G networks, ubiquitous connectivity, and GPS-based services didn’t replace the Internet—it amplified it. This was an incremental but deeply transformative shift. 

Standing at the Next Inflection Point 

Now we stand at the precipice of the next tectonic technology transition. 

AI is not hype. Businesses are adopting AI faster than they adopted personal computers or the Internet. Global investment in AI—across software, infrastructure, and services—has reached hundreds of billions of dollars annually and is projected to grow into the trillions. This puts AI on a scale that rivals, and in some cases exceeds, previous technology waves. 

The Risk Created by Speed 

What makes this transition different is speed. AI is evolving at a historically rapid pace. Models improve continuously. Costs decline quickly. Capabilities expand faster than most organizations can absorb. This creates a new kind of risk: the AI technology or vendor you select today may be obsolete in months, not years. 

Why Incremental Adoption Matters 

For that reason, AI must be deployed incrementally. Organizations need to learn by doing. They need to build internal skills and institutional knowledge while applying these tools to real business problems—inefficiencies, compliance risk, manual bottlenecks, and legacy systems that constrain growth. 

Based on my experience deploying AI solutions over the past few years, I can say with confidence that this technology is living up to the hype. We are seeing results in weeks and months that previously took years using traditional approaches. 

What It Takes to Achieve ROI 

However, achieving a high return on investment requires discipline. 

First, leaders must be clear about which areas of the business will truly move the needle if AI is applied. Not every process needs AI. Second, organizations should start with one or two discrete initiatives that can be deployed and operationally integrated within a short time horizon—ideally around 90 days. From there, teams should learn, measure, adapt, and continuously improve both the process and the technology. 

Where to Start 

Based on AXI’s experience in the healthcare and manufacturing industries, the best starting points are high-volume, repeatable processes that rely heavily on human effort. Examples include sales order processing, accounts payable and invoice processing, electronic medical record evaluations, claims remittance, quality and regulatory reporting, and other workflows dominated by manual data entry, document review, and spreadsheet-driven work. 

Rethinking Return on Investment 

It is also important to think beyond basic productivity metrics such as hours saved or headcount reduction. The return on AI automation is far more nuanced. Manual processes often carry error rates in the low single digits, and even small error rates can have outsized consequences. A single mistake in a compliance workflow or a high-value transaction—such as a spreadsheet error during month-end close—can have a material financial impact. Delays can lead to lost customers, revenue leakage, and reputational risk. 

Evaluating AI investments is therefore a multifaceted business decision. Labor savings matter, but so do risk reduction, revenue protection, operational resilience, and decision quality. 

The Cost of Doing Nothing 

Finally, there is a strategic cost to not acting. When organizations fail to invest in tools that reduce mundane, error-prone work, employees notice. Over time, this impacts job satisfaction, retention, and institutional knowledge. Just as with the Internet and mobile technologies before it, AI-enabled solutions improve incrementally—day by day, month by month, year by year. 

A Call to Action for Leaders 

It is imperative for leaders of companies—large, small, and everywhere in between—to choose a business problem to solve with AI. Learn from it. Expect some missteps. Adjust. Keep moving forward. 

The promise of AI is real.
Embrace the hype. 

 

About the Author 

George K. Mehok is the Founder and Chief Executive Officer of ApertureXI (AXI), where he leads strategy and execution in intelligent automation, analytics, and AI-driven business transformation. A technology leader with experience across publicly held, private, and early-stage ventures, George has spent his career building and scaling high-impact technology platforms in healthcare, manufacturing, and enterprise services. He is also the author of Going Dark: A Liberty Unit Novel, a techno-historical thriller that blends cyber intrigue with Revolutionary-era history.Â