For leaders currently trying to implement AI, the roadmap often looks simple on paper: identify a problem, buy a tool, and reap the rewards. Yet, as thousands of companies rush to adopt generative AI and automation in 2026, many are hitting a wall. The technology is performing, but the projects are stalling. This has led to a critical debate in boardrooms and operations centers alike: Is the bottleneck the technology itself, or the humans required to wield it?
We asked thought leaders…ranging from CEOs of SaaS companies and construction firm owners to legal experts and healthcare directors…what has been harder to find: the right software, or the right people?
The Consensus: The “People Problem” is the Real Bottleneck
The verdict was overwhelming. Of the total leaders surveyed, the vast majority identified talent and human judgment as the significantly harder resource to acquire. While AI software has become commoditized, accessible, and relatively inexpensive, the “human operating layer”..the individuals capable of translating business problems into AI workflows, remains critically scarce.
Here is what the experts had to say about why the “people part” is keeping them up at night, and the rare exceptions where software remains the primary hurdle.
The Software is a Commodity; Judgment is Scarse
For many leaders, the market is oversaturated with capable tools. The challenge isn’t access to intelligence; it’s the application of it.
Nate Nead, CEO of DEV, puts it bluntly: “The software is easy to find; the people are not. AI tools are abundant, inexpensive, and increasingly commoditized. What’s consistently harder is finding people who can translate business problems into AI-ready workflows… The hidden challenge is accountability. When AI underperforms, leaders often realize they never assigned a human owner with both technical authority and business context.”
This sentiment is echoed by Xi He, CEO of BoostVision, who notes that talent must sit in the middle of three worlds: business, data, and technology. “You can buy great software and still get zero ROI if no one knows which workflows are worth automating or where humans must stay in the loop,” He explains.
Edward Tian, Founder/CEO of GPTZero, highlights the risks of this gap: “Not only does the licensing and implementation of software pose issues, staffing people who possess Technical Skills along with Domain Knowledge has proven to be a nearly insurmountable effort… Audits routinely revealed inconsistent and non-compliant labeling along with improper threshold application.”
The “Translator” Gap
The most coveted skill isn’t coding; it’s context. Leaders are struggling to find “bilingual” employees who speak both the language of the industry (whether that is law, construction, or marketing) and the language of AI.
Mike Fullam, CEO of Togo, illustrates this perfectly in supply chain management: “I’ve interviewed plenty of people who can talk about algorithms… but when I ask them how they’d handle a situation where the AI makes the wrong call and a customer is waiting on a shipment, they freeze. The AI isn’t perfect. Somebody has to step in, make the call, and then figure out how to improve the system.”
Scott David Stewart, an attorney at Arizona Law Group, points out that in high-stakes fields, this gap is ethical, not just technical. “AI can help with intake… but only if someone knows how to ask the right questions and when not to rely on automation. That judgment does not come from software. It comes from experience, ethics, and context.”
The Strategy: Build Talent, Don’t Buy It
Because “unicorns” who possess both deep domain expertise and high-level AI literacy are so rare, many successful leaders have stopped looking for them. Instead, they are building them.
Michael Podolsky, CEO of PissedConsumer.com, successfully pivoted to an internal approach: “We set up an employee-driven AI training program where staff choose and test tools… This builds practical skills and creates a bench of managers who can effectively oversee AI initiatives.”
Bell Chen, Founder of Superdirector, suggests changing hiring criteria entirely: “I don’t look for perfect resumes anymore. I look for curiosity, for people who aren’t afraid to try something new… The AI landscape changes every week.”
Mike Wislinsky, Owner of Denver Floor Coatings, found that domain expertise trumps tech skills every time. “Our scheduler had never touched AI software before, but she understood our customer flow cold. Gave her two weeks of YouTube tutorials and vendor training—now she’s running predictive models that cut our no-show rate by 18%.”
The Counterpoint: When Software Is the Hard Part
While the majority pointed to talent, a distinct minority found software to be the hurdle…specifically in industries requiring hyper-specialized or “white glove” service.
Joseph Agresta, President of Benzel-Busch, argues that for luxury markets, generic AI is a liability. “We tested four different AI platforms… Three of them treated a $180,000 AMG GT the same way they’d handle a used Civic. Generic AI tools are everywhere, but they’re built for scale, not for businesses where every transaction matters individually… If you’re in a relationship-driven business, plan to spend 70% of your AI budget finding or customizing the right software.”
Mike Wislinsky also noted that while his people adapted, the software search itself took months because “most tools promised features we’d never use while missing basic stuff.”
How Webuters Bridges the Gap
The consensus is clear: the gap between “buying AI” and “getting results” is people. This is where Webuters steps in. We understand that AI implementation is not a plug-and-play software installation; it is an operational overhaul that requires the very “translators” and “architects” that are so hard to hire.
Webuters helps organizations solve the “People vs. Software” dilemma by providing the missing expertise:
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Custom AI Strategy & Governance: We don’t just deploy models; we design the guardrails, ethical frameworks, and “human-in-the-loop” workflows that experts like Nate Nead and Edward Tian identify as critical.
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Domain-Specific Customization: For businesses like Benzel-Busch that cannot use generic tools, we build custom AI solutions tailored to specific industry nuances, ensuring the software fits the business, not the other way around.
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Workforce Enablement: We help bridge the “Translator Gap” by designing systems that are intuitive for your domain experts, allowing your existing team to leverage high-level AI without needing to become data scientists.
Bottom Line
As we move through 2026, the barrier to entry for AI is no longer technology…it is operational maturity. As Niclas Schlopsna of spectup summarized, “Leaders who invest early in the right people build durable advantage, while those chasing tools alone end up with expensive experiments and very little impact.”
The software is ready. The question is: Is your team? If the answer is “no,” the solution likely isn’t another SaaS subscription…it’s an investment in training, culture, and the right strategic partners.
Don’t let a lack of operational maturity stall your innovation. At Webuters, we specialize in bridging the gap between powerful software and empowered teams. We provide the strategy, governance, and technical expertise needed to ensure your AI investment delivers real ROI, not just new subscriptions.
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