The loudest story in enterprise AI today is capability: bigger models, agentic workflows, and the march toward systems that match a person. The subtler story, and the one that shows up on balance sheets, is the distance between what those systems are sold to do and what they actually do once a company tries to run them. That gap doesn't close itself. Companies fill it with people, tooling, and services, which means the real cost of adopting AI is the software line item plus the human scaffolding required to make it deliver. Understanding that gap and who has visibility into it is becoming one of the most consequential questions in workforce strategy.
Working toward an answer is Wael Sabra, the founder and CEO of global talent platform SiiRA, which connects fast-growing companies with vetted specialists around the world. His vantage point sits at the seam between AI's promise and its implementation, since the calls his company fields tend to start where the technology stops. This perspective gives him a concrete read on what organizations are actually spending to turn AI pilots into outcomes.
"The total cost of ownership of AI is skyrocketing because organizations are paying both for the technology and for the people to figure out how it's going to work," Sabra says. In his view, that double payment is the predictable result of a promise that keeps arriving ahead of the product.
The vendor demo is not the deployment
Sabra's first observation is one every experienced buyer recognizes from the pre-AI era. The pitch and the rollout are different animals. "In every technology, in every software, if you've been on a vendor presentation, they come in and promise you the world, and then you go in and implement it and the implementation is different," he says. AI has only amplified that pattern, because the promises are now framed around human-level intelligence while the day-to-day reality lags well behind.
Part of why the gap stays underreported, in Sabra's view, is that the messaging is doing double duty. The headline claims aren't aimed solely at the companies deploying the technology. "You've got to remember, those AI labs are also appealing to investors with their news. They're not only appealing to companies and buyers," he notes. The buyer, he explains, is already sold and eager to use the technology, which makes the inflated framing partly a capital-markets performance.
That leaves practitioners to absorb the difference, and Sabra frames his own company's role as having a direct line of sight into it. "We really have a front-seat view into this gap that exists between what could be and what is. We're able to see the skills and the resources that are needed to bridge those gaps." As he sees it, the bridge is made of people.
Filling the gap means hiring, not just licensing
The practical consequence is that adopting AI extends beyond a procurement task to a hiring event. Companies discover that the technology alone doesn't close the loop, so they staff around it. "You may need developers and a service ecosystem to fulfill the technology promise. You may need operations people, you may need all of that," Sabra says. Each of those hires is a cost the original software business case rarely accounted for.
This is the mechanism behind the skyrocketing total cost of ownership he describes. Instead of choosing between spending on tools or spending on talent, organizations are doing both at once, because the tools don't yet perform without the talent. "Companies have not really started to realize the full potential of this tech, or any potential out of that technology," he asserts, even as their combined spending climbs.
Remote work is where the specialized talent lives
Because the skills that close the gap are specific and scarce, companies increasingly must look past their own geography to find them. This pulls Sabra toward a structural caution about cross-border employment. The demand is global, but the infrastructure underneath it isn't finished. "Benefits, insurance, these types of things are not standardized across companies, let alone something like calculating the appropriate tax rate."
He casts this as an existential matter for distributed work rather than a back-office nuisance. "Work is no longer a place, it's a thing we do," Sabra explains, noting that output now routinely lands far from where the work happens. History has little precedent for this arrangement. If the compliance and payments layer fails to keep pace, his worry is that companies may either back into becoming reluctant experts in remote-work administration or abandon distributed hiring altogether, cutting themselves off from the very specialists the AI gap requires.
The durable advantage is the judgment AI can't copy
All of this reframes what companies should value in the people they hire. If AI has commoditized the production of strategy documents and first drafts, the scarce input becomes the human judgment layered on top, not the output the machine can generate on its own. Sabra points to an idea circulating in the technology space. "A lot of tech leaders have been saying that the future belongs to the neurodivergent because they can think in ways that most of us can't, and AI has been trained on 'most of us'," he says, casting genuinely outside perspectives as the thing models struggle to reproduce.
His conclusion is a useful correction to the anxiety that AI erases the value of people. It does the opposite to the part that matters. "AI has leveled the playing field for strategy. I think where the advantage is going to come from is going to be human, and is going to continue to be human for the foreseeable future," Sabra says. The technology closes some gaps and opens others, and the companies that come out ahead will be the ones that invest in the judgment, expertise, and perspectives the models can't manufacture rather than assuming the software made them optional.
