AI changed the staffing equation

The AI talent shortage is not simply about finding people who understand artificial intelligence. It increasingly involves the broader technical ecosystem required to turn AI initiatives into reliable business capabilities: data engineering, machine learning, application development, integration, cloud infrastructure, automation, and governance. ManpowerGroup reports that AI model and application development ranked first globally at 20%, followed by AI literacy at 19%, while traditional IT and data skills ranked seventh at 17%. The challenge is therefore not limited to a handful of research specialists. Organizations need people across the technology stack, and those professionals are being pursued by companies simultaneously trying to modernize their products, operations, and customer experiences.

Hiring cannot move alone

A conventional hiring pipeline works well when talent supply and demand are reasonably balanced. AI skills are changing that assumption. A company can approve a position, publish a job description, screen applicants, conduct interviews, and still discover that the available candidates do not match the technical requirements. Meanwhile, the business initiative continues moving. KORE1's 2026 IT staffing analysis describes the AI shortage as particularly pronounced in applied areas such as MLOps, data engineering, and applied machine learning. The result is a growing disconnect between when a company needs a capability and when its internal recruiting process can realistically provide it. That disconnect is where alternative staffing models become relevant.

Flexibility becomes capacity

A flexible engagement model changes the question from “When can we hire this person?” to “What capability do we need, and how can we add it?” An IT leader might need one specialist to strengthen an existing team, several professionals to accelerate a program, or a multidisciplinary group to support an entire technology initiative. Those needs do not necessarily justify the same employment structure. A dedicated nearshore team can provide sustained capacity while an individual specialist can address a narrower gap. The value of flexibility is precisely that the delivery model can follow the work rather than forcing every technology requirement into a permanent-headcount template.

Nearshore expands the pool

Nearshoring adds another dimension to that flexibility by expanding access to professionals beyond the local hiring market while maintaining close collaboration. Digitech describes its model as combining Latin America nearshore staffing with overlapping U.S. business hours, experienced technology professionals, and multidisciplinary delivery capabilities. That operating model can be particularly relevant when projects require frequent interaction between U.S.-based stakeholders and technical teams. Instead of treating geography as a barrier, organizations can use a nearshore footprint to access additional capacity while maintaining communication rhythms that resemble an integrated extension of the internal organization.

Dedicated means integrated

A dedicated team is different from simply receiving a larger list of contractors. The objective is to create stable technical capacity that understands the organization's environment, priorities, and ways of working. Digitech describes dedicated nearshore teams as multidisciplinary teams working as an extension of the customer's organization during overlapping U.S. business hours. That distinction matters because AI initiatives rarely operate inside a single technical silo. A data engineer may need to coordinate with application developers; an automation specialist may depend on enterprise integrations; an AI capability may require cloud, security, QA, and data expertise around it. A team structure can therefore reduce the coordination burden that emerges when every capability is sourced separately.

Match structure to demand

The strongest argument for nearshore teams is not that every company should replace its internal hiring strategy. It is that workforce models can coexist. A permanent employee may be the right choice for a long-term strategic responsibility. A specialist may be appropriate for a defined technical gap. A dedicated team can make sense when the organization needs several complementary skills over an extended period. Project delivery may be more suitable when the scope and outcome are already defined. Digitech explicitly supports individual specialists, dedicated nearshore teams, project delivery, and hybrid engagement models. Flexibility works when the structure reflects the actual shape of the work.

Certification reduces friction

Access to more people only helps if those people have the right capabilities. Certified professionals can give technology leaders a clearer starting point when evaluating specialized expertise. Digitech states that its professionals are certified across areas including CRM, platform integration, automation, data analytics, and quality assurance. That matters because the staffing challenge is not simply increasing the number of available résumés. It is reducing the time spent separating relevant technical experience from generic claims of expertise. A partner that understands the technology requirement and pre-vets professionals can make the path from need to productive capacity more direct.

Build for changing priorities

AI roadmaps rarely remain static. A project may begin with data preparation, expand into application development, and then require additional automation, QA, or platform expertise. Rigid staffing structures can make those transitions harder because the team was designed around yesterday's requirement rather than tomorrow's. Flexible engagement allows organizations to adjust capacity as priorities evolve. Digitech emphasizes adapting engagement models to different organizational constraints and levels of internal capacity, while maintaining accountability and integration. That adaptability is especially useful when technology leaders are still learning which AI capabilities will create the greatest business value.

Flexibility is not less accountability

Flexible staffing should not mean ambiguous ownership. The opposite is more useful: clear responsibilities with adaptable capacity. Technology leaders should still define objectives, technical expectations, communication practices, delivery responsibilities, and success measures. The difference is that the organization can adjust the amount or composition of external capacity without redesigning its entire workforce every time the roadmap changes. Digitech positions its nearshore model around experienced professionals, multidisciplinary capabilities, overlapping working hours, and an established technology organization supporting each engagement. Flexibility becomes valuable when it creates options without creating uncertainty.

A practical staffing model

For an IT leader facing an AI-related skills gap, the starting point can be simple: identify the capability bottleneck, determine how long it is likely to remain critical, and then select the engagement model around that reality. One specialized role may call for an individual professional. A sustained AI initiative may require a dedicated multidisciplinary team. A defined transformation may be better suited to project delivery. A hybrid approach can combine internal employees with external specialists. The point is not to choose one model permanently; it is to create a workforce structure capable of responding as technical requirements change. When demand moves quickly, the ability to adjust capacity becomes part of technology strategy itself.

“72% of employers” report difficulty finding skilled talent, according to ManpowerGroup's 2026 Talent Shortage Survey.