By George Denlinger, President of Technology Talent Solutions, Robert Half
Tech and IT teams aren’t short on work in 2026. As technology continues to advance at a rapid pace, teams across organizations of all sizes are navigating growing complexity alongside heightened demands from the business. Their priorities include many asks from stakeholders—protect systems and data, modernize aging systems, move AI beyond pilots and strengthen data foundations—with the expectation that teams deliver as quickly as possible.
However, many priority initiatives, especially those involving security, data and AI, now overlap, and most teams lack the bench strength and expertise to keep pace. Research for Robert Half’s Demand for Skilled Talent report found only 7% of technology leaders across organizations of all sizes believe their teams have sufficient headcount and skills to achieve strategic priorities this year.
A deeper dive into our research shows where tech and IT teams at small and midsize companies (under 1,000 employees) and large enterprises (1,000+ employees) are focusing their efforts this year, and which skills gaps are creating challenges. Here’s are the key insights, and what tech leaders and their teams can do to maintain project momentum in 2026.
Tech talent shortages: How to strengthen your skills bench
Shared priorities, concentrated talent demand
Technology leaders across company sizes share nearly the same strategic priorities for early 2026. This overlap is significant because it concentrates demand for the same hard-to-find skill sets in an already tight labor market. When many organizations are pushing forward on security, AI integration and data initiatives at the same time, competition intensifies for talent in the same domains, including AI, data engineering, cloud operations, IT governance and compliance.
Learn more about in-demand roles and hiring trends in technology in early 2026.
Tech priorities for 2026 by company size
SMBs:
Security of IT systems and information
Data initiatives
AI integration
Software engineering and development
AI governance and ethics strategy
Large enterprises:
Security of IT systems and information
AI integration
Data initiatives
Software engineering and development
AI governance and ethics strategy
Source: Robert Half survey of more than 350 technology leaders at small and midsize companies (under 1,000 employees) and large enterprises (1,000+ employees) in the U.S.
© 2026 Robert Half Inc. An EOE M/F/D/V.
SMBs seek more versatility than specialization
SMBs have a tall agenda for the year with security, data initiatives and AI at the top of their list. However, a common situation at SMBs is that the team that builds the technology for the business is often the same team that maintains, supports and modernizes it.
The most evident skills gap SMB leaders report in their departments is IT operations and infrastructure support. That shortage makes it harder to keep systems stable as cloud work expands, integrations multiply and new tools like AI become part of daily workflows.
In SMBs, increasing tech team bench strength is more about expanding coverage and versatility than adding deep specialization in multiple areas. Tech leaders in these organizations face a constant balancing act. They must keep vital upgrades moving forward while protecting core services and preventing their team from getting mired in reactive work.
Large enterprises need skilled talent for AI integration
Our research shows large enterprises place AI and ML integration slightly higher on their list of priorities for 2026 than SMBs. AI and ML is also the most evident skills gap in tech departments at large enterprises (52%), compared with those at SMBs (36%).
AI integration is not a stand-alone project and scaling AI consistently across an enterprise environment is a complex undertaking. Once pilots conclude, the work expands quickly from building a model to building the full system around it . Here’s what changes:
Integrations expand across identity, access and data pathways, which raises risk considerations and increases coordination across teams.
Data readiness sets the pace for what can move into production and perform reliably.
Governance expectations increase as AI use grows, especially around approved tools, access controls, auditability and data handling.
Ongoing operational support becomes essential because AI systems require monitoring, retraining, incident response and ongoing performance management.
This is why large enterprise tech teams tasked with delivering AI initiatives need to have depth across integration, data, governance and operations—not just a few specialists who can build a model.
See these tips for creating an AI readiness plan.
Tech and IT skills gaps are widespread and becoming harder to ignore
Both SMB and large enterprise tech leaders are dealing with skills gaps, but Robert Half’s research shows the latter group is feeling it more. Large enterprise tech leaders are more likely than SMB leaders to report skills gaps in their departments and to say the impact of those gaps is more evident than a year ago.
Tech skills gaps by company size
64% of SMB and 79% of large enterprise tech leaders report a skills gap in their department.
Where skills gaps are the most evident:
AI and machine learning: SMBs 36%, Large enterprises 52%
IT operations and infrastructure: SMBs 40%, Large enterprises 32%
IT governance and compliance: SMBs 27%, Large enterprises 24%
Cloud architecture and operations: SMBs 21%, Large enterprises 26%
Data engineering and analytics: SMBs 21%, Large enterprises 24%
Source: Robert Half survey of more than 350 technology leaders at small and midsize companies (under 1,000 employees) and large enterprises (1,000+ employees) in the U.S.
© 2026 Robert Half Inc. An EOE M/F/D/V.
When skills gaps persist and grow, tech leaders must start making trade-offs such as:
Delaying work
Narrowing project scope
Accepting more risk
Giving more work to key contributors
Trying to hire talent to help close critical skills gaps is another pressure point. Large enterprise (69%) and SMB (60%) leaders say hiring is more challenging than a year ago. In other words, many organizations are trying to do more with already-stretched teams in a market where skilled talent is increasingly hard to find.
What SMB tech leaders can do next
Tech leaders at SMBs are often operating with fewer specialists and broader ownership across multiple priorities and types of work. These strategies can help keep key initiatives moving forward while reducing the risk of disruption or stall-outs:
Strengthen infrastructure and support. Address the IT operations and infrastructure support skills gap proactively to improve stability and reduce reactive work. Provide practical upskilling opportunities so more team members can take on support, modernization and basic governance tasks and help share the load across the team.
Bring in specialized, temporary talent as needed. Engage contract professionals and consultants when projects require deeper expertise or added bandwidth, allowing your core team to stay focused—and even pick up new skills and approaches through proximity.
Choose AI use cases that enhance productivity. Focus first on applications that can improve existing workflows or strengthen reliability. Expand only when data quality, integration and governance basics are in place, so scaling doesn’t create avoidable risk.
What large enterprise tech leaders can do next
Tech leaders in large enterprises are dealing with complex environments—multiple business units, a larger IT footprint, more integration points, and more potential points of failure for security, governance and compliance. With AI integration high on the list of priorities for 2026, measures like these can help tech leaders advance these projects more confidently while working to bridge skills gaps:
Staff AI integration efforts like a production capability, not an innovation project. Identify the roles required to integrate and support AI in production, and then build depth in those positions through targeted hiring, upskilling and the use of resources like highly skilled contract talent.
Invest in the systems around AI. AI performance and reliability depend on data readiness, integration and operational support. Focus on the work that connects AI to workflows and business systems, where many teams experience slowdowns.
Make governance part of delivery. Governance is a requirement for scaling AI effectively. Define responsible AI use in practical terms, and then embed standards for approved tools, access and data handling into how teams work so expectations are consistent as adoption expands.
Talent strategy: Another priority for 2026
From AI to security to data, most technology leaders have multiple, complex and interconnected priorities they need to achieve this year, all while keeping operational systems running smoothly. But without a robust bench of skilled talent to tap, they face making constant trade-offs that hinder progress and risk project success.
To develop the necessary bench strength, leaders should consider talent strategy as a tech priority in its own right. Whether they’re leading tech and IT teams in a large enterprise or SMB, they can use targeted upskilling to build internal capability where skills gaps exist or are most likely to emerge. And with a flexible staffing approach, they can bring in specialized expertise—from contract talent to consultants—when and for as long as they need it.
