More than three-quarters of employers are still struggling to find the skilled talent they need in 2026. According to recent global workforce research, tech and data-related roles represent the most persistent gaps across industries.

At the same time, many organizations are more cautious about hiring. Only 35% of tech employers plan to add headcount this quarter, placing increased pressure on learning and development (L&D) and transformational leaders to fulfill critical capability needs internally.

This reality creates a dual imperative: understanding which digital capabilities will be most essential in the near future and determining when it makes sense to hire externally versus investing in upskilling existing employees. To overcome ongoing talent scarcity, 32% of organizations are actively upskilling or reskilling their workforce, while 26% are targeting new talent pools. The most effective organizations are pursuing both strategies simultaneously, balancing targeted hiring with intentional, outcome-driven capability development.

Deciding When to Hire and When to Upskill

The decision to hire or upskill is rarely straightforward and often depends on how specialized a skill is and how urgently the capability is needed.

Highly specialized skills that require deep expertise, such as cloud architecture or data engineering, are often best addressed with new hires. Likewise, when organizations need these capabilities immediately to support mission-critical initiatives, external talent may be the most practical option.

By contrast, skills that build on existing capabilities or support longer-term transformation efforts might be best built in house. Upskilling is particularly effective when employees can apply what they learn to real projects and when managers actively support learning as part of everyday work.

With that context in mind, several digital capability areas stand out as particularly important in 2026.

Data Expertise Continues to Anchor Digital Transformation

Data engineering and analytics remain central to nearly every digital initiative, from automation to artificial intelligence adoption. Organizations are looking to hire professionals who can design reliable data pipelines, manage cloud-based analytics platforms and ensure data quality and governance.

When gaps exist in these capabilities, the impact is often immediate and far-reaching. As a result, many organizations choose to hire for advanced data engineering roles, particularly when they require experience with scale, security or regulatory compliance.

At the same time, there is significant opportunity for internal development. Employees with adjacent technical skills can often be trained to support data platforms, analytics enablement and operational reporting, especially when learning is tied directly to business use cases and transformation goals.

Cloud Skills Remain Foundational, Not Optional

Cloud engineering ranks among the most in-demand tech capabilities entering 2026. While cloud adoption is no longer new, the complexity of modern environments has increased significantly. Organizations are seeking professionals who can design, manage and optimize cloud architectures across hybrid and multi-cloud ecosystems.

This demand extends well beyond infrastructure teams. Application developers, security professionals and data specialists are increasingly expected to understand how cloud platforms operate within a company’s broader technology ecosystem. As a result, cloud literacy is becoming a baseline expectation across many IT roles, while deep engineering expertise remains a specialization that is difficult to develop quickly.

For learning leaders, this distinction is critical. Advanced cloud engineering roles often require years of hands-on experience and may be best addressed through selective hiring. However, cloud-adjacent capabilities such as operations, automation and cost optimization can be developed with internal training programs, especially when learners have the opportunity to put those skills to work in real projects.

AI Curiosity Is Becoming a Core Capability

In 2026, organizations increasingly expect IT professionals to understand how artificial intelligence (AI) tools fit into workflows and infrastructure, even if they’re not AI specialists.

In many cases, the most valuable skill is not deep technical mastery but applied understanding. Employers are looking for individuals who demonstrate curiosity about AI, an ability to experiment with proofs of concept and a willingness to explore how emerging tools can solve real business problems. This mindset is particularly important as AI capabilities continue to evolve faster than formal curricula.

From a development perspective, AI presents a strong case for upskilling. Structured learning experiences that emphasize experimentation, agent development and practical use cases —particularly those that embed AI into day-to-day workflows rather than treating it as a standalone technology— can help employees build confidence and relevance quickly.

Modernization Creates New Skill Pressures

As organizations continue to modernize legacy systems, new capability gaps are emerging. Many experienced technologists with deep legacy expertise are retiring or transitioning away from older platforms, while newer professionals may lack exposure or interest in maintaining them.

This gap cannot be solved through hiring alone. In many cases, the most effective approach is to upskill internal employees who already understand the organization’s platforms and processes and can be trained to support modernization initiatives. These roles often benefit more from experiential learning, mentorship and project-based training than from traditional classroom instruction.

Designing Upskilling Programs That Deliver Results

Successful IT upskilling programs prioritize practical application over theory. Short, intensive learning experiences, such as boot camps or modular programs, often produce stronger outcomes than broad, generalized training because they focus on the specific tools and challenges employees will encounter on the job.

Equally important is creating an environment that encourages experimentation. Learners need safe spaces to test ideas, build prototypes and learn from failure. Communities of practice and collaborative learning models can help reinforce skills while strengthening team connections, particularly in hybrid and remote environments.

While experience remains critical to performance, curiosity and adaptability are increasingly valuable traits. Organizations that recognize and reward these qualities are better positioned to keep pace with rapid technological change.

At Experis, we increasingly see organizations succeed when they align talent strategies with transformation roadmaps — combining targeted hiring, structured upskilling and AI-enabled tools to accelerate business impact without over-reliance on any single approach.

Ultimately, organizations that treat hiring and upskilling as complementary strategies —supported by strong data, applied AI and continuous learning cultures— will be best equipped to close critical capability gaps and build workforces ready to deliver sustained digital transformation.

Gain frameworks to design AI learning programs that drive workforce readiness and AI adoption with this certificate.