Cloud and artificial intelligence are no longer experimental technologies reserved for innovation teams. Businesses across industries are using cloud platforms, data engineering, machine learning, automation, and generative AI to modernize operations and improve how they serve customers.
But there is a major challenge behind this transformation: finding people with the right skills to build and manage these technologies.
For many organizations, the cloud and AI skills gap is slowing projects, increasing pressure on existing teams, and making technology modernization more difficult than expected. The solution is not always to hire more permanent employees. Increasingly, businesses are looking at flexible talent models that allow them to access specialized skills when and where they are needed.
Why Cloud and AI Talent Is Difficult to Find
Cloud and AI projects often require highly specialized capabilities. A single initiative may need cloud engineers, DevOps professionals, data engineers, machine learning specialists, security experts, software developers, and technical project leaders.
The challenge becomes even greater when companies need experience across specific technology environments.
Cloud teams may need expertise in AWS, Azure, or Google Cloud, along with Infrastructure as Code, CI/CD, containers, Kubernetes, security, and cloud cost optimization. AI initiatives may require data pipelines, machine learning models, generative AI integration, data governance, predictive analytics, and MLOps.
Traditional hiring processes can take weeks or months to identify and onboard these specialists. Meanwhile, project deadlines continue moving forward.
The Cost of Waiting
A skills shortage affects more than recruitment.
When a critical role remains open, existing employees may have to take on additional responsibilities. Projects can become dependent on a small number of specialists. Product releases may slow down, and modernization initiatives can remain stuck between planning and implementation.
This is particularly challenging for organizations that need to respond quickly to changing business requirements.
Instead of treating every skills gap as a permanent hiring problem, companies can separate their needs into two categories: capabilities they need permanently and specialized expertise they need for a specific period.
That distinction can create a more flexible workforce strategy.
Flexible Talent Can Close Short-Term Gaps
On-demand talent allows businesses to bring pre-vetted professionals into projects for a defined period. This can be useful when an organization is experiencing a project surge, launching a cloud migration, filling a temporary skills gap, or requiring a specialist who is not needed permanently.
Team extension provides another option. Rather than outsourcing an entire project, organizations can add specialists directly to their existing engineering teams.
For example, a company with a strong application development team may have limited DevOps capacity. Instead of delaying a modernization initiative until a permanent DevOps engineer is hired, the organization can add an experienced specialist who works within the existing processes, tools, and management structure.
The result is additional capacity without giving up control of the project.
Hiring for Skills and Team Fit
Speed matters, but speed alone is not enough.
A strong cloud or AI hiring strategy should consider technical capability, communication, working style, and the candidate’s ability to operate within the existing team.
Structured screening and skills assessment can help organizations identify qualified professionals faster while maintaining quality.
This is especially important for specialized technology roles. A candidate may have experience with a particular cloud platform, but the organization also needs to understand how that experience applies to its architecture, development process, security requirements, and business objectives.
Build a Workforce Model Around the Project
There is no single hiring model that works for every technology initiative.
Some roles should be permanent because they represent long-term organizational capabilities. Others may be better suited to contract staffing or team extension.
A cloud migration, for example, may require additional specialists during the implementation period but fewer resources once the migration is complete. Similarly, an AI initiative may initially require data engineering and machine learning expertise before transitioning to an ongoing product support model.
The key is to match the engagement model to the business requirement.
From Skills Gap to Technology Opportunity
The cloud and AI skills gap is a genuine challenge, but it does not have to stop modernization.
Companies can combine permanent hiring with flexible staffing, team extension, and technology consulting to create a workforce that adapts to changing priorities.
The Globalwise brings staffing and technology consulting together under one roof, supporting organizations that need both the right people and the right technology capabilities. Its services include On-Demand Talent and Team Extension Services, while its technology capabilities include Cloud Engineering & DevOps and Data & AI Solutions.
The objective is simple: help businesses move from needing specialized skills to putting those skills to work.
When workforce strategy and technology strategy are planned together, organizations can respond faster, close critical skills gaps, and keep important initiatives moving forward.