AI Rewrites the Rules of IT Talent

Artificial intelligence (AI) is reshaping every corner of the enterprise, but nowhere is the change more consequential than in hiring, staffing, and organizational design. The last two years have proven that AI’s impact on the workforce is no longer hypothetical. Large scale layoffs driven by automation began in the second half of 2025 and will continue through 2026 as organizations optimize workflows and streamline repetitive tasks.

Yet the picture is far more nuanced within IT. Contrary to early predictions of sweeping displacement, IT careers are stabilizing. According to a new survey on unstructured data management we recently conducted, fewer than 25 percent of IT organizations expect to downsize in the coming year, while fully half plan to add staff. The challenge ahead is not a reduction in the importance of IT talent. It is a dramatic reconfiguration of what IT employees need to know.

The Shifting Landscape of IT Talent

AI is changing the fundamentals of how digital organizations operate. Infrastructure, data platforms, cloud services, cybersecurity, and automation are becoming increasingly interconnected through AI‑enabled workflows. As a result, the composition of IT teams is being recalibrated with new priorities. When surveyed, IT leaders identified a clear top need for the year ahead.

• Most (62%) require professionals who understand AI data management, up from 43% in 2024. This signals a profound shift in what it means to build and operate modern data environments.
• Cloud storage expertise follows at 60%. The rise of multi‑cloud architectures and AI training pipelines has placed immense pressure on storage performance, elasticity, placement strategies, and cost transparency.
• Data security and compliance skills remain essential, with 59% of leaders stating that their incoming hires must be prepared to manage sensitive data across AI‑infused workflows.

These are not incremental adjustments. They are structural changes to the core competencies that define the next generation of IT professionals. Additional gaps emerge in data analysis techniques (52%) and financial operations knowledge at 43%. Vendor analysis and procurement skills register at 35%, while 33% of leaders say their teams lack experience collaborating with departments to develop data services strategies.

Collectively, these gaps illustrate a broad reality. AI is turning the enterprise IT organization into a data‑driven function where operational success requires fluency across governance, analytics, architecture, and economics.

See also: The Cost of Doing Nothing About Your Data: Why IT Is Drowning

Why AI Data Management Dominates

AI data management has quickly become the anchor skill for modern IT organizations. This domain spans an expanding set of data services that underpin effective AI operations:

• It includes data classification, which ensures that information is stored, accessed, and fed into models based on sensitivity and business context.
• It includes metadata enrichment, which improves discoverability and enhances model quality.
• It extends across data ingestion processes, the governance frameworks that define responsible use, and the security controls that protect data pipelines from internal misuse and external attack.
• It also encompasses cost management, a discipline that is becoming increasingly critical as AI workloads require substantial infrastructure investment.
• Data classification and intelligent AI ingestion have moved from tasks handled by data scientists to IT-enabled processes. The reason why is because AI needs unstructured data, typically 90% of an organization’s footprint, which is untenable for data scientists to manually classify. IT’s role here has materialized to automate unstructured data management processes and bring in departmental stakeholders as needed.

AI data management and governance is a set of strategic capabilities rather than a technical subset of data storage or analytics. IT leaders recognize that the quality, governance, and security of data are now direct determinants of model accuracy, operational reliability, and regulatory alignment. Even sophisticated AI tools underperform or introduce unacceptable risk. Unstructured data management is becoming a key capability in the age of AI especially because of the huge volume of unstructured data that requires IT operationalization.  This is why demand for these skills is growing faster than any other segment of IT hiring. 

A New Class of IT Leaders

Beyond altering skill requirements, AI is redefining leadership structures. According to the survey, many (55%) of IT organizations will reorganize or reskill their teams for AI, while more than half expect to hire new IT leadership for AI. These new leaders will assess how automation affects job roles, redesign organizational models, and define the workforce strategy for AI‑enabled operations.

For example, at Thoughtworks, Lead AI Infrastructure Engineers design and maintain high‑performance, scalable, and resilient infrastructure for modern AI workloads. They are responsible for developing and managing advanced inference systems across cloud and on‑premises environments and ensuring AI systems meet demanding requirements for throughput, latency, availability, and compliance.

Early career roles, on the other hand, are being structurally redesigned. In some cases, IT is collapsing traditional entry‑level tiers and embedding AI directly into core platforms. This reduces the need for large junior teams while raising baseline expectations for those who remain. The new entry point into IT increasingly requires people who can validate AI outputs, manage exceptions, and oversee AI decision making.  Think of their role as the “human in the loop” for overseeing AI-based execution.

Recent research from the Indeed Hiring Lab reinforces this trend. Job postings requiring at least five years of experience rose from 37% to 42% between 2022 and 2025. Employers want seasoned technical staff who can navigate hybrid cloud environments, manage AI‑driven infrastructure, interpret model outputs, and ensure that systems operate within governance and compliance boundaries.

The Rise of the AI-Native IT Professional

AI is redefining what it means to be an IT practitioner. Professionals must learn to work alongside AI tools across coding, monitoring, quality assurance, support, and data analysis. They must reskill for emerging opportunities in AI engineering, which now encompasses model integration, workflow design, data pipeline optimization, and continuous model monitoring. The traditional division between infrastructure and application roles is blurring as AI becomes embedded in every layer of the technology stack.

This shift demands an AI‑native mindset. IT employees will need to understand the relationships between data, policy, infrastructure, and automation. For example, a data storage systems administrator may need to ensure that AI‑driven provisioning tools comply with access policies, respect data lineage rules, and operate efficiently across hybrid cloud environments. They contribute to cross‑functional design decisions and collaborate closely with cybersecurity, finance, operations, and line of business leaders.

The value of IT talent will increasingly be measured by how effectively workers can align AI capabilities with business outcomes. AI is forcing IT organizations to rethink their operating models, revise their talent strategies, and update their workforce planning assumptions. IT skills are also pivoting to manage unstructured data for successful AI outcomes. The mandate for IT executives is clear. This is not a moment for incremental change. It is a moment to rebuild the talent foundation for a new era of digital operations.