The organizational shift from investing in AI to operationalizing AI is causing significant structural changes in the skills market, transforming roles and recalibrating how employers award premium pay.
In 2026, companies will concentrate around a very different mandate: to operationalize, secure, govern and economically optimize increasing complex digital systems. This applies especially those driven by AI, explained David Foote, chief analyst and research partner for research group Foote Partners, LLC.
As a result, employers are hiring professionals with broad skills for roles that control risk, cost and outcomes, not those who specialize narrowly in a single technology.
As further proof, the Certified Artificial Intelligence Scientist (CAIS) certification (which covers multiple concepts and techniques for building AI ecosystems) recorded a 50% increase in market value during the six months ending October 1, 2025, based on data from Foote Partners IT Skills and Certifications Pay Index. And thousands of people have enrolled in courses for IBM’s recently updated AI Engineering Professional Certificate, which saw an 11% increase in market value.
With that as a backdrop, here's a look at the skill groups and roles that gained (and lost) value during 2025 and are positioned to continue down the same path in 2026.
Skills and Roles Gaining Value
AI Infrastructure
The skills that saw the biggest rise in demand and market value are the ones that allow AI to become core infrastructure- the backbone that powers modern operations.
“Skills that reduce cost, manage model drift, ensure portability across hardware and integrate AI into business workflows without regulatory or reputational risk are going to be rewarded disproportionately in 2026,” Foote said.
For example, the following non-certified skills garnered premiums ranging from 18% to 23% of base pay, and market value increases ranging from 4.8% to 38% between July 1 and October 1, 2025.
- AWS Codebuild
- Artificial Intelligence Ops
- AI Mode Optimization
- Amazon Athena
- Apache Iceberg
- Azure OpenAI
- Computer Vision
- Deep Learning
- Machine Learning
- MLOps
- Neural Networks
- ONNX
- Prompt Engineering
Note that companies will be looking to fill emerging roles that utilize a combination of these skills such as AI platform and operations architects or sustainable AI engineers.
AI Reliability Engineering
Poor data quality is generating hallucinations and biases in AI output. As a result, professionals who can architect, govern and operationalize data at scale will be paid high premiums compared to core AI engineers Foote said.
Specifically, data and AI reliability engineers who can ensure data consistency, schema evolution, real-time availability across hybrid or cloud native architectures are in high demand. Knowledge of Apache Iceberg, for instance, grew in value because of its role in enabling lakehouse architectures, which is critical for analytics and AI pipelines.
Other skills such as Amazon Athena, data integration, data modeling, DataOps and data science, have risen in value because they address the problem of inconsistent data feeding AI models.
Defensible Security
For the first time non-certified security-related skills joined foundational certifications such as the CyberSecurity Forensic Analyst (CSFA) and the GIAC Certified Intrusion Analyst (GCIA) in dominating Foote Partners’ list of top paying skills.
Why is this happening?
Boards and regulators want to see security and risk designed into systems, not just patched on after deployment Foote explained. As a result, the market will reward security and risk architects and other professionals who can model risk, quantify impact, design secure architectures, and translate technical exposure into business language.
Some of the non-certified skills that you need to qualify include security auditing, security architecture, cyber threat intelligence, NIST skills, risk analytics and assessment and knowledge of governance risk management compliance, or GRC. In fact, these hot skills garnered premiums ranging from 18% to 19% of base pay, and market value increases ranging from 12.5 % to 26.7% in the Foote Partners survey.
AI Ops
In 2026, DevOps and DevSecOps will converge with operational intelligence, particularly around AI.
By consolidating these functions, organizations can not only achieve faster delivery, and enhanced security but also systems resilience, operational predictability, cost visibility and control, and automated response to failure.
Professionals who can integrate DevOps pipelines, security automation, AI-driven operations into some coherent control layer are becoming financial risk managers, not just engineers. As Foote pointed out: “That's a very, very different role for them.”
Distributed Systems and Trust Engineering
As the adoption of blockchain and its regulation continues to grow, professionals must increase their skills to keep pace.
Both jobs and premium pay will favor pros who can secure smart contracts and integrate blockchain with enterprise systems to automate processes, enhance supply chain transparency, improve data security, and create more efficient, trusted workflows. In fact, trust in blockchain technology has faced challenges that must be addressed to increase adoption.
So, in 2026, blockchain persists, but only where trust, auditability, and compliance matter.
Skills and Roles Losing Value
Organizations are eliminating roles where work is repeatable, decisions are rules-based and output requires execution but not ownership of the results. Here are some examples:
Report Builders and Basic BI Dashboard Developers
Dashboard creation has been automated and commoditized, driving down demand and pay. The demand has shifted to data modeling, governance and domain-facing analytics as companies hire more analytics engineers, data modelers and domain analytics leads.
SOC Analyst
SIEM/SOAR automation and AI-assisted investigation increasingly handle first-pass
triage, deduping, enrichment, and routing - allowing humans to shift toward threat hunting, detection engineering and incident command.
Manual QA Testers
Test automation and AI-assisted test generation are reducing the need for large manual regression teams. Professionals who adopt a combined approach of hybrid QA, automation and CI/CD stand a better chance of remaining employed and maintaining their market value.