Future-Proofing Your KM Initiatives

For their part, organizations seeking quality AI workers are experiencing a shortage of them. In a survey conducted by the Manpower Group, AI skills for the first time surpassed engineering and IT skills as being the most difficult for employers to find globally (manpowergroup.com/en/news-releases/news/ global-talent-shortage-reaches-turning-point-as-ai-skills-claimtop-spot). As a result, employers are building literacy within their workforces. Moreover, they are recognizing that they may need to hire inexperienced individuals who have potential and train them. 

Companies that rely on KM systems will need to bolster their AI staffing as they move forward with their digital transformations. According to Gartner, “by 2029, at least 50% of knowledge workers will develop new skills to work with, govern or create AI agents on demand for complex tasks” (gartner. com/en/newsroom/press-releases/2025-08-26-gartner-predicts-40-percent-of-enterprise-apps-will-feature-task-specific-ai-agents-by-2026-up-from-less-than-5-percent-in-2025). In a survey of HR leaders conducted in December 2025, Gartner found that 40% of the respondents had already eliminated outdated roles, and, of those, half had redesigned team structures to be more cross functional and agile. According to a May 2026 Gartner survey, beginning in 2028, AI will create more jobs than it replaces, but it will be a disruptive process.

The DeWinter Group, a staffing and recruiting firm for technology, finance, and accounting professionals, cited a Rise LinkedIn post (linkedin.com/pulse/ai-talent-salary-hiring-report-2026-riseworks-5abpf) saying that AI and machine learning hiring grew 88% from 2025 to 2026.

DeWinter reports that these are among the related skills employers are seeking (dewintergroup.com/blog/5-high-demand-ai-roles-shaping-the-tech-job-market-in-2026):

♦ MLOps/AI infrastructure engineers for model deployment, CI/CD pipelines for code building and testing, and cloud scalability

♦ AI product engineers to integrate foundation models and vector databases into customer-facing software

♦ AI security and governance specialists for AI risk management, regulatory compliance, data privacy, and vulnerability testing

♦ Data and AI platform engineers to build data pipelines and storage architectures to feed AI models

♦ AI solutions architects to bridge business strategy and AI execution, design scalable enterprise systems, and evaluate vendor technology

Although they may not be labeled as such, AI workers also include those subject matter experts who review AI-generated content and validate answers. 

Key elements for future-proofing KM initiatives are governance, providing searchable and actionable content, and finding highly qualified personnel. This list is easy to write, but difficult to execute. To be sure, the age of agentic AI will be a challenging one for KM.