What does "digitally transformed profession" mean for M&E?

The New Economy Skills report concludes that 68% of digital skills will be transformed by AI, compared to only 35% of more human-centric skills. Evaluation sits precisely at this intersection. For M&E professionals, this means data collection, cleaning, coding, and descriptive analysis are increasingly AI-assisted or AI-led. Evaluators are shifting from manual processing to oversight, interpretation, and judgment. The value of an evaluator is less about running analyses and more about asking the right questions of data and AI systems. M&E roles are evolving from "data handler" to "evidence interpreter and decision advisor."

• ▹ Routine data tasks become AI-assisted or automated.
• ▹ Evaluators focus on oversight, interpretation, judgment.
• ▹ Asking the right questions replaces running analyses.
• ▹ Resistance to digital transformation risks low-value task confinement.

Why is technology literacy now a core evaluation skill?

The report highlights technology literacy as the most widely demanded digital skill, appearing in 34% of all job postings—far more than advanced AI or programming skills. For M&E professionals, this is a critical signal. Technology literacy in M&E now includes understanding AI-assisted survey tools, dashboards, and analytics platforms; knowing how data flows from collection to decision; and being able to critically assess AI-generated insights. This does not mean evaluators must code. It means they must work confidently with digital systems, supervise AI outputs, and explain AI-assisted findings to non-technical stakeholders.

• ▹ Technology literacy appears in 34% of job postings.
• ▹ Understand AI-assisted tools and data flows.
• ▹ Critically assess AI-generated insights.
• ▹ Supervise AI outputs and explain findings to stakeholders.

How will advanced AI skills be distributed in M&E?

The report shows that AI and big data skills appear in only 2% of job postings, mostly in highly technical sectors. However, this does not mean AI is irrelevant to M&E professionals. Instead, it signals a division of labour: a small group builds AI systems, while a much larger group uses, supervises, and applies them. For M&E professionals, the implication is clear: you are unlikely to be hired as an AI engineer, but you are increasingly expected to work alongside AI-powered systems. Examples include AI-assisted qualitative coding, automated data quality checks, and predictive risk analysis.

• ▹ Only 2% of postings require advanced AI engineering.
• ▹ Most evaluators will use, supervise, and apply AI systems.
• ▹ AI-assisted coding, quality checks, and prediction are already common.
• ▹ Understanding AI transformation matters more than deep technical expertise.

Why do human-centric skills become more valuable?

A key insight from the report is that human-centric skills—judgment, ethics, systems thinking, communication—are far less exposed to AI transformation than digital skills. For M&E, this is good news. Evaluation quality depends on contextual understanding, ethical judgment, stakeholder engagement, and sense-making across complex systems. AI can process data faster and detect patterns at scale, but it cannot decide what evidence is credible, balance political and ethical considerations, or translate findings into meaningful action. The future evaluator is a hybrid professional: digitally fluent, analytically rigorous, and human-centred.

• ▹ Judgment, ethics, and systems thinking resist automation.
• ▹ Contextual understanding remains uniquely human.
• ▹ AI cannot balance political, ethical, and cultural factors.
• ▹ Future evaluators combine digital fluency with human-centred rigor.

How does skills-based credentialing affect M&E careers?

One of the report's strongest calls to action is the need for portable, practical, skills-based credentials. This has major implications for M&E careers. Employers now trust demonstrated skills more than formal degrees. Micro-credentials, applied projects, and tool-based certifications gain importance. Continuous learning replaces static qualifications. For M&E professionals, this means your CV must show what you can do with data and AI, not just years of experience. Demonstrable competence with dashboards, AI-assisted analysis, and digital surveys becomes a hiring signal. Evaluation credibility increasingly depends on applied digital competence.

• ▹ Demonstrated skills now outweigh formal degrees.
• ▹ Micro-credentials and applied projects gain importance.
• ▹ CVs must show applied data and AI competence.
• ▹ Continuous learning replaces static qualifications.

What is the professional risk of the skills gap?

The report notes that only 20% of business leaders believe their workforce is proficient in AI and data skills, despite soaring demand. In the M&E sector, this creates clear risks. Evaluators without digital fluency may be excluded from higher-value assignments. Organisations may outsource advanced analysis rather than build internal M&E capacity. Junior professionals with stronger digital skills may leapfrog senior roles. This is not about age or experience—it is about adaptation speed. Digital skills gaps are widening faster than systems can respond. M&E professionals who wait for formal training to catch up may fall behind.

• ▹ Only 20% of leaders rate workforce AI-proficient.
• ▹ Digital fluency determines access to high-value assignments.
• ▹ Junior staff with digital skills may leapfrog senior roles.
• ▹ Adaptation speed matters more than years of experience.

What does the future M&E professional look like?

Taken together, the report's conclusions point to a clear trajectory for Monitoring & Evaluation. The future M&E professional will oversee AI-assisted evaluation processes, combine digital evidence with contextual judgment, translate complex data into decision-ready insights, and continuously update skills through applied learning. The future M&E system will use more real-time and predictive data, depend on AI-supported analysis, demand higher standards of evaluation quality and transparency, and reward professionals who bridge technology and impact. The New Economy Skills report does not signal the end of M&E—it signals its evolution.

• ▹ Oversee AI-assisted evaluation processes.
• ▹ Combine digital evidence with contextual judgment.
• ▹ Translate data into decision-ready insights.
• ▹ Bridge technology and impact through continuous learning.

Frequently asked questions about New Economy Skills and M&E

Authoritative resources and further reading