Why Every M&E Professional Should Pay Attention to McKinsey’s Latest Warning

Picture this: You’ve just spent three days cleaning survey data from a multi-country evaluation. Your eyes are tired from staring at Excel spreadsheets, your coffee has gone cold, and you’re only halfway through coding the qualitative responses. It’s tedious work, but it’s what you were trained to do. It’s what makes you valuable as an M&E professional.

Now imagine an AI agent doing that same work in three hours. With higher accuracy. While you sleep.

This isn’t a dystopian future scenario. According to McKinsey Global Institute’s latest research, “Agents, robots, and us: Skill partnerships in the age of AI,” released in November 2025, this is happening right now.

The report’s most striking finding? 57% of current work hours in the United States could be automated with technology that already exists today. Not technology that might exist by 2030. Technology that’s available right now, in 2026.

For M&E professionals, this should be more than just another headline about AI disruption. This is a wake-up call that demands our immediate attention, because the skills we’ve spent years developing are precisely the ones McKinsey identifies as most exposed to automation.

The Skills Change Index: Understanding What’s at Risk

McKinsey developed something called the Skill Change Index (SCI), a time-weighted measure of automation’s potential impact on each skill used in today’s workforce. Think of it as a disruption forecast for your professional toolkit.

The results are sobering for those of us in the research and evaluation field: Digital and information-processing skills rank highest on the index, meaning they face the greatest potential for disruption by 2030.

Let’s break down what this means in M&E terms:

Skills with High Automation Exposure

Data Collection and Processing: Survey administration, data entry, basic data cleaning, and standardised data processing are increasingly handled by AI agents. These systems can now manage complex survey logic, flag inconsistencies, and even conduct initial quality checks with minimal human intervention.

Literature Reviews and Desk Research: AI tools can now scan thousands of academic papers, grey literature, and evaluation reports in minutes. They can identify themes, extract key findings, and synthesise information across multiple sources. The desk research that used to take weeks now takes hours.

Routine Analysis: Standard statistical procedures, regression analysis, basic econometric modelling, and descriptive statistics are increasingly automated. AI can run multiple model specifications, check assumptions, and even suggest appropriate tests based on data characteristics.

Report Drafting: AI writing assistants can now generate evaluation reports from templates, populate findings sections with data, create standard visualizations, and even draft recommendations based on predefined frameworks. The first draft that used to take days can now be generated in minutes.

Document Formatting and Visualization: Creating charts, tables, infographics, and standardised report layouts, tasks that many M&E professionals spend significant time on, are easily automated.

The uncomfortable truth? If your primary value proposition as an M&E professional centres on your ability to execute these tasks, you’re competing with technology that works 24/7, doesn’t take vacations, and costs a fraction of your salary.

But Here’s What the Headlines Miss: The $2.9 Trillion Partnership Opportunity

Before you start updating your resume for a career change, here’s the critical nuance that most coverage of this research misses: McKinsey isn’t predicting mass unemployment. They’re describing a fundamental reorganization of how work gets done.

The research estimates that by 2030, AI-powered agents and robots could generate about $2.9 trillion in economic value annually in the United States. But here’s the catch: this value can only be realized “if organizations prepare their people and redesign workflows, rather than individual tasks, around people, agents, and robots working together.”

In other words, the future isn’t “humans OR machines.” It’s “humans AND machines working in partnership.”

For M&E professionals, this distinction is everything. The question isn’t whether AI will take over evaluation work; it’s already taking over the routine parts. The question is: what’s your role in this new partnership?

The Skills That Will Define M&E Excellence in 2030

McKinsey’s research reveals a fascinating pattern: while technical and information-processing skills are highly exposed to automation, interpersonal skills like negotiation, coaching, and relationship-building show the least change on their Skills Index.

For the M&E field, this creates a clear roadmap of where to invest your professional development efforts.

The Rising Stars: Skills That Increase in Value

1. Stakeholder Negotiation and Management

In a world where AI can generate evaluation designs and analyze data, the human skill of navigating complex stakeholder politics becomes more valuable, not less. Convincing a resistant government partner to share sensitive data, building trust with community members who’ve been over-researched, negotiating access in conflict-affected areas, require emotional intelligence, cultural competence, and relationship-building skills that AI cannot replicate.

McKinsey’s research shows that demand for “social and emotional skills” will grow by 26% in the United States and 22% in Europe through 2030. For M&E professionals, this means your ability to build coalitions, manage difficult conversations, and navigate institutional politics will become a core differentiator.

2. Ethical Judgment and Research Integrity

As AI tools become more capable of conducting research, the need for human oversight of ethical considerations becomes paramount. Consider informed consent with vulnerable populations, balancing power dynamics in participatory evaluations, making judgment calls about cultural sensitivity, deciding what data to collect versus what crosses ethical boundaries; these require moral reasoning and contextual judgment that algorithms cannot provide.

The report notes that “people will remain vital to make [AI tools] work effectively and do what machines cannot.” In M&E, much of what machines cannot do involves ethical discernment in complex, ambiguous situations.

3. Evaluation Design for Complex, Unique Contexts

While AI can suggest evaluation methodologies from a database of past approaches, designing an evaluation for a genuinely novel intervention in a specific context requires creative problem solving. Understanding local power dynamics, adapting methods to cultural contexts, designing data collection that respects community norms, and balancing rigour with feasibility constraints, this kind of bespoke design work requires human judgment and creativity.

McKinsey found that creativity is one of the higher cognitive skills expected to remain highly sought after, with a potential increase of 12% by 2030.

4. Quality Assurance and AI Output Verification

Here’s the paradox of AI adoption: as AI handles more routine work, the human skill of quality assurance becomes more critical, not less. Someone needs to verify that the AI’s analysis is sound, check that automated coding hasn’t missed important nuances, ensure that AI-generated recommendations are contextually appropriate, and catch algorithmic biases that could skew findings.

Notably, McKinsey reports that job postings mentioning “quality assurance” are rising as AI adoption increases. For M&E professionals, becoming the trusted expert who can validate and improve AI outputs is a valuable niche.

5. Translating Data into Decision Intelligence

AI can identify patterns in data. What AI struggles with is understanding what those patterns mean for a specific decision maker in a specific context. Taking evaluation findings and translating them into actionable intelligence for program managers, explaining the implications of mixed results to diverse stakeholders, and helping donors understand trade-offs between different programmatic options; this is interpretive work that requires deep contextual knowledge and communication skills.

6. Process Optimization and Workflow Redesign

McKinsey emphasizes that the maximum economic value from AI comes not from automating individual tasks but from redesigning entire workflows. M&E professionals who can map evaluation processes, identify where AI can add value, design human-AI collaboration workflows, and continuously optimize based on results will be invaluable.

This is less about being a great evaluator and more about being a great evaluation systems architect.

7. Capacity Building and Mentoring

As AI tools proliferate, there’s growing demand for people who can teach others how to use these tools effectively, build capacity in partner organizations to leverage AI for M&E, mentor junior professionals in the skills AI can’t replace, and facilitate learning about the evolving M&E landscape.

McKinsey notes that demand for “teaching” skills is rising alongside AI adoption, a counterintuitive finding that makes sense when you consider that new technologies create new learning needs.

The Critical Skill Everyone’s Talking About: AI Fluency

Perhaps the most striking finding in McKinsey’s research is this: demand for “AI fluency”—the ability to use and manage AI tools has grown sevenfold in just two years (2023-2025). It’s now the fastest-growing skill in U.S. job postings.

And this isn’t just for tech roles. Job postings for roles as diverse as “SEO specialists, organic chemists, financial reporting managers, and engineers” are increasingly requiring AI fluency.

For M&E professionals, AI fluency isn’t optional, it’s foundational. But what does it actually mean?

What AI Fluency Looks Like in M&E

Effective Prompting: Knowing how to ask AI tools the right questions to get useful evaluation related outputs. This includes crafting prompts for literature synthesis, data analysis code, survey questions, or report drafting.

Output Evaluation: Understanding AI capabilities and limitations well enough to assess whether an AI-generated output is reliable, biased, or needs human correction.

Tool Selection: Knowing which AI tools are appropriate for which evaluation tasks, and understanding when human judgment should override AI suggestions.

Workflow Integration: Designing evaluation processes where AI tools and human expertise are strategically combined for maximum impact.

Ethical AI Use: Understanding issues like data privacy, algorithmic bias, and consent when using AI tools in evaluation work, especially in development contexts with vulnerable populations.

The good news? You don’t need a computer science degree. McKinsey’s research shows that AI fluency is appearing in job postings across all sectors and roles. It’s about being a sophisticated user of AI tools, not an AI developer.

The Identity Shift: From Executor to Orchestrator

Perhaps the most profound implication of McKinsey’s research is the identity shift it demands from professionals across all fields, including M&E.

The traditional M&E professional identity centered on execution: “I am valuable because I can execute high-quality evaluations. I can collect data rigorously, analyze it correctly, and write comprehensive reports.”

The emerging M&E professional identity centers on orchestration: “I am valuable because I can design evaluation systems where AI tools, human expertise, and systematic processes work together to generate actionable insights for decision-makers.”

This is the shift from being the person who runs the regression to being the person who decides what questions need answering, designs the analytical approach (which may involve AI), interprets what the results mean in context, and guides decision-makers toward evidence-informed action.

It’s less about technical mastery of evaluation methods and more about strategic thinking about how evaluation creates value for users.

McKinsey describes this as shifting from “executor (doing the thing) to orchestrator (managing the system).” For many M&E professionals, this will require not just learning new skills but fundamentally reconceiving our professional identity.

What’s Declining (But Still Essential)

McKinsey’s research shows that mentions of certain skills are declining in job postings, even though, as they note, “these skills remain essential for much of the workforce.”

For M&E, this includes:

Routine Writing and Editing: First-draft report writing, standardized documentation, formatting and copyediting; these are declining in job postings because AI handles them well. But note: strategic writing, storytelling for impact, and writing for specific audiences remain critical.

General Research Skills: Basic literature searching, information gathering, and research synthesis are declining because AI excels at these tasks. But note: critical appraisal of research, contextual interpretation, and research design remain vital.

Basic Data Analysis: Descriptive statistics, standard tests, routine quantitative analysis are declining in job postings. But note: complex analytical thinking, choosing appropriate methods, and interpreting results in context remain essential.

The key insight: these skills aren’t disappearing from M&E work; they’re being augmented by AI. The question is whether you’re positioned as someone who performs these tasks (competing with AI) or someone who guides and validates these tasks (partnering with AI).

Your 2026 Action Plan: Five Moves to Make Now

Based on McKinsey’s findings, here are concrete steps M&E professionals should take in 2026 to position yourself for 2030:

1. Start Building AI Fluency Today

Don’t wait for formal training. Start experimenting with AI tools in your current work:

• Use ChatGPT or Claude to draft literature reviews and then practice evaluating and improving the outputs
• Try AI-powered data cleaning tools and compare results to your manual process
• Experiment with AI for generating survey questions or evaluation frameworks
• Practice prompting AI to generate analysis code and learn to verify its accuracy

The goal isn’t to replace your judgment but to develop intuition about what AI does well, where it fails, and how to get the best results.

2. Invest in Distinctly Human Skills

Seek out training and experiences that develop:

• Negotiation and conflict resolution skills
• Cross-cultural communication competencies
• Ethical reasoning and applied ethics in research
• Stakeholder engagement and facilitation skills
• Creative problem-solving and design thinking

These are the skills McKinsey identifies as least automatable and most likely to increase in value.

3. Become a Process Designer

Start thinking about your evaluation work as a series of workflows, not just a collection of tasks:

• Map out a typical evaluation process from inception to final report
• Identify which steps require human judgment and which could be augmented by AI
• Experiment with redesigning processes to leverage AI while maintaining quality
• Document what works and what doesn’t

This builds the systems-thinking muscle that will be critical in an AI-augmented future.

4. Develop Quality Assurance Expertise

Position yourself as the person who ensures quality in an AI-augmented evaluation process:

• Learn about algorithmic bias and how it manifests in data analysis
• Develop frameworks for verifying AI outputs in evaluation contexts
• Build expertise in spotting when AI-generated insights miss critical context
• Create quality checklists specific to AI-augmented evaluation work

5. Cultivate Your Strategic Voice

Shift from being primarily a technical expert to being a strategic advisor:

• Practice translating evaluation findings into strategic recommendations
• Develop your ability to facilitate evidence-informed decision-making conversations
• Build skills in helping stakeholders understand trade-offs and implications
• Learn to communicate complex findings to non-technical audiences

This is about positioning yourself as a strategic partner, not just a service provider.

The Bigger Picture: Reimagining M&E in an AI Partnership Era

McKinsey’s research makes clear that realizing the $2.9 trillion opportunity requires “redesigning processes, roles, skills, culture, and metrics so people, agents, and robots create more value together.”

For the M&E field, this means rethinking some fundamental assumptions:

From Linear to Iterative: Traditional evaluation follows a linear path: design → collect → analyze → report. AI enables more iterative approaches where preliminary analysis informs ongoing data collection, where real-time synthesis guides adaptation, and where continuous learning replaces periodic reporting.

From Evaluation as Product to Evaluation as Service: Instead of delivering evaluation reports (a product AI can largely generate), M&E professionals may increasingly provide ongoing evaluation intelligence services; continuous sense-making, strategic interpretation, and adaptive learning support.

From Individual Expertise to System Performance: Success may depend less on individual technical excellence and more on designing evaluation systems that perform well; combining AI capabilities, human judgment, organizational learning, and stakeholder engagement into coherent, value-generating systems.

From Technical Monopoly to Democratized Practice: As AI makes evaluation tools more accessible, M&E professionals may shift from being the only ones who can do evaluation to being guides who help others do evaluation well, quality assurance experts who ensure rigorous practice, and strategic advisors who ensure evaluative thinking shapes organizational decision-making.

The Future Is Already Here: It’s Just Not Evenly Distributed

William Gibson famously said, “The future is already here; it’s just not evenly distributed.” This applies perfectly to McKinsey’s findings about AI and the future of work.

In some organizations, AI tools are already deeply integrated into evaluation workflows. Automated data cleaning is standard. AI assists with literature reviews. Machine learning models identify patterns in qualitative data. These organizations are learning what works, what doesn’t, and how to maximize the human-AI partnership.

In other organizations, evaluation still looks much like it did a decade ago. Manual data entry. Word-by-word literature reviews. Analysis conducted entirely by humans.

The gap between these two realities is widening. And by 2030, McKinsey suggests, that gap will determine which professionals and which organizations thrive.

A Call to Reflective Action

The most important thing McKinsey’s research should prompt is not panic but reflection.

Take an honest inventory:

• What percentage of your current work could be augmented or automated by AI?
• Which of your skills are distinctly human and difficult for AI to replicate?
• How much of your value comes from task execution versus strategic thinking?
• Are you positioned as someone who does evaluation or someone who designs evaluation systems?
• How would you describe your professional value in a world where AI handles routine analytical work?

These aren’t comfortable questions. But they’re essential ones.

The researchers at McKinsey are clear: “Integrating AI will not be a simple technology rollout but a reimagining of work itself, redesigning processes, roles, skills, culture, and metrics so people, agents, and robots create more value together.”

For M&E professionals, this reimagining is already underway. The question isn’t whether our field will be transformed by AI. The question is whether we’ll be active architects of that transformation or passive subjects of it.

Conclusion: The Partnership Imperative

McKinsey’s vision of the future of work isn’t humans versus machines. It’s humans and machines in partnership, each bringing complementary strengths.

Machines bring: tireless data processing, pattern recognition at scale, rapid synthesis of vast information, consistent application of rules and frameworks, and 24/7 availability.

Humans bring: ethical judgment in ambiguous situations, stakeholder relationship management, creative problem solving in novel contexts, strategic interpretation of findings, and contextual wisdom that transcends data.

The M&E professionals who thrive in 2030 will be those who can orchestrate this partnership—who know when to leverage AI and when human judgment is irreplaceable, who can design systems where technology amplifies human expertise rather than replacing it, and who can translate the avalanche of AI-generated insights into actionable wisdom for decision-makers.

The future of M&E work isn’t about being replaced by machines. It’s about becoming something different—and arguably more valuable—than we are today.

The transformation is already underway. The skills you develop in 2026 will determine your relevance in 2030.

What will you choose to become?

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About the Research

This article draws on “Agents, robots, and us: Skill partnerships in the age of AI,” published by McKinsey Global Institute in November 2025. The report analyzes automation potential across more than 800 occupations, examines skill shifts through 2030, and introduces the Skill Change Index to measure disruption across 7,000+ workplace skills. The research focuses primarily on the United States but notes that findings apply broadly to advanced economies.