Working smarter: Leveraging AI for monitoring and evaluation

Monitoring & Evaluation (M&E) is a powerful tool to determine whether certain interventions are reaching their intended outcomes—not just by measuring what happened, but by exploring why it happened, who was impacted, and at what cost. M&E challenges assumptions, surfaces insights, and brings clarity to decisions that shape lives.

Yet despite its value, M&E is often underused by organizations. When done well, it can be time consuming and resource intensive. In a world that demands quick decisions and faces serious time and funding constraints, traditional M&E methods sometimes feel like a luxury.

AI offers a way forward. When used responsibly, AI can help M&E professionals work faster, dig deeper, and deliver more timely insights without compromising rigor. Drawing on real-world examples and grounded in our commitment to ethical, human-centered innovation, we’ve developed several considerations for using AI to elevate your M&E projects.

Accelerate M&E

M&E is largely about making sense of information. But the volume and complexity of that information often exceeds human capacity. AI technologies, including large language models (LLMs), can boost M&E efficiency by rapidly synthesizing data, simulating reasoning, and communicating clearly—without fatigue.

These capabilities are already enhancing how M&E professionals work. AI can scan literature and stakeholder feedback to extract key themes and summarize findings. It can also rapidly assess sentiment in media coverage or evaluation narratives. Off-the-shelf chatbots can surface insights from structured data, uploaded documents, or online sources, accelerating early-stage research and analysis. Ctrl+F and keyword searches have served us well, but they’re training wheels compared to these new tools.

Beyond synthesis, AI supports evaluative thinking. It can help refine research questions, identify logic gaps, and assess alignment with legal or ethical standards. AI also enhances communication by helping evaluators wordsmith content for clarity and brevity as well as translate technical concepts—such as mathematical equations—into language that resonates with diverse audiences.

Building on these ideas, the table below illustrates some of the ways that LLMs can support M&E activities across the project lifecycle. Organized by functional categories—ideation, research, stakeholder engagement, coding, analysis, and writing—the table highlights sample tasks where AI can add value, from generating hypotheses and summarizing text to categorizing qualitative data and drafting reports. Each category is paired with a guiding tip to promote responsible and effective use.

As outlined in Guidehouse’s 2025 Tech Guide, AI doesn’t replace human judgment—it amplifies it. Along these lines, Steve Jobs once described the personal computer as a “bicycle for the mind.” To extend that metaphor, think of AI as a teammate in a cycling race: creating a slipstream, reducing resistance, and helping you surge ahead of the peloton. By lightening the mental load and streamlining knowledge integration, AI allows you to focus on what matters most—asking sharper questions, interpreting results with additional nuance, and fine-tuning strategic recommendations.

Apply discipline and purpose to AI use

To avoid common pitfalls, AI should be approached with the same care and discipline that underpins successful projects. Ideally, it should integrate into your broader project delivery framework.

Effective AI use in M&E comes down to deft management across four key stages:

1. Information sourcing: Curate high-quality data and content to ensure that AI outputs such as summaries and evaluations are grounded in reliable sources.
2. AI engagement: Select fit-for-purpose tools and craft thoughtful prompts that foster collaboration to maximize value for tasks like brainstorming or sentiment analysis.
3. Output validation: Rigorously review AI-generated outputs for accuracy, relevance, and ethical alignment, including bias mitigation.
4. Activation: Translate AI insights into actionable recommendations that support confident decision-making.

Treat AI as a managed capability, not a mere novelty. As per Executive Order 14179, the U.S. aims to remove regulatory barriers and reaffirm its global leadership in AI by promoting innovation. Building on this vision, OMB Memorandum M-25-21 provides agencies with a roadmap to accelerate responsible AI adoption—emphasizing innovation, governance, and public trust. For M&E professionals, this means integrating AI into project workflows with discipline: selecting fit-for-purpose tools, applying rigorous validation, and aligning with federal expectations around transparency, ethical safeguards, and human oversight. By treating AI as a strategic asset governed by clear policies and accountable leadership, evaluators can unlock smarter insights while upholding public confidence and institutional integrity.

Approach the work with humility and curiosity

AI is powerful but certainly not perfect. It can misinterpret context, overlook subtle distinctions, and generate outputs that sound plausible but are factually incorrect. It can, moreover, introduce cognitive distortions and even a false sense of subject matter mastery—aka, the illusion of explanatory depth. That’s why M&E teams must engage with AI without overreliance.

Ask:

• Where does an AI tool such as an LLM excel, and where are its limitations?
• How can an LLM enhance, rather than replace, human judgment?
• What do our teams need to apply AI tools responsibly?
• How can we structure our engagement with AI tools—by scoping tasks, formatting content, and designing prompts—to align with their processing strengths and ensure stepwise, purposeful collaboration?

The best approach is grounded in humility and driven by curiosity. As AI evolves, so should your commitment to continuous learning. Experimenting with emerging techniques and sharing lessons across teams will help you navigate this landscape with confidence and care. Your skills in evaluation design, mixed-methods research, and data visualization remain essential for validating and interpreting AI-generated insights. Critical thinking and good judgment are as critical as ever.

This mindset is one that Guidehouse actively cultivates. Whether we’re designing maturity models, conducting key informant interviews, or building performance monitoring systems, we combine AI’s speed and scale with our proven, human-centered approach—helping clients unlock smarter insights faster and responsibly.

Exploring AI in M&E: From practice to possibility

To illustrate how AI can be thoughtfully embedded into M&E workflows, the following examples showcase two distinct applications: one drawn from a real-world use case, and the other constructed as a theoretical scenario. Together, they help paint a fuller picture of how generative tools can enhance M&E efforts.

AI powered analysis for strategic communication

To monitor and evaluate the soft power impact of a high-profile diplomatic summit, our team conducted a media sentiment analysis using generative AI as part of a broader assessment framework. 

We curated dozens of translated news excerpts from global outlets and applied an LLM to score each on a sentiment scale from -3 to +3, providing a measure of public perception. The average score of +1.22 suggested generally favorable coverage, serving as an evaluative metric for the summit's effectiveness in shaping positive narratives. 

To assess reliability, we validated the model’s outputs through repetitive prompting and human cross-checks, aligning with best practices in monitoring and evaluation to promote data accuracy and objectivity. The distribution of scores included a healthy mix of neutral and negative excerpts, confirming the dataset's balance and the AI-generated results' credibility. 

This approach delivered a timely, data-driven evaluation, demonstrating how AI can enhance the speed and rigor of monitoring and evaluation processes in public diplomacy, especially when paired with human oversight and methodological care. Our experience using AI to conduct sentiment analysis resulted in a reduction of about 50% in time required and enabled our team to focus more on assessing the results and building more detail into our determination of what they meant.

AI-augmented mixed-methods benchmarking

To help government clients assess their performance management capacity—the people, processes, and culture required to track results, analyze evidence, and ensure programs achieve strategic goals—we have led mixed-methods benchmarking initiatives within U.S. government agencies. Here we consider how a future iteration of the project could combine human expertise with AI-driven efficiency.

The methodology would begin with strategic case selection across subdivisions of the agency, guided by purposive sampling. Purposive sampling is often necessary because achieving full coverage across all subdivisions would require resources beyond what most projects can support. It is also helpful because purposive sampling—a non-probabilistic method—enables evaluators to capture deep, contextualized insights from a diverse set of subunits that differ in size, function, and performance profile. Selection would be informed by multiple indicators such as funding levels, compliance history, and perceived evaluation capacity. Here, AI can accelerate the process by scanning administrative datasets, identifying patterns, and surfacing candidate cases that best represent the variation of interest.

During data collection, AI-powered tools could assist in designing interview and focus group guides, simulate respondent perspectives to stress-test questions, and even provide real-time prompts for follow-up questions during interviews. However, caution is warranted: this AI-enabled approach is best suited for contexts where the information collected is not highly sensitive and can be shared with an LLM without compromising confidentiality. In projects involving vulnerable populations or sensitive data, alternative methods that avoid exposing raw content to external AI systems would be essential to uphold privacy and ethical standards.

On the analysis side, LLMs can supercharge qualitative processing by automating transcription, coding, and thematic classification—tasks that often consume weeks of manual effort—thereby accelerating timelines without sacrificing rigor. AI can also perform sentiment analysis, detect latent themes across interviews, and link qualitative insights to quantitative performance metrics, strengthening triangulation. 

The results from the initial stages of the study would feed into a conceptual maturity model, which serves as the organizing framework for assessing performance management capacity. The model breaks down performance management into key dimensions—such as staffing, technical skills, and evidence-based culture—and defines what progress looks like along a continuum from nascent to advanced. By anchoring analysis in this structured framework, the model enables consistent scoring across units and highlights where targeted improvements are needed, providing a roadmap for capacity building. Here, AI can assist reviewers by synthesizing evidence from multiple sources, detecting gaps or inconsistencies, and generating preliminary summaries to support consensus-based scoring of performance management capacity. Throughout, human oversight remains essential to validate outputs, mitigate bias, and ensure ethical compliance.

This proposed methodology does more than benchmark current practices—it creates a scalable, adaptive framework for continuous improvement. 

Use smarter tools for stronger insights

As AI reshapes the M&E landscape, it offers the potential to accelerate analysis, expand reach, and deepen understanding. By curating quality data, guiding AI tools with clarity and purpose, validating outputs with critical thinking, and communicating findings in transparent and actionable ways, you can ensure that AI delivers maximum impact.

To explore how your agency or organization can move from pilot to scale, visit The Tech Guide 2025.