14 AI Tools
EvalCommunity Tutorial
14 AI Tools That Can Save M&E Professionals Hours Every Month
A Practical AI Toolkit for Monitoring, Evaluation, Learning and International Development
M&E professionals are often expected to do more with the same amount of time.
You may be reviewing a 200-page evaluation report, preparing a donor presentation, analysing interview transcripts, writing an evaluation proposal, documenting meetings, producing training videos, managing spreadsheets, responding to emails, creating dashboards or trying to understand a completely new programme area.
The problem is not necessarily that M&E professionals are inefficient.
The problem is that many M&E workflows still contain a huge amount of repetitive work.
AI can help.
But the goal should not be to collect dozens of AI tools simply because they are new.
The better approach is to identify the tasks that consume the most time and then choose the right tool for that specific workflow.
This tutorial introduces 14 AI tools that can be particularly useful for M&E, MEL, evaluation, research, programme management and international development work.
Some are research tools. Some work with documents. Others help with meetings, presentations, video, data, automation, email, applications and AI agents.
The important question is not “Which AI tool is the best?”
It is: “Which part of my M&E workflow can this tool make easier, faster or more reliable?”
The 14-Tool M&E AI Stack
- NotebookLM — Source-Grounded Research
- Runway — AI Video
- Beautiful.ai — AI Presentations
- Fathom — Meeting Intelligence
- ElevenLabs — AI Voice and Audio
- Glide — No-Code Apps
- Bardeen — Browser Automation
- Claude — Long-Form Analysis and Writing
- Adobe Acrobat AI — PDF and Document Analysis
- Superhuman — AI Email
- Leonardo.Ai — AI Images
- Bolt.new — AI App Building
- Airtable — AI Data and Agents
- Descript — AI Video Editing
The tools are not equally relevant to every evaluator.
The value comes from matching them to the right task.
1. NotebookLM — Source-Grounded Research
One of the most useful applications of AI for M&E is working with large collections of source material.
Google’s NotebookLM is designed around this use case. You can provide sources such as PDFs, websites, YouTube videos, audio files, Google Docs and Google Slides, then ask questions about the material.
NotebookLM can provide citations back to the sources and generate different types of research and learning outputs.
For evaluators, this can be particularly useful.
M&E source collection examples
- Programme design documents
- Theory of Change
- Logframe
- MEL plan
- Baseline report
- Previous evaluation
- Donor requirements
- Interview transcripts
- Programme guidelines
- Monitoring reports
- Research papers
Then ask questions such as:
Identify the programme’s five most important intended outcomes and cite the source for each.
Compare the Theory of Change with the MEL plan. Identify any important indicators or assumptions that appear in one but not the other.
Review the baseline report and identify findings that should influence the evaluation design.
Identify contradictions between the programme design and monitoring reports.
This is much more powerful than simply asking AI to summarize a document.
The evaluator can use AI to interrogate the evidence base.
Upload → Ask → Cross-check → Investigate → Extract evidence
Do not treat the AI summary as the final evidence.
Use the citations to return to the source material.
2. Runway — AI Video
Video is increasingly useful for development organizations.
M&E teams may need to create:
- Training videos
- Programme explainers
- Evaluation dissemination videos
- Stakeholder presentations
- Social media content
- Research summaries
- Animated concepts
- Short learning materials
Runway provides AI-assisted video generation and editing capabilities.
M&E use cases
Imagine you have completed an evaluation and need a short dissemination video.
You could combine:
- Evaluation findings
- Programme photographs
- Charts
- Interviews
- Narration
- AI-generated visual elements
You could also use AI video to create a short explanation of a Theory of Change or evaluation methodology.
Do not use generative video to fabricate programme beneficiaries, field activities or evaluation evidence. If a visual represents a real event or real participant, make that distinction clear.
3. Beautiful.ai — AI Presentations
M&E professionals frequently spend too much time preparing presentations.
You may already have the analysis completed but still spend hours:
- Formatting slides
- Aligning objects
- Rewriting headings
- Adjusting layouts
- Creating visual summaries
- Making charts fit
- Fixing inconsistent formatting
Beautiful.ai uses AI and smart layouts to help create professional presentations.
M&E example
Then provide:
- Evaluation findings
- Recommendations
- Key indicators
- Charts
- Programme context
The important point is that AI should not decide what your evaluation means.
You should determine:
- The findings
- The interpretation
- The recommendations
- The evidence
- The limitations
AI can help with the communication layer.
4. Fathom — Meeting Intelligence
Meetings create a surprisingly large amount of hidden M&E work.
Someone has to:
- Take notes
- Record decisions
- Identify action points
- Assign responsibilities
- Write follow-up emails
- Update project records
Fathom is an AI meeting assistant that can transcribe and summarize meetings and produce action items and follow-up information.
M&E example
Consider a weekly programme monitoring meeting.
Instead of manually documenting:
- What happened?
- What changed?
- What decisions were made?
- Who is responsible?
- What needs follow-up?
You can use AI-generated meeting information as the first draft.
Extract all decisions made during the meeting.
Identify all actions, responsible people and deadlines.
Identify any unresolved risks that require follow-up.
Meeting recordings and transcripts can contain sensitive information. Check organizational policy, participant consent, storage arrangements and data-processing requirements before using AI meeting tools.
5. ElevenLabs — AI Voice and Audio
Audio can be useful for M&E communication, training and knowledge products.
ElevenLabs provides AI voice generation, text-to-speech, voice cloning, dubbing and related audio capabilities.
M&E use cases
- Training modules
- Evaluation explainers
- Research summaries
- Presentation narration
- Accessibility versions of documents
- Short educational videos
- Multilingual communications
You could take a two-page evaluation summary and turn it into a narrated five-minute briefing.
Voice cloning requires particular care. Do not clone someone’s voice without appropriate authorization.
6. Glide — Build Internal M&E Apps Without Coding
Many M&E teams have spreadsheets that have gradually become informal applications.
You may have a spreadsheet tracking:
- Indicators
- Field visits
- Beneficiary feedback
- Risks
- Evaluation actions
- Programme activities
- Data quality issues
Glide can turn structured data into applications without traditional software development.
M&E use cases
Imagine a field monitoring tracker where users can:
- Submit monitoring information
- View assigned activities
- Record observations
- Upload information
- Track follow-up actions
- View status dashboards
Another example is an Evaluation Action Tracker containing:
- Recommendation
- Responsible person
- Due date
- Status
- Evidence of implementation
- Verification
- Notes
7. Bardeen — Browser Automation
A huge amount of M&E work happens inside browsers.
You may repeatedly:
- Search websites
- Copy information
- Update spreadsheets
- Collect research findings
- Move information between systems
- Fill forms
- Collect programme information
Bardeen is designed for browser-based automation and AI-assisted workflows.
M&E examples
The goal is no longer simply:
“Ask AI a question.”
It becomes:
“Make this repetitive workflow happen automatically.”
8. Claude — Long-Form Analysis and Writing
Claude is particularly useful for long documents, structured writing, analysis and iterative editing.
For M&E professionals, possible applications include:
- Evaluation reports
- Research reports
- Proposals
- Methodology sections
- Evaluation questions
- Interview guides
- Learning products
- Donor reports
- Policy briefs
- Theories of Change
- MEL plans
But there is an important distinction.
“Write my evaluation report.”
Instead, use AI as a structured collaborator.
Review this evaluation report outline and identify gaps in the logic.
Identify claims that require stronger evidence.
Rewrite this section for clarity without changing the findings.
Identify where the language sounds more certain than the evidence supports.
This is a much more defensible workflow.
9. Adobe Acrobat AI — PDF Intelligence
PDFs are everywhere in M&E.
Think about how many documents an evaluator may encounter:
- Donor guidelines
- Evaluation reports
- Research papers
- Programme documents
- Baseline studies
- Surveys
- Policies
- Contracts
- Technical reports
- Country strategies
Adobe Acrobat’s AI features allow users to work with documents using AI-assisted analysis, summaries and questions.
M&E example
What methodology was used in this evaluation?
What were the main limitations identified by the authors?
Identify all recommendations related to data quality.
Locate the evidence supporting recommendation number three.
For multiple documents, you could ask:
This can improve document triage.
Document analysis is not document verification. For important findings, return to the original page and source.
10. Superhuman — AI Email
Email is another hidden source of M&E workload.
M&E professionals may receive:
- Data requests
- Donor questions
- Consultant communications
- Meeting invitations
- Review requests
- Programme updates
- Follow-up questions
- Evaluation comments
Superhuman uses AI to help draft, rewrite, summarize and organize email.
M&E example
AI can produce the first draft.
You remain responsible for:
- Accuracy
- Tone
- Commitments
- Confidentiality
- What actually gets sent
11. Leonardo.Ai — AI Images
M&E teams increasingly need visual communication.
You may need:
- Training illustrations
- Social media graphics
- Concept visuals
- Programme diagrams
- Background images
- Educational materials
- Presentation visuals
Leonardo.Ai provides AI image generation and image editing capabilities.
M&E example
Suppose you are creating a training module about community participation.
You could generate an illustrative visual representing:
- Community consultation
- Participatory monitoring
- Data collection
- Learning workshops
- Programme implementation
Do not generate a fictional image and present it as evidence of a real programme activity.
12. Bolt.new — AI App Building
Bolt.new is aimed at building web applications using natural-language instructions.
For technically confident M&E professionals, this opens up an interesting possibility:
Prototype small M&E tools without starting from traditional software development.
Possible M&E applications
- Indicator tracking tools
- Evaluation calculators
- Sampling calculators
- Data-quality checkers
- Recommendation trackers
- Survey planning tools
- Evaluation dashboards
- Simple evidence databases
For example:
A prototype is not automatically production-ready. Professional M&E systems still require testing, data validation, security review, accessibility, user testing and quality assurance.
13. Airtable — AI Data and Agents
Airtable sits at the intersection of databases, workflows and AI.
Its AI capabilities can help organizations retrieve, analyze, classify, generate and work with structured information.
This makes it particularly interesting for M&E.
M&E example: Evaluation Evidence Tracker
- Source
- Programme
- Evaluation question
- Finding
- Evidence type
- Population
- Geography
- Methodology
- Date
- Confidence
- Link
- Notes
AI can help enrich, classify or organize records.
Another example: Beneficiary Feedback Database
AI could help:
- Categorize feedback
- Detect themes
- Identify recurring issues
- Flag urgent issues
- Summarize comments
- Generate structured tags
A structured M&E workflow with AI embedded into the data.
14. Descript — Edit Video Like Text
Video editing has traditionally required learning timelines, tracks, cuts and editing software.
Descript takes a different approach.
It transcribes video and allows users to edit content through the transcript.
Its AI-assisted features can support transcription, text-based editing, filler-word removal and audio improvement.
M&E use cases
- Evaluation interviews
- Training videos
- Webinars
- Learning events
- Research presentations
- Stakeholder interviews
- Knowledge products
Imagine recording a 30-minute training session.
- Upload the recording.
- Let AI transcribe it.
- Edit the transcript.
- Remove unnecessary sections.
- Remove filler words.
- Create a shorter version.
- Export the final video.
The editing process becomes much closer to editing a document.
How Should an M&E Professional Choose Between These Tools?
Do not start with the tool.
Start with the workflow.
What takes too long?
- Reading documents?
- Preparing presentations?
- Taking meeting notes?
- Writing emails?
- Creating videos?
- Building small applications?
- Updating spreadsheets?
- Collecting information?
- Creating visuals?
- Repetitive browser work?
Then choose the tool.
A Simple M&E AI Tool Selection Framework
| Your problem | Tool to consider | Typical M&E use |
|---|---|---|
| Research | NotebookLM | Interrogate source collections |
| Long-form writing | Claude | Reports, proposals, analysis |
| PDFs | Adobe Acrobat AI | Document analysis |
| Meetings | Fathom | Notes, actions, summaries |
| Presentations | Beautiful.ai | Evaluation and donor decks |
| Video | Descript / Runway | Training and dissemination |
| Audio | ElevenLabs | Narration and learning |
| Images | Leonardo.Ai | Illustrations and visuals |
| Superhuman | Drafting and inbox work | |
| Browser automation | Bardeen | Repetitive online workflows |
| Small apps | Glide | Trackers and internal tools |
| Custom applications | Bolt.new | Prototypes and tools |
| Structured data + AI | Airtable | Evidence and feedback workflows |
The More Important Question: What Should You Automate?
Not everything should be automated.
Automate the repetitive
- Formatting
- Transcription
- Initial classification
- Document triage
- Meeting summaries
- First-draft emails
- Data enrichment
- Repetitive research collection
- Video cleanup
Keep humans responsible for judgment
- Interpreting findings
- Making evaluation conclusions
- Assessing evidence quality
- Understanding context
- Ethical decisions
- Sensitive stakeholder interpretation
- Programme recommendations
- Attribution and contribution judgments
- Final reporting decisions
This distinction is critical.
AI should reduce administrative friction without removing professional accountability.
Build an M&E AI Stack Instead of Collecting Random Tools
You do not need all 14 tools.
A practical M&E professional might start with only five:
- Research: NotebookLM
- Writing and analysis: Claude
- Documents: Adobe Acrobat AI
- Meetings: Fathom
- Automation: Bardeen or Airtable
That already covers a large proportion of common knowledge-work tasks.
Then add specialist tools when a specific workflow requires them.
A Practical Exercise for EvalCommunity Users
Choose one workflow that you repeat every week.
For example:
Every Friday I prepare the programme monitoring update.
Break it down:
- Collect information
- Review documents
- Extract findings
- Check indicators
- Identify risks
- Draft summary
- Create charts
- Prepare presentation
- Send email
- Archive the final version
Now identify which steps are:
Manual → Repetitive → Rule-based → Automatable
Then ask:
Which parts could AI assist with without removing the professional judgment required from an M&E specialist?
You may discover that the biggest productivity gains do not come from one magical AI tool.
They come from redesigning the workflow.
A Better Way to Think About AI Productivity
The future of AI productivity in M&E is not:
“I know 14 AI tools.”
It is:
“I know how to redesign my M&E workflows using AI.”
Old workflow
Receive 150-page report → read manually → take notes → write summary → prepare presentation.
AI-assisted workflow
Upload report → interrogate source → extract evidence → verify findings → structure summary → generate presentation draft → human review.
The evaluator still makes the important decisions.
But much of the mechanical work can be reduced.
The Next Step: Move From AI Tools to AI Agents
There is an important progression here.
Level 1 — AI Tool
You open an AI tool and ask it to perform a task.
Level 2 — AI-Assisted Workflow
AI becomes part of a repeatable process.
Level 3 — AI Agent
An agent can be configured to perform multiple steps toward a defined objective, using tools and context as part of a workflow.
This is particularly relevant to M&E because evaluation work contains many repeatable processes.
Examples include:
- Indicator tracking
- Evidence collection
- Document review
- Beneficiary feedback analysis
- Evaluation report review
- Donor reporting
- Research monitoring
- Data-quality checks
- Literature scanning
- Programme learning workflows
The shift is from “Which AI tool should I use?” to “Which M&E workflow should I redesign?”
Final Takeaway
The 14 tools in this tutorial are useful for very different reasons.
NotebookLM can help you work through evidence.
Claude can help with long-form analysis and writing.
Acrobat AI can help interrogate PDFs.
Fathom can reduce meeting documentation.
Beautiful.ai can accelerate presentation development.
Descript and Runway can reduce video-production friction.
ElevenLabs can accelerate narration and audio production.
Leonardo can help with visual content.
Glide and Bolt.new can help build lightweight applications.
Bardeen can automate repetitive browser tasks.
Superhuman can reduce email workload.
Airtable can combine structured data, workflows and AI agents.
But the biggest productivity improvement comes from something else:
Workflow redesign.
Do not ask:
“How can I use AI?”
Ask:
“Which part of my M&E workflow should no longer require me to do it manually?”
That is where the real productivity opportunity lies.
EvalCommunity Academy
Go Beyond Knowing AI Tools
Knowing which AI tools exist is only the beginning. The next step is learning how to apply AI responsibly and effectively to actual M&E work — and then move from individual prompts to reusable AI workflows and agents.
Build Your AI-for-M&E Skillset
Learn AI. Apply AI. Build AI Workflows.
Start with the foundations of AI in M&E, then learn how to design, build, test and use practical no-code AI agents for repeatable evaluation workflows.
AI in M&E → AI Workflows → AI Agents
The goal is not to become an AI specialist. The goal is to become an M&E professional who knows how to use AI effectively, critically and responsibly.
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Practical AI skills for Monitoring, Evaluation, Learning and Development professionals
