Explore societal challenges, KPIs, public statistics, assessment and calculation methods, and reference cases.
AI / IMM / MCP
Connect AI to
evidence-based impact management
Bring your preferred AI assistant together with structured impact data, established assessment methods and repeatable IMM workflows.
AI adoption is rising. Embedding it in workflows is harder.
AI is now commonplace in impact practice, but its use is less established in specific workflows such as data analysis, implementation and ongoing monitoring.
AI in impact practice
98%Use generative AI at workSIMI 2026 / N=42
In SIMI’s survey of 42 impact and evaluation practitioners, nearly 98% used generative AI at work. Usage is more widespread in planning and reporting than in day-to-day monitoring.
Widespread AI use has yet to translate into routine IMM workflows
Share using AI at specific stages of impact practice (SIMI 2026; N=42).
AI used in specific IMM workflows
General AI use (about 98%) is more widespread than AI use at individual stages of IMM. Connecting AI to recurring evidence collection and monitoring remains a practical gap.
Source: SIMI, Generative AI Use in Evaluation and IMM (revised September 2026; N=42). The 98% figure measures use at work; 57% and 45% measure use at specific IMM stages. These are different questions, not successive conversion rates or a participant drop-off.
AI × IMM
Accumulated domain expertise × AI flexibility
impactlake™ goes beyond a standalone AI tool. It combines data, assessment models and operational processes developed through IMM practice with the flexibility of AI, making exploration broader and everyday use easier.
| Comparison dimension | General-purpose AI alone | impactlake™: domain foundation × AI | Conventional systems alone |
|---|---|---|---|
| Early-stage exploration | Explore and organize ideas flexibly in natural language | Explore with AI and convert findings into structured outputs | Start with predefined forms and settings |
| KPIs and evaluation methods | Can suggest options, but definitions and evidence require checking | Use referenced information and methods while maintaining evaluation models | Use preset indicators and formulas reliably |
| Ongoing management | Often stays within individual conversations and files | Combine AI flexibility with continuous records and reuse | Make it easier to manage actuals, history and responsibilities |
| Organizational processes | Often depends on individual prompts and human checks | Define where AI is used and reviewed, then embed it in workflows | Define approval and operational procedures |
| Updating and sharing information | Information often needs reorganizing by project or conversation | Reuse information for analysis by different users | Use managed, consistent data |
This comparison reflects typical use patterns. Actual capabilities depend on the AI model, system architecture and operations.
UTILIZE IMM
Ground AI workflows in trusted impact data
We combine datasets, methods and assessment models developed since 2022 with designs for responsible AI use. The goal is not simply to generate an answer, but to make it usable in later operations.
Link logic models, indicators, calculations, evidence and performance results so that outputs remain usable.
Use retrieval-augmented approaches such as RAG to reference data and documents. Combine sourced information with human judgment rather than relying solely on generation.
Separate AI drafting, structuring and calculation, and human review, then design their use to meet organizational and information-management needs.
01Refer to data and evidenceChallenges, indicators, statistics and methods
02Explore with AIHypotheses, options and questions
03Structure and recordModels, formulas and actual results
04Review and reuseDecisions, monitoring and reporting
Use impactlake™ from your AI assistant
Choose the AI tool you use and enter what you would like to explore. You can also start with an example question below.
Other AI services, including Gemini, are planned for future support. For details, contact us.
From AI-assisted analysis to ongoing IMM
Combining AI with a data foundation can support parts of work traditionally requiring specialist expertise. Our goal is to help more people participate in and sustain impact exploration, measurement and improvement.
Make impact exploration more accessible
Identifying relevant issues and indicators depends on specialist support
Ask in natural language and explore relevant cases, KPIs and evidence
Business and investment teams can develop hypotheses for expert review
Accelerate impact assessment design
Document review and indicator research are repeated for every project
Build an initial assessment using existing models and calculation methods
Spend more time validating assumptions and reuse structured models
Support continuous monitoring
Plans, results and assumptions are scattered across files and people
Manage KPIs, formulas, evidence and update histories in one structure
Make performance reviews and improvement part of routine work
Inform action and investment decisions
Assessments often end once a report has been prepared
Reuse the same information for investment, business management and IR
Support decisions, operational improvement and stakeholder dialogue
Put AI to work throughout the IMM lifecycle
Start exploring through the AI you already use. Contact us about specific integrations and organizational deployment.