Making SME progress measurable.
The MGS methodology team publishes working notes on SME evidence, programme design, finance readiness, sector practice, and responsible AI. Each note connects institutional research to the operating decisions Mothusi makes in the platform: how SMEs are assessed, how evidence is graded, how interventions are measured, and how progress becomes visible.
In-house research. Primary-source citations. Versioned working notes. Explicit bylines. No sponsored content.
Working notes on the evidence standards behind SME development.
Each note examines a specific failure point in SME-development practice, and the operational standard MGS uses to address it. Versioned, dated, and revised in public.
Attendance is not impact.
J-PAL's reviews of business-training programmes - drawing on randomised studies across Mexico, Peru, Tanzania, Sri Lanka, the Philippines, and other markets - consistently find that average measurable effects on revenue, employment, and survival are small or null. The conclusion is not that training does not work. It is that the data the sector has cannot distinguish what works from what does not.
A donor agency reports 240 workshops, 6,400 SMEs attending, 4,100 completions, and a 4.3-out-of-5 satisfaction score. Three years later the donor asks: are the businesses we supported measurably stronger than the ones we did not support? The programme team cannot answer in evidence at the operator level. This article argues that the binding constraint is the metric, not the intervention.
Read the articleObserved and verified maturity are not the same.
A construction subcontractor with eight years of trading, a workforce of twenty, and three regional buyers joins a development programme. Within an hour the operator is asked to define a value proposition. The mismatch between programme content and operator maturity is one of the most consistently documented design failures in business-development services literature.
Mature SMEs joining development programmes are routinely treated as blank pages. The Inter-American Development Bank's reviews of business-development services in Latin America, the OECD's SME policy assessments, and the World Bank's analysis of entrepreneurship-programme effectiveness have all flagged programme-content mismatch as a recurrent design failure. This article describes the operational separation between observed and verified maturity that resolves it - and why lender-side accountability makes the separation non-optional.
Read the articleThe credit officer's blank-page problem.
An institutional credit officer evaluating an SME application typically has 60 to 90 minutes per file. The pack arrives with a 12-month bank statement, two years of unaudited financials, a tax-clearance certificate, and a one-page profile. The officer is being asked to reconstruct the business from a thin set of artefacts. The OECD has named information asymmetry as the binding constraint behind the global MSME finance gap for over a decade.
The IFC estimates the global MSME finance gap at $5.2 trillion. The OECD's consistent diagnosis is that the gap is not primarily a shortage of capital - it is a shortage of information. Lenders cannot read the operators in front of them at the granularity the credit decision requires. This article describes what the credit officer actually sees, what the application form misses, and the operational fix.
Read the articleWhy agriculture needs a sector evidence layer.
The FAO estimates roughly 500 million smallholder farms worldwide, supporting an estimated 2 billion people. Agriculture remains, by ILO employment data, the single largest sector of employment in many developing economies. Yet generic SME software cannot reason about production cycles, biological risk, animal health, or seasonal cash. The sector-specialisation gap is one of the costliest blind spots in SME development.
According to FAO data, smallholder farms account for the majority of food production in many developing economies. ILO employment statistics consistently place agriculture as a leading employment sector across sub-Saharan Africa and South Asia. The MSME finance gap for agri-operators is documented as among the most severe sub-segments of the broader $5.2 trillion shortfall. Generic SME software, designed for generic SMEs, cannot reason about the dimensions that matter most for these operators. This article argues for a sector-layer architecture.
Read the articleWhy SME development needs a shared maturity standard.
The global MSME finance gap is $5.2 trillion. Forty years of programmes have not closed it. One of the binding constraints is the one nobody talks about: every country, every programme, every funder uses a different SME maturity framework - and cohort-level outcomes therefore cannot be compared.
The IFC estimates the global MSME finance gap at $5.2 trillion. The World Bank reports that SMEs account for roughly 90% of businesses worldwide and over half of formal employment, with more than 600 million jobs needed by 2030. Four decades of programmes have not closed the gap. This article argues that one binding constraint, rarely surfaced, is the absence of a shared SME maturity framework - and that the operational fix is a standard built the same way IFRS, ICD-10, and Basel were built.
Read the articleModels capture. Frameworks decide.
The EU AI Act classifies credit scoring and creditworthiness assessment as high-risk AI uses. The US Federal Reserve's SR 11-7 has set model-risk-management standards for over a decade. The Bank for International Settlements has published successive reports on AI/ML in banking. The institutional bar for "Responsible AI in finance" has been rising for years. Most SME-development AI vendors are not built to clear it.
Institutional procurement teams evaluating AI in SME development face a question AI vendors rarely answer directly: where does the model decide, where does a rule-based framework decide, and where does a human decide? The EU AI Act, the US Federal Reserve's model-risk-management guidance, the Bank for International Settlements, and the OECD AI Principles have all converged on the same architecture for high-stakes financial decisions. This article draws the operational line in the language a model-risk-management team or methodology committee would use to evaluate it.
Read the articleWhy programme reporting has to move from endline evaluation to live evidence.
Most SME programmes discover too late which businesses were stuck, which interventions failed, and which cohorts were ready for capital. The programme team is asked to report on outcomes after the work is finished, when the only remaining option is explanation. This note argues that programme delivery is a live measurement problem, not an endline evaluation problem.
Endline evaluation tells you what happened after it is too late to change the result. MGS treats programme delivery as live measurement: tier movement, evidence confidence, issue clusters, readiness, and next actions tracked while the programme is still running.
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Read the methodology and the platform documentation.
Articles place the framework in context. The methodology page documents the framework itself. A briefing covers the deployment model.
From business support to measurable enterprise development. Across sectors, countries, and real operating environments.