Heading to AIAE 2026? 12 Questions to Answer Before Your Next Agricultural Growth Decision

The decisions begin after the expo

When the agricultural sector gathers at KICC in Nairobi for the Africa International Agricultural Expo (AIAE) 2026, from 23 to 25 October, the conversations will be familiar ones: new markets, new partnerships, new technologies and new programmes. Expos are where plans get sharper and decisions get started. The harder part comes afterwards, when a decision has to stand up to real farmers, real customers and real numbers.

Imagine a company that launches in a new county because the early numbers looked promising. Or a programme that scales up after a strong pilot, then struggles to explain what actually changed. Or a digital tool that was designed with care, but that farmers quietly stopped using after a few weeks. These situations rarely come from a lack of effort. More often, the decision was made with less evidence than the team believed it had.

Agriculture across Africa is becoming more data-driven. Digital agriculture, agricultural finance, market access and food processing all generate more information than they used to. But having data is not the same as having useful intelligence. That gap is where costly assumptions tend to hide, and it is why we created a short checklist to take into AIAE 2026 and use long after it.

Start with one question

Before investing in a new market, launching a product, expanding a programme or introducing a technology, ask: do we actually have the evidence we need to make this decision confidently?

If the answer is ‘not quite’, that is useful to know early. It is far cheaper to find an information gap before a decision than after it.

Part 1: Understand your market

The first three questions look at who you are serving and where you might grow.

A product designed for an ‘average farmer’ can overlook real differences between customer segments, such as geography, farm size, gender, age or production system. It also helps to separate the user, the buyer, the influencer and the beneficiary, because they are not always the same person. Measuring whether people use something is only half the job. Understanding why they do or do not is where the useful insight sits.

• Do we clearly understand who our customers or beneficiaries are?
• Do we know what drives adoption?
• Do we understand the market beyond our existing customers?

Part 2: Listen to the market

Next, the checklist asks what farmers, customers and users actually think, and whether you have tested your product or service before scaling it. It also looks at the last mile: where distribution bottlenecks occur, whether retailers are well informed, and whether stock-outs are affecting demand.

For agri-tech in particular, adoption should be studied from the user’s point of view, not only from the technology provider’s. What customers tell you can reveal opportunities that internal reports never will.

Part 3: Measure what matters

This part is about evidence you can trust. Can you show whether a programme or intervention is working? Is there a clear baseline, a credible comparison point, and are indicators tied to programme objectives? And can you trust your field data?

Bad data can produce precise-looking but unreliable conclusions. Who collects the data, how enumerators are trained, whether questionnaires are tested, and whether digital tools include validation rules and quality checks all shape how far the results can be relied on.

The part closes with a test that every team should apply: what decision will this information help us make? If there is no clear answer, it is worth reconsidering whether to collect it at all.

Part 4: Turn data into intelligence

The final part asks whether you are looking beyond the numbers. Quantitative research tells you what happened, how many, how often and where. Qualitative research helps explain why it happened, how people experienced it and what needs to change. Strong agricultural intelligence often combines both.

It also asks whether you can see patterns across locations, customer groups and time, and it ends on the question that ties everything together: what decision will the evidence change? If the decision is unclear, the research question may not be ready.

A five-minute health check

The checklist includes a short self-assessment made up of ten statements, such as ‘We have reliable field data’ and ‘Research findings directly inform decisions’. You score each one as yes, partly or no, then add up your total out of 20.

There is no pass or fail. A higher score suggests you have many of the foundations for data-informed decision-making. A middling score points to useful systems with some important gaps. A lower score suggests that important decisions may currently rely on assumptions, incomplete information or fragmented data. In every case, the aim is to show you where to look next.

Before you commission your next study

If your team is thinking about commissioning research, a survey or an evaluation, it helps to answer seven questions first. The checklist gives you space to write your answers down. Teams that work through these questions tend to brief research partners more clearly, and that usually leads to more useful findings.

• What decision are we trying to make?
• What do we already know?
• What do we not know?
• Who has the information we need?
• What is the most appropriate method?
• What would make the findings credible?
• How will we use the findings?

Where Sonivance can help

Sonivance Ltd is a research, field data, M&E and insights partner. We support organisations across agricultural value chains with market and customer research, mobile data collection and field supervision, monitoring, evaluation and learning, and analytics and insights. We are not an agricultural producer or technology vendor. Our job is to help you get reliable evidence from the field and turn it into insight you can act on.

The checklist is an independent resource from Sonivance Ltd, prepared for attendees of AIAE 2026. It is not an official AIAE publication.