Cocoa’s AI Revolution May Be Won With Better Data

Artificial intelligence may transform the cocoa sector, but its most useful contribution is unlikely to arrive in the form of autonomous machines roaming farms.

At the recent The Cocoa Association of Asia (CAA) Conference in Singapore, moderator William Gaultier, partner at Enfactum, asked the ‘The Bittersweet Byte’ panel where AI is already creating value in cocoa – and where enthusiasm is running ahead of what the technology can reliably deliver.

The discussion pointed to a sector interested in AI but wary of exaggerated promises. Panellists identified the strongest near-term opportunities in relatively modest applications: analysing field data, forecasting demand, preserving institutional knowledge, reducing administrative work, supporting product development and delivering agricultural guidance through mobile devices.

The emerging picture was not one of technology replacing people, but of carefully designed systems helping them make faster, better-informed decisions.

Better Intelligence Begins With Better Data

Hans Christian Fleischer, chief commercial officer at Farmforce, highlighted the potential to extract new insights from years of first-mile data. Over time, AI could also provide farmers with better information about agricultural practices and disease detection.

The value of such systems, however, will depend on the quality of the information underpinning them. AI cannot compensate for field data that is missing, unreliable or poorly collected.

Technology intended for remote cocoa-producing areas must also be robust and scalable. It needs to remain useful where internet connectivity is limited and digital infrastructure cannot be taken for granted.

That makes data collection and system design central to cocoa’s adoption of AI. Sophisticated models will offer little practical value if the information reaching them is incomplete or farmers cannot reliably access their output.

Giving Small Businesses More Capacity

For smaller cocoa businesses, the immediate appeal of AI may lie in its ability to stretch limited resources.

Eduardo Burg, founder of COA&Co, described using the technology to undertake work that might otherwise require additional staff or capital. Applications have included brand development and the exploration of product and flavour trends.

He also identified potential uses in demand and supply forecasting. For smaller manufacturers, better forecasting could support decisions about stock and cash flow – areas in which mistakes can be particularly difficult to absorb.

His experience with AI-generated content and social media tools has been less convincing. This limitation points to a broader requirement for successful adoption: users must understand what a good result should look like before they can judge whether an AI system has produced one.

AI can expand a small company’s capacity, but it does not remove the need for subject knowledge. Without that expertise, greater speed and volume may simply make weak output harder to detect.

The Smartphone as a Gateway

Mishari Muqbil, fractional chief technology officer at Mishari.net, suggested that the smartphone could become the principal interface between farmers and AI.

Farmers would not need to understand the underlying technology. They could instead ask questions in natural language and receive information through a device they already use.

That accessibility could make sophisticated tools available well beyond specialist technical teams. It also creates a serious verification problem.

Muqbil described an instance in which an AI system suggested a cover crop. Local agricultural specialists subsequently indicated that the plant was likely to become invasive and difficult to remove.

The system had generated a potentially useful line of enquiry, but local expertise prevented a recommendation from becoming a damaging intervention. The example demonstrated both the value and the limits of AI: it can rapidly propose possibilities, but it cannot be assumed to understand the full environmental and agricultural context in which its suggestions will be applied.

Preserving Knowledge Before It Disappears

AI could also help cocoa organisations retain expertise that might otherwise be lost.

Muqbil described how one organisation recorded conversations with a senior engineer and processed the material into an AI-supported knowledge base. Junior colleagues could consult the system before approaching the expert directly, accelerating the transfer of specialised knowledge while preserving a point of human escalation.

A similar approach could be used to document the practical knowledge of experienced cocoa farmers. Insights developed over decades are often difficult to capture in formal manuals or databases. Recording that expertise could make it accessible to younger farmers and future generations.

The model is significant because it treats AI as a bridge to human knowledge rather than a substitute for it. The technology organises and retrieves expertise; people remain responsible for providing, interpreting and validating it.

Image shows William Gaultier (left) and a lively panel at the CAA Conference in Singapore — William Gaultier (left) moderated a lively panel on the use of AI in cocoa. Image: cocoaradar.com

A Prototype Is Only the Beginning

Natalia Poliakova, founder of Unscript IKIGAI, focused on the gap between creating an AI prototype and operating a dependable system.

An initial tool can now be built surprisingly quickly. Turning it into something suitable for routine business use is considerably more demanding.

Reliable implementation requires systems thinking, testing, appropriate data architecture, encryption and privacy controls. Businesses must also understand how commercially sensitive or highly personal information will be handled before placing it in a general-purpose AI system.

This distinction becomes particularly important as companies move from automation towards more autonomous systems. Automation can reduce repetitive work, but it should not be confused with independent judgement. Human emotion, accountability and the ability to understand context remain essential.

Technology Cannot Fix Farming’s Economics

The panel was similarly cautious about suggestions that AI alone could attract a younger generation to cocoa farming.

Fleischer argued that the fundamental condition is economic viability. Young people must first be able to see a realistic living in agriculture. Once that foundation exists, technology could make farming more attractive by improving access to advice, helping producers document their production history, supporting access to financial services and reducing risk.

Connectivity and digital services may also help narrow the gap between rural and urban life. But technology cannot compensate for an occupation that does not provide a viable livelihood.

AI may enhance the proposition of cocoa farming; it cannot create that proposition by itself.

Setting Boundaries for Agentic AI

Agentic AI – systems capable of carrying out multi-step tasks – could have substantial implications for cocoa businesses.

Instead of navigating multiple dashboards and repetitive processes, employees could use a single interface to assemble information, produce reports and complete administrative work. Tasks that once required teams to spend months compiling information could become considerably faster.

Yet greater capability increases the need for clear boundaries. Businesses must understand the processes they intend to automate, identify decisions that require human involvement and retain the expertise needed to evaluate the system’s output.

Delegating a task to AI does not remove accountability for the result.

A Revolution Built on Practical Gains

The panel’s message was one of restrained optimism.

AI’s most valuable early contribution to cocoa may be the removal of small, repetitive obstacles across the value chain, rather than a spectacular technological transformation. These practical gains could help farmers obtain advice, enable businesses to make better use of existing information and free employees from time-consuming administrative work.

Success will depend on reliable data, connectivity, privacy safeguards, local expertise and meaningful human oversight. The industry will also need a clear way to ensure that efficiency gains benefit farmers as well as companies.

AI can propose, organise and accelerate. People must still decide what is credible, appropriate and worth doing.

Cocoa’s AI Revolution May Be Won With Better Data – Not Robots