PowerPoint Presentation

[Audio] Welcome to Module 3: From technology to business model: new value creation.

[Audio] We now reach the central transition of the seminar. In Module 1, we defined digital transformation as broader than buying technology. In Module 2, we looked at the technologies that make new capabilities possible. Now we ask the question that matters most to a business owner: what do we actually do differently with those capabilities? A business model describes the logic through which an organisation creates value for a customer and captures part of that value for itself. In practical terms, we can reduce the question to four elements: what are we offering, who is it for, how do we deliver it, and how do we earn from it? Digital technologies can change any one of those elements. A farm might continue selling the same crop but add a digital advisory service. A cooperative might move from selling equipment to offering equipment-as-a-service. A food producer might use traceability data to justify a premium product. Or a producer might use an online channel to reach customers directly instead of relying entirely on intermediaries. This is the key distinction: the technology does not automatically create the business opportunity. The opportunity emerges when the capability created by the technology changes the economics or the customer experience of the business..

[Audio] A common mistake in digital transformation is to begin with the technology: 'We have sensors, so what can we do with them?' or 'We should use AI.' A stronger approach reverses the sequence. Start with the customer problem. What is costly, slow, risky, uncertain, inaccessible, or frustrating for the customer today? Then ask what outcome the customer would value. Only after that do we ask which digital capability could help us deliver that outcome better. For example, imagine a group of farms struggling to make irrigation decisions during increasingly variable weather. The customer problem is not 'lack of sensors'. The problem is uncertainty and the cost of poor timing. A digital service could combine sensor readings, weather forecasts and crop information to provide irrigation recommendations. The value proposition is not the sensor; it is better decisions and reduced risk. This customer-first sequence is especially important for SMEs because resources are limited. A small organisation cannot afford to digitise everything. It needs to identify the few problems where digital capability can produce a meaningful and repeatable customer benefit..

[Audio] One of the most accessible forms of digital business-model innovation is to combine a physical product with an ongoing service. This is sometimes described as servitisation or a product-service model. The traditional agricultural transaction is often simple: a supplier sells a machine, input, piece of equipment or food product, and the transaction ends. Digital capabilities make it possible to keep the relationship active after the initial sale. A machinery company can add remote monitoring and predictive maintenance. An irrigation supplier can provide continuous monitoring and recommendations. A food producer can combine the physical product with digital information about origin, production practices or sustainability performance. The business benefit is not only additional revenue. A service relationship can create a stronger and more continuous customer relationship, provide better information about customer needs, and make the offering harder to compare purely on price. But there is a warning. A service is not free to deliver. It requires people, data infrastructure, customer support and clearly defined responsibilities. The business must calculate whether the additional customer value is large enough to justify those costs..

[Audio] A second major pattern is recurring access: subscription or data-as-a-service. Instead of asking the customer to purchase the entire technology stack, the provider sells continuing access to a capability. This can apply to farm-management software, analytics, monitoring, digital advisory services and traceability platforms. The customer might pay monthly, annually, per hectare, per monitored asset, or according to usage. This model can be attractive in agriculture because it reduces the upfront investment required from the customer. A small farm may not want to purchase a complete analytics platform, but it may be willing to pay a manageable annual fee for access to recommendations. For the provider, the attraction is recurring revenue and a longer customer relationship. But recurring revenue also creates a recurring obligation. The service has to keep delivering value. If the customer stops seeing useful outcomes, the subscription will not survive. This means that pricing cannot be separated from value measurement. A provider should understand what outcome the customer is buying: saved inputs, reduced labour, better quality, lower risk, better compliance, higher selling price, or something else..

[Audio] A third pattern is the platform or marketplace model. Instead of simply selling a product to one customer, the organisation creates an environment that connects different groups. In agriculture, a platform might connect farmers with buyers, producers with consumers, farms with contractors or service providers, or technology startups with agricultural businesses. The platform creates value by making transactions, information exchange or coordination easier. Revenue can come from commissions on transactions, subscriptions, premium services, advertising, or complementary digital services. The economics can become powerful because the platform may benefit from network effects: more participating farmers can attract more buyers, and more buyers can make the platform more attractive to farmers. However, platforms also introduce strategic risks. Recent research on agricultural platformisation highlights the possibility of fragmented data ecosystems, vendor lock-in and increasing dependence on a small number of powerful technology and agribusiness providers. citeturn0search0turn0search1 For an SME, the practical lesson is not that every business should build a platform. It is to recognise when the business is in a coordination problem where connecting participants creates more value than simply selling another product..

[Audio] Digital transformation can also change the route to market. Instead of selling entirely through wholesalers, distributors or retailers, an agricultural or food business may use digital channels to build a direct relationship with customers. This could be a farm e-commerce store, an online ordering system, a recurring produce box, a digital community, or a combination of physical and digital customer experiences. The opportunity is not simply that the producer receives a higher selling price. Direct relationships can generate customer knowledge, repeat purchasing, loyalty and better understanding of demand. Digital information can also support personalised offers and make provenance or sustainability information part of the customer experience. But direct-to-consumer does not mean friction-free. The business takes on responsibilities that an intermediary previously handled: order processing, fulfilment, delivery, returns, customer service and marketing. So the strategic question is very practical: which intermediary function could our organisation perform better, more efficiently or more distinctively through digital tools? The answer may be the foundation for a new channel..

[Audio] By now we have repeatedly encountered data. It is therefore worth asking a more precise question: when does data become an economic asset? Data can create value by improving decisions, predicting events, personalising recommendations, supporting transparency, coordinating several actors, monitoring performance or enabling an entirely new service. A farm's raw sensor readings may have little standalone value. A reliable service that turns those readings into a decision that saves water may have substantial value. This distinction is important because collecting more data is not the same as creating more value. Data has to be relevant, reliable, accessible and connected to a use case. Most importantly, there must be a customer or internal business decision that benefits from it. There is also a governance dimension. Agricultural data can raise questions about who owns it, who can access it, whether it can be transferred to another provider, how it is protected, and how it can be combined with other datasets. The OECD has highlighted unclear and fragmented agricultural data governance as a barrier to trust and adoption. citeturn0search9 So the goal should not be maximum data collection. The goal should be responsible data use that produces measurable value for the organisation and its customers..

[Audio] Let's turn the patterns we have discussed into a simple design tool. When you have a potential digital business opportunity, walk through these seven questions. First: who has the problem? Be specific. 'Farmers' is usually too broad. You might mean small vegetable producers with limited irrigation expertise, or processors that need faster traceability verification. Second: what outcome are we improving? This is the value proposition. Third: what digital capability enables that outcome? Only here do we select the relevant technology or combination of technologies. Fourth: how does the customer access the value? Through a product, a service visit, an app, a platform, a subscription, a marketplace or a combination? Fifth: how and when do we get paid? Sixth: what capabilities and costs are required to deliver reliably? And seventh: what evidence will tell us the model is working? This last question is essential. A digital business model should have measurable assumptions that can be tested. Notice the logic: customer first, value second, technology third, economics and delivery after that. This prevents technology from becoming the answer to a question nobody actually asked..

[Audio] The final message of Module 3 is about experimentation. A business model is a set of assumptions. Digital transformation becomes much less risky when those assumptions are tested in small steps. Choose one customer segment, one problem and one value proposition. Then build the smallest version of the digital service that allows you to test the idea. The purpose of the pilot is not to demonstrate that the technology works in isolation. The purpose is to learn whether customers actually use the capability, whether they perceive value, and whether the economics can work. For example, if the idea is a digital irrigation advisory service, do not begin by building a complete platform for thousands of farms. Start with a small number of customers and test whether the recommendations are understandable, whether customers change behaviour, whether measurable savings occur, and whether they would pay for continued access. Measure both customer value and provider economics. How much time does the service require to operate? What is the cost per customer? How often do customers use it? Do they renew? Does the value generated exceed the price and delivery cost? This is the bridge into Module 4. We are now going to look at real cases and ask not only what technology they adopted, but what business model they built around it, how they captured value, and what lessons smaller agricultural and agri-food businesses can take from those examples..

[Audio] Let's proceed to module 3 recap: Digital transformation creates business opportunity when digital capabilities change how value is created, delivered, or captured. Start with a customer problem and desired outcome — not with a technology. Common digital business-model patterns include product + service, subscription/data-as-a-service, platforms/marketplaces, and direct-to-consumer channels. Data creates economic value when it is transformed into useful decisions, predictions, transparency, coordination, or services — with appropriate governance. A strong digital business model connects customer value, digital capability, delivery model, revenue logic, costs, and measurable evidence. The safest route is iterative: test one business-model hypothesis, measure the result, learn, and scale. Up next: Module 4 — Case Studies & Success Stories.

References. European Commission. (2024). Unlocking the potential of digital and data technologies. Directorate-General for Agriculture and Rural Development. https://agriculture.ec.europa.eu/vision-overview/research-innovation/digital-transition_en Florez, M. (2026). The role of solution business model patterns in digital agriculture: Linking business model components and sustainable outcomes from a startup business perspective. Business Strategy and the Environment. https://onlinelibrary.wiley.com/doi/10.1002/bse.71316 OECD. (2021). Issues around data governance in the digital transformation of agriculture. OECD Publishing. https://doi.org/10.1787/53ecf2ab-en Sauvagerd, M., Mayer, M., & Hartmann, M. (2024). Digital platforms in the agricultural sector: Dynamics of oligopolistic platformisation. Big Data & Society. https://doi.org/10.1177/20539517241306365 Uyar, H., Rizou, S., & Fountas, S. (2024). The role of platforms in agricultural data value creation. The Journal of Applied Engineering and Agriculture, 1(2). Hackfort, S., Marquis, S., & Bronson, K. (2024). Harvesting value: Corporate strategies of data assetization in agriculture and their socio-ecological implications. Big Data & Society. https://doi.org/10.1177/20539517241234279 Sun, Y., Miao, Y., Xie, Z., & Wu, R. (2024). Drivers and barriers to digital transformation in agriculture: An evolutionary game analysis based on the experience of China. Agricultural Systems, 221, 104136. https://doi.org/10.1016/j.agsy.2024.104136.

[Audio] Thank you!. thank you!. TALLHEDA has received funding from the European Union's Horizon Europe research and innovation programme under Grant Agreement No. 101136578. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the European Union nor the granting authority can be held responsible for them..