PowerPoint Presentation

[Audio] Welcome to Module 4: Case studies and success stories.

[Audio] We now move from frameworks to examples. The purpose is not to show a list of impressive technologies. It is to train you to recognise the business logic underneath successful digital transformation. For every case, ask the same seven questions: What problem is being addressed? Who is the customer or beneficiary? What digital capability is being used? What value does it create? What business model delivers that value? What evidence suggests the model works? And can it scale or be adapted elsewhere? A case can look impressive because of its technology, but technology alone does not tell us whether a business model is viable. We are looking for the mechanism that converts digital capability into customer value and economic value. These cases are patterns, not recipes. A solution that works for a large machinery company may not suit a small farm. The objective is to extract transferable principles..

[Audio] Let's begin with a case particularly relevant to our European and Greek context. Research on precision agriculture in Greece documents farms using digital technologies not simply as isolated equipment, but as part of broader entrepreneurial and business-model configurations. One example is the collaboration between Agrostis and Ecodevelopment around the ifarma farm-management platform and the PreFer site-specific fertilisation service. ifarma is a cloud-based Farm Management Information System organising fields, crops, activities, inputs and resources. PreFer adds a specialised analytical service. It uses spatial and agronomic information, including soil properties, crop indices, climatic parameters and yield records, to produce prescription maps for site-specific fertilisation. The research describes big-data analysis and machine-learning methods in producing these recommendations. The business-model lesson is more important than the technical detail. A digital platform becomes more valuable when complementary specialist services can be built on top of it. The customer is not simply buying data; the customer is buying a better farming decision. This illustrates the transition from Module 3: technology capability becomes a service, and the service can support an ongoing customer relationship rather than a one-time equipment sale..

[Audio] Let's analyse this case using our seven-question framework. The customer problem is not lack of machine learning. It is uncertainty about how to manage variation within the farm. Applying the same fertiliser rate everywhere may be simple, but it can be inefficient when field conditions vary. The digital capability combines farm-management data, spatial information, agronomic knowledge and analytical methods. The value proposition is therefore decision support that translates complex data into an actionable recommendation. Raw data is rarely the complete value proposition. The customer normally pays for an outcome or decision that the data makes possible. For SMEs, another lesson is that you do not need to develop every digital capability internally. Partnerships can combine a farm-management platform, agronomic expertise and technology into a stronger offering..

[Audio] Our second case moves from an individual farm service to a platform model. Borrero and Mariscal studied the design of a digital data platform for agriculture in Andalusia, Spain, with particular attention to small farmers. The proposed farmdata platform aggregates public and private data, including weather and satellite information and farmer-generated information from sensors, UAVs and applications. It uses cloud infrastructure and provides decision-support functions, including visualisation and predictive tools. What makes this case particularly useful is that governance is treated as part of the value proposition. Farmers wanted security and transparency and needed clear information about who could access data, how it would be used and how value would be distributed. This takes us beyond the idea that digital transformation is simply technical architecture. A data platform is also an institutional arrangement. Trust, access rules and transparency can determine whether customers participate. For a business considering a data-driven model, governance cannot be left until the end. It is part of the product..

[Audio] The platform logic is powerful because the same infrastructure can support several services. Once data from different sources is integrated, additional applications can be developed without rebuilding everything. The research describes a modular platform in which approved providers and university spin-offs can contribute tools and complementary services. This creates an ecosystem rather than a single application. But participants need to perceive a fair exchange. Farmers provide data and need to understand what they receive in return. If the platform captures most of the value while the data contributor sees little benefit, trust and participation can weaken. That is why platform business models require two architectures at once: technology architecture—databases, APIs, cloud services and analytics—and governance architecture—rules about access, ownership, transparency, security and value distribution. This is also relevant to TALLHEDA's skills focus: digital transformation requires people who understand technology as well as the organisational and economic arrangements around it..

[Audio] Our third case illustrates transformation from a physical-product business toward a connected digital service ecosystem. John Deere's Operations Center is a useful illustration. The company describes how connectivity and digital tools allow growers to monitor operations, make tactical decisions and plan strategically using data from their farms. The platform also supports work planning, field progress monitoring and machine-performance analysis. The strategic significance is the customer relationship. A tractor or combine traditionally creates value through physical performance, with the transaction concentrated around equipment purchase. Connectivity allows the manufacturer to remain part of the customer's operating environment across the machine lifecycle. This is the product-plus-service pattern from Module 3. The physical asset remains important, but software, connectivity, analytics, support and data create additional value layers. For smaller businesses, the lesson is not to imitate a global machinery manufacturer. It is to ask: after the sale, what information, monitoring, optimisation or support could we continue to provide?.

[Audio] Our fourth case focuses on digital agronomy. CropX provides a connected platform combining soil and agronomic information with analytics and decision support. Its published customer stories use outcome-oriented language. For example, CropX reports a Colorado family farm reducing irrigation by approximately one-third using its sensors in partnership with the Colorado Ag Water Alliance, while other recent cases describe yield and irrigation improvements. These are company-reported customer stories, so they should be treated as illustrative evidence rather than independent causal evaluations. The business-model lesson is the shift from selling a sensor to delivering an ongoing agronomic capability. Sensors are part of the system, but the relationship is built around monitoring, interpretation and recommendations. A digital service becomes more commercially persuasive when the customer can connect it to an operational outcome: water saved, input reduced, yield improved, labour time reduced or risk managed. When hearing a success claim, ask what exactly was measured, over what period, against what baseline, and whether the result can be replicated. Digital transformation should be evidence-driven, not hype-driven..

[Audio] Let's bring the cases together. They look different on the surface—precision fertilisation, a regional data platform, connected agricultural machinery and digital agronomy. But the underlying logic is similar. First, they begin with a real business or customer problem. Second, they combine technologies rather than relying on one tool in isolation. Third, value comes from something the customer can use: a better decision, a service, coordination, transparency or a measurable outcome. Fourth, the customer relationship changes. Digital capability allows the provider to remain connected to the customer over time rather than completing the relationship at the point of sale. Fifth, data governance and trust become strategic because information flows between organisations. Sixth, transformation should be treated as an iterative learning process. We need evidence about adoption, customer value and economics before scaling. That gives us a practical bridge to Module 5. We have covered what digital transformation means, the technologies involved, the business models they can enable, and examples of those models in practice. The final module will bring this into a roadmap for participants' own organisations..

[Audio] Let's proceed to module 4 recap Analyse cases through the business problem and value-creation logic, not technology alone. Digital platforms can combine data sources and support multiple services, but governance and trust are essential. Precision-agriculture technologies can become recurring advisory and decision-support services rather than one-off purchases. Connected machinery illustrates how a physical product can anchor a continuing digital customer relationship. Digital agronomy becomes more compelling when value can be expressed through measurable operational outcomes. Transferable pattern: problem → digital capability → customer value → business model → evidence → scale.

References. Borrero, J. D., & Mariscal, J. (2022). A case study of a digital data platform for the agricultural sector: A valuable decision support system for small farmers. Agriculture, 12(6), 767. https://doi.org/10.3390/agriculture12060767 Partalidou, M., Paltaki, A., Lazaridou, D., Vieri, M., et al. (2021). Business model canvas analysis on Greek farms implementing precision agriculture. Agricultural Economics Review, 19(2), 28–45. Lepore, F., Ortolani, L., Iliopoulos, C., Vergamini, D., & Brunori, G. (2025). Assessing costs and benefits of agricultural digitalisation: The case of data collection support tools in agricultural-pastoral farms. Italian Review of Agricultural Economics, 80(2), 89–104. https://doi.org/10.36253/rea-15851 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 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://doi.org/10.1002/bse.71316 John Deere. (2026). U.S. Model Farm: Economic and sustainable outcomes from the model farm. https://www.deere.com/en-us/our-company/sustainability/us-model-farm CropX. (2026). Customer case studies and success stories. https://cropx.com/customer-case-studies/.

[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..