PowerPoint Presentation

[Audio] Welcome to Module 1. 1 From Research Outcome to Market-Ready Product.

[Audio] Let us begin Module 1 with a distinction that sounds obvious but has deep consequences: research success and market success are two different things. Research success is measured by novelty, rigour, and contribution to knowledge. Did you discover something new? Is your method sound? Has it been peer reviewed and published? Has the prototype worked in a trial? These are excellent achievements, and they matter, but they answer questions that are asked inside the research community. Market success is measured by a completely different set of questions, asked by completely different people. Does a farmer, a cooperative, or a food company have this problem often and painfully enough to act? Will they change what they do today in order to use your solution? Will they pay for it, and keep paying? And will they tell their neighbours about it? These two kinds of success rely on different kinds of evidence. In research, evidence is an experiment, a dataset, a statistical test. In the market, evidence is a conversation with a customer, a pilot that continues after the trial period, a signed agreement, a payment. They also run on different timelines. A research project may last three or four years and be judged at its end. A farmer may decide in a few weeks, within one growing season, and if your solution does not fit that window, you may wait another year. This is why researchers sometimes feel frustrated: a technically excellent result does not sell itself. And it is why business people sometimes feel frustrated with researchers: a technology that looks impressive in a demonstration may solve a problem nobody has. Neither side is wrong. Commercialisation is the work of building a bridge between the two, and that bridge has to be built deliberately, with methods, rather than hoped for..

[Audio] Innovation policy has a well-known image for this gap: the valley of death. The term is used for the stage between a technology that has been developed with public research funding and a product that is attractive enough for private investors, companies or customers to fund. Murphy and Edwards (2003) discussed this transition in the context of moving technologies from public to private sector financing, and the image has been used ever since because it describes something that many of you will recognise. Picture a project that ends. The grant has been spent, the deliverables are submitted, the team disperses, and the prototype sits on a shelf. Revenue has not started, because there are no customers yet. New research funding is hard to obtain, because the work is no longer new enough. And private investors are not interested, because the outcome carries too much uncertainty. That is the valley, and many promising results fall into it. Why does this happen? There are four recurring causes. First, there is no evidence of customer demand: the team believes the solution is valuable, but has never tested that belief with the people who would buy it. Second, ownership is unclear: nobody is certain who holds the rights to the software, the data or the invention, and this uncertainty makes partners and investors walk away. Third, there is no business team: researchers are brilliant at research, but nobody has taken responsibility for sales, pricing, support, and customers. Fourth, there is no bridge funding to carry the outcome through the months between the end of the project and the first revenue. The encouraging message is that every one of these causes can be addressed in advance. Customer evidence can be gathered through interviews and pilots. Ownership can be mapped and clarified. A business role can be assigned, or a partner found. Bridge funding can be planned for while the project is still running. That is why I said that the valley is crossed by method and planning, and not by luck. The remaining modules give you the method..

[Audio] Everything we have said so far applies to any sector. But digital agriculture has some particular features that shape how commercialisation works, and it is worth naming them directly. First, seasonality. A farm operates in cycles. A solution designed to help with a specific crop stage has a limited window each year in which it can be tested and sold. Feedback loops are long: you may need a full season to see whether a recommendation actually improved yield or reduced costs. This means that commercial learning is slower than in many other digital sectors, and that planning pilots around the agricultural calendar is essential. Second, margins. Many agricultural businesses operate with thin and variable margins, so they look hard at the return on any new investment. A solution must show a clear financial or practical benefit, and ideally in terms the farmer already uses: cost per hectare, hours saved, or risk reduced. Third, trust. In agriculture, decisions are strongly influenced by peers, by advisers, by cooperatives and by suppliers. A farmer will often trust a neighbour who has used a tool for two seasons more than any brochure or university presentation. This makes references and demonstration farms extremely important. Fourth, fragmentation. There are many small buyers rather than a few large ones, working with many crops, soils and climates. A solution validated in one context may not transfer directly to another, which means that the evidence you collect needs to be specific and local. Fifth, interoperability. Farms already use machinery, software and data systems from many suppliers. A new digital tool that does not connect to what exists adds work instead of saving it, and that is a very common reason for rejection. And sixth, data. Digital agriculture runs on farm data, and farmers rightly ask who owns it, who can see it, and how it will be used. Klerkx, Jakku and Labarthe (2019), in their review of social science research on digital agriculture, highlight that digitalisation raises questions that go beyond technology, including questions about data, power and the structure of value chains. For commercialisation, this means that how you handle data is part of your product, not an afterthought. None of this makes commercialisation impossible. It means that successful products in this sector are designed with these realities in mind from the start..

[Audio] Let us now be concrete about what exactly you might be commercialising, because "research outcome" is a broad phrase, and different kinds of outcomes lead to different kinds of offers. If your outcome is an algorithm or a model, for example a pest risk model, a yield forecast, or an image analysis method, the natural offers are software: a subscription service, an interface that other systems can call, or a licence that allows a larger company to build your model into its own product. If your outcome is a dataset, perhaps a carefully curated collection of field measurements, imagery or soil information, the offer might be a data service, a licensed dataset, or a benchmarking product that lets users compare themselves with others. Here, rights and consent matter enormously, and we will return to them in Module 3. If your outcome is a sensor or a device design, the offer may be a finished product, a kit, or a licence to a manufacturer that already has production and distribution. If your outcome is a method, a protocol, or accumulated know-how, such as a way of monitoring irrigation or managing a pest, the offer might be an advisory service, a training programme, or a certification scheme. This is often the quickest route to first revenue, because it requires less development than a product. And if your outcome is a prototype application or platform, the offer may be a product in its own right, or even the foundation of a new company. Now here is the key idea on this slide. An outcome is not yet a product. A product, or more precisely an offer, is the outcome plus everything that makes it usable and trustworthy for a customer: packaging, so that it can be installed or accessed without the research team; support, so that someone answers when it fails; proof, so that the customer believes it works in their conditions; and a price, so that it can be bought. A model that runs perfectly on the researcher's laptop is an outcome. The same model, wrapped in an interface, documented, supported, tested on three real farms and available for a fixed annual fee, is an offer. A useful habit starting today is to write the sentence: "My outcome is X; the offer a customer could buy is Y." If you find that difficult, you have identified the first piece of work..

[Audio] The first tool for measuring readiness is one you may already know: Technology Readiness Levels, usually abbreviated TRL. The scale was developed within NASA and was described in a widely cited 1995 white paper by John Mankins. It is also the scale used in the work programmes of Horizon Europe, which is why many of you will have written TRL numbers into proposals. The scale has nine levels. At the bottom, TRL 1 and 2, basic principles have been observed and a technology concept has been formulated. At TRL 3 and 4, there is experimental proof of concept, and the technology has been validated in a laboratory. At TRL 5 and 6, the technology has been validated and then demonstrated in a relevant environment. For agriculture, that means in conditions that resemble real farms, for example in a field trial or a pilot greenhouse. At TRL 7 and 8, a prototype is demonstrated in an operational environment, and the system is complete and qualified. And at TRL 9, the actual system has been proven in operation. What does this mean for you? Most research outcomes, at the end of a project, sit somewhere between TRL 3 and TRL 6. That is where research projects are designed to take them. But customers, as a rule, buy at TRL 8 or 9. They want something that is complete and proven in conditions like theirs. The distance between where a research outcome ends and where a purchase can begin is exactly the valley we talked about. The TRL scale is helpful because it forces honesty about the evidence. To claim TRL 6, you need a demonstration in a relevant environment, not a hope. To claim TRL 7, you need a prototype running in real operational conditions. Many teams find that they have overestimated their level by one or two points, and that is a useful discovery to make early. But I must add a warning, and it leads directly to the next slide. TRL measures only technical maturity. A technology can be at TRL 7 and still have no customer, no business model, unclear ownership, and no team to sell it. For commercialisation, we need a second measure..

[Audio] To complement TRL, we need a view of commercial readiness. Several frameworks exist. One example is the Commercial Readiness Index, developed by the Australian Renewable Energy Agency in 2014 for the energy sector, which describes progress from a hypothetical commercial proposition toward a mature, bankable market. You do not need to adopt a specific framework. What matters is that you look at the commercial side with the same discipline that TRL brings to the technical side. For this seminar, I suggest eight dimensions, and we will use them as a scorecard. First, customer and problem evidence. Have real potential users confirmed that the problem exists, that it matters, and that they are looking for solutions? Second, value proposition. Is the benefit to the customer clear, measurable, and better than what they use today, including doing nothing? Third, business model and pricing. Do you know how value will be captured, who pays, and roughly how much? Fourth, intellectual property and data rights. Is ownership clear, are the necessary permissions in place, and is the outcome protected in the way that suits your plans? Fifth, regulation and standards. Are there rules, certifications or standards that must be met before the offer can be sold or used, for example data protection, product safety, or sector rules? Sixth, team and roles. Who is responsible for the commercial work? Not the research, the commercial work: talking to customers, negotiating, selling, and supporting. Seventh, partners and channels. Who already has the customers' trust and could give you access, such as cooperatives, advisers, distributors, or machinery makers? And eighth, funding and resources. What will carry you from the end of the research funding to the first revenue? Each of these dimensions can be assessed on a scale. And here is the important point: the weakest dimension, not the average, usually decides how fast you can move. A technology at TRL 7 with a commercial readiness of one or two out of five is not ready for market, however impressive the demonstration. Equally, a strong team with a clear customer but a TRL 3 technology needs further development first. Seeing both sides together tells you where to invest your next euro and your next month..

[Audio] Now it is your turn. This is the first working exercise of the seminar, so please pause the video when I finish speaking. Step one: choose one research outcome. It should be a real one, ideally something you could imagine offering to customers within the next two or three years. Do not choose the most impressive outcome; choose the one you know best. Step two: write the sentence we discussed: "My outcome is X; the offer a customer could buy is Y." Keep it to a single sentence. Step three: score your outcome from one to five on each of the eight dimensions shown on the previous slide. One means there is no evidence at all. Five means there is strong, documented evidence. Be honest, and where possible, think about what evidence you could show to a sceptical stranger. A good test is this: if somebody asked, "How do you know?", could you point to something concrete? Step four: add your TRL, from one to nine, again being strict about what you have demonstrated rather than what you expect. Step five: circle your two lowest scores. These are your priorities. You will see that they connect directly to the remaining modules. Customer evidence and value proposition are the subject of Module 2. Intellectual property and data rights are Module 3. Business model and pricing are Module 4. Partners, channels and funding are Module 5. Please take as long as you need; five to ten minutes is a good guide. Keep your scorecard, because we will use it again in Module 5 and in your ninety-day plan. Pause the video now..

[Audio] Let us consolidate what we have covered in Module 1. We began with the idea that research success and market success are different things, with different evidence and different timelines, and that commercialisation is the bridge between them. We looked at the valley of death, the gap between publicly funded research and a product that customers and investors will fund, and we saw that its main causes, missing customer evidence, unclear ownership, no business team and no bridge funding, can all be addressed in advance. We discussed why digital agriculture has its own particular conditions: seasonality, thin margins, the importance of trust, fragmented buyers, interoperability with existing equipment, and sensitive data. We saw that a research outcome becomes an offer only when it is packaged, supported, proven, and priced. And we introduced two ways of measuring progress: the Technology Readiness Level for technical maturity, and an eight-point commercial readiness scorecard for everything else. If you completed the exercise, you now have something valuable: a first honest picture of where your outcome stands, and two priorities to work on. If you skipped it, I encourage you to go back, because everything that follows builds on it. In Module 2, we turn to the first and most important of those priorities for almost every outcome: the customer. We will learn how to identify who really has the problem, how to talk to them in a way that produces honest answers, how to express your value in terms they care about, and how to choose the first market segment to win. Let us move on..

References. Australian Renewable Energy Agency. (2014). Commercial readiness index for renewable energy sectors. ARENA. European Commission. (n.d.). Horizon Europe work programme: General annexes (technology readiness level definitions). [Add the version and URL of the work programme used.] Klerkx, L., Jakku, E., & Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS – Wageningen Journal of Life Sciences, 90, 100315. https://doi.org/10.1016/j.njas.2019.100315 Mankins, J. C. (1995). Technology readiness levels: A white paper. NASA, Office of Space Access and Technology, Advanced Concepts Office. Murphy, L. M., & Edwards, P. L. (2003). Bridging the valley of death: Transitioning from public to private sector financing. National Renewable Energy Laboratory..

[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..