From October 18 to 20, 2023, we had the opportunity to conduct an exciting workshop on “Resilience and Sustainability in Smart Service Ecosystems” at the Mobiliar Forum in Thun, Switzerland. The workshop was organized by the Databooster program, which supports innovative partnerships and is a hub for data-driven innovation in Switzerland. The workshop brought together three companies from the energy and logistics sectors, as well as researchers and experts from the field of service innovation and digitalization, to discuss how companies can become more resilient and sustainable with their partners and customers through new and existing services.
The workshop was motivated by the high volatility of the business ecosystems in recent years, which was unexpected and caught society and economy unprepared. Both on the human resources side and in the material logistics, there were unforeseen bottlenecks. The market situation was determined by the insufficient availability of resources and not by the demand. As a result, there was a wave of short-term oriented management reactions that were not sustainable in the medium and long term and thus laid the foundation for the next wave of challenges.
Against this background, it became clear that the business ecosystems need to become more resilient, i.e., able to withstand shocks and adapt quickly to dramatically changed situations. Resilience can be achieved by increasing the ability to absorb shocks (e.g., through more redundancy, robustness, or agility) and by developing dynamic capabilities to adjust to changing needs (e.g., through creativity, adaptability, or flexibility).
Smart service systems are closely linked to the ability of dynamic adaptation. By their inherent design of tightly interconnected resources, smart service systems inherently contain the ability to dynamically realign and rewire existing resources to adapt to dynamically changing needs.
The workshop started with a discussion to gain a deeper understanding of resilience and sustainability in smart service systems. Then, the situations of the three participating companies were examined from this perspective in sub-groups and opportunities for new services were identified. Building on this, the moderation very smartly guided the development of common solution approaches that involve all three companies. As a result, joint project ideas are now available, which are to be further elaborated in a follow-up step towards implementation.
The workshop groups had access to various design and prototyping tools and sources provided by the Mobiliar Forum. The groups had to present their results and recommendations repeatedly during the workshop.
The workshop was a great learning experience for all of us as we gained new insights into the topic of resilience and sustainability in smart service systems. We also enjoyed interacting with other participants from different backgrounds and perspectives. It became apparent that such workshops are very valuable for fostering collaboration and innovation among companies, researchers, and experts in Switzerland.
A big thank you goes to Sophie Bürgin for the excellent moderation, to Fabrizio Laneve for providing the beautiful and inspiring Mobiliar Forum infrastructure and to all participating company representatives for their spirited participation and co-creation.
On October 25 and 26, 2023, we conducted the Smart Data Forum, a two-day event co-organized by Easyfairs and the databooster, at the Messe Zürich. The theme of the forum was “Smart Services and Resilience”, and it featured presentations and discussions from experts and practitioners in the field of smart service systems.
The forum aimed to explore how smart services can help businesses and society cope with the challenges of volatility, uncertainty, complexity, and ambiguity (VUCA) in the current and future environment. The speakers shared their insights and experiences on how to design smart services that are resilient, sustainable, adaptable, and innovative.
Some of the topics that were covered in the forum included:
How to use data and analytics to support decision making and service innovation in smart service systems
How to leverage digital platforms and ecosystems to create value and network effects in smart service systems
How to apply design thinking and agile methods to develop user-centric and co-creative smart services
How to balance resilience and efficiency in smart service systems
How to measure and improve the performance and impact of smart services
The forum was moderated by Jürg Meierhofer, senior lecturer and director for studies (CAS Smart Service Engineering, MAS Industry 4.0) at the ZHAW Zurich University of Applied Sciences. He also provided some useful frameworks and tools for understanding and designing smart services.
The speakers were Reto Zuest, Mirko Maurer, Daniel Politze, Nikolas Schaal, Nikolaus Obwegeser, and Jean Paul Potthoff. They represented different sectors and domains, such as manufacturing, IT management, transport, or research and development. They presented some interesting case studies and best practices from their own organizations or projects.
We learned a lot from the forum and I enjoyed networking with other participants who shared a common interest in smart service systems. The forum was a valuable opportunity to exchange knowledge and ideas on how to create smart services that can enhance resilience and sustainability in business and society.
On September 29, 2023, the Expert Group Smart Services held its first presence meeting since 2019 to discuss strategic topics for the Expert Groups in the upcoming years. There was broad agreement that service value creation using generative artificial intelligence has significant future potential and that there is a considerable need for research in this area. From the perspective of service value creation, embedding artificial intelligence in service concepts along the value chain and customer lifecycle represents a promising topic. Interestingly, the multidisciplinary perspectives of the Expert Group turned out to be relevant for successful projects, such as consumer and work psychology aspects, data analytics and data management, as well as business modelling.
The Expert Group’s strategic core team meeting was followed by a very inspiring presentation by Dr. Sebastian Domaschke from our member company eraneos (https://www.eraneos.com/). The presentation showed by 3 impressive case studies how data could be used in practice to create business value. Many thanks to Sebastian!
By Markus Konz, Swiss Re, and Jürg Meierhofer, ZHAW
On August 29, 2023, we conducted an idea mining workshop in the field of financial risks of smart services in Zurich. The topic of the workshop was centered around the following two risk types, which emerge from the new characteristics of data-driven services:
If a provider (typically in manufacturing, i.e., providing a machine) goes to output oriented services, it takes over the risk of temporarily being not able to provide the output promised. This may create financial damage to the customer and depending on the contract, the provider must pay a penalty for this or does not get the fee from the customer because there is no output. How can this financial risk for the provider be quantified and what would be possible instruments to mitigate this risk?
If the provider going to a XaaS (e.g., machine as a service) offering, it lowers the investment hurdle for the customer because there is only OpEx, no CapEx. However, the provider needs a priori financing for the machine, e.g., by a loan. It pays interests for this loan which need to be re-financed by the recurring service fees. Additionally, there is an uncertainty regarding these future cash flows (e.g., due to insolvency).
Applying the service design methodology, the workshop had the goal to shed light on these questions:
Desirability: To what extent do machine builders actually have the pain assumed above and need a solution?
Feasibility, viability: To what extent do financial service providers have the capacity to deliver services for these needs in an economical way?
Almost twenty attendees from several countries contributed to the rich and multifaceted discussion, which brought up the potential for further going analysis and projects.
By Miriam Baumgartner, Agroscope, Stefan Rieder, Identitas and Jürg Meierhofer, ZHAW
On July 3, 2023, we conducted an insightful databooster idea shaping workshop in a domain with traditionally rather low degree of digitalization, but yet a considerable potential to create value for the actors of the ecosystem. Precision livestock farming allows the automatic monitoring of animal health and welfare. Its potential has been proven particularly in the dairy sector. Not so much attention has been paid to the digital monitoring of equine welfare, although the well- being of horses is of great concern to their owners and of public interest.
We created a layout of the business and private ecosystem and evaluated the needs terms of jobs and pains of different actors. At the same time, a strong focus was put on the economic quantification of the pains and their potential mitigation or elimination, which provided some results which were previously not obvious in their qualitative and quantitative dimension and which contribute relevant elements for a business case.
The workshop was substantially complemented by the expert knowledge from «Hochschule für Wirtschaft und Umwelt Nürtingen-Geislingen (HfWU)» and BestTUPferd GmbH (Berlin) and moderated by Jürg Meierhofer from the databooster.
Co-creation for solving tomorrow’s challenges today
On June 22nd-23rd, the IEEE 10th Swiss Conference on Data Science (SDS2023) took place in Zurich/Schlieren. The first day was dedicated to interactive workshops and held in the amazing JED event location in Schlieren. With 15 workshops, more than 350 participants and a lot of enthusiastic feedback, we can draw a very positive conclusion. At this point, we would like to thank again all workshop organizers and the spirit of all participants – without your commitment and active participation, the workshop day would not have been possible! In this blog, we would now like to briefly present 4 selected workshop formats which, within the framework of the databooster program of Innosuisse, had a particular focus on co-creation and ideation.
The workshop Responsible AI – Explainability, Transparency and Fairness of data-based applications in practice, was organized by CLAIRE (R. Chavarriaga), the Expert Group Data Ethics (C. Heitz), Eraneos (B. Müller) and the applied universities FHGR (C. Hauser) and ZHAW. The event started with short keynote speeches by Xavier Renard (AXA Group Paris) and Arman Iranfar (CertX), who addressed the challenges around the concepts and levels of fairness, non-discrimination, explainability or transparency as well as the upcoming regulatory frameworks in the frame of the AI Act and the related requirements.
Subsequently, various break-out groups were formed, and challenges and solutions with a focus on practical implementations were developed and discussed. The key challenges identified were: i) What is a structured approach to develop responsible AI?, ii) How to set up a risk assessment which is suited for addressing the risk-based approach required by the EU AI act?, iii) How can data scientist be connected with other stakeholders for making sure that the engineering of AI is fully connected with an integrated risk assessment?, and iv) How to measure the “degree of responsibility” of responsible AI?
For identified challenges for which no solution was found during the workshop, specific innovation support programs were presented offering possibilities to be able to continue the work in depth afterwards.
In The Power of Knowing Where, the participants discuss existing and brainstorm potential future use cases across various sectors. Using open-source datasets, the participants were able to implement their ideas in the subsequent hands-on session and to investigate and predict natural hazards relating to climate-change in Switzerland – from the exploration, visualization, and manipulation of location-based data towards the use of it in predictive modelling.
In Innovation & Business Cases, the participants got insights into the business potential of geospatial data and value-added service presented by Jonas Weiss (IBM). Furthermore, legal requirements on ESG reporting and risk assessments of large investments were discussed and challenges but also opportunities presented – paving the way to completely new business perspectives and new business models that can be exploited. The STDL presented success stories and use this to lead to an open and interactive discussion round allowing participants to bring their specific questions and case studies to the table for constructive feedback – and to push ideas further with new approaches, new motivation, and new contacts.
P. Hutzli and B. Russinello (la Mobilière) gave in their workshop an Introduction and ROI of Knowledge Graphs, based on three examples in watch industry, energy and insurance. They presented, why Swatch, BKW and Mobiliar have chosen Knowledge Graph technology over classical approaches to combine data and metadata from many different sources into one coherent data network. The complete process was discussed – from the initial pain points and how they built their solutions to the business cases and the positive returns on investment. Inspired by these specific journeys, the participants were motivated to identify similar pain points and use cases in their own organization, to develop a simple road map and to calculate a quick Return on Investment (ROI). Afterwards, the specific business cases were presented for immediate feedback, mutually inspiring everybody to look for new use cases.
The Expert Group Smart Maintenance (Lilach Goren Huber, Manuel Arias Chao – ZHAW) organized the workshop Deep Learning for Predictive Maintenance: Scalable Implementation in Operational Setups. In this workshop, the gap between the state-of-the-art research on the one hand, and industrial implementation, on the other hand was discussed.
One of the underlying theses for this was that the technological and algorithmic development is driven primarily by academia and less by industry which stands in contrast to other applications of DL such as image recognition, speech recognition and gaming, which are driven by industry giants like Google, Meta, or Microsoft. In a co-creation setup, the following challenges were addressed, and solutions discussed based on the use-cases of wind turbines and aircraft engines: i) dealing with the lack of labeled historical faults, ii) effective combination of domain knowledge for fault isolation, iii) upscaling the Fault Detection and Isolation (FDI) algorithms to multi-component systems, and iv) quantifying uncertainty in fault detection problems.
The SDS2023 workshop day convinced with the awesome atmosphere, highly committed workshop organizers and open-minded participants interested in exchange and cooperation! Thanks a lot for this inspiring day!
At this year’s IEEE Swiss Conference on Data Science there was a very informative workshop on Generative AI in Practice. The presentations and discussions in this workshop made clear that the generative AI technologies, and Large Language Models (LLMs) in particular, are very versatile and powerful. It also became apparent, however, that the business potential of LLMs largely remains unclear.
OpenAI’s AI chatbot ChatGPT has already gained over 100 million users within the first two months of its release. This makes this internet service the fastest growing of its kind. In comparison, the runner-up, Tiktok, took a full nine months to reach a similar number of users. The potential of the underlying technology, LLMs, is undisputedly recognized by business leaders. According to a study by Gartner among business executives1, 45% of respondents have already intensified their investment in artificial intelligence (AI) as a result of the success of ChatGPT.
LLMs are extremely large-scale artificial neural networks that are trained with terabytes of textual content to complete texts. LLMs can therefore generate new content and thus belong to the class of generative AI solutions. In contrast, discriminatory AI models can only establish assignments or classifications between different inputs and predefined outputs.
LLMs can be used for a variety of purposes, such as text summaries, sentiment analysis, or named entity recognition. Although there are already initial indications of the productivity gains that can be achieved through the use of LLMs, the role of this technology for the business models of industrial companies is still largely unclear.
The Impact of LLMs on Business Models
In the following, we present the findings derived from own prototypes and a comprehensive analysis of more than 50 real-world use cases pertaining to the application of LLMs within various companies. A distinction can be made between four mechanisms of how business models are changed with LLMs:
new customer benefits
new sales and communication channels
increased business process automation
improved use of information resources
New Customer Benefits
LLMs play a crucial role in operating personal assistance systems. Instacart, a grocery delivery service, utilizes LLMs to address nutrition queries and offer personalized product recommendations.
Furthermore, LLMs serve as personal coaches, particularly valuable in the realm of learning. Khan Academy, an educational platform, employs LLMs to detect errors in programming tasks and generate helpful solution hints.
LLMs also possess the capability to independently generate content that is relevant to customers. Copy.ai, an online service, exemplifies this by creating blog posts, social media content, and website material based on bullet-style keywords and predefined language styles.
Additionally, LLMs facilitate voice-based interactions with machines. Mercedes, for instance, integrates LLMs into the infotainment systems of their premium vehicles to provide comprehensive answers to complex customer questions while driving.
New Sales and Communication Channels
LLM-based chatbots offer significant advantages in automating sales and customer service processes. In Switzerland, the insurer Helvetia has successfully implemented an LLM-based chatbot to handle inquiries regarding their product range.
Another notable example is Solana, a blockchain operator, leveraging ChatGPT in their customer service operations. By utilizing LLM-based chatbots, Solana effectively assists customers in resolving intricate service-related challenges, ensuring a seamless user experience.
Increased Business Process Automation
LLMs offer remarkable potential in enhancing automation within information-intensive business processes. The Radisson hotel chain has effectively employed LLMs to automate the handling of customer inquiries and cancellations, enabling swift and accurate responses. Additionally, LLMs generate helpful suggestions for emails and review responses, streamlining communication and enhancing customer satisfaction.
Another notable application is observed in Swiss Migros Bank, where LLMs play a pivotal role in partially automating mortgage application processing. By intelligently recognizing case-specific requirements and evaluating text-based customer documents, LLMs assist in expediting and improving the efficiency of the application review process.
Improved Use of Information Ressources
The fourth mechanism focuses on the enhanced exploitation of information resources. Morgan Stanley, a securities trading company, exemplifies this by leveraging LLMs to facilitate employee access to and evaluation of internal documents. Through the application of LLMs, Morgan Stanley streamlines the process of retrieving and analyzing crucial information, ensuring efficiency and informed decision-making within the organization.
Likewise, Zurich Insurance capitalizes on LLMs to automate contract evaluation and ascertain the validity of insurance claims. This strategic employment of LLMs empowers Zurich Insurance to effectively and efficiently assess the presence of a claim, ultimately leading to enhanced operational processes.
Current Challenges in the Commercial Application of LLMs
When evaluating the strategic importance and necessity for action concerning the commercial application of LLMs, companies are faced with three fundamental questions.
What are technical risks associated with the use of LLMs?
One significant challenge arises from the risk of LLMs generating false or inaccurate statements, commonly known as hallucinations. However, advancements in prompt engineering, which involve carefully formulating textual instructions, have already proven effective in mitigating this risk to a considerable extent. Additionally, the development of fact-checking methods is underway to ensure that the output generated by LLMs is rooted in accurate and verified information.
Another crucial technical concern revolves around the security of sensitive data shared during the prompting process. The potential exists for malicious actors to employ targeted prompts, referred to as Training Data Extraction Attacks, to extract training data from LLMs. Consequently, it is imperative to eliminate the possibility of shared data being utilized to train publicly accessible LLMs. Alternatively, dedicated LLMs can be utilized to safeguard the confidentiality of shared data.
What legal framework conditions need to be taken into account?
When utilizing LLMs, it is essential to adhere to relevant data protection regulations, particularly if personal data is being processed. This entails fulfilling information obligations and respecting individuals’ rights to information, similar to other AI applications.
Additionally, companies need to consider the evolving legal landscape, such as the European Union’s AI Act. The current draft of the AI Act specifies certain requirements for LLMs, including the prevention of generating illegal or discriminatory content and the disclosure of copyrighted content used during training. However, a comparison of different LLMs reveals that most models do not fully comply with these requirements, particularly regarding copyright compliance. Therefore, it is crucial for companies to carefully assess and ensure compliance relevant legal obligations when making long-term technology decisions involving LLMs.
In which areas should companies invest in LLMs?
Providers of standard software and Internet services are already investing heavily in LLMs. This includes areas such as sales management, customer service, marketing and knowledge management. Non-software companies will likely source such software rather than build it themselves.
More interesting for non-software companies are application areas in which LLMs have a direct impact on their value propositions or business-critical business processes. For example, robotics manufacturer Boston Dynamics uses LLMs to enable voice-based interaction between users and machines. Ivaldi, a distributed production specialist, uses LLMs to help maintenance teams troubleshoot. Rolls-Royce uses artificial intelligence to harness unstructured data and optimize supply chain management.
These illustrations highlight the substantial innovation potential of LLMs, extending beyond software companies to various other industries. Particularly noteworthy is the possibility for non-software companies to harness this potential by reimagining user interactions or unlocking significant optimization opportunities.
Warum brauchen wir eigentlich digitale Business Ökosysteme?
Mit diesen Hypothesen sind wir in den Tag gestartet:
Kundenseitig: Kunden benötigen nicht mehr einfach Produkte für “Punktlösungen”, sondern Begleitung entlang der Customer Journey, mit über die Zeit wechselnden Bedürfnissen, die im Netzwerk eines Business Ökosystems abgedeckt werden können.
Anbieterseitig: Diesen vielfältigen Bedürfnissen können einzelne Unternehmen oft nicht gerecht werden, da sie nicht über die vielfältigen Ressourcen verfügen, sondern spezialisiert sind. Im Verbund mit anderen Unternehmen lässt sich aber die Vielfältigkeit erreichen.
Digital: die Kundeninteraktion sowie die Organisation der Ökosysteme lassen sich über digitale Plattformen und Schnittstellen effizient und mit Skaleneffekten implementieren.
Die gehaltvollen und lebhaften Referate sowie der interaktive Workshop haben diese Hypothesen über den Tag hinweg wiederholt aus verschiedenen Blickwinkeln beleuchtet und interpretiert. So ergab sich bis zur Abrundung des Tages vor dem Abschluss-Apéro ein umfassender Eindruck der Leistungsfähigkeit von Business Ökosystemen, wovon hier nur ganz rudimentär und ohne Anspruch auf Vollständigkeit ein Eindruck wiedergegeben werden soll:
Ökosysteme bieten einen Mehrwert für alle Beteiligten und unterstützen die Ökologie.
Sie festigen die Kundenbindung und bieten direkte und indirekte Netzwerkeffekte.
Ein Produkt mit einer DNA versehen für die Rückverfolgbarkeit und die Kunden für den Wert davon sensibilisieren.
Ein Ökosystem für die “smarte” Gestaltung einer geographischen Region (vs. ein transaktionales Ökosystem).
Mit einem Ökosystem den KMU Kunden alles rund um ihre Administration abnehmen und sich dabei nicht selber ins Zentrum setzen (Anmerkung der Redaktion: “Ecosystem vs. Egosystem”!).
Ein vorerst digital aufgebautes Ökosystem zur Vernetzung von Robotern mit dem Potenzial, später menschliche Aktivitäten (z.B. Servicepersonal) zu integrieren.
Durch Transparenz im Ökosystem Kosten sparen und dieses resilienter machen gegen externe Schwankungen.
Der Workshop hat den Konferenz-Teilnehmenden die Möglichkeit geboten, ihre eigenen Challenges einzubringen und diese aus den Perspektiven Ressourcen, Geschäftsmodellen und Datennutzung zu beleuchten. Daraus sind zahlreiche potenzielle Projektideen entstanden, die nun auf eine Weiterverfolgung warten, z.B. im Databooster Innovationsprozess (https://databooster.ch/innovation_process/).
Herzlichen Dank an alle Referierenden (Reihenfolge gemäss Programm): Andrin Egli – Swisscom, Amrit Khanna – Concircle, Oliver Walter & Filipa Pereira – Rieter / Haelixa, Pascal Gurtner – Smarter Thurgau, Natalie Jäggi & Linus Schenk – Die Mobiliar, Gundula Heinatz Bürki – databooster & data innovation alliance, Marc Wegmüller – Wegmüller AG, Remo Höppli – Earlybyte & Kemaro, Rainer Deutschmann – Migros.
6th Conference Perspectives with Industry 4.0: Digital Ecosystems – May 31, 2023, Winterthur
By Jürg Meierhofer, ZHAW
Why do we actually need digital business ecosystems?
We started the day with these hypotheses:
On the customer side: customers no longer simply need products for “point solutions,” but rather assistance along the customer journey, with needs that change over time, which can be covered in the network of a business ecosystem.
On the supplier side: Individual companies often cannot meet these diverse needs because they do not have the diverse resources but are specialized. However, in association with other companies, variety can be achieved.
Digital: customer interaction as well as ecosystem organization can be implemented efficiently and with economies of scale via digital platforms and interfaces.
The substantial and lively presentations as well as the interactive workshop throughout the day repeatedly illuminated and interpreted these hypotheses from different angles. Thus, by the rounding off of the day before the closing aperitif, a comprehensive impression of the performance of business ecosystems emerged, of which only a very rudimentary and non-exhaustive impression will be given here:
Ecosystems offer added value for all actors involved and support ecology.
They strengthen customer loyalty and offer direct and indirect network effects.
Digital partner integration enables circular economy (keyword “fully loaded trucks”).
Adding a DNA to a product for traceability and making customers aware of the value of it.
An ecosystem for “smart” design of a geographic region (vs. a transactional ecosystem).
Using an ecosystem to relieve SME customers of everything around their administration, while not putting themselves at the center of it (editor’s note: “ecosystem vs. egosystem”!).
An ecosystem built digitally for the time being to connect robots with the potential to integrate human activities (e.g. service personnel) later on.
Saving costs through transparency in the ecosystem and making it more resilient to external fluctuations.
The workshop gave the participants the opportunity to bring in their own challenges and to address them from the perspectives of resources, business models and data use. Numerous potential project ideas emerged from this, which are now waiting to be followed up, e.g. in the databooster innovation process.(https://databooster.ch/innovation_process/).
With the CAS Smart Service Engineering of ZHAW School of Engineering we had the chance to spend two days this week at the Mobiliar Forum Thun (many thanks Fabrizio Laneve) . Moderated by Ina Goller in very focused and agile way, we further developed and refined the mutual value creation in the ecosystems of our service innovation cases.
It turned out that ecosystem design is a) extremely important for value creation, b) not trivial, c) creates new resources by successfully integrating existing resources. Our ambition was to quantify the value stream in the ecosystems. It turned out that while even the quantification of economic value is very demanding, but often possible, the quantification of social and emotional value streams is a widely open field which requires further research. We are looking forward to this challengin yet extremely rewarding work.
The practical conference on digital ecosystems in Winterthur just before this workshop (http://www.zhaw.ch/i40konferenz) was the ideal kick off to this workshop, in which we all became co-creative ecosystem designers ourselves.
By Manuela Hürlimann, ZHAW and Thomas Zaugg, Roche
On May 10th, 2023, the “Natural Language Processing in Action” Expert Group of the data innovation alliance and SwissNLP organised a meeting in Zurich with three exciting presentations on speech processing.
Oscar Koller, a principal applied scientist at Microsoft, presented on the use of end-to-end neural systems for automatic speech recognition in Swiss German. He discussed how the current industry paradigm of hybrid ASR is being replaced with end-to-end models, such as those that have been winning recent benchmarks. Oscar shared the results of his team’s comparison of different neural network architectures, and highlighted the advantages of using transducers for improved real-time performance in their work.
Claudio Paonessa, a researcher at FHNW, discussed how recent advances in speech-to-text, text-to-speech, and translation for Swiss German can be combined with a large language model to create a voice-based conversational assistant. He shared a demo of the model in action, showcasing its ability to give apt replies. However, he also acknowledged that processing time still needs to be reduced to give a real-time feeling, and suggested reducing model size as one possible solution.
Dr. Edith Birrer, a senior researcher at iHomeLab, HSLU, presented results from her team’s work on using speech processing in the context of home care. Together with international project partners, they ran interviews and workshops to identify potential use cases for home care workers. While they had originally planned to focus on care documentation, their results showed that most care workers found supporting services – such as a to-do list that can be ticked off verbally – to be more useful. They implemented three use cases and tested them in a lab with carers, showing a high level of enthusiasm among users, but emphasizing the need to address data privacy concerns before such technologies can become widely used.
After the presentations, attendees enjoyed an apéro and continued discussing the topics at hand.
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