AI regulation: EU vs. USA - opportunities and challenges for companies
Artificial intelligence (AI) has made enormous progress in recent years and has become an integral part of the modern working world. From the automation of business processes to support with complex decisions - the potential applications are many and varied. But with the opportunities also come challenges, particularly in the area of ethics and responsible use. In this article, we take a look at current developments in AI regulation, with a particular focus on the European Union and the United States, as well as voices from the industry itself.
The EU AI Act: a pioneer in terms of regulation
With the AI Act, the European Union has taken a pioneering step towards AI regulation. This law aims to create clear rules for the development and use of AI systems. Für Unternehmen, die sich auf KI-gestützte Lösungen spezialisiert haben, bedeutet dies eine sorgfältige Prüfung und Anpassung ihrer Produkte.
Key points of the EU AI Act:
- Risk-based approach: AI applications are divided into different risk categories.
- Strict requirements for high-risk AI systems
- Prohibition of certain AI practices that are considered unethical
- Transparency obligations for AI systems
Early preparation as a competitive advantage
One particularly interesting aspect of the EU AI Act is the opportunity for companies to gain a significant competitive advantage through early preparation. According to a report from Artificial Intelligence News, this could look like this:
- Building trust with customers: Companies that are already integrating the principles of the AI Act into their products and services are signaling a high degree of responsibility and future orientation to their customers.
- Optimization of internal processes: Early adaptation to the upcoming regulations enables companies to gradually optimize their internal processes.
- Innovative edge: Companies that address the requirements of the AI Act at an early stage can develop innovative solutions that are both compliant and competitive.
- Attractiveness for investors: Investors and stakeholders increasingly value sustainability and compliance. Companies that respond proactively to regulations can therefore be more attractive for investment.
- Helping to shape industry standards: Early adopters have the opportunity to help shape industry standards and position themselves as thought leaders.
The USA: A different approach
While the EU is focusing on comprehensive regulation, the USA is taking a slightly different approach. President Biden recently issued a national security memorandum on AI that focuses on specific areas.
Key aspects of the US strategy:
- Focus on national security and economic competitiveness
- Promoting AI innovation while minimizing risks at the same time
- Cooperation between government, private sector and research
According to a report by CoinGeek, the US Housing & Finance industry is looking for AI-supportive regulation that encourages innovation while addressing risk.
Voices from the industry: Anthropic's call for AI regulation
Interestingly, voices calling for stronger regulation are also coming from within the AI industry itself. A notable example of this is Anthropic, a leading AI company that recently has made an urgent appeal to the public and political decision-makers.
Anthropic's main arguments:
- Potential disasters: Anthropic warns of potential catastrophic consequences that could result from the uncontrolled use of advanced AI systems.
- Need for state supervision: The company emphasizes that the industry's self-regulation is not sufficient and that stronger state supervision is required.
- Focus on advanced models: Particular attention should be paid to the regulation of AI systems that could develop human-like capabilities.
- Global coordination: Anthropic calls for international cooperation on AI regulation to establish global standards.
These demands underline the growing realization within the industry that responsible development and use of AI is essential.
Effects on companies
These developments present companies with both challenges and opportunities:
- Increased safety standards: Implementation of stricter safety protocols for AI systems.
- Transparency and accountability: Need to make the decision-making processes of AI systems more transparent and comprehensible.
- Ethical considerations: Increased inclusion of ethical aspects in AI development and application.
- Investment in research and development: Greater investment in research into AI safety could become necessary.
Conclusion and outlook
The regulation of AI is a complex and rapidly evolving field. While the EU is creating a comprehensive framework with the AI Act, the USA is taking a more focused approach. At the same time, voices from the industry such as Anthropic are calling for even stricter controls.
Our advice to CIOs and innovation managers
- Stay informed about regulatory developments
- Invest early in adapting your AI systems to upcoming regulations
- Seize the opportunity to gain a competitive advantage through proactive action
- Promote a culture of transparency and accountability in your company
- Take an active part in discussions on AI regulation in your respective industry
The future of AI lies in the balance between innovation and responsibility. Companies that recognize and implement this early on will be successful in the long term.
For further insights into the differences between European and American approaches to AI innovation, we recommend the article “The AI Divide: How European Firms Can Harness Their Regulatory Strengths and Learn from US Innovation”.
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Pro FIT grant for Tucan.ai: Advanced conversation and data analysis through generative AI
Tucan.ai from Berlin has set itself the goal of fundamentally improving the way companies analyze their internal conversations and data. With the support of Pro FIT grant from IBB Berlin (Investitionsbank Berlin) with a financial contribution from the European Union (ERDF), Tucan.ai is developing an innovative solution that overcomes the limitations of modern generative artificial intelligence (AI) to deliver accurate, traceable and privacy-compliant results.
Goals of the project
The main objective of the project is to develop a tool that makes it possible to store large volumes of internal company conversations and information in a structured manner in an intelligent archive and to make it usable for analyses and queries. This solution is realized through the integration of advanced Large Language Models (LLMs) and innovative speech recognition technologies.
Specific goals:
- Traceability of data sources: Ensuring transparency by linking text chunks with origin metadata.
- Consideration of language variations and context jumps: Adapting AI to understand and process different accents, dialects and non-continuous context jumps.
- Integration into existing enterprise SaaS solutions: Integration of the new technology into Tucan.ai's existing enterprise solution.
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Expected results
By successfully implementing the project, companies are able to maximize the value of their meeting notes and internal information archives. The expected results include:
- Increased efficiency and productivity: Employees can access relevant information faster and more precisely, which leads to considerable time savings.
- Improved employee satisfaction: The simple and rapid availability of information reduces the workload and increases employee satisfaction.
- Compliance and data protection: The solution meets the strict requirements of the General Data Protection Regulation (GDPR) and offers secure and transparent data processing.
- Competitive advantage: Companies that use the new solution can gain a competitive advantage thanks to more efficient workflows and a better basis for decision-making.
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Financial support by the European Union
The total cost of the project amounts to EUR 952,335.38, of which EUR 343,305.08 is financed by the European Regional Development Fund (ERDF). This significant financial support underlines the relevance and potential of the project to drive digitalization and efficiency in European companies.
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Case Study: Steigerung der Effizienz des Kundendienstes um 76% in 6 Monaten
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Entdecken Sie, wie eine Versicherungsgesellschaft durch die Implementierung des KI-Systems von Tucan.ai den Kundenservice revolutioniert hat:
Die wichtigsten Ergebnisse
Diese Fallstudie zeigt, wie strategische Planung in der Anwendung von KI nicht nur die Kundenzufriedenheit steigert, sondern einen wichtigen Geschäftszweig zukunftssicher macht:
- Effizienzsteigerung: Automatisierungsrate von 76% bei E-Mail-Anfragen und Reduzierung der Antwortzeiten von 3,5 Tagen auf nur 4 Stunden.
- Erhöhte Genauigkeit: Durch die proprietäre "Chunking"-Technik erreichte die KI eine Antwortgenauigkeit von 98%, was zu einer erheblichen Steigerung der Kundenzufriedenheit führte.
- Kostensenkung: Deutliche Reduzierung der Betriebskosten durch Verringerung der Abhängigkeit von manueller Arbeit.
- Positive Rückmeldungen: Verbesserte Arbeitsabläufe und höhere Arbeitszufriedenheit bei den Kundendienstmitarbeitern.
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Case Study: Enhancing Customer Service Efficiency by 76% in 6 months
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Discover how an insurance company transformed their customer service operations by implementing Tucan.ai's AI system:
Key Results
This case study shows how strategic planning in the application of AI not only increases customer satisfaction, but also future-proofs an important line of business:
- Increased efficiency: Automation rate of 76% for e-mail inquiries and reduction of response times from 3.5 days to just 4 hours.
- Increased accuracy: Using proprietary "chunking" technology, the AI achieved 98% response accuracy, resulting in a significant increase in customer satisfaction.
- Cost reduction: Significant reduction in operating costs by reducing dependence on manual labor.
- Positive feedback: Improved work processes and greater job satisfaction among customer service employees.
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Strategies for analyzing huge amounts of text with AI: the use of chunking and vector databases
Table of contents
In the age of digital transformation, large language models (LLMs) are opening up new ways of analyzing huge volumes of text stored in databases. These advanced AI systems deliver on the promise of gaining deep insights and competitive advantages from the wealth of data. However, they reach their limits due to the so-called token limitation, a technical restriction on the amount of data that can be processed. This becomes a particularly challenging hurdle when trying to analyze millions of text documents in depth or gain new insights:
Suppose a company wants to analyze 1,000,000 text documents. If special methods are not used, it faces major problems: Due to token limitation, an LLM can only analyze parts of the documents per run, which leads to a loss of information. Without a breakdown into manageable units and without a semantically intelligent database, there is a considerable loss of context. Documents have to be viewed in isolation, which makes it difficult to gain deeper insights. The analysis is also extremely time-consuming and resource-intensive, as each document has to be processed in full individually.
Chunking & vector data: easily analyze 1,000,000+ documents
An effective method of circumventing this limitation is the combination of smart chunking and the use of vector databases. By breaking down complex texts into smaller sections that can be handled by LLMs (chunking), it is possible to analyze large volumes of data without the restrictions imposed by token limits. In addition, vector databases make it much easier to access and analyze relevant information thanks to their ability to process and query semantic vector representations quickly and efficiently. This combination significantly increases the processing capacity and precision of LLMs and opens up the possibility of using the full power of the technology to gain valuable insights from the flood of data.
Download AI knowledge management one-pager
When analyzing large amounts of data, such as 1,000,000 text documents, the analysis process changes significantly:
- Efficient data processing:Splitting documents into smaller units (chunking) makes them easier for LLMs to process, as token limitations are bypassed.
- Advanced contextualization: Vector databases enable a deeper context analysis by quickly assigning semantically similar text parts. This significantly improves the understanding and classification of information.
- Time efficiency and scalability: The documents are broken down into smaller parts and information is retrieved efficiently using vector databases. This significantly speeds up processing, optimizes analysis and saves resources.
Real-world examples
Example for the legal department of a private equity fund
A private equity fund uses LLMs to check the compliance of its extensive and transnational contract database. The challenge lies in the enormous amount of data and the need to efficiently identify specific regulatory requirements in different countries.
- Chunking application: Before the analysis, all documents are divided into thematically relevant sections. This enables the LLM to apply its analysis skills specifically to relevant text segments and significantly improve the accuracy of the results.
- Vector database integration: Relevant sections and legal provisions are stored in the vector database. The LLM uses these to retrieve the most relevant legal texts and compliance requirements for specific legal issues.
Download AI knowledge management one-pager
The results are a much more efficient and in-depth analysis of compliance, minimizing regulatory risks and facilitating adaptation to international laws.
Example for the market research department of a large company
A market research department uses LLMs to derive trends and patterns from millions of consumer feedbacks, market reports and social media posts.
- Chunking application: Splitting the data into smaller, thematically focused segments allows LLM to work more precisely and in a controlled context, improving the accuracy of trend analysis.
- Vector database integration: By storing thematic vectors from the analyzed text chunks in the vector database, LLM can consistently and efficiently track relevant topics and trends across a comprehensive and diverse data set.
Download AI knowledge management one-pager
This strategy enables the company to react quickly to changing market conditions and develop customized marketing strategies based on in-depth, data-driven insights.
In both cases, chunking and vector databases prove to be indispensable tools for fully exploiting the strengths of LLMs. Through these techniques, companies can increase the power of AI in text analytics, allowing them to gain deeper insights and make more accurate decisions.
Efficiently manage information floods with AI
In the age of information overload, it is more important than ever for companies not only to manage their data, but also to use it intelligently. With its chunking technology developed in Germany and integration into vector databases, Tucan.ai offers a pioneering solution that emphasizes precision, efficiency, and data protection. Whether it's analyzing complex contracts, identifying market trends or making privacy-compliant decisions, Tucan.ai enables companies to revolutionize their data processing and make informed decisions based on verifiable and accurate data. Discover the transformative power of Tucan.ai and ensure your organization is at the forefront of data-driven decision making.
Manage your knowledge in a precise, scalable and GDPR-compliant way!
Fed up with low-quality AI tools for market researchers?
You are most likely reading this, because you are not satisfied with the solutions and technologies the AI hype has generated for market researchers.
And you shouldn't be.
Artificial intelligence was first envisioned as a technology, that would massively reduce the workload of humans, drive automation and enable new ways of working. ChatGPT has proven that AI is capable of assisting humans during work, but sophisticated industries like market research require more than just a beautiful front-end that links to a generalistic and basic service like GPT 3.5. Now, more than ever, there is a need for quality AI tools for market researchers.
But what is a quality AI solution?
The most valuable resource any market research firm has, is it's human capital and the unique minds that are capable of designing and evaluating intelligent studies. As such, the quality of a solution is decided by how powerful the support mechanisms of said solutions are. AI-platforms need to be non-intrusive, easy to use and should never infringe on a human's ability to have the last say in intellectual matters. At the same time, they should massively reduce time spent on rudimentary tasks and enable new ways of working.
tucan.ai - a proven AI platform for market research.
As our award for being the most innovative market research solution of 2023 shows, tucan.ai is a real AI-solution that was established well before the AI hype and uses proprietary algorithms and models. And because we are more than just a pretty mask for API calls to 3rd party providers, we are able to provide our services both online and offline and customize them to perfectly match the workflows, processes and IT-infrastructure of our partners. It comes as no surprise that some of the biggest market research firms across the globe are currently implementing our solution to ensure they stay ahead of the curve.
We enable qualitative researchers...
Our cutting-edge AI assistant allows you to get your interviews and focus groups encoded automatically on predefined categories or themes. In addition to the encoding of answers, researchers can define additional analysis tasks or datapoints they would like to extract from the data medium and receive results in mere minutes, whereas manually completing this task often takes the average researcher multiple days. Beyond market research, this function can also be used for opinion polling and similar fields that rely on qualitative data analysis. Learn more in our factsheet "AI-powered encoding with Tucan.ai" or contact us for a copy of the presentation we used for our viral "qualitative market research and AI" webinar.
...and quantitative researchers!
A recent addition and much requested feature of our existing market research customers, is the inclusion of functionalities that assist in quantitative market research. Our first release in this feature group allows you to analyze an unlimited number of datapoints based on a predefined code-plan or even ask the AI to create a code-plan for you. In our test, a researcher analyzed 400 datapoints with a partial code-plan. The AI assistant had the objective to complete the code-plan based on the given data and then quantify all entries based on all code-plan categories. The results were perfect and the entire process only took 1 minute and 43 seconds, all the way from opening our platform to opening the exported results. That is 228 times faster than even a fast researcher could have done it manually. To learn more about our new quantitative research features, contact us directly or sign up for our upcoming "quantitative market research and AI" webinar.
Contact us today and see how tucan.ai can transform your studies by enhancing every single workflow and enabling your researchers to 10x their results in a single month.
In case you wish to learn more about Tucan.ai's solutions for teams and enterprises, please schedule a short online call with our CEO, Florian Polak (florian@tucan.ai).
CUSTOMER VOICES
What they say about us
"We at Axel Springer have been using Tucan.ai for already over two years now, and we continue to be very satisfied with the performance of the software and the development process as a whole."
Lars
Axel Springer SE
"I have known the founding team for over a year. At Porsche, we are very satisfied with their work so far. I have recommended the use of Tucan.ai to my colleagues and business partners and I have been getting highly positive feedback back across the board - both on the service and the software."
Oliver
Porsche AG
"Tucan.ai has been a game-changer for our team. The software is incredibly intuitive and easy to use. It has saved us countless hours of work and has allowed us to focus on what really matters - our clients. I would highly recommend Tucan.ai to anyone looking for an AI-powered productivity tool."
Alex
Docu Tools
Keine Lust mehr auf neue KI Tools mit mangelnder Qualität?
Wahrscheinlich liest du das hier, weil du mit den Lösungen und Technologien, die der KI-Hype für Marktforscher hervorgebracht hat, nicht zufrieden bist.
Und das solltest du auch nicht sein.
Künstliche Intelligenz wurde zunächst als eine Technologie erdacht, die die Arbeitsbelastung von Menschen massiv reduzieren, die Automatisierung vorantreiben und neue Arbeitsweisen ermöglichen würde. ChatGPT hat bewiesen, dass KI in der Lage ist, Menschen bei der Arbeit zu unterstützen, aber anspruchsvolle Branchen wie die Marktforschung erfordern mehr als nur ein schönes Frontend, das mit einem generalistischen und grundlegenden Dienst wie GPT 3.5 verbunden ist. Heute besteht mehr denn je ein Bedarf an hochwertigen KI-Lösungen für Marktforscher.
Aber wie sieht eine hochwertige KI-Lösung aus?
Die wertvollste Ressource eines Marktforschungsunternehmens sind die Forscher, die in der Lage sind, intelligente Studien zu konzipieren und auszuwerten. Die Qualität einer Lösung wird also dadurch bestimmt, wie leistungsfähig die Unterstützungsmechanismen dieser Lösung sind. KI-Plattformen müssen unaufdringlich und einfach zu bedienen sein und sollten niemals die Fähigkeit des Menschen beeinträchtigen, in intellektuellen Angelegenheiten das letzte Wort zu haben. Zugleich sollten sie den Zeitaufwand für rudimentäre Aufgaben massiv reduzieren und neue Arbeitsweisen ermöglichen.
tucan.ai - eine bewährte Plattform für Marktforschung.
Wie unsere Auszeichnung als innovativste Marktforschungslösung des Jahres 2023 zeigt, ist tucan.ai eine echte geschäftsfähige KI-Lösung, die lange vor dem KI-Hype entstanden ist und eigene Algorithmen und Modelle nutzt. Das befähigt uns unsere Dienste sowohl online als auch offline anzubieten und sie perfekt an die Workflows, Prozesse und IT-Infrastruktur unserer Partner anzupassen. Es ist also kein Wunder, dass einige der größten Marktforschungsunternehmen unsere Lösung schon heute implementieren, um sicherzustellen, dass sie der Zeit immer einen Schritt voraus sind.
Wir befähigen qualitative Forscher...
Unser hochmoderner KI-Assistent ermöglicht es, Interviews und Fokusgruppen automatisch nach vordefinierten Kategorien oder Themen zu kodieren. Neben der Kodierung der Antworten können Forscher zusätzliche Analyseaufgaben oder Datenpunkte definieren, die sie dem Erhebungsmedium entnehmen möchten und erhalten die Ergebnisse in nur wenigen Minuten. Das ist beachtlich, wenn man bedenkt, dass diese Prozesse, wenn sie manuell ausgeführt werden, oft mehrere Tage in Anspruch nehmen. Über die Marktforschung hinaus kann diese Funktion auch für Meinungsumfragen und ähnliche Bereiche genutzt werden, die auf die Analyse qualitativer Daten angewiesen sind. Erfahre mehr in unserem Factsheet "AI-powered encoding with Tucan.ai" oder kontaktiere uns direkt für eine Kopie der Dokumentation unseres viralen Webinars zu qualitativer Forschung und künstlicher Intelligenz.
...und quantitative Forscher!
Unsere bestehenden Kunden aus der Marktforschung bitten uns seit längerem um die Entwicklung von Funktionalitäten, die bei der quantitativen Marktforschung helfen. Unser erstes Update für quantitative Forschung macht es möglich, eine unbegrenzte Anzahl von Datenpunkten auf der Grundlage eines vordefinierten Code-Plans zu analysieren oder sogar die KI zu bitten, einen neuen Code-Plan zu erstellen. In unserem Test analysierte ein Forscher 400 Datenpunkte mit einem unvollständigen Codeplan. Der KI-Assistent hatte die Aufgabe, den Codeplan auf der Grundlage der gegebenen Daten zu vervollständigen und dann alle Einträge auf der Grundlage aller Codeplan-Kategorien zu quantifizieren. Trotz perfekter Ergebnisse dauerte dieser Prozess nur eine Minute und 43 Sekunden, vom Öffnen unserer Plattform bis zum Öffnen der exportierten Ergebnisse. Das ist 228 Mal schneller, als selbst ein schneller Forscher es manuell hätte tun können. Wenn du mehr über unsere neuen quantitativen Forschungsfunktionen erfahren möchtest, kontaktiere uns direkt oder melde dich für unser kommendes Webinar zu quantiativer Marktforschung und künstlicher Intelligenz an.
Kontaktiere uns noch heute und finde heraus, wie tucan.ai deine Studien revolutioniert, indem es Prozesse optimiert und Forschern ermöglicht, ihre Ergebnisse in einem einzigen Monat zu verzehnfachen.
Wenn du mehr über Tucan.ai's Lösungen für Teams und Unternehmen erfahren möchtest, vereinbare bitte einen kurzen Online-Termin mit unserem CEO, Florian Polak (florian@tucan.ai).
Kundenbeiträge
Was sie über uns sagen
"We at Axel Springer have been using Tucan.ai for already over two years now, and we continue to be very satisfied with the performance of the software and the development process as a whole."
Lars
Axel Springer SE
"I have known the founding team for over a year. At Porsche, we are very satisfied with their work so far. I have recommended the use of Tucan.ai to my colleagues and business partners and I have been getting highly positive feedback back across the board - both on the service and the software."
Oliver
Porsche AG
"Tucan.ai has been a game-changer for our team. The software is incredibly intuitive and easy to use. It has saved us countless hours of work and has allowed us to focus on what really matters - our clients. I would highly recommend Tucan.ai to anyone looking for an AI-powered productivity tool."
Alex
Docu Tools
Missing information from last weeks meeting? Ask Tucan!
Learn here how you can use Tucan.ai to automate your meetings and create a smart knowledge archive for conversational content:
Step 1: Connect your conferencing apps and other tools
Tucan.ai integrates with popular calendar and video conferencing apps such as Google Calendar, Zoom, Microsoft Teams, Google Meet and more. You can easily connect your accounts and invite our bot to join your meetings as a participant. Tucan.ai will automatically record the audio of your meetings and upload them to its secure cloud platform. Alternatively, you can upload any audio or video file to your account yourself. Tucan.ai will take care of the rest.
Step 2: Review, edit and share the content provided by Tucan.ai
Right after your meeting Tucan.ai generates a transcript and a summary using its own speech recognition algorithms. You can easily edit, annotate, highlight and share content with your team members, followers or clients. Furthermore, it is now even possible to ask questions about past meetings and get answers based on the gradually improving speech recognition and natural language understanding capabilities of our AI.
Step 3: Keep track of and manage your conversations with ease
Tucan.ai also provides you with data and insights from your meetings, such as talking times, sentiments, action items, keywords and topics. These metrics may be used to improve your communication skills, track your progress, identify gaps or opportunities and optimise your task management. You can also integrate Tucan.ai with other tools like CRM systems, project management and collaboration platforms to automate your entire workflow.
Step 4: Gain deeper qualitative insights through automated encoding
Tucan.ai also offers a smart feature which allows you to get your interviews and focus groups encoded automatically on predefined categories or themes. This function is particularly well-suited for market research, opinion polling and similar fields that rely on qualitative data analysis. You can use Tucan.ai to extract relevant insights more quickly and accurately from your conversations without spending hours on manual encoding. Learn more in our factsheet "AI-powered encoding with Tucan.ai":
Step 5: Use our new prompt feature for swift inquiries and analyses
We are constantly developing new functionalities to enhance Tucan.ai’s capabilities. One of our most exciting new releases allows you to prompt various kinds of data extractions and summaries from your conversations. For example, you can ask Tucan.ai – almost like a company-internal GPT – to provide you with the contents needed for a SWOT analysis from strategy meetings or customer persona from sales calls, and it will get back to you with customised outputs based on your prompts.
Outpace your competition - Book a free consultation call with our CEO Florian!
In case you wish to learn more about Tucan.ai's solutions for teams and enterprises, please schedule a short online call with our founder and CEO, Florian Polak.
CUSTOME VOICES
What they say about us
"We at Axel Springer have been using Tucan.ai for already over two years now, and we continue to be very satisfied with the performance of the software and the development process as a whole."
Lars
Axel Springer SE
"I have known the founding team for over a year. At Porsche, we are very satisfied with their work so far. I have recommended the use of Tucan.ai to my colleagues and business partners and I have been getting highly positive feedback back across the board - both on the service and the software."
Oliver
Porsche AG
"Tucan.ai has been a game-changer for our team. The software is incredibly intuitive and easy to use. It has saved us countless hours of work and has allowed us to focus on what really matters - our clients. I would highly recommend Tucan.ai to anyone looking for an AI-powered productivity tool."
Alex
Docu Tools
5 Golden Rules for a Better Meeting Culture
1. Plan structurally.
A productive meeting starts with structural planning. Set an agenda and a goal to define the focal points for discussion. Especially in case of online meetings you should also clearly communicate whose attendance is expected and who may join in as a passive participant.
2. Communicate requirements.
Inform participants about the topics and goals of the meeting and include a short reminder to ensure attendance, preparation and a certain sense of involvement. If applicable, it is also advisable to let them know in advance about necessary equipment and tools for the meeting.
3. Ensure commitment.
Participants bring different personalities to the room. Some tend to prefer to just listen while others show a tendencies towards striving to dominate a conversation. It is thus important to ensure everyone can speak up and all views are being considered.
4. Focus on follow-ups.
The tangible outcome of a meeting should always be some sort of summary or list of take-aways such as follow-ups and to-do's. Make sure people know about, agree and commit to carrying out follow-up actions. Then evaluate the meeting with all its outputs to determine what has worked and what not. Be sure to include your take-aways in follow-up meetings.
5. Incorporate feedback.
As mentioned above, all participants must be included in the process and commit to their follow-up responsibilities. Positive effects in the overall performance of a team or company are often also indicative of effective meetings. To arrive there continuous improvement is required in terms of technical execution as well as successful completion of follow-ups. The credo must be: Stay on top of things.
Meetings are an established method for developing ideas and making decisions together, but when poorly organised they can kill morale and productivity. According to recent studies, 30 to 70 percent of our professional meetings today are unnecessary or even counterproductive.
Tucan.ai is a Germany-based deep-tech startup developing AI software aimed at improving our professional communication. We build productivity tools based on automatic speech recognition, transcription and summarisation to help B2B customers organise, carry out and post-process meetings and other structured conversations more efficiently and productively. Our vision is to outsource exhausting bureaucratic tasks to AI systems so that we can focus more on what is really important and exciting: being innovative and creative together.
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