Data-driven Generative AI

Introduction
Just as the world changed on August 12, 1908, when the first Ford Model T rolled off the assembly line, the world changed on November 20, 2022, with the arrival of ChatGPT (Generative Pretrained Transformer). Within two months, ChatGPT has more than a hundred million users. ChatGPT is one of the many Generative AI applications. This form of artificial intelligence (AI) is being adopted faster than any other technology. What can Generative AI do, for example:

  • Create a summary, (re)write and translate texts.
  • Create an information flyer.
  • Generate software code.
  • Write a film script or opinion piece, but also, of course, answer questions.

In addition to advantages, there are also disadvantages to this new technology. In particular, the datasets used, the trainings models, business interests, and ethical aspects will play a major role in further development and applications. However, many will have experienced the simplicity and power of this technology. If we use this technology properly, we can better solve problems such as labor market shortages, low productivity growth, the energy transition, and fake news.

In this academy solution, we explore the foundations of Generative AI in a non-technical way. You ask a question (text that you enter and is also called a ‘prompt’) and receive an answer. Generative AI interprets your prompt and creates an appropriate response. This is not only in terms of content (a kind of search result such as Google Search) but also in the form of generated composite texts. Generative AI predicts, as it were, the next most likely word in a sentence. It uses (public) texts and websites to train and produce answers to questions. However, it is only sometimes certain or clear to the user whether the facts the AI bases its answers on are correct. It is better to use data-driven organizing for this. This concept ensures a shared, assured, and accessible reality for reliable, rich data.

We will also discuss the relationship between Generative AI and data-driven organizing during this academy solution. It starts with establishing facts (for example, with calibrated and validated sensors or authorized senses). After that, you no longer want to be able to adjust facts on which there is consensus, unilaterally and afterward (shared and distributed ledger, blockchain), after which you use facts for processes (smart contracts, learning models). Finally, you will enrich facts with artificial intelligence. Combining these steps creates the digital assembly line.

If you can predict the next word in a sentence, you can also schedule the next truck with the lowest CO2 emissions for a route or make complex personnel planning within a hospital. There are numerous possibilities for replacing human work with data-driven Generative AI. You can see this negatively but also positively: people have more time for activities that they are better at, such as attention, care, education, and local security.

We dive into various aspects of data-driven organizing, including collecting, storing, making accessible, and enriching data. Furthermore, we will explore how data-driven Generative AI can be applied in various sectors such as government, the financial sector, supply chain companies, the construction chain and HR functions.

We will also discuss the principles of Generative AI in relation to data-driven organizing to make informed decisions and develop an effective vision and strategy. The government would probably have made different choices during the coronavirus period if they had rich data and Generative AI. We must remember that the computer is much better and faster at organizing, analyzing, and interpreting large amounts of data with many roles, variables, processes, and relationships between them. We discuss the importance of Generative AI and its benefits and limitations.

At the end of this academy solution, you will better understand Generative AI in relation to data-driven organizing and how you can use these technologies not only to be ESG/CSRD or GDPR compliant but also to become more productive and future-proof. You can also apply what you have learned through practical exercises and real-life examples.

What you’ll learn
This academy solution aims to learn what data-driven Generative AI is, why it is important, and what the participant can and cannot do with it. Participants can also discuss their use cases and share how they want to set up an innovation project or develop a vision and strategy.

This academy solution aims to increase awareness, transfer knowledge, and provide insight into practical applicability. Participants develop a ‘FutureNext radar’ to make the right decisions at the right time in vision and strategy development, new business and operating models, innovation and IT investments in general, and Generative AI in particular.

Who should attend
This academy solution is especially important for professionals involved in the design and development of new organizations and IT systems that better fit new social contexts, ecosystems, and data technologies and take into account challenges in the areas of: sustainability, digitalization, decentralization, scarce talents, productivity growth, privacy, power of tech companies, cyber security and (digital) waste.

In general, this academy solution is mainly aimed at professionals (strategists, innovators, policymakers, business developers, project leaders, organizational consultants, IT architects, and community managers) who must be able to provide answers to questions from management, customers, suppliers and other stakeholder about future-driven organizing, strategy, new business and operating models, data-driven organizing, disruptive technologies, digital transformation in general, and the development and use of generative AI in particular and (helping to) set up projects and programs for this.

Furthermore, this academy solution is also suitable for (general) managers and professionals in HR, finance, legal, facility management and logistics, strategists, and policy staff who want to know more about Generative AI in a non-technical way and the application possibilities for their organization.

Results
The result of this academy solution is that the participant can answer questions about the usefulness and necessity of data-driven organizing in relation to Generative AI for his organization, customers, and network and can develop a use case.

Investment
The investment for this service is:

  • including: preparation, follow-up, Weconomics books, (learning) materials, licenses, local travel time/costs
  • excluding: customization costs, VAT, accommodation costs and optional: other book(s), (case) materials, licenses; follow-up and assessment of use cases, not local travel time/costs.

(see also overview page with advice tariffs for groups/Incompany).

Summary program proposal (1-4 hours)

  1. Introduction, introduction
  2. Historical and social context
  3. What is data-driven generative AI?
  4. Why (attention) for this theme?
  5. What can and can’t you do with it?
  6. Practical examples, cases
  7. What does this mean for your business model/function/organization
  8. How do you start a transformation?
  9. Questions, dialogue, statements

Program suggestion / outline (1 day)
(Also possible in an afternoon or evening session from 13:00-17:00. or from 16:00-22:00, for example)

09:00 – 09:30 Walk-in with coffee and tea

09:30 – 10:00 | Introduction and context

  • Introduction
  • Social context: durable, digital, decentral and more humane
  • Organizational problems: more rules, fewer people, outdated (IT) systems
  • What is the impact on the organization?

10:00 – 11:00 | What is data-driven Generative AI?

  • What are data and what is data-driven organizing?
  • What is AI, Artificial General Intelligence (AGI), Generative AI and what is ChatGPT?
  • What is the relationship between data-driven organizing and Generative AI?

11:00 – 11:15  | Pause

11:15 – 11:45 | Why is it important?

  • Solving social and organizational problems
  • New business and operating models
  • Improve productivity, security and democracy

11:45 – 12:15 | Which aspects are important

  • Purpose of work
  • Ethical aspects and legislation
  • Power of tech companies
  • Disinformation, fake news, polarization and propaganda

12:15 – 12:30 | Questions about the morning program

12:30 – 13:15 | Lunch

13:15 – 15:15 | Practice and cases

  • Practical applications
  • Weconomics cases
  • Discuss submitted use cases / papers (vision, position, white)
  • Assignments (with Lego)

15:15 – 15:30 | Pause

15:30 – 16:30 | Transformation

  • Double-track Strategy, Community Model Canvas, Idealized Design, Backcasting
  • Solution Development Journey
  • What does it mean for the participant’s position/organization/business model?
  • How do you start tomorrow?

16:30 – 17:00 | Questions and dialogue

Program proposal (2 days)

(Also possible, for example, in 2 afternoon and evening sessions from 13:00 to 21:00, or 4 separate half-days)

Day 1

09:00 – 09:30 Walk-in with coffee and tea

09:30 – 10:00 | Introduction and context

  • Introduction
  • Social context: durable, digital, decentral and more humane
  • Organizational problems: more rules, fewer people, outdated (IT) systems
  • Double-track strategy: being compliant and future-proof
  • What is the impact of Generative AI on the organization

10:00 – 11:00 | What is data-driven Generative AI?

  • What is AI, Artificial General Intelligence (AGI), Generative AI and what is ChatGPT?
  • What is the difference between closed source and open source?
  • What is the relationship between data-driven organizing and Generative AI?

11:00 – 11:15 | Pause

11:15 – 12:15 | What is data-driven organizing?

  • What are data and what is data-driven organizing?
  • How do you organize supply and demand of data?
  • What are important components: IoT, blockchain, smart contracts, AI
  • What is a digital assembly line?

12:15 – 12:30 | Questions about the morning program

12:30 – 13:15 | Lunch

13:15 – 15:00 | Preparation case ‘Data-driven Generative AI’

  • Data-driven Generative AI case with Harvard Case method
  • Handing out and explaining the case
  • Preparation for case discussion: individual and group (dialogue)

15:00 – 15:15 | Pause

15:15 – 16:30 | Data-driven Generative AI in practice

  • Practical applications
  • What does it mean for your organization?
  • How do you set up a sustainable AI strategy?
  • Which components are important?
  • Which data and data processes are relevant?
  • What technology is available?
  • Discuss submitted use cases / papers (vision, position, white)
  • Work on your own use case under supervision
  • Assignments (with Lego)

16:30 – 17:00  | Questions and dialogue

Day 2

09:00 – 09:30 Walk-in with coffee and tea

09:30 – 10:15 | Why is it important and what should you arrange?

  • Solving social and organizational problems
  • New business and operating models
  • Improve productivity, privacy, security and democracy
  • Seize opportunities and distinguish yourself

10:15 – 11:00 | Organize data in a fundamentally different way

  • Which patterns do you recognize and can you use?
  • From what perspectives can you organize Generative AI?
  • There are only five organizational principles
  • What are important insights from organizational theory?
  • Which organizational model fits better?

11:00 – 11:15 | Pause

11:15 – 12:15 | Focus for AI impact

  • Perspective change, brave leadership, system innovation, relevant technology, transformation
  • From the right attitude to behavioral change
  • Complex contagiousness: how do you change beliefs, reflexes and norms?

12:15 – 12:30 | Questions about the morning program

12:30 – 13:30 | Lunch

13:30 – 15:00 | Case ‘Data-driven Generative AI’ discussion

  • Explanation of case discussion
  • Case discussion in group
  • Case evaluation

15:00 – 15:15 | Pause

15:15 – 16:30 | How do you start tomorrow

  • Community Model Canvas versus Business Model Canvas
  • Double-track strategy, backcasting, systems theory, design thinking
  • Solution Development Journey
  • Examples of projects and project implementation
  • How do you set up a data-driven ecosystem?
  • What are parts of an AI project?
  • How do you deal with hypes and resistance?
  • Approach: project methods, first steps
  • How do I start a project and how do I move afterward?

16:30 – 17:00 | Questions and dialogue

Duration
1 day or 2 day parts
2 days or 4 day parts

Product type
Awareness, knowledge transfer with practical examples and opportunity to ask questions and personal input.

In addition to concept, theory and practical examples, the participants will also enter into dialogue about their own AI practical examples that can or cannot be organized in a data-driven manner.

First introductory webinar?
If you would first like to get an impression of our way of working and vision on future-driven leadership and data-driven organizing, please first participate in an introductory webinar for free and without obligation.

Information
If you want additional information, please contact us.

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