AI is no longer a ‘nerd’ topic. Now, everyone is interested in AI, including the Board of Directors who lack knowledge.

AI tools have grown exponentially in popularity.

Tools such as Chat GPT / Einstein Salesforce / Bing AI Microsoft seen in areas such as financial services/radiology/manufacturing

Democratization of AI

How might we rethink how organisations adopt AI in the light of recent developments?

Participant 1:

• Seen many recent changes in IT creation capabilities in the business units
• Mendix for example
• AI expands this ability to other users, allowing people to be more creative and access more areas.
• Improves access to software development, generating code and fundamentally changing jobs
• More enjoyable and menial as routine tasks disappear

Participant 2:

• Automate manual work
• No cognitive work is not safe – this can now be automated with AI

Markus Sontheimer – ISS:

• Problem with change management,
• Agile teams in functional departments need to spread out more.

Participant 4:

• A lot of the debate has been focused on organisations
• From a managerial point of view, I need to make clear the specific tasks (not jobs) that are being automated.
• How to we facilitate hybrid development, AI as ‘assistants’ to workers?
• Language plays an important part in acceptance, Assistants v robots.

Participant 5:

• AI does not replace workers but can help existing workers.
• Collaboration is key, AI does not replace doctors/lawyers, but doctors with AI will replace doctors without

Participant 6:

• What is the specific algorithm? Who controls it?

Participant 7:

• Skill development and training will be much higher for organisations.
• Need to keep up to speed with developments.

Participant 8:

• If tasks disappear some hours of work will disappear.
• Some jobs will have less capacity needs
• 94% of German citizens would accept chatbots / ‘digital assistance’

Participant 9:

• Requires a robust governance framework.
• Academia should help define certain standards. Advisory boards.

Tasks will change, the way we frame communication, change management.

Potential solution: democratisation of AI.

Positive framing needs to be relevant and meaningful for people.

People are afraid of AI and we need to improve this. The world is changing, need to communicate that AI is positive and when used in the right way can provide value

Felix Kruger – Volkswagen Digital Solutions:

• People don’t trust AI to make sound decisions.
• Digitization is creating fear, must trust technology
• Is this in the job profile of a CIO?
• Who should be the leader of educating the people about AI? CIO? Leadership team? Not a question of technology but of change management, making sure the chosen educator has the competence

Participant 11:

• Not everyone’s perception.
• There is an open-minded atmosphere with AI.
• Company had to push back: do not expose company data to Chat GPT
• A productivity tool, nothing more.
• Maturing of the organisation but reflects poorly on employee awareness of what information should be exposed online.
• It needs to be supervised but not by IT. This is more a legal issue. Might be down to the CIO?

Participant 12:

• This is a company-wide issue.
• Keywords need to be made appealing and relevant to the company and people in the company.

Need to clarify the roles and what can be expected from whom. Propose a model.

Participant 13:

• Do not like the attitude that there is a business function and IT.
• IT are also domain experts.

Interplay of competences.

Participant 14:

• Can’t expect one perspective to solve it all.
• Agile team with different roles.

So, who pays the bill? It only works if you have somebody who takes leadership.

If the AI creates efficiency for the business then the business pays for it – it’s a longer term ROI, if it provides immediate value to the consumer, then they pay for it

Participant 15:

• If you want to be profitable as a company, you need to scale AI.
• If you just have AI and it doesn’t scale, then it will not have a positive impact.

Markus Sontheimer – ISS:

• Implementation of analytics
• One job of the CIO is to reduce complexity
• AI can be reduced to mathematics and analytics can be explained to board members
• But how do we provide a simple explanation for NLP to board members
• Translate what is happening outside to what is happening inside the company, how does Chat GPT impact the institution

Participant 16:

• Present what does generative AI mean for their area
• Opportunity to put something together, good for the reputation of IT to be proactive
• Go to the board about Microsoft Co-Pilot – show that it is tangible and useful for the business – not starting from scratch, standing on the shoulders of giants – utilising existing Ai models
• Make it tangible and show concretely what can be done with it
• What are the repercussions?

Participant 17:

• What are the pros and cons, how can AI be leveraged

Jan-Niklas Keltsch – Deloitte:

• How to scale out AI
• Can’t put client data in a third-party AI model
• Have to also explain to customers how their data is being used
• Control access completely
• AI team consisting of data privacy, technical and legal specialists, for employees to ask questions such as when and when not to use AI
• What master agreement sits on that cloud environment
• Professional Secrecy Amendment

Participant 19:

• Chinese walls between different language models.
• Technology is faster than policy and regulation

Separate into 5 breakout groups discussing how to prepare executive team for the board meeting on AI:

• How does AI impact our strategy
• How do we deal with new AI risks?
• How do we budget and spend capital on AI?
• How do we build AI competencies at the board level?

CIO Presentations

Presentation 1

Two options.

Must be relevant appealing and available.

1. Recall. Generative AI, give the CIO the green light and they report back, pros/cons/risks/opportunities
2. Shocker video, all in. If you don’t act now, you will miss the chance

Presentation 2

Give an overview of generative AI, explain what it is and what it isn’t’

Demonstrate it. Deep Fake video of CEO. Show something very clear to see the idea behind it.

Chances and risks

Risks

• Deep fakes
• Security
• Manipulation
• Bios

Chances

• Sales
• New business model
• Productivity

Do a deep dive on the potential that AI have.

Propose guidelines,

Do whatever it costs.

Presentation 3

Overview, be specific, what is the technology, show use cases.

Demystify, show that there is an interest.

Then get specific for the company. Make it clear that it is not a silver bullet.

Propose a guidance, what should be used in the company, what’s in it for us

What do we expect from the board. Pilot is too specific. How to organise it for the company.

Future of IT is networking for the company.

What are the business/legal risks involved and how can these be addressed.

Shared governance

Presentation 4

Result at the end would be the next steps.

Moment of impact – understand AI. Get some real tangible examples.
Specific examples to allow them to get the feel of it.
What does this mean for us? What does this mean for the business?
Chances and risk

What have we done already in the direction and what’s coming next

Doing a POC. Need to investigate the regulations because people are already using it.

Need to communicate to employees how to use AI.

Chat GPT does not address the risks.

Digitisation is not a high priority topic for CEOs, they have many other topics to cover.

How relevant was the topic?

The topic was extremely relevant, Chat GPT played a large role in the discussion and has been in the news only just recently. Was great to have CIOs discussing a topic that is happening right now, across all industries and walks of life.

What did I take away from this topic?

There is a constant theme throughout this topic, and other topics that have been raised by the CIOs, of accountability and the role of the CIO ever growing and expanding. Becoming more about managing the rapid change and the adoption rather than the technology itself. As organisations are becoming more and more digitized, the CIO is expected to take a further responsibility to know everything there is to know about all upcoming technologies, and to keep the board informed about future opportunities. There seems to be widespread confusion/hesitance across all industries about the use of AI and the urgency that is needed to adopt it, and who’s job it is to educate the higher stakeholders about the value it can bring to a company.

About

The modern enterprise is a data-driven business. Generative AI is responsible for analyzing and interpreting the data, but the journey goes much further – to autonomous units where artificial intelligence increasingly takes control and decision-making.

Discussion

One year ago, during the CIOMove conference in Zurich, AI was not as relevant of a discussion nor talked about as commonly as it is today. In this year’s conference in Porto, AI and especially Chat GPT dominate the discussion. Things have changed quite a lot.

What has happened in the last 12 months with AI?

– There has been continual and steady progress in the AI space, however this year, public perception of AI has heightened.

– In enterprise IT, disruptions tend to happen when certain technologies hit the public.

• This is an important factor to be considered.

Where do we currently stand with Generative AI?

– A functioning and ready to use Generative AI model is years away, as is complete autonomous driving Level 5

• This type of technology tries to predict and assume the next thing to come. This may not always be a good thing, as seen in the case of autonomous driving.

– Generative AI is a tool such as Excel and we will have to find solutions and ways to apply it in the best manner once we start utilizing it in our everyday work.

• We will need to learn the limits of Generative AI so and define processes on how to use it.
• The most important thing is we need to get it right and ensure the regulation of AI. The CEO of Chat GPT agreed to this as well.

– Why is it a race against complexity?

• The complexity of traffic is so large with autonomous cars that we are going back to Level 3 from Level 5
• Controlled complexity is a drive on a straight road from point A to point B
• However, driving in New York City for example would be too complex and therefore presents the issue we are facing with complexity.

– There also exists narrow domains and specific use cases for these AI solutions to advance a business, rather than reliance on Generative AI

• For example: At Wendy’s fast-food restaurant, the individual ordering will speak to an AI model, and it then will process your order.
• Wendy’s will train the model with their specific business language to make this happen.
• We want to start with IP and data on a domain model and not wait for a complete generative model because that is years out.

– Pan is the model working behind the scenes that allow AI technology to become more specialized.

• there will be a medical or finance version of Pan. A company will take the finance model and enrich it with its own IP and company-specific data.

What should regulations on AI look like?

– Regulating AI becomes both necessary and potentially risky.

– Large-scale regulation does not refer to the regulation of “criminals” because those individuals will find ways around regulations regardless.

• We need to determine how to regulate and standardize AI for everyone.

– When it comes to political or spiritual beliefs, it is difficult to know how to regulate them.

• Who is able to delete certain content? Who censors what is wrong or right?

What should AI Authenticity look like? How do we have proof of authenticity?

– To “clarify” the rules of the game, AI-generated content needs to be flagged and shown that it is an AI image.

• We are currently working on creating a watermark to understand to do just this. This is something that should be coming very soon.
• The goal is for the watermark to become so commonly used and expected to be used, that providers of AI have to adhere, Make it a global standard. The watermark will not remove “fake” or false information from the internet, rather making the user aware of the content they are consuming.
• For example, football records are already generated by AI, individuals are okay with this as long as they have the knowledge and therefore a choice to consume.

– Technicalities of an AI watermark:

• Any document with a certain set of metadata or picture can be digitally signed to indicate this is generative AI created content. 
• The developer conference is available on YouTube for additional information

– Is it always possible from a tech perspective, to determine if it is AI generated or not?

• Usually yes, and today there is software on the market that can tell you which is AI, and which is not.
• However, it is not 100% possible, but if usage becomes broad, then the mere absence of proven authenticity speaks for itself.  

– Currently, Google and Microsoft are companies producing those models to show AI transparency, but what happens when the everyday person starts generating AI?

• This is already commonplace, such as adhering to HTML.
• Technologies will also be developed that can sift through and find the AI watermark.
• This currently occurs within YouTube for example.

– New AI models will eventually be using the content of the internet that has already been generated by other AI models. Are we in jeopardy of everything becoming AI and cannot trust it?

• Watermarks will help a little bit but not solve the general problem.
• There will inherently be bias in the AI space since it input base dataset is largely biased to a degree.

AI Education

– As a society, we will need to find ways to not lose basic skills and qualities such as geography that can be lost when depending heavily on AI

– We need to adjust our education expectations and understand how to utilize AI in the best way.

• This would be the job of not just schools and technology companies, but your organization and even taught within families.

– There is no return from where we are, things are rapidly developing. So, it is our responsibility to get things done the right way and stay on top of AI education.

Job Security and AI

– Instead of worrying about AI making an individual obsolescent in their job, understand that it will transform the way you do your job.

– You can leverage the building of the neuro network for a model and have an algorithm that processes a lot of information, for the tasks required in your job.

– The benefit of AI is it never gets tired nor makes mistakes on repetitive tasks and lacks fatigue and increased error rate.

• The jobs in which these human error rates could occur due to repetition over time are the ones that will benefit the most from AI usage.
• For example, a data processing role will benefit greatly from the incorporation of AI tools.
• It is predicted that ~80% of code can be created from AI but will still require human revision.

– Examples of AI usage in different professions:

• Medicine: AI can be used to make a suggestion or catch an error; however medical decisions still are done by the physician. AI is used as a support tool.
• Lawyers: only lawyers can give legal advice. So, AI cannot legally give legal advice, rather again can be used as a support tool.

– Overall, it is very difficult to predict how AI will affect jobs until its implementation.

• Then we learn the benefits and dangers and evolve from there, it is all a learning experience.

How can CIOs implement AI? 

– How can a CIO use these technologies to automate and synthesize and accelerate tasks?

– For example: tracking the efficiency of a car is an intense simulation and is very expensive.

• There is an AI model that will determine the efficiency of the model with 95% accuracy.

– It would be beneficial to use AI to create content that you know is so appealing to a specific target audience to enhance marketing to drive up revenue. Do not think we are at a place where we can use technology to do this?

• For example, AI has been given input data to create something unique and successful.
• AI was given 30 pictures of shoes and then trained to build proposals for a shoe as though it was from the 70s. It created shoes that fit the mold exactly.
• A marketing test was done with one ad created by Chat GPT and one created by a marketing expert. The chat GPT one had a 50% more click rate.

– But how do you know how much you will sell?

• With AI, you can tell how often a shoe is being sold, sold for, how much is in stock in the supply chain, etc. This is currently being utilized.

– Who is responsible for the outcome of AI usage? Would the CIO be held responsible?

• We must first ensure that individuals are aware of the content generated by algorithms and AI.
• if an autonomous vehicle knocks someone off of the sidewalk, this is very bad press, and you question who is liable in this scenario.
• These are unsolved questions.
• To avoid issues with the outcome of AI, we need to create sets of rules similar to that of regulated industries such as banking.
• Ideally, if you feed the same input, you want to see the same output every time.
• This needs to be the expected norm to avoid issues with the outcome of AI.
• The pharmaceutical industry has been doing this for a long time now.

Who own’s AI generated data? 

– AI models that are trained using the company’s data should have no issues with copywriting.

• When you fine-tune the model, it becomes the customer’s intellectual property.
• After that, you feed it with data from your own infrastructure.

– For example, Med-Palm in the medical domain, you train with your company IP data, which is then never fed back into the central library for security purposes.

– Med-Pam has 500 billion parameters; however, every parameter has a compute cost.

• If you choose to trim down the neuro network and complexity of the AI, you need less computing power and bring therefore down costs.

– Information that hits cyber networks can see a lot of patterns and correlations within the network. Are companies allowed to use this information?

• Even today, reinforced AI algorithms will give you a hint which is gathered from identifying patterns and correlations within the data it has access to.

AI Advantages Expected in 5-10 years.

– In individuals’ private lives, AI will help a lot too, such as GPS systems.

– AI can act as a “stand-in” for meeting for a meeting or virtual event and return with a summary, enhancing the efficiency of time.

– Ai will be able to read an official document or sift through the transcript of a video conference and then present a thorough summary.

–      The importance of data quality and collection is increasing. 

• Improving data quality is difficult.
• Knowledge about external and traffic data is also important.

– Skip L and emberscript

• Emberscript: transcribe conversation from one language to another and can do so in ~ 5-10 minutes.

– In many years AI may be better than we are. Reminds me of when machines started making clothes and individuals did not want clothes made by machines.

Three Interesting Theses

1. Companies will not benefit from AI if they cannot manage or control their data. Those who do not have a data strategy today will be left behind by competitors.
   Agree: 29
   Disagree: 2
   
2. A centralized multiparty generalize AI model will lead to data loss. It will be important to manage isolated instances of AI to prevent IP data leakage.
   Agree: 27
   Disagree: 2
   
3. The union of OT And IT is the starting point for an autonomous manufacturing future where generative AI can control processes without human intervention.
   Agree: 19
   Disagree: 4.5

“In your business there is no business without technology”, summed up the fundamental requirements of supply chain and IT resilience for CIOs present at the Deep Dive session held at Kuehne+Nagel, Porto for CIOMove2023. Stefan Brock and Martin Kolbe presented their theses to CIOs. Stefan focused on how the geopolitical, environmental, security and technological risks in the current operating environment necessitate a re-evaluation of an organisation’s IT architecture principles, whilst Martin considered how cloud choices and technical dependencies need to be managed to ensure resilience. Both argued that the CIO’s role was the only one that could become the strategic link to advise the board on risk, dependencies and opportunities.

CIOs debated these issues during an engaging and wide-ranging discussion, the key points of which were:

• The choice of cloud or on-premises architecture is an individual organisational choice based on the problems trying to be solved.
• You can’t avoid technology dependencies, instead you need to be able to manage your choices.
• Resilience must be built into architecture and development choices from the start.
• The cloud offers resilience but often the availability, access, and connectivity dependencies of the cloud are not considered.
• The organisational culture and understanding of the board and the business needs to evolve so that they are better informed about resilient technologies.
• The responsibility for resilience should belong to the whole business, but the CIO has to retain the accountability for technological resilience.
• CIOs cannot however hold all the accountability, particularly in instances where business services should be ensuring legal compliance.
• When a business is service orientated then its IT is the differentiator.
• Research shows that cyber security attacks are most likely to be by professional groups, will involve infection techniques, and are more likely to target customer information.
• Cyber security considerations are changing due to: machine learning and deep fake algorithms becoming more sophisticated; the complexities of state funded threats; and, the lack of global cyber security experts.

Given the fundamental nature of IT as the cornerstone of a dynamic organisation, the group agreed that the role of the CIO needed to evolve to become the strategic bridge between technology teams, the business and board. CIOs need to manage competing viewpoints within these groups to build organisational technical responsibility and resilience whilst retaining overall accountability.

Summary

The Metaverse is a highly relevant and developing area that has received a lot of attention both inside and outside of IT. It has the potential for significant impact in many areas of industry and can allow consumers to be innovative and  create their own solutions. However, the CIOs expressed disappointment in the lack of implementable progress in this area and were concerned with what could be done to accelerate growth as well as how Accenture and university academics could support enterprises during this progression.

The discussion largely focused on the degree of accuracy required for industrial over consumer use. Many companies, particularly manufacturing groups, consider the lack of precision as a major limiting factor for Metaverse as the associated level of risk is high. Balancing precision and cost is imperative, with some companies requiring such high accuracy for simulations that Metaverse does not appear feasible to them in its current state. Others, however, suggest that exact digital replicas are not needed for the Metaverse to provide benefit. There was also focus on the importance for collaboration and decomposability with the Metaverse in order to unlock its full industrial potential, and the importance of planning and agility in adapting to technological advancements and disruptions, especially for legacy companies that may struggle to embed developing technologies into their culture, where the risk is somewhat unknown.

The session highlighted that the Metaverse is still in its early stages of development, with gradual progress expected to continue for now. Exponential growth could happen at any time but we do not know when due to aforementioned limiting factors. Collaboration, leadership support, and building blocks from platform providers were mentioned as necessary for advancing the Metaverse and integrating it into the production pipeline, as well as combining it with the use of AI.

The final voting indicated divided opinions on the mindsets of the participants. Overall:

“The metaverse is not only about technology, it’s about mindsets.”
15 agreed with the statement. 14 disagree. Remainder undecided

“Not gaming or entertainment but industrial production is the big driver of the metaverse experience.”
19 agreed with the statement. 10 disagree.

“People totally underestimate that the state-of-theart is about metaverse”
16 agreed with the statement. 14 disagree. Remainder undecided.

 

Long Read: Presentation

Hosted at eDreams ODIGEO in Porto. Presented by Tobias Regenfuss from Accenture (German Speaking Region) and Dr. Arthur Langer from Northeastern and Columbia Universities.

Most commonly known area of the Metaverse is the Consumer Metaverse. The presentation begins by showing a short film which details how Accenture has brought the Ocean Race to the Metaverse. This has created a new experience for sailing fans, providing them with access to real time info and footage as well as information on sustainability. Users are able to follow the teams as they compete to sail across the globe from within the Metaverse. Opens new levels of reach and allows thousands of fans to be immersed in the race.

Accenture sees even more traction in the Corporate Metaverse. The current opinion on the Metaverse is that it has not delivered what was anticipated, and progress is slow. However, massive evolution is expected in industrial enterprises re the Metaverse.

What is the Metaverse? The Metaverse is based on:

1. Advances in AR, VR and XR. Much more immersive experiences in 3D high definition rendering. This can be via a headset, mobile or engaging and immersive experiences over a browser.
2. Internet of ownership. Through trading digital items, such as NFTs, artwork that only exists in VR. Users can purchase digital items for their avatars, possessing unique, identifiable virtual goods that can be represented in the blockchain. This allows for digital ownership.
3. Internet of place. Provide places that we can inhabit in the Metaverse. Consumers can go and meet with others, interact with objects or buy virtual land.
4. Trend and need to combine individual platforms into uniform. Interoperability hurdles between platforms presents a challenge. Partnerships in the Metaverse are essential. Items ought to be able to be taken from place to place within the Metaverse, rendering in equal quality. Physical items can also be reflected in the Metaverse as digital twins. A vision for the future involves immersive Metaverse experiences where the process of buying a not yet manufactured car is rendered in a 3D game-like environment such as those provided with Unreal engine, consumers can then follow their purchased vehicle along the production line.

Many of these points are already happening, and we see each of these gradually evolving. Perhaps as the next step in digitisation.

Metaverse technology can allow for the decomposition down to reusable parts – like LEGO blocks. With these parts consumers can become more innovative and creative about what they want. Notion of consumer revolution. 5G has also provided speed and the ability to do more providing consumers with more access.

With Metaverse, universe of reach increases – price may come down due to reusability but reach will grow.

The Metaverse has uses as:

• Consumer Metaverse: a more immersive shopping experience, services on products can be conducted in a virtual space.
• Public Metaverse: learning and education based. Education can be provided to the workforce and allow them to engage with material in more meaningful ways. Onboarding experiences in the Metaverse have shown to be more efficient. Eg. a meeting can be held virtually and immersively whilst in numerous different places.
• Industrial Metaverse: digitizing the production process through production simulations. Includes component design and maintenance, product testing. Simulates and tests new product flows and can be used to train both bots and employees (particularly those which are deployed in hazardous areas or where there is a level of personal risk).

Discussion

• Change happens gradually and the Metaverse hasn’t brought digital revolution yet, what is holding us back?

• One theory: Metaverse reality can only create (for example) 95% accuracy in relation to reality, that’s okay for us as consumers but is this good enough for an industrial environment? For industrial simulations we have to be much closer to 99%.

• Where would you see the current status in this area and what is the next step before this really takes off?

• High definition depends on use cases. Crashes will need to be much closer to reality but this may be an isolated use case. Agree that we are at the bottom of the development potential of the Metaverse. Pare being deployed at relatively low cost along product lines CIO need to look at how we can gather more data in real time in order to deploy more accurate representations of reality.

• What additionally needs to come together for this to be exponential? What are the mechanics? Expresses disappointment in the Metaverse.

• The challenge is Legacy organizations. Protecting stock prices and pressure to preserve what you already have whilst improving and doing new things is a challenge. Linear development is a result of not taking these risks. Fedex example, who retire and rebuild whole systems in order to catch up with current trends.  Where is the risk module for digital production? Where does that money come from? RII of digital companies is flat. Amazon effect, lose money before you make money whilst building up the infrastructure.  Private equity allows innovation very quickly. New places can quickly disrupt in certain areas legacy companies and can happen very quickly. Need to balance being bold.

• Argues that Metaverse does not have to be about mimicking reality. Does a ball in a game have to travel at the same rate as Earth? Mimicking behavior fully is a limitation. Do we need to be so precise, a twin can not be exact. Metaverse philosophy can be used to create a general replica. Many cases in industry where similarity down to such small parts is not required and this needs to be reflected.

• Yes, digital twins of toxic substances or flight simulations are going to require an accurate replica when training handlers but use in the Nutrition industry may require less precision. Also, when replicating the classroom experience for example, it is not about creating a digital copy but looking to change and understand how people learn. Consumers and industries have different perspectives and see the world differently. Experiment with parts. Private equity model is interesting – 1 out of 80 success rate but level of eventual success is high/

• Old business models, completely different mechanics, cannot always work on new things. Higher chanch of survival for non-legacy companies.

• S curve keeps shrinking. How do we at the same time invest in new things?

• Emotion component. Consumer companies are rooted in consumer experience. Oculus feels unpleasant to wear for long times and can be a barrier, which technology is leading and how do we test them out?

• Agreed that end user device needs improving. But highlights that Metaverse is not limited to just the 3d virtual reality, browser interface, mobile phones are all new ways to engage with customers compared to classical user interface in the past. Not just goggles.

• What is the definition of metaverse? 3D immersive experience? Interact with the environment and social content. Clear on the difference between industrial metaverse.

• Trend to bring user experience forward in all spaces. Enterprises need to think about how this opens up for new interactions for consumers and the workforce. Can we use an immersive learning experience to train faster?

• What is the limiting factor at the moment? Previously discussed the lack of 3D data, due to need for accuracy, in some industries.  What support can we expect from digital leaders such as Accenture? What is coming from universities?

• Universities  as institutions do not work together. Need corporate and governmental support to make progress. The system is detrimental to collaborative academic support. Where is the leadership supporting this? With the right leadership in technology we could do a lot of things.

• Teaching universities need to facilitate learning. Metaverse should help us look at issues in our real world in a different way. Digital twins should be considered in a looser way. Not cost effective to make it a 100% accurate replica in most use cases. Full immersion VR is not feasible. Smaller part of many worlds helps us in specific instances where 100% precision is not required.

• Work with companies to determine what is possible today. Metaverse Operating Module helps industries create building blocks from existing data. Global collaborative village – drive this forward. Platform providers need to supply building blocks to make digital twin space accessible for industries.

• How to prepare today for what may be relevant in 5-10 years? What is Accenture offering?

• Evidence that planning is very difficult. Can you create an economy and culture in a company and respond fast enough? Be Agile. Black Swan events such as Covid can really disrupt organizations. Resilient companies have historically proven to do better economically. Invest in building AI-based models and combine with a good enough digital twin for a good enough cost. Only do real tests when necessary. Cost and speed need to be minimized.

• Planning process – major mistakes are committed early on.

• No real predictor, the beauty of the digital twins.

• Focus a lot of resources not on potential value. How can we create network effects/scaling effects in the Metaverse? Non linear effects.

• Digitisation increases reach with low cost.

• Transfer and networking logic is needed.

• Every public board should have three technological capable people on the board. Supply is nowhere near there. Boards don’t understand technological issues and don’t have the capacity to explain it to them.

Final thoughts

At the end of the discussion, the participants were given three statements and voted whether they disagreed or agreed regarding the Metaverse in which there was a reasonably even split between participants.