Closing the AI Skill Gap Together

The majority of generative AI (GenAI, e.g. ChatGPT) predictions point towards massive gain in productivity. Studies range from articles positing up to a 40% increase in efficiency (1) to forward-looking analyses by consultancies like Gartner and McKinsey with statements like “30 percent of hours currently worked across the US economy could be automated” (2).

We stand on the doorstep to a fantastic opportunity, but also a great challenge to get everyone on board with the change.

I’ve stitched together a few recent pieces and thoughts on this important subject💡. All references are at the end.


😃 It’s for everyone

For companies to harvest the full potential of GenAI will require more than simply hiring for new skills, it's about re/upskilling your current workforce. Everyone must be onboard, bridging the knowledge gap between those proficient in GenAI and those who aren't will be of major importance. Not doing so could significantly set back your company.


👥 Consider two perspectives

🏢 The Business Perspective: Investing in reskilling shall be a long-term strategic play, make it a core strategic pillar that gets full support from all the leadership layers.

👩‍💼 The Employee Perspective: Reskilling supplies a pathway for career development and job security. But it’s also tough challenge to our egos and worldviews, imagine your top skill can now be automated and you are forced to re-skill - that’s not fun 😬.

Having a clear “why” is helpful to get employees onboard.

A recent HBR (3) article stresses how employees are more willing to engage in reskilling when they understand its value and when programs are designed from their perspective.


🏆 Give GenAI a positive vibe through your project managers

Successful practitioners seem to agree that it’s important with a few early successful pilots under the belt to inspire others. It makes sense to start up-skilling project managers and have them taking active roles in the GenAI journey. Project managers often have a wide exposure to different functions and can help to understanding where the chance of successful GenAI pilots is the greatest.

❓ What's the one skill you're looking to upgrade for the AI era? ❓

📖 References
1. Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality by Fabrizio Dell'Acqua, Edward McFowland, Ethan R. Mollick, Hila Lifshitz-Assaf, Katherine Kellogg, Saran Rajendran, Lisa Krayer, François Candelon, Karim R. Lakhani :: SSRN
2. https://www.mckinsey.com/mgi/our-research/generative-ai-and-the-future-of-work-in-america
3. https://hbr.org/2023/09/reskilling-in-the-age-of-ai

Nu finns allt för att våga prova Generativ AI, eller?

 Är “Copilot Copyright Commitment” från Microsoft det som krävs för att alla slutligen ska våga experimentera brett med generativ AI och rulla ut skarpt?

Generativ AI förväntas öka vår produktivitet rejält, McKinsey uppskattar ökningens värde till 4.2-6.4 trillioner dollar per år. Jättemycket pengar. Generativ AI kommer bli viktigt i dom flesta organisationer, kanske på samma sätt som internet eller rent av elektricitet ⚡️

Men, vågar man testa?

Det är många utmaningar som behöver hanteras:

♥️ Vi måste värna om våra anställda och inte skapa panik för att bli av med jobbet.

🕵️ Vi behöver lita på att AI systemen inte exponerar våra hemligheter, eller utsätter oss för IT-säkerhetsrisker.

👩‍⚖️ Vi måste våga använda det som kommer ur AI verktygen utan att riskera att bli stämda för IP intrång.

Tar man ett stort bolag som Microsoft har dom förstås adresserat allt detta och bitvis kommunicerat ut det:

♥️ Tidigt började man använda “Co-”, Copilot, Co-create, o.s.v., för att var tydliga med att vi människor sitter i förarsätet och AI-verktygen hjälper till oss bättre. Inte ersätta.

🔒 Man är tydlig med att all insamlad data är anonymiserad och aggregerad, vilket säkerställer att enskilda användare eller organisationer inte kan identifieras. Samma rigorösa säkerhetsstandarder gäller för Copilot som för Mirosoft 365.

📑 För någon vecka sedan släpptes Copilot Copyright Commitment som ska göra oss trygga i att IP som genereras tillhör skaparen. Man sträcker sig så långt att till och med täcka upp för eventuella stämningar och skriver i sin blogg:

“Copilot Copyright Commitment. As customers ask whether they can use Microsoft’s Copilot services and the output they generate without worrying about copyright claims, we are providing a straightforward answer: yes, you can, and if you are challenged on copyright grounds, we will assume responsibility for the potential legal risks involved.”

Är Copilot Copyright Commitment det sista som behövdes för att ni ska börja experimentera med generativ AI på riktigt?

Referenser

https://blogs.microsoft.com/wp-content/uploads/prod/sites/5/2023/07/Microsoft-Voluntary-Commitments-July-21-2023.pdf

Data, Privacy, and Security for Microsoft 365 Copilot - Deploy Office | Microsoft Learn

Microsoft announces new Copilot Copyright Commitment for customers - Microsoft On the Issues

Economic potential of generative AI | McKinsey

Hey Enterprise, you can start using AI now!

During the spring we read about companies such as Apple, Samsung, JPMorgan, and Goldman Sachs etc., banning the use of large language models (e.g., ChatGPT) due to concerns regarding data privacy, quality, and ethics. These concerns involve potential exposure of confidential information, generation of inaccurate outputs, and violation intellectual property rights.

This might apply for you as well…

There is good news, Microsoft just introduced Bing Chat Enterprise which gives you access to GPT-4 directly in your browser. It addresses some of the mentioned blockers:

🔒 Strict data protection, ensuring your business data stays within your organization.

🚫 Microsoft does not have access to view or use your chat data. Your information remains strictly yours and is not used for AI model training.

Since Microsoft is backing these statements with its reputation, it should really be safe to start picking the AI fruits.

Are you already using ChatGPT in your business? What are the greatest benefits? If not, will you start now when the Enterprise option is available?

https://blogs.microsoft.com/blog/2023/07/18/furthering-our-ai-ambitions-announcing-bing-chat-enterprise-and-microsoft-365-copilot-pricing/

ChatGPT in Life Science?

Having worked in the Life Science sector for many years, I was curious to explore how large language models (LLM) like ChatGPT are steadily making its way into this traditionally slow-moving sector, especially given the multitude of compliance factors that need to be considered.

LLMs are proving to be highly effective at:
📌 Understanding and processing natural language.
📌 Identifying patterns and trends in large data sets.
📌 Synthesizing complex data into meaningful, actionable insights.

These capabilities make LLMs promising tools for addressing common challenges within Life Science such as:
💊 Processing vast amounts of data, scientific literature, and patents.
💊 Accelerating drug discovery and development processes.
💊 Streamlining the design of clinical trials, creating control groups, and interpreting complex data.

It's exciting to see that major companies like AstraZeneca, Pfizer, and Sanofi are already tapping into these tools.
💊 AstraZeneca use ChatGPT to analyze scientific literature and identify new drug targets for development.
💊 Pfizer leverage ChatGPT to automate the literature review process in drug discovery and development. This not only streamlined the process but also cut down costs.
💊 Sanofi, on the other hand, is actively collaborating with AI-focused biotech firms to integrate these language models into their drug discovery process, with the goal of accelerating knowledge sharing and progress.

However, implementing these tools in such a highly regulated business sector does come many challenges, many of which are relevant for several sectors .
📌 Lack of transparency in how the LLMs operate can be a significant barrier.
📌 Training data, the question arises whether current models are trained on the right or enough data? Gigantic life science companies own an abundance of internal data that is crucial for development but is not yet included in the open models.
📌 Privacy and security, ensuring that data is not leaked, is a top concern.

How do we solve these? For transparency, initiatives such as 'explainable AI' aim to make the decision-making process of AI models more understandable. With regards to training data, collaborations between Life Science companies and AI researchers could lead to more nuanced models trained on a wider range of data. Lastly, for privacy and security, leveraging technologies like differential privacy could offer a path forward. Still some work to be done here…

🥳 We are likely to witness a surge in collaborations between AI-focused companies and traditional life science organizations. This could ultimately lead to the faster development of safer drugs, which is a win for us all!

❓ Are you working within Life Science and using large language models?
❓If you're not using it for core business functions, what do you use it for?
❓Or is it still on the strategic level?

🔍 Explore AI without Spreading Your Corporate Secrets

😱 Ever felt the icy chill of dread realizing your confidential data might be at risk while experimenting with AI? Recently, we read about Apple banning their employees from using ChatGPT due to concerns that confidential information could be leaked. It makes perfect sense and Apple is not alone.

💡 Yet, we know that generative AI models are going to play an important role for us, and the best way to stay ahead of the curve is to experiment with it. NOW.

⌛ Until the big IT providers, Google and Microsoft, roll out AI in all enterprise IT environments, there are a few ways to run pilot AI projects. Yes, we heard the announcement that Microsoft is releasing it natively in Windows 11 during the summer, but enterprises are seldom on the latest Windows release.

🚀 There are several solutions 😄

Feel save while exploring letting AI loose on your confidential documents…

🛡️ Stay super safe by running GPT locally, i.e., on a computer that is not connected to the internet. What? This sounds super nerdy, but it is not. With very limited computer skills, I managed to install privateGPT on my computer and ask questions on documents that I provided. How cool isn't that!!! It's extremely safe but the answers were not on par with say, ChatGPT-4. See https://lnkd.in/dGrpw2cd

🔒 Use the encrypted service CapeChat. It automatically encrypts your documents and censors any sensitive data, allowing your information to stay private and secure. This sounds like an opportunity to be safe and get state-of-the-art AI results. See https://lnkd.in/dpFVDhcY Do you like it?

💻 Microsoft also provide a method for deploying ChatGPT based models, which I haven't tried yet, see https://lnkd.in/dS4TknJV. Have you tried it? Does it do the magic for you?

If you are happy using publicly available information and not your own confidential data…

🖥️ Set up specific computers without access to company logins (i.e., no access to company data), use mobile internet access, and have clear instructions on what you are allowed to do and not do. Your company will likely have some publicly available information that can be used in AI experiments. This is not the safest method but quite easy to set-up.

❓What is your approach to leverage the recent developments within AI? Is it OK to use ChatGPT within your company? Is it not? Are you eager to start now? Or wait until Google/Microsoft rolls out the solution for you? 🥳

Two Highlights from the Microsoft Build 2023 Keynote

The ongoing developer-focused Microsoft Build conference offers a glimpse into the future. As the event unfolds, I anticipate hearing announcements that eventually will benefit us regular users, reshaping the way we work.

🎙️ Microsoft's CEO, Satya Nadella, kicked-off the conference with several big announcements. As expected, the spotlight was on AI, with two thoughts stuck with me.

🔑 Trust is the cornerstone of AI integration
One demo involved MS Word, supercharged by AI and plugins, editing a legal document. The AI was tasked with making a specific change, which it accomplished by locating the correct paragraph, implementing the change, and then analyzing the enforceability of the law under California jurisdictions.

Would you trust the outcome?

The current workflow is to forward the draft legal document for final approval by (carbon based) lawyers. This is an excellent initial step that promises to reduce costs. Now, imagine a scenario where you trust the system enough to bypass the human element. This could significantly expedite the process and drastically reduce costs!

This thought process can be applied to any situation where AI assists us. However, the stakes are considerably higher with legal documents compared to, say, creating a Spotify playlist.

The question then arises: what would it take for us to trust the system enough to eliminate human verification?

I'm not tempted by a future where my job is to check the output from AI systems.

Let’s build trust! How!?

🗣️ The Evolution of User Interfaces: From Clicks to Conversations
We are witnessing a transition from mouse-click interfaces to natural language interfaces across all applications, including navigating Windows!

Given that text has a low bandwidth, will we soon be conversing with our computers to accomplish tasks?

If so, the ambient noise in office landscapes could shift from 'tak-tak-tak-tak' of keyboards to a of 'bla-bla-bla-bla' as we speak to our computers.

How might this constant verbal interaction with computers reshape office layouts? Could this signal the end of traditional office landscapes?

🥳 Interesting things are happening to the tools we use every day! Looking forward to diving into more presentations from the conference, have you seen anything interesting?

i’M Making May My Microsoft Month

💯 Hundreds of new AI tools are released every week, which is great, and I love exploring them.

However, for me, it's mostly about getting a sense of what's happening in the world of AI, not for professional work. Agile startups might be able to keep up with the pace, but for enterprises, it's a slower rollout cycle.

🏢 Most of us rely on Microsoft or Google for office productivity, and when these companies roll out a new AI tool, the effect is huge. Hundreds of millions of users, including the "laggards", suddenly gain access to new tools that might revolutionize their work – my work.

Microsoft is currently in an extremely interesting position, owning a large stake in OpenAI and implementing AI in their popular office suite. However, the ecosystem is complex, with at least 15 different tools in M365 and many other services. How is this playing out? What are they up to?

🔍 To get my head around this, I will focus on the month of May to understand how Microsoft is leveraging AI, with the ultimate goal of understanding:

📌 How will daily work in the Microsoft suite change in 3, 6, and 12 months?
📌 How will it be rolled out? considering privacy and confidentiality aspects?
📌 Examples of companies using it today with internal data.

📢 If you have any suggestions on where to start (podcasts, YouTube, websites...) or who to talk to? have you already started using Microsoft AI tools at your company? feel free to contact me. I'm eager to start 🥳

AI is on the doorstep of big enterprises

Currently, companies concerned about information security are not allowing their employees to use AI-products such as ChatGPT. This makes perfect sense, as the data entered into ChatGPT will be used by OpenAI for training models and stored on their servers. At least that how it was until recently.

🔐 The other day, OpenAI rolled out the possibility to opt out from using your data in training new models, and they are planning to release a business subscription for "professionals who want to have more control over their data."
https://lnkd.in/dufUWU8v

📈 This opens up the possibility of using ChatGPT with your company data, such as improving the language of a document or make a summary of it. Massively great.

📌 If you're not "enterprise-level concerned" about confidentiality but still want some level of privacy, opting out from sharing your data with OpenAI might be sufficient to get start using ChatGPT in your profession, check the fine print before going all-in. If this is not enough, the solution is just around the corner...

🚀 The real revolution will come when the AI-model has access to all your internal data (documents, e-mails, chat conversations, etc.). Then, your own "ChatGPT" will provide answers based on your company data, allowing you to ask it, for example, to summarize the top 10 lessons learned from all projects in your company. This is getting closer—a lot closer.

A few weeks ago, Microsoft demonstrated how to leverage ChatGPT's "brain" to interact with your enterprise data while respecting information protection controls. You can test it now.
https://lnkd.in/dzDcAaBk

🎓 As said many times, AI will revolutionize knowledge work, and it will happen very soon. My bet is that 2024 will be the year of re-skilling and up-skilling.

💭 What do you think? Have you started using ChatGPT professionally yet? What's holding you back? Has AI already improved your productivity or provided other benefits? How do you think about re-skilling your entire force of knowledge-workers?