AI is not breaking EU law: Revealing the musician interviewed // Dave on Data

Dear visitor!

My latest newsblog article for Schorberg Analytics started with a reference to a song title by a famous metal band.

And I wrote a little bit about the new EU AI Act and how its affects AI in HRM in my text at the beginning, before I focused on the main purpose of it: Presenting you 7 books on AI for work and HRM.

Note: I updated this list a day later by adding 20 more books at the bottom on Artificial Intelligence(AI), ChatGPT, Business, and Large Language Models(LLMs), so there are no excuses for you for not reading and not knowing how to get into AI or, as you have probably touched the keyboard for it, for improving your skills and business!

But back to music: “Breaking the Law” is a song title by the successful British band Judas Priest founded 1969 in Birmingham, and the interviewee was their singer and songwriter: Robert Halford. The music group sold over 50 million albums and is considered to be on of the greatest metal bands.

German/Austrian/Swiss culture TV station 3sat spoke with their lead vocalist on the occasion of their new and 19th studio album “Invincible Shield”. You can watch the interview with Rob Halford, now 72 years old, in the media library of the broadcaster and the program “Kulturzeit”:

Image: Rob Halford, Source: 3sat (embedded)


So, that was the solution. When I was younger (and my hair was of course longer) I used to listen to such music and from other rock genres of the 80s and 90s as well. As now I am just a little bit older, I still enjoy this music, but my main focus since 15 years is dancing and listening to Salsa music.

Interesting: There are others like business psychologist Dr. Nico Rose who write and speak about the connection of Psychology and Hard Rock/Heavy Metal music (see his website too), and as I know some (or many? who has a statistics? ;-)) tech people (and with other backgrounds as well like design and law) banged their heads too.

Here a closing tip fits which returns us to AI, data science, and Python (the programming language of course, not the lovely animal 😉):

David Langer, an applied computer scientist and experienced IT trainer (and according to his shirts in his latest videos with some interest for metal bands like Metallica and Iron Maiden), offers great online-tutorials, videos, and live training to learn Python, and Python with Excel, Machine Learning, and special Data Literacy courses. Check out his work on his website and free resources on his YouTube channel!

I learned about Dave on Data by the terrific newsletter by Data & Analytics, so you should, if this your field of work, check it out too.

That’s it!

Holidays are coming for many of us and I wish you some recreational and inspiring free days where ever you may roam! (the next song riddle, hoho!)

Stefan Klemens

Note: The image for this newsblog article was created with the help of DALL·E 3 as part of OpenAI’s ChatGPT-4. For those interested in the prompt and the chatbots accompanying words, here it is:

You: Create a HD pop art image in horizontal format of the relationship of heavy metal music on one side and data analytics as well as artificial intelligence on the other side. Surprise me!

ChatGPT: Here’s a pop art image that creatively captures the relationship between heavy metal music and the worlds of data analytics and artificial intelligence. It blends the vibrant and chaotic energy of a heavy metal concert with the precise, orderly realm of AI and data analytics in an imaginative and cohesive way. Enjoy exploring the details and the surprising ways in which these two seemingly disparate worlds are visually connected!

Hint: I created another picture with it, and a different prompt, and the results with skulls, stats, guitars, and charts were artistic, but maybe a little bit to scary for a header! ;-).

PS: Want to exchange ideas on Human Resources, people analytics, digital assessment, or artificial intelligence in HRM? Then network, write a message and/or make an appointment for an online meeting. Or the classic way: phone call.

And: You like my work and the content I regularly share? Then I’m happy about a Like or comment on LinkedIn. Thank you! 🙂 🙋‍♂️🌳


7 Books on AI for Work and HRM + AI Act

Updated, 20/03/2024: Book list with 20 more books on AI at the end; some text extensions and corrections.

Dear visitor!

We live in fast times, information travels with light speed around the globe, and artificial intelligence is not breaking the law anymore with the AI Act that just passed the EU parliament on March 13, 2024.

To put it casually, alluding to a song by a well-known British hard rock band. Which I remembered recently while watching an TV interview on German/Austrian/Swiss culture channel 3sat with the front man of the group. (Can you guess who it was?)

Update 02/04/2024 (d:m:y): I published the solution for the little riddle today in this newsblog article and last week on this LinkedIn post.

The Artificial Intelligence Act

But I don’t want to write today about the Artificial Intelligence Act as a European Union regulation yet (nor about Heavy Metal music), but about what’s in the first part of this newsblog’s title: 7 Books on AI for Work and HRM (five published in 2024 and two in 2023; one in English).

However, anyone who works professionally with these mathematical-computer-based tools should take a close look on this new and unique law on AI and its key content: the five level of risk from artificial intelligence. You can find more on the AI Act in this article by Deutschlandfunk (in German, March 16) and on this special EU website.

For HR leaders and professionals it is crucial to know and act on that AI applications for employment like CV-scanning, worker management, vocational training, and the like are classified as “high risk” by the AI Act – as others areas like health or law where AI systems negatively affect safety or fundamental rights of people.

Important to know: The law is expected to come into force with certain delays depending on the risk level in May 2024 after its publication in EU’s own journal.

Note: There is standard for HR Tech in Germany already since 2021: Guidelines and further publications like the “Ethik Check KI” (February 2024) regarding the application of technology and artificial intelligence in HRM were developed by the “Ethikbeirat HR Tech” (Ethics Council HR Tech).

So much for that! Keep in mind this law and guidelines, but let’s us now turn to some more practice and input on how to use AI in your work and for a better HR – and how AI is challenging us and what we else we can do about it.

One more thing: As I am also a fan of science-fiction and thrillers, I added a new novel concerning this areas too. Although such books (and films) can be misleading and they exaggerate in terms of technical, psychological, logical, and other issues more or less (but this is of course one main reason to read fiction at all), some also inspire scientists and engineers, thus drive progress and help to design the future. Good examples are the books by Jules Verne, the novels by Isaac Asimov, or the technical devices designed for Star Trek.


(a) As you will see, all but one books of my list are in German. You can find good books on AI and AI in human resources management in English language of course, and a search at O’Reilly or Amazon will help.

(b) And: The first three books presented below are, by accident (no commission for me!) published by renowned Rheinwerk Verlag (formerly: Galileo Press), specialized in IT, Marketing, Photo, and Video. Because of their Eastern sale you will receive until March 24, 2024, a print book and an ebook for one price in a bundle!

But let’s start with the serious books (I will write a separate newsblog article on AI in novels and films):

Eigene KI-Anwendungen programmieren

Image: Rheinwerk (embedded)

Metin Karatas (2024). Eigene KI-Anwendungen programmieren. Bonn: Rheinwerk. [February 2024]
https://www.rheinwerk-verlag.de/eigene-ki-anwendungen-programmieren/ [Note: English language edition in preparation an on sale in November 2024 according to this publisher’s website]

KI für Kreative

Image: Rheinwerk (embedded)

Jenny Habermehl (2024). KI für Kreativ: Künstliche Intelligenz für Grafik und Design nutzen. Bonn: Rheinwerk. [March 2024]

ChatGPT & Co.

Image: Rheinwerk (embedded)

Rainer Hattenhauer (2024). ChatGPT & Co.: Wie du KI richtig nutzt – schreiben, recherchieren, Bilder erstellen, programmieren. Bonn: Rheinwerk. [November 2023]

Richtig texten mit KI

Imag: mvg (embedded)

Kai Spriestersbach (2023). Richtig texten mit KI – ChatGPT, GPT-4, GPT-3 & Co. München: mvg. [May 2023]

Inside KI

Image: Herder (embedded)

Larissa Holzki, Stephan Scheuer (2024). Inside KI: Wie Künstliche Intelligenz und ihre Pioniere unser Leben und Arbeiten revolutionieren. Freiburg im Breisgau: Herder. [March 2024]

The Algorithm

Image: Hatchette Books (embedded)

Hilke Schellmann (2024). The algorithm: How AI decides who gets hired, monitored, promoted, and fired and why we need to fight back now. New York: Hachette Books. [January 2024]

Die Burg

Image: Droemer (embedded)

Ursula Poznanski (2024). Die Burg. München: Knauer. [February 2024]

See my note in the following block on this novel and further links regarding the discussion about the risks of AI.

Note: Novel “Die Burg”
This novel deals with an AI that becomes (super) intelligent, forms it own goals, and acts on these. The risks and chances of AI are widely discussed, one result being the AI Act by the EU mentioned at the beginning of my newsblog article. Whether AI will eventually become human-like or even reaches superpowers, is not clear and the positions of leading AI experts are split. Read more on risks and harms in the Wikipedia here and about existential risks from AI here.

The books by Ursula Poznanski and Hilke Schellmann show AI in a negative light or are critical towards the application of AI in HRM. I don’t share such negative views who overdraw what AI will be capable in the near future or which emphasize the risks much more rather than weight the chances equally.

But it is always good to see the other side and be aware of what some people and employees positions are – and address them appropriately for seeking acceptance by making clear that HR decisions are made by humans and that AI follows rules.

What is need here is far more AI competence and data literacy and their promotions are also part of the AI Act, as well as in projects like those that are announced these days by Microsoft Deutschland within the scope of their planned third server location (next to Bedburg and Bergheim) for AI and cloud computing in the state of Nordrhein-Westfalen. See the next block for more.

Note: Microsofts Investment in Germany
Marianne Janik, CEO of Microsoft Germany, says to the press on her visit to an event in the region with politicians on March 18, 2024: “Niemand wird einer Technologie vertrauen, wenn die Beurteilungsfähigkeit dafür nicht da ist.” [No one will trust a technology if the ability to assess it is not there.] Read more in this newspaper article here and here for the companies huge education campaign for AI, and what the involved local politicians think about it.

That was it! I hope you have gotten some suggestions for books that will help you with your professional AI journey. There are of course many other books out there on AI with or without mathematics, on specific areas (like HRM), or on today’s dominating fields: machine learning and deep learning – or short: data science.

What is your favorite book on AI for work? Which one would you recommend to your colleagues? And how do you see our AI future? Please comment on my LinkedIn post regarding this newsblog article.

I wish you a good week, and: Happy reading and applying AI!

Stefan Klemens

Update (20/03/2024): I expanded my list on a number of books on AI, ChatGPT, Business, and Large Language Models (LLMs). See the results in the following block. And I did some text extensions and corrections as well.

Update: 20 more books on Artificial Intelligence (AI), ChatGPT, Business, and Large Language Models (LLMs)

1. Alexander Loth (2024). KI für Content Creation: Texte, Bilder, Audio und Video erstellen mit ChatGPT & Co. Frechen: mitp.
2. Wolfhart Fabarius (2023). ChatGPT in der Unternehmenspraxis: Anwendungsbeispiele für Risikomanagement, Controlling und Compliance. Berlin: Erich Schmidt.
3. Andreas Berens, Carsten Bolk (2023). Content Creation mit KI. Bonn: Rheinwerk.
4. Stephen Wolfram (2023). Das Geheimnis hinter ChatGPT: Wie die KI arbeitet und warum sie funktioniert. Frechen: mitp.
5. Ulrich Engelke, Barbara Engelke (2024). ChatGPT – Mit KI in ein neues Zeitalter: Wie KI-Tools unser Leben und die Gesellschaft verändern. Frechen: mitp.
6. Eric Sarrion (2023). ChatGPT for Beginners: Features, Foundations, and Applications. Berkeley, CA: Apress. [507 pages]
7. Codi Byte (2023). hat GPT Bible: 10 books in 1 : everything you need to know about AI and its applications to improve your life, boost productivity, earn money, advance your career, and develop new skills. Wroclaw: Amazon Fullfilment.
8. Eric Sarrion (2023). Exploring the Power of ChatGPT: Applications, Techniques, and Implications. Berkeley, CA: Apress. [204 pages]
9. Ken Huang, Yang Wang, Feng Zhu, Xi Chen, Chunxiao Xing (Eds.). (2023). Beyond AI: ChatGPT, Web3, and the Business Landscape of Tomorrow. Cham: Springer Nature Switzerland.
10. Tom Taulli (2023). Generative AI: How ChatGPT and Other AI Tools Will Revolutionize Business. Berkeley, CA: Apress.

11. Olivier Caelen, Marie-Alice Blete (2024). Anwendungen mit GPT-4 und ChatGPT entwickeln. O’Reilly.
12. Paul Dupin (2023). Chatgpt: Profitables Geschäft in der Ära der KI Textgenerierung. Epubli.
13. Andreas Helfrich-Schkarbanenko (2023). Mathematik und ChatGPT: Ein Rendezvous am Fuße der technologischen Singularität. Berlin, Heidelberg: Springer Spektrum.
14. Charles Waghmare (2023). Unleashing The Power of ChatGPT: A Real World Business Applications. Berkeley, CA: Apress.
15. Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, Dilip Gudivada (2023). Applied Generative AI for Beginners: Practical Knowledge on Diffusion Models, ChatGPT, and Other LLMs. Berkeley, CA: Apress.
16. Tom Taulli (2023). ChatGPT and Bard for Business Automation: Achieving AI-Driven Growth. Berkeley, CA: Apress.
17. Michael McTear, Marina Ashurkina (2024). Transforming Conversational AI: Exploring the Power of Large Language Models in Interactive Conversational Agents. Berkeley, CA: Apress.
18. Thimira Amaratunga (2023). Understanding Large Language Models: Learning Their Underlying Concepts and Technologies. Berkeley, CA: Apress.
19. Stefan Brunnhuber (2024). The Third Culture: The Impact of AI on Knowledge, Society and Consciousness in the 21st Century. Cham: Springer Nature Switzerland
20. Stefan H. Vieweg (Ed.). (2023). KI für das Gute: Künstliche Intelligenz und Ethik. Cham: Springer International Publishing, Springer Gabler.

Tip: Still not enough? Need a special book? Looking for upcoming publications? Then please use your favorite book search engine! For my German-speaking readers: buchandel.de shows almost all deliverable books in German language.

Note: Be aware that ChatGPT by OpenAI is although the leading GenAI tool, there are others similar Chatbots (as you probably know) that you should check out and get familiar with by testing them, watching videos and online-tutorials, reading books and articles, and participating in training and workshops:

Note: Major competitors and alternatives for ChatGPT

(a) Gemini by Google (formerly: Bard; see more here) as the most important competitor;
(b) Anthropic (see more here), founded by former members of OpenAI and financed greatly by Amazon and Google;
(c) Chatbots based on the model LLaMA by Meta, now open source.

Get an overview here about Large language models (LLMs) which are the basis for today’s chatbots. Check out also:

(1) French company Mistral AI (more here) with investors like Andreessen Horowitz, BNP Paribas, and Salesforce.
(2) German Aleph Alpha (more here) with investors like Schwarz Gruppe, SAP, Bosch, and Hubert Burda Media.

I will write more on these companies and models in an upcoming newsblog article: So stay tuned and connect with me on LinkedIn!

PS: Want to exchange ideas on Human Resources, people analytics, digital assessment, or artificial intelligence in HRM? Then network, write a message and/or make an appointment for an online meeting. Or the classic way: phone call.

And: You like my work and the content I regularly share? Then I’m happy about a Like or comment on LinkedIn. Thank you! 🙂 🙋‍♂️🌳

Artificial Intelligence

How do I find the right AI tool? 10 search engines will help!

Dear visitor!

The number of AI tools is growing exponentially as you know if you are following AI newsletters, AI newsblogs and articles, and AI-related posts on LinkedIn.

Which makes it hard to keep an overview and find a solution that could be better for your demands than standard-choice ChatGPT (although it is still one of the best). As a result there are now special search engines for AI software.

For this newsblog article I have collected ten AI search engines – And, as my focus is on human resource management, I tested the directories on the term “Human Resources” and put the numbers of the resulting AI HR tools in parentheses. Some tools offered a category on this term too, while others as you can see list just few ones.

So here is my list on 10 AI search engines in alphabetical order (number of HR tools):

AI Library: 2,150+ tools and colabs (1 HR)

AI Search: 13,088+ tools (23 HR)

AI Tool Directory: no numbers on tools (32 HR)

AI Tool Hunt: Best Free AI Tools (30 HR)

AI Tools Arena: no numbers on tools (6 HR)

AllthingsAI: no numbers on tools (25 HR)

Find My AI Tool: 1,500 tools (26 HR)

Futurepedia: 5,748 tools (12 HR)

There’s An AI For That: 12,757 tools (56 HR)

Toolify: 10,723 tools (321+ HR)

So, there you have a bunch of search engines: Test them on what kind of AI tool you are looking for, and compare their results. This search engines on AI tools provide more or less further functions like categories and several filters, so you can fine tune your AI search.

And as the results above suggest, it is a good idea to start with “There’s An AI For That”, “AI Tool Director”, or the other four which follow close. Toolify is a special case: Although it shows a very high number of AI HR tools, there is no category “Human Resources”. Instead we find there the two HR-related categories “AI Recruiting” and “AI Interview Assistant”, and AI tools regarding further HR topics like “Teams” among “Other > Other”.

And be aware of course: All this special AI HR tools that you will find can help you in your HR work, but as you know yourself, more general GenAI tools like ChatGPT can strongly support you as well – and even enable you to create your own HR AI tool!

I wish you a good week, and: Happy searching, and testing afterwards!

Stefan Klemens

PS: Want to exchange ideas on Human Resources, people analytics, digital assessment, or artificial intelligence in HRM? Then network, write a message and/or make an appointment for an online meeting. Or the classic way: phone call.

And: You like my work and the content I regularly share? Then I’m happy about a Like or comment on LinkedIn. Thank you! 🙂 🙋‍♂️🌳


HR Data Science: Salary Prediction at Trivago and StepStone

Dear guest!

On Tuesday evening, 29th of August 2023, I was at the Düsseldorf Data Science Meetup at Trivago, the Online search company for hotels, in their extraordinary headquarters in the Medienhafen of Düsseldorf, my birth city.

I left my office in Solingen early in the afternoon that day since I met with my business friend Dominik Rühl before the event – and walked in the sun from the Düsseldorfer Landtag (state parliament) at the Rhine to our meeting point at UCI cinema near Trivago.

Stefan Klemens with Dominik Rühl at the meetup. Foto: Stefan Klemens

It was good to see Dominik after about five years – And we had a fruitful exchange on our common topics artificial intelligence (AI), recruitment, skills, and digital assessment as well as some private issues. He is now working as a HR & Recruiting Manager at Advance Business Partner GmbH based nearby in the city of Neuss on the other side of the Rhine. The consulting company focuses on mobility services in different areas like recruitment, innovation, and transformation management.

Although the summer and weather this year in Germany is pretty unstable, we enjoyed sitting outside with our drinks at unique brewery bar Eigelstein.

Find out more about the Düsseldorf Data Science Meetup Group with its interests in Data Science, Machine Learning and Python/R, on this website.

Arriving at Trivago

The Trivago building as seen from the north-east of the Medienhafen Düsseldorf. Foto: Stefan Klemens

At 6 pm it was time to walk to nearby Trivago building, finished in 2018. The individual modern styled entrance area and the café behind offers a glimpse on how the interior of the building is decorated (see this article and this article about the New Work culture at Trivago and the architecture of the headquarter´s spaces.)

Surprisingly we, with another guest, were the first participants arriving (ok, it was half our before the official start and talks started even later), but were soon picked up by Gina from Trivago. Together we (and a cart full of pizza in yellow boxes for the data people) were lifted by one of the elevators to the top floor for the location event.

A stunning view to the south-west skyline from the roof terrace reached our eyes, and Dominik, the coming participants, and me enjoyed drinks and pizza before the event started at 7 pm.

Our co-host Aida Orujova gave us a very warm welcome, she introduced the speakers, and broke the ice by asking who is from data science, who is from engineering, and who just there to know more about salaries.

Co-host and moderator of the evening Aida Orujova welcoming the Data Science crowd. Foto: Stefan Klemens (with approval of the her)

First talk: Alexander Fischer, Trivago

Alexander Fischer from Trivago started with his talk about is passion for the programming and statistics software R, and his (and the economists´) “Swiss knife” methodical approach for prediction outcome variables: Linear Regression. He showed how he and his team used this classical algorithm with packages R´s fixest, and PyFixest to predict wage by using the variables education and ability (e.g. intelligence).

In his presentation of the problem in doing that (“The error term is correlated with the dependent variable”) he referred to a recent study using data from 59,000 Swedish men published 2023 by Marc Keuschnigg, Arnout van de Rijt, and Thijs Bol in the European Sociological Review (number 20, pages 1-14), titled “The plateauing of cognitive ability among top earners” (online article published here on January 28, 2023).

Since AB-Testing (or randomized experimental and control group design) is not feasible in the model (sending randomized individuals in one group for example one year more to college) the classical solution in Social Sciences and Psychology are Quasi-Experiments which were first introduced in the literature by standard book “Quasi-Experimentation: Design and Analysis Issues for Field Settings” written by Cook and Campell (1979).

As a solution for not manipulation experimental the years of education as predictor of the wage Alexander used therefore a variable called “distance to college” as a natural differing factor between people regarding their years of education.

The data scientist from Trivago further pointed out in his “The Secret Sauce” slide that taking the role of companies into account in the corresponding regression model, the computation is quite demanding (millions of employees, thousands of companies, 20 years of data) – But he presented of course a solution for it (and that was not Spark!).

At the end with the help of programming language Python and package PyFixest Alexander showed that the prediction of salary can be done, and he answered the questions of the audience.

Second talk: Michael Matuschek & Tim Elfrink, StepStone

In the second talk this evening we learned from Michael Matuschek and Tim Elfrink how StepStone is predicting the salaries of all kinds of jobs for their salary products.

Michael begun the session, and gave an overview about StepStone´s salary products include the Salary Planer, Salary on Listings, and Auto-generated Salary SEO pages.

As a result of a 2020 study and further research before it turned out that salary is for 96 % of the respondents the most important criteria when choosing a job (flexible working ours, career & training opportunities, and corporate culture, reach only 90 % resp. 91 %).

Michael Matuschek with Tim Elfrink from StepStone answering a question from the audience. Foto. Stefan Klemens. Thanks both for their approval of the picture!

Michael told us also about the challenges in prediction salary at StepStone regarding data distribution and features (more white collar jobs and little part-time data for example) and: The gender pay gap, quality assurance, feature engineering, the underlying model and the used algorithm, as well as the metrics (main business KPIs) accuracy and generalisation.

After him Tim Elfrink took the mic and explained the broader infrastructure of the predicting IT system with AWS and the auto deployment of the model. Further subtitles of his presentation were for example: Creating scalable infrastructure and development environment.

A number of questions (and some hints for improving their model) came from the participants, and Michael and Tim were happily answering them.

Closing, socks, and outlook

At 8.30 pm presenter Aida Orujova returned to the stage again and thanked all guests and speakers for being there. As several others I took the chance to talk with some participants (see header picture), before I needed to catch my tram to travel home.

Trivago-Logo in front of the building after sunset. Foto: Stefan Klemens

My second Düsseldorf Data Science Meetup was another wonderful experience (read about my first here), and the scheduled next event in October 2023 is of course on my list.

Oh, one last thing (we learned this from the apple guy, right?) I did not mention yet. Before the start the participants could grab one, two, or three promotional gifts from Trivago as shown in the picture: One for using your hand to write (still common among a few people I was told), one for storing big data in a small piece of metal, and one to keep your feet between 28 ° C and 33 ° C (surface temperature of the extremities as I learned writing this sentence) when external temperatures fall in later autumn.

Promotional gifts for the participants of the Düsseldorf Data Science Meetup from Trivago. Foto: Stefan Klemens

As I like to test digital and analogue things (I have high scores on openness to experience (see the Big Five Personality Traits) and curiosity which is one of my signature strengths according to the VIA-Model), the usefulness of the trivagonian socks to prevent cold toes needed to be proven also.

Note: If you like to know more about psychological traits and psychometric assessment of these for HR recruiting, selection, and development, then click on my work as a Work Psychologist as presented here: https://www.digitalassessment.de/

I can say that my feet got warmer but the real test of course – and perhaps then like a case study (N = 1) with more treatments like a stepstonian, a sipgatian, and quantopian fabric as well a control (no treatment, that is walking without socks! preparing for that right know!) – will be conducted in colder times which are coming soon to Germany. I will report on it! 😉 And perhaps you wanna join the experiment to lift the “N”, so results will be more valid?

Me testing the Trivago socks: And I am smiling realizing the double meaning of the words and symbols matching the two main areas of the company. Foto: Stefan Klemens

With this of course rather funny ending, I thank very much the organizers and speakers for this evening, and Trivago for hosting the meeting! Will we see us next time on a Düsseldorf Data Science Meetup (or another place if you like)?

Many greeting and all the best to you!

Stefan Klemens

PS: Want to exchange ideas on people analytics, digital assessment or artificial intelligence in HRM? Then network, write a message and/or make an appointment for an online meeting. Or the classic way: phone call.

And: You like my work and the content I regularly share? Then I’m happy about a Like or comment on LinkedIn. Thank you! 🙂 🙋‍♂️🌳


Analysis by BCG: Generative Artificial Intelligence in Human Resources

Dear guest!

Generative Artificial Intelligence (GenAI) has gone public since OpenAI released its ChatGPT 3 on November 30, 2022. You know it, tested it, and it is now likely that you are using it productively for your work.

As I am into Human Resources (HR) and Analytics (People & HR Analytics, Digital Assessment) any new content that deals with Artificial Intelligence and HR catches my eyes. Today it was the following article published by Boston Consulting Group (BCG) on August 24, 2023:


The six authors from six different BCG locations (Julie Bedard, Katie Lavoie, Renee Laverdiere, Allison Bailey, Vinciane Beauchene, and Jens Baier from my home town Düsseldorf!) analyse the impact of GenAI on HR, show increased productivity with ethical AI, and how HR executives can lead the GenAI transformation by example.

How GenAI impacts HR

According to the authors GenAI shifts HR into a more strategic function in four ways:

  • a dramatically increased self-service,
  • productivity and experience enhancements across hire-to-retire processes,
  • truly personalized and always-on delivery of HR services, and
  • a comprehensive, data-driven talent ecosystem to power people decisions and workforce transformation.

Especially GenAI´s ability to collect and combine less structured data will enable these impacts in talent assessment, developing career pathways, talent sourcing, and learning and development (L&D) as their trend and use case examples and case study indicate.

As their results show BCG reduced with GenAI in self-service 40 % in employee request approval time. Regarding creating a responsive online course productivity outcomes were significant with duration time dropping dramatically from 6 weeks (before GenAI) to 9 days, 60% cost savings to develop video content, and a half-sized project team (5 persons instead of 10).

Moreover the company improved the whole hiring process with the help of GenAI the: content creation for roles, candidate analysis, interview and selection, and onboarding.

The article describes also which the lessons learned after the change and how one can putting it the pieces together in an AI enabled more comprehensive and data-driven ecosystem by answering four questions:

  1. What skills do I need? In what quantity? (Work redesign and demand forecast, job-skills matrix with proficiency)
  2. What is the current workforce potential? What new career paths exist? (Employee skills mapping, job-matching algorithm)
  3. How do I best fill supply-demand gaps? What are no-regrets talent moves? (Dynamic workforce plan, sourcing and upskilling strategy)
  4. How do I support comprehensive change management? (GenAI and AI use cases like Copilot to redesign work)

Increased productivity with ethical AI

The authors expect that GenAI with the right mix of certain factors “could boost HR productivity up to 30% in the not-so-distant future”. And state: “One early adopter in AI for HR has been able to reap financial benefits, cutting its annual budget by 10% year over year for the past three years.”

After that the article offer two scenarios how a GenAI-enhanced HR business partners (HRBP) may support a global industrial goods company for example. Both scenarios create time savings as the article illustrates.

In the following part the authors on one hand ask HR to be aware of the risks of GenAI when dealing with sensitive personnel data, and on the other hand to take the chance to decrease bias in bias in real-time decision making, if well done.

Working together with legal and business leaders for a responsible AI is the key for “business decisions that are sound, just, and well documented.

Closing remarks

The article ends with a call to HR leaders to drive the Generative AI Transformation along with their own function, and the offer 7 steps to do so.

Further the authors describe 6 points each the Chief HR officer in his dual role can activate now in (a) supporting the enterprise and (b) leading the change in implementing GenAI.

Thanks to Barry Swales for sharing this article on LinkedIn! And: All the best him and the Tucana team for the final week before the People Analytics World Conference in Amsterdam.

Have a good weekend and many greetings!

Stefan Klemens

PS: You want to exchange ideas on People & HR Analytics, digital assessment or artificial intelligence (AI) in HRM? Then network, write a message and/or make an appointment for an online meeting. Or the classic way: phone call.

And: You like my work and the content I regularly share? Then I’m happy about a Like or comment on LinkedIn. Thank you! 🙂 🙋‍♂️🌳