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Leveraging AI in Software Development

Munich, March 2024

Leveraging AI in Software Development

Munich, March 2024
I

n 2023 the hype around artificial intelligence (AI) and specifically GenAI reached its peak.

This can be seen in 2 major aspects. First, the valuation of leading AI companies like Microsoft or OpenAI went through the roof. OpenAI is in early talks to raise a fresh round of funding at a valuation at or above $100 billion¹. Second, the high number of projects that have been launched in the automotive industry across the entire value-chain created a high demand on AI expertise. Companies formed teams or complete organizations defining hundreds of use cases for launching pilot projects to increase the customer experience, create efficiency or support other strategic goals in their company & product strategy.

Berylls Digital & Technology serves as the center of competence AI within the Berylls group. Therefore, we are leveraging AI for our own business, working with customers on projects and observing market developments closely. Over the last 12 months we saw that companies tend to be more cautious with the implementation of GenAI at the beginning. While early pilot projects are often rolled out with standardized services of business software providers, e.g. copilots, later stage GenAI use cases are more customized and developed inhouse or with in cooperation with partners to target specific problems.

Successfully navigating through a plethora of potential use cases as well as implementing the most impactful ones necessitates a robust validation process and significant organizational change. The pace and extent of these transformations are influenced by a company’s strategic decision to either lead as an innovator and first mover or to adopt a fast follower approach. Drawing from this study and our experience in client engagements, we advocate for at least being an early adopter, particularly in integrating Copilots into standard enterprise software. Our observations from numerous projects highlight an immediate beneficial impact: the license costs per user are quickly offset by short-term efficiency gains. Proper integration and customization of these tools within existing processes can further enhance productivity and reduce labor requirements in specific roles.

 

At Berylls, we specialize in guiding automotive car makers and suppliers through their journey from initial exploratory ideation to becoming efficient, scaling organizations.  Indeed, the potential of AI is immense in multiple fields of application, yet we also see a significant number of failed pilots & projects through our work with clients. We will discuss the reasons for failing in AI use cases in another Berylls insights edition in more detail. However, in this article we want to set the focus on an area where AI delivers a significant improvement on speed and efficiency for coding projects: The usage of GitHub Copilot by Microsoft in “real” software development projects.

 

Case Study

The automotive industry is spending over 4 billion euros annually due to poor vehicle launches caused by ineffective project management, missing information, and late execution of measures. As a result, many vehicle launches end up in task force mode. Properly managing these task forces in stressful situations requires full focus, which is why efficient collaboration is essential. Nonetheless, modern processes are complex and multilayered dependencies across departments pose information asymmetries, leading to the distribution of outdated information and time inefficiencies.  

Leveraging our decades of experience in managing task forces at Berylls, we have developed a solution to overcome these challenges. Together with our software development partner Avochoc, Berylls is launching “elyvate”, a digital task force solution custom-built for the automotive industry. This is our digital approach to optimize performance in project and shop floor management.

Our software as a service (SaaS) solution is the next level of task force management and focused on user-friendliness as well as simplicity, ensuring an intuitive experience. elyvate is designed to integrate seamlessly into your workflow, enhancing efficiency without the complexity. It offers an all-in-one reporting solution that includes automated KPI and measure tracking to optimize performance in project and shop floor management. elyvate allows to digitally steer task force projects with ease, proactively plan and follow up on KPIs, implement measures, as well as most importantly, track their efficiency.

Source: Berylls Strategy Adivsors

Through “elyvate”, Berylls provides our customers with a tool to address these challenges.

  • Cloud-based SaaS tool with 24/7 availability across multiple devices.
  • Smart project management and reporting including creation of
    KPI charts, tracking and planning for taskforce project teams.
  • Fast set-up and preconfigured work package structure based
    on project needs.
  • Integration into shop floor and machinery management and reporting systems (in development).

     

During the development phase we jointly decided to leverage AI development support tools as far as possible, not only to make the process more efficient but also to get hands on understanding of the advantages and challenges of these.

Using AI tools in a business context needs clear and robust legal governance model for using such applications. Particularly AI usage must comply with applicable laws and regulations as well as aspects like intellectual property (IP), data protection (GDPR), and business secrets become critical. We will cover this in detail in our latest newsletter. This is especially challenging since the local / regional regulations are just being updated to reflect the questions in IP and data protection.

Today we want to share our experiences leveraging GitHub Copilot by Microsoft.

Our experiences with GitHub and AI features:

Coding can often be a laborious and lengthy process. Nowadays, developers are perpetually seeking innovative methods to enhance their productivity, precision, and efficiency in programming. One tool of choice for development teams is GitHub.

GitHub is an AI-powered developer platform that allows developers to create, store, and manage their code.² Features like access control, bug tracking, software feature requests, task management, continuous integration, and wikis are included in the software. Acquired by Microsoft in 2018, the platform is now used by more than 50,000 organizations and 1.3 million paid subscribers, representing the most broadly facilitated AI developer tool worldwide.³

The GitHub Copilot

Introduced in late 2021, GitHub Copilot represents one of the latest innovations by GitHub. Marketed as an ‚AI pair programmer‘, Copilot employs AI to auto-generate code within your editor. It is accessible as an extension for Visual Studio Code, the JetBrains IDE suite, and Neovim, broadening its usability across different development environments.

GitHub Copilot is an innovative AI tool driven by OpenAI Codex, designed to improve the coding experience for developers. Its primary function is to propose code snippets based on the context within the file, such as function names, code comments, docstrings, file names, cursor position, and more. By simply pressing the Tab key, developers can accept these auto-generated proposals, which are derived from open-source code within GitHub’s public repositories. This AI tool is proficient in numerous common languages including TypeScript, Python, JavaScript, Ruby, among others.

One of the key aspects that sets GitHub Copilot apart from similar tools is the level of control it offers to the users. Unlike other products, GitHub Copilot empowers the user with the flexibility to accept or reject code suggestions, manually modify them, and explore different alternatives. As the user interacts with the tool, it gradually adapts to the coding style, leading to progressively refined and relevant suggestions over time.

A distinctive feature of GitHub Copilot, further differentiating it from other solutions, is its ability to comprehend natural language, encompassing both programming and human languages. This feature is especially beneficial for developers working with unfamiliar frameworks and libraries, as Copilot can navigate through open-source documentation quickly, reducing the need for manual searches.


To maximize the benefits of GitHub Copilot, it is recommended to divide the code into smaller functions, write effective comments and docstrings, and use meaningful names for function parameters. These practices aid Copilot in understanding your intent more accurately.

However, it is important to note that GitHub Copilot does not generate flawless code. While it strives to comprehend the developer’s intent, some proposals may not function as expected or even make sense. It does not test any of the code it suggests, which means the suggested code might not compile or run. Therefore, developers still need to meticulously review and test the code before assuming it’s usable.

In general, GitHub Copilot is likely the best autocomplete tool available, offering a wide array of solutions to problems beyond basic suggestions. The variety of proposals for a code snippet is impressive, often reducing the need to consult external sources like Stack Overflow.

Despite its capabilities, it is crucial to understand that GitHub Copilot is merely a tool and far from replacing human developers. You cannot depend solely on Copilot, and it is still up to the developer to accept the suggestions and make necessary modifications.

Let’s delve deeper into different examples:

GitHub Copilot is able to transform your comments into usable output (executable code), similar to the ChatGPT overlay. Simply by writing a prompt that describes the desired logic, Copilot can automatically generate code suggestions based on that description. As a programmer, you can then easily accept these suggestions with a press of the Tab key.

Consider, for instance, you are crafting a Python function and have included a comment detailing its intended functionality. For Copilot to generate high-quality code proposals, it is vital to write clear and precise comments and docstrings. Ambiguous or poorly written comments can pose a challenge for Copilot in accurately grasping your intended functionality.

GitHub Copilot can immensely cut time spend for coding, especially when it comes to speeding up the writing of repetitive code segments. When dealing with large blocks of standard code, simply providing a few examples of the desired pattern is enough. Copilot then efficiently handles the rest, streamlining the coding process.

As previously stated, GitHub Copilot does not perform testing on the code it generates. Nonetheless, it can be used to recommend tests that are compatible with your code implementation. This provides an effective way to quickly incorporate a test unit package. While it is still necessary to verify the logic and functionality of the code, using Copilot for this purpose offers a faster option compared to writing the test code entirely on your own.

This scenario is perhaps the most effective use of Copilot. It acts as a crucial aid for developers using an unfamiliar language or framework. Imagine, for example, you wish to create a specific function in a new programming language. The approach to coding can vary greatly based on the language you are using. Even if you have some proficiency in this new language, the autocomplete feature of Copilot remains a significant time-saving tool.

Our experienced developers have found great value in using Copilot while delving into languages they are not familiar with. Although Copilot’s suggestions may not always be perfect, they often accurately capture the basic syntax. Furthermore, it guides users towards common programming idioms, library functions, and more. Copilot can even function as a self-teaching tool for programmers, aiding in their learning process.

Developers frequently know precisely what needs to be done but sometimes overlook the finer details of the process. For example, they might need to normalize matrices or create complex functions with particular parameters. Copilot’s autocomplete feature enables developers to concentrate on the method, while Copilot handles the completion of the code. This capability saves developers valuable time, as they do not have to consult books or websites to look up methods.

Since copilot makes use of a lot of historical data, it also has an in depth understanding on which tools developers use. As tech evolves, so do the tools the developers use. Generally, the way of accomplishing certain tasks stays relatively consistent, with small changes in function names or syntax. As Copilot has insight to this, it can generate the correct code if guidance is given.

Much of the functionality in the software realm is possible, because computers adhering to specific sets of rules for communication, commonly known as protocols. Among the most prevalent protocols are RADIUS (for your ISP to grant internet access), CSMS (to communicate with electric vehicles), TCP/IP (for reliable data transmission over the internet), etc. While these protocols are thoroughly documented, they often encompass hundreds of pages, necessitating extensive reading for developers to implement them. Copilot, however, can adeptly predict the methods developers should use and accurately assist in implementing these protocols.

As copilot has context of the code, it is able to correctly predict which functions and variables need to be used for the next part of the code. This assists developers by reducing cross referencing in their own code base, allowing the development to be streamlined.

Hands-on Insights:

While it is not strictly necessary to do this before using Copilot, it is highly beneficial to define the purpose and objective, especially if you are new to the tool. Avoid diving into GitHub Copilot with a “let’s just see what happens” attitude. This could lead to confusion and prevent you from fully utilizing the tool’s capabilities. Once you have determined how you intend to use Copilot for your upcoming project, the subsequent steps will be much simpler.

Many coders utilize GitHub Copilot when they have a clear picture of what they want to create but might need to deal with a programming language they are not familiar with. Copilot then assists them in getting the syntax correct and understanding basic library functions.

GitHub Copilot is not a standard feature of many programming platforms and their editors. Therefore, you will need to register and install the extension before you can begin using it. The platform offers different subscription packages⁴, choose which option suits your needs best.

After installing the extension, Copilot will ask you to authorize the plugin by logging into GitHub. Once authorized, you should be automatically redirected back to the editor.

As you write, GitHub Copilot will start to automatically suggest autofill options based on the context. It is your decision whether to accept or reject these suggestions. If you are not satisfied with what Copilot is suggesting, you can always view other suggestions to see if they are more applicable.

It may take some time to get used to Copilot, but with more usage, you will become more comfortable with the suggestions as well as the Copilot building those based on your objective.

As mentioned earlier, Copilot is not flawless. Therefore, you cannot blindly accept the suggestions and assume they are perfect. You will probably need to make adjustments to the code. As always, you should run tests before incorporating the code into your project.

GitHub evaluated Copilot’s accuracy by reviewing a set of Python functions in open-source repositories. They removed the function bodies and asked Copilot to fill them in. Copilot correctly completed the functions 43% of the time on its first try. When Copilot was given 10 attempts, the code was correct 57% of the time.⁵ Our experiences were slightly better but still manual checks and adaptations are a must do.

While copilot can drastically improve a developer’s workflow, as it really shines with trivial repetitive tasks, this only holds when a developer can properly guide it. Copilot cannot solve new problems, but it can save hours in repetitive tasks and documentation reading.

Since the tool is always trying to suggest potential edits based on the code you build, it might not unleash its full potential. For example, in the hands of a strong developer, copilot is a tool that will save the developer hours of time. In the hands of an inexperienced developer, the suggestions may be unsecure and may cause the developer to take longer to complete a task as they would need to do more debugging than usual.

As the AI tool is trained with public data and code, discussions arose about potential copyright / IP infringement by GitHub CoPilot over the last months in the US. Dating back to a claim from November 2022, a judge looked into these claims which will have a significant impact on GitHub and other AI large language models (LLMs), but potentially also your generated code by the Copilot.

These claims represent developers’ allegations of the algorithmic reproduction of their source code by CoPilot, filed against GitHub, OpenAI, and Microsoft. Some cases have been dismissed, yet there is still a high risk of infringement. In fact, the judge allowed three damage claims so far, which gives room for more as well as anticipated changes to the services of Copilot in the future.

Do not miss this opportunity to revolutionize your business with AI technology! Take the first step towards a transformative journey and discover how our cutting-edge automotive reference projects at Berylls can enhance your business processes. Act now and contact us to explore a world of possibilities with AI – Let’s innovate together.

For more insights go to:

Github

We will cover governance models for AI usage in business context in detail in our latest newsletter.

About AvoChoc

At AvoChoc, since our inception in 2015, we have been dedicated to delivering unparalleled digital experiences that captivate audiences and elevate businesses across diverse sectors. Our expertise in experience development, blending artistic vision with technological prowess, allows us to create exceptional web designs and complex systems. Our diverse portfolio includes work in education, medical, children’s development, coding for kids, manufacturing, clean energy, electric vehicles, sports, and private security, highlighting our versatility and commitment to innovation.

We extend our services beyond technical solutions, providing business consulting and boasting an in-house team of UI/UX designers. This ensures holistic insight and design excellence in every project. Our dedication to exploring the latest tech, frameworks, libraries, and languages keeps us at the forefront of technology, enabling us to offer the most advanced and effective solutions to our clients.

About Berylls Digital & Technology

The Berylls Group unites expertise in strategy consulting, data-driven marketing transformation, venture building and equity investing. We focus exclusively on what we call the global automobility sector – because we believe that today’s automotive industry is about much more than the manufacturing and selling of cars.

Berylls Digital & Technology, for instance, is dedicated to the digital consulting & project execution and scaling of digital products. Our two focus areas are consulting in the area of software-defined vehicles (SDV) and the execution of software taskforce projects, safeguarding projects that are in distress. With our strong set of external partners, we are uniquely positioned to serve our customers, “but different”.

Our focus on digital innovation and its commitment to driving advancements in the automobility sector demonstrate our role as a transformative force in this industry, particularly through our technological and digital offerings.

Notes:
¹ Bloomberg: https://www.bloomberg.com/news/articles/2023-12-22/openai-in-talks-to-raise-new-funding-at-100-billion-valuation
² Wikipedia: https://en.wikipedia.org/wiki/GitHub
³ Microsoft: https://www.microsoft.com/en-us/Investor/events/FY-2024/earnings-fy-2024-q2.aspx
⁴ Github: https://github.com/features/copilot/plans
⁵ Medium: https://medium.com/codex/what-is-github-copilot-6c3e99ba7c41

Author
Christian Kaiser

Partner & Head of IT

Christian Kaiser

Christian Kaiser (1978) is Partner and Head of IT at Berylls by AlixPartners (formerly Berylls Strategy Advisors), specialising in software and digitalisation. He started his career at DaimlerChrysler AG in 1997 and has 27 years of industry and consulting experience in the automotive sector and has worked as CDO, CIO and CEO in various international OEMs and software companies.
Mr Kaiser has also held roles as chairman or board member of various companies in the software industry.
At Berylls, he specialises in the areas of software defined vehicles, software development, digital business models, digital operating models and software task forces.
Christian holds a degree in ‘Business Economist (EBW)’ from the University of Applied Sciences Würzburg.

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Vehicle to Grid Readiness Worldwide

Munich, February 2024

Vehicle to Grid Readiness Worldwide

Munich, February 2024
I

n the worldwide movement towards carbon neutrality Germany aims to reach 80% in renewable electricity share by 2030 from around 50% in 2023.

This increased share of renewable energies (wind, solar and hydro) also necessitates temporary storage of electric energy to stabilize the electric grid. A prominent and cost-effective way to increase storage capacity drastically and thereby stabilize energy supply is the integration of electric car batteries into the electricity grid. The method to not only charge car batteries from the electric grid, but also supply energy back from the car to households or factories through grid is known as V2G (vehicle to grid).

V2G serves as an alternative to other grid stabilization options such as pumped storage power plants, with few suitable locations left, or the significantly more expensive stationary battery storage. In the long run, commercial returns from V2G can reduce the total cost of ownership of an EV through reduction in charging and overall electricity bill.

The V2G score corresponds to the readiness of a country to utilize V2G potential. The most significant factors for the same are smart meter rollout (to enable bidirectional energy flow between EVs and grid) and share of V2G capable EVs in the fleet. The study shows that the V2G potential in most countries is not limited by smart meter roll-out, but the amount of available V2G capable vehicles. It also strikingly shows that Germany is significantly behind the leading nations in terms of V2G readiness. This is mainly due to a very low smart meter rollout in the country so far (around 1% of all household are equipped with a smart meter currently). The smart meter rollout in Germany was slowed down in the past by regulatory uncertainties and a lack of dynamic electricity pricing models requiring the smart meters. In the beginning of 2023, a new law came into place requiring a 95% smart meter rollout by 2032.

So far V2G has only been demonstrated through pilot projects and studies. For unleashing its true potential, both electrical and electric vehicle (EV) infrastructure of a country plays a prominent role. For example the amount of required charging points is increased, as vehicles serving as grid stabilizers block the charging point for this time.

If you want to discuss the opportunities and challenges that come with V2G, please feel free to contact Dr. Alexander Timmer.

Highlights 

  • In the worldwide movement towards carbon neutrality Germany aims to reach 80% in renewable electricity share by 2030 from around 50% in 2023. This increased share of renewable energies (wind, solar and hydro) also necessitates temporary storage of electric energy to stabilize the electric grid.

     

  • A prominent and cost-effective way to increase storage capacity drastically and thereby stabilize energy supply is the integration of electric car batteries into the electricity grid. The method to not only charge car batteries from the electric grid, but also supply energy back from the car to households or factories through grid is known as V2G (vehicle to grid).

     

  • V2G serves as an alternative to other grid stabilization options such as pumped storage power plants, with few suitable locations left, or the significantly more expensive stationary battery storage.

     

  • In the long run, commercial returns from V2G can reduce the total cost of ownership of an EV through reduction in charging and overall electricity bill.

     

  • The V2G score corresponds to the readiness of a country to utilize V2G potential. The most significant factors for the same are smart meter rollout (to enable bidirectional energy flow between EVs and grid) and share of V2G capable EVs in the fleet.

     

  • The study shows that the V2G potential in most countries is not limited by smart meter roll-out, but the amount of available V2G capable vehicles. It also strikingly shows that Germany is significantly behind the leading nations in terms of V2G readiness. This is mainly due to a very low smart meter rollout in the country so far (around 1% of all household are equipped with a smart meter currently).

     

  • The smart meter rollout in Germany was slowed down in the past by regulatory uncertainties and a lack of dynamic electricity pricing models requiring the smart meters. In the beginning of 2023, a new law came into place requiring a 95% smart meter rollout by 2032.

     

  • So far V2G has only been demonstrated through pilot projects and studies. For unleashing its true potential, both electrical and electric vehicle (EV) infrastructure of a country plays a prominent role. For example the amount of required charging points is increased, as vehicles serving as grid stabilizers block the charging point for this time.
Berylls Insight
Vehicle to Grid Readiness Worldwide
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Authors
Dr. Alexander Timmer

Partner

Lars Behr

Senior Consultant

Rishab Harlalka

Consultant

Dr. Alexander Timmer

Dr. Alexander Timmer (1981) joined Berylls by AlixPartners (formerly Berylls Strategy Advisors), an international strategy consultancy specializing in the automotive industry, as a partner in May 2021. He is an expert in market entry and growth strategies, M&A and can look back on many years of experience in the operations environment. Dr. Alexander Timmer has been advising automotive manufacturers and suppliers in a global context since 2012. He has in-depth expert knowledge in the areas of portfolio planning, development and production. His other areas of expertise include digitalization and the complex of topics surrounding electromobility.
Prior to joining Berylls Strategy Advisors, he worked for Booz & Company and PwC Strategy&, among others, as a member of the management team in North America, Asia and Europe.
After studying mechanical engineering at RWTH Aachen University and Chalmers University in Gothenburg, he earned his doctorate in manufacturing technologies at the Machine Tool Laboratory of RWTH Aachen University.

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Quo Vadis, China 2024 – A rat race with no end in sight

Munich, February 2023

Quo Vadis, China 2024 - A rat race with no end in sight

Munich, February 2024
2

023 was a highly turbulent year for the Chinese auto market, which saw it engulfed in a vicious circle – a “rat race” with no end in sight and price wars as the weapons of choice.

In the following insight report, we want to explore this rat race in greater detail and provide an outlook for 2024, including the challenges that lie ahead as well as the focal points of foreign OEMs in the highly complex Chinese market.

RECAP 2023

“卷” or the involution of the Chinese auto market Overall performance

The term “卷” (spelled “Juǎn” in Chinese Pinyin) was undoubtedly the prevailing expression in the Chinese automotive market in 2023. It is understood to mean “involution” or “rat race,” depicting a scenario in which all market participants are entangled in an intense battle for market share with no foreseeable conclusion.

And that exactly describes the Chinese auto market with the emergence of a price war as the ultimate solution, while favorable governmental subsidies are on the way out – a vicious circle.

Curious? Download the full insight now!

Berylls Insight
Quo Vadis China 2024 - A rat race with no end in sight
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Authors
Dr. Jan Burgard

Berylls Group CEO

Willy Wang

Managing Director China

Lois Yang

Consultant

Dr. Jan Burgard

Dr. Jan Burgard (1973) is CEO of Berylls Group, an international group of companies providing professional services to the automotive industry.

His responsibilities include accelerating the transformation of luxury and premium OEMs, with a particular focus on digitalization, big data, connectivity and artificial intelligence. Dr. Jan Burgard is also responsible for the implementation of digital products at Berylls and is a proven expert for the Chinese market.

Dr. Jan Burgard started his career at the investment bank MAN GROUP in New York. He developed a passion for the automotive industry during stopovers at an American consultancy and as manager at a German premium manufacturer. In October 2011, he became a founding partner of Berylls Strategy Advisors. The top management consultancy was the origin of today’s Group and continues to be the professional nucleus of the Group.

After studying business administration and economics, he earned his doctorate with a thesis on virtual product development in the automotive industry.

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Willy Wang

Willy Lu Wang (1981) joined Berylls Strategy Advisors in 2017. He started his career participating in the graduate program of Audi focusing on production planning. After stations at another strategy consultancy as well as being the strategy director for a German Tier-1 supplier, he is now responsible for the China business at Berylls.

He has a broad consulting focus working for all clients in China, whether they are JVs, WOFEs or pure local players. He is also responsible for the development of AI and Big Data products dedicated towards the Chinese market further strengthening the Berylls End-to-End strategy and product development capabilities.

Wang studied Electronics & Information Technology with focus on Systems and Software Engineering and Control Theory at Karlsruhe Institute of Technology.

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Pressemitteilung: Wachstumsmarkt China bekommt Risse

München, Januar 2024

Pressemitteilung: Geschäftsmodell China bekommt Risse

München, Januar 2024
G

eschäftsumfeld der Zulieferer trübt sich in diesem Jahr ein, nach spürbarem Aufwind bei Umsatz und Marge im Jahr 2023.

  • Berylls-Rück- und Ausblick auf die Zuliefererbranche: 2024 wird erneut ein herausforderndes Jahr.

  • Die ersten drei Quartale 2023 waren eine Phase der Zuwächse bei den weltweit größten Zulieferern, Umsatz und Marge stiegen teilweise signifikant. Bereits in Q4 2023 trübten sich die Bedingungen ein, dieser Trend wird 2024 fortbestehen.

  • China bleibt größter Fahrzeugmarkt, aber bedingt durch die Währungsschwäche, die zunehmende Komplexität der Beziehungen zu China und Entscheidungsfindungen vor Ort, bekommt das positive Bild Risse.

  • Osteuropas Bedeutung für die Automobilindustrie ist hoch und wird weiterwachsen.

  • Steigende Lohnkosten in der Region und geopolitische Risiken, machen Standortentscheidungen zu Osteuropas Gunsten, jedoch zunehmend schwieriger.

  • Berylls-Experten sehen wachsende Relevanz von Zulieferer-Produktionsstandorten in Nordafrika.

Jetzt die gesamte Pressemitteilung herunterladen!

Berylls Pressemitteilung
Pressemitteilung: Wachstumsmarkt China bekommt Risse
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Dr. Alexander Timmer

Dr. Alexander Timmer (1981) ist seit Mai 2021 als Partner bei Berylls by AlixPartners (ehemals Berylls Strategy Advisors) tätig, einer internationalen und auf die Automobilitätsindustrie spezialisierten Strategieberatung. Er ist Experte für Markteintritts- und Wachstumsstrategien, M&A und kann auf eine langjährige Erfahrung im Operations-Umfeld zurückschauen. Dr. Alexander Timmer berät seit 2012 Automobilhersteller und -zulieferer im globalen Kontext. Er verfügt über ein fundiertes Expertenwissen in den Bereichen Portfolioplanung, Entwicklung und Produktion. Zu seinen weiteren fachlichen Schwerpunkten zählen unter anderem Digitalisierung und der Themenkomplex rund um die Elektromobilität.
Vor seinem Einstieg bei Berylls Strategy Advisors war er unter anderem für Booz & Company und PwC Strategy& als Mitglied der Geschäftsführung in Nordamerika, Asien und Europa tätig.
Im Anschluss an sein Maschinenbaustudium an der RWTH Aachen und der Chalmers University in Göteborg promovierte er im Bereich der Fertigungstechnologien am Werkzeugmaschinenlabor der RWTH Aachen.

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Status Quo Automobilproduktion in Deutschland

München, Januar 2024

Status Quo Automobilproduktion in Deutschland

München, Januar 2024

B

erylls erwartet für 2030 ein Volumen in der Automobilproduktion von 5,2 Millionen Fahrzeugen – unter starkem Vorbehalt.

  • Berylls-Experten erwarten, dass die deutsche Automobilproduktion in den nächsten Jahren deutlich stärker wächst als das BIP. Das Wachstum generiert Tesla, nicht die deutschen OEMs.
  • Die Abhängigkeit vom Premiumsektor und die Gefahr für Produktionsverlagerungen ins Ausland, sind Risikofaktoren für das Wachstum.
  • Im Vergleich zu den aktuellen Stückzahlenprognosen bis 2030 erwartet Berylls im Idealfall einen weiteren Anstieg von 13 Prozent. 
  • Stellen sich die erwarteten positiven Effekte nicht ein und sinkt das Volumen, kann im Worst Case mit einem negativen Effekt auf das BIP von knapp zwei Prozent gerechnet werden.
  • Die Weichen in der Automobilproduktion müssen heute in die richtige Richtung gestellt werden, die Entbürokratisierung gehört zwingend dazu.

Zum Jahreswechsel 2023/2024 prägt Pessimismus die deutsche Wirtschaft, zumindest, wenn man dem Institut der deutschen Wirtschaft (IW) glaubt. Die Gründe dafür sind vielschichtig. Zu ihnen gehören die schwächelnde Weltkonjunktur, Zinserhöhungen, aber auch Unklarheiten beim Bundeshaushalt. Zudem schwindet das Vertrauen in den Standort Deutschland, nicht zuletzt das abrupte Ende der E-Autoförderung trägt dazu bei. Wenig verlässliche Rahmenbedingungen und der Fachkräftemangel machen es schwierig, optimistisch auf das Jahr 2024 zu blicken. Die Berylls-Experten sehen dennoch Anlass zumindest für vorsichtigen Optimismus in der Automobilbranche.


Automobilproduktion auf Wachstumspfad


Denn nach vielen Tiefschlägen in der jüngeren Vergangenheit, ist die deutsche Automobilindustrie wieder auf einen Wachstumspfad eingeschwenkt. In den kommenden Jahren wächst die deutsche Fahrzeugproduktion mit 2,5 Prozent jährlich deutlich stärker als das reale Bruttoinlandsprodukt (BIP) mit prognostizierten 1,4 Prozent pro Jahr. Setzen wir dieses Wachstum an, werden im Jahr 2030 in Deutschland 5,3 Millionen Fahrzeuge produziert, im vergangenen Jahr waren es 4,3 Millionen. Bis 2030 wird ein Wachstum von 24 Prozent erwartet, das klar über dem von China liegt. Die dortige Steigerung der Fahrzeugproduktion wird im gleichen Zeitraum bei 17 Prozent liegen.

Figure 1: Vergleich Wachstum Automobilproduktion und BIP

Anmerkung: TOP 25 Länder nach Fahrzeugproduktion, bereinigt um Russland und Iran
Quelle: Berylls Strategy Advisors, S&P Global

Die für Deutschland gegebene Produktionszahl stellt allerdings einen Mittelwert dar, der in einem Korridor mit erheblicher Bandbreite liegt. Denn die Automobilindustrie am Standort Deutschland steht gleich vor zwei großen Herausforderungen. Zum einen vor ihrer Transformation in Richtung e-Mobilität und zum anderen vor industriepolitischen Verwerfungen. Zu denen gehören die Kriege in der Ukraine und im Nahen Osten, aber auch der zunehmende Markt-Protektionismus in den verschiedenen Weltregionen.

Produktionsverlagerungen und negative Auswirkungen auf die Geschäftsmodelle können die Folgen sein. Sie würden die Inlandsfertigung um bis zu 32 Prozent schrumpfen lassen, wie die Berylls-Analyse nahelegt. Im Best Case allerdings, werden hierzulande im Jahr 2030 sechs Millionen Fahrzeuge produziert, ein Plus von 13 Prozent gegenüber dem angenommenen Mittelwert. Tatsächlich steigt das Produktionsvolumen aber nicht bei den einheimischen OEMs, sondern nahezu ausschließlich bei Tesla im Werk in Grünheide. Die deutschen Hersteller planen im Mittelwert mit keinem Wachstum der lokalen Produktion und sind zudem stark von den Entwicklungen im Premiumsegment abhängig.

Figure 2: Anteil von Premiumfahrzeugen und OEMs an Gesamtproduktion

Anmerkung: Top 15 Länder nach Fahrzeugproduktion
Quelle: Berylls Strategy Advisors, S&P Global

Herausforderung: Automobilproduktion im europäischen Ausland

 

Problematisch für die heimische Industrie sind zudem Hersteller, die sich im nahen europäischen Ausland niederlassen. Hier mag beispielhaft die Ankündigung von BYD genannt werden. Der chinesische Hersteller plant ein Werk in Ungarn und wird von dort Europa mit vergleichsweise erschwinglichen Fahrzeugen versorgen. Um dem Standort Deutschland auch in Zukunft eine angemessene Bedeutung zu sichern, wäre es aber wünschenswert, wenn in hiesige Produktionsstandorte investiert werden würde oder wenn neue Hersteller, analog zu Tesla, ihre europäische Produktion in Deutschland ansiedeln.

Es wäre die Aufgabe eine engagierten Industriepolitik dafür die Weichen zu stellen. Dazu gehört ganz sicher eine Entbürokratisierung vieler Prozesse. Als wenig hilfreich sehen die Berylls-Experten dagegen die zeitlich begrenzte Einführung eines Industriestrompreises.

Schrumpft die Automobilindustrie in Deutschland, hat das unmittelbare und erhebliche Auswirkungen auf das BIP. Schon das von Berylls prognostizierte Worst Case Szenario, mit dem Produktionsrückgang von 1,7 Millionen Fahrzeugen bis 2030 (gemessen am Mittelwert), hätte einen negativen Effekt auf das BIP von 1,6 Prozent. Um diesem Abschwung vorzubeugen, sind alle relevanten Akteure aufgefordert wirksame Gegenmaßnahmen zu ergreifen. Die OEMs allein, können dies nicht bewerkstelligen.

Figure 3: Prognostizierte Automobilproduktion und Szenarien in Deutschland

*) Ausprägung abhängig von Szenario, mit oder ohne Risiko von Verlängerungen
Quelle: Berylls Strategy Advisors, S&P Global

Die gesamte Studie zur Automobilproduktion in Deutschland auch hier zum Download.

Berylls Insight
Status Quo Automobilproduktion in Deutschland
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Autoren
Stefan Schneeberger

Project Manager

Samuel Schramm

Consultant

Successful transformation starts at the top: the importance of good leadership

Munich, December 2023

Successful transformation starts at the top: the importance of good leadership Munich, December 2023

A

total of 7 Chinese car manufacturers presented themselves impressively at the IAA with large stands and high-quality, innovative vehicles. However, brands such as Fiat, Peugeot and Jeep were not represented. The profound change in the automotive industry cannot be denied.

The industry is undergoing massive changes that go beyond new drive concepts and designs and lead to a reorganization of brands and market shares in Europe and worldwide. The uncertainty in the German automotive and supplier industry is noticeable, and managers must actively shape the transformation to remain competitive. 

In the midst of transformation processes towards electric drives, agile supply chains and autonomous vehicle technologies, a better balance is needed between leadership and management. Management controls and monitors the present. It focuses on business processes, plans budgets and organizes the necessary resources. Leadership, on the other hand, focuses on future viability. It creates an environment and a culture in which the company can be successful in the long term by breaking down rigid thought patterns and motivating employees to help shape the change. 

However, there is no standardized definition of leadership, and it requires regular discussion so that individual scope for interpretation can come together. The basic prerequisite is a shared understanding of leadership attitudes and mindsets within the leadership team. This significantly impacts the way they make decisions, treat their employees, and shape the corporate culture. Without debate and reflection within the leadership team, individual mission statements develop and inevitably lead to contradictory behavior and decisions. For example, a „charismatic manager“ or a „good coach“ is not automatically a strong leader. There is no doubt that these are desirable qualities. However, they can also be a risk for a company, as seen in the DeLorean Motor Company in the early 1980s. Its founder, John Z. DeLorean, was a charismatic but controversial engineer who quickly gained fame and influence at General Motors and is still considered the youngest department head in the company’s history. Through his courage, self-confidence, and innovative spirit, he quickly made a name for himself outside the automotive industry. However, his business practices, his personal lifestyle and his financial and strategic decisions quickly led to problems and ultimately to the collapse of the company. 

If charisma alone is not enough to successfully transform a company, what are the decisive elements?  

Our observations reveal that successful leadership teams possess five specific characteristics, drawing inspiration from Jim Collins‘ principles: 

1. „Entrepreneurial spirit“ characterizes managers who are distinguished by a unique combination of two essential qualities. Through personal humility, they focus on teamwork and devote their ambition to the good of the organization rather than their own personal recognition. In addition, they are highly committed to the long-term success of the organization through professionalism, high work ethic, high standards and willingness to take tough decisions. 

Eiji Toyoda is an example of true „entrepreneurial spirit“. During his 15-year presidency, Toyota became one of the leading automobile manufacturers, setting new standards for quality, efficiency, and continuous improvement. Rather than personal enrichment or self-promotion, Eiji’s focus was on improving the company and promoting the Toyota Production System. 

2. „Fanatical discipline“ involves setting a clear performance framework that must be demanded and adhered to with the utmost rigor. Even under the most unfavorable conditions, no restraint is granted, even if it seems tempting due to short-term profits or external pressure.  

Although his disciplined approach is not without controversy, and his leadership style attracts both admiration and criticism, Elon Musk is a living example of fanatical discipline. His enthusiasm and dedication to his products became clear in 2018. During this time, there were reports that he slept on a couch in the factory to monitor production processes and intervene immediately in the event of problems to achieve the ambitious production increases.

3. „Empirical creativity“ encompasses the relentless urge to first subject innovative ideas with experiments, facts and figures. Instead of putting extensive resources into an idea from the outset, the „bullets first, cannonballs later“ approach is pursued. 

For centuries, „Hongbao,“ traditional red envelopes filled with money, have been cherished in Chinese culture, especially during Chinese New Year. In 2014, Tencent transformed this tradition by introducing a digital version on WeChat. In an advertising campaign, around EUR 70 million was digitally distributed, marking the success of this innovation. This breakthrough paved the way for WeChat Pay, allowing users to link their bank accounts for seamless digital transactions. The founder, Allen Zhang, is integral to WeChat’s cult-like success in China, akin to Steve Jobs in the USA and Europe, with a staggering 1.3 billion active users. 

4. „Productive paranoia“ aims to identify and anticipate future challenges and problems at an early stage before they occur and the ability to react decreases. Productive paranoia represents a sensible form of prevention and risk management. The key motivation of leaders with productive paranoia is not to take past success for granted and remain self-satisfied. Instead, they think about what dangers may lurk in the future and what reserves need to be built up for protection in the form of time, money or even knowledge. 

Alan Mulally, the former CEO of Ford Motor Company, became known for his pragmatic approach to the automotive business and his use of big data for decision making. Unlike GM and Chrysler, Ford did not have to turn to the US government for funds from the Troubled Asset Relief Program (TARP) during the financial crisis of 2008/2009.  With empirical data and well-founded analyses, Ford had already prepared itself for economically uncertain times and reacted early to changing consumer preferences with more economical models, such as the Superior Fuel Economy Package of the Ford F150 in 2009. 

5. „Radical Candor” is a catalyst for transformation by encouraging controversial thinking and creating an environment that values diverse perspectives. Conventional thinking is broken, and innovative solutions are identified by encouraging active listening and open conversations. Conflicts are not seen as an obstacle to be avoided, but as a source of creative tension for collective learning and growth. With curiosity and openness, leaders lead through conflict to solutions.  

 

Netflix embodies radical candor, with a culture deeply ingrained in seeking a „diversity of thought.“ Originating from Reed Hastings, co-founder and CEO, this conviction is rooted in the belief that diverse perspectives foster creativity and enable agile adaptation to changing conditions. Netflix actively encourages debates, where decisions prioritize the quality of proposed solutions over authority or charisma. The company operates on the principle that the best idea, regardless of its origin, prevails. Embracing „Fail Fast and Learn,“ Netflix recognizes that not every idea succeeds, fostering experimentation and risk-taking crucial for innovation. In acknowledging that failure is a natural part of the process, individuals are not solely held responsible for setbacks, contributing to a culture of continuous improvement.  

The escalation of competition from the Far East is not a passing challenge but the emerging norm. The demand for continuous competitiveness and innovation places unrelenting pressure on companies and their managers. Resilience is key. For leaders steering their companies through transformative times, the emphasis is not on embodying all leadership characteristics individually. Instead, the goal and challenge lie in cultivating and fostering these qualities within the leadership team. 

Successful leadership teams create trust as the basis for all actions

Leadership teams wield numerous levers to navigate effectively. They must inspire through their actions, setting an example for others to follow. Establishing a culture of openness requires the management team’s receptivity to feedback, offering employees a platform for expression. Personnel decisions, serving as guides for valued behaviors, necessitate a commitment to entrepreneurial thinking. This, in turn, demands the creation of structures fostering innovation, risk-taking, and efficient problem-solving. Actively shaping the organization enables the desired culture to flourish. Transparent resource distribution clarifies priorities, fostering not only understanding but also trust. Trust, the cornerstone for effective collaboration, forms the basis upon which a culture of success is constructed. By proactively engaging with these levers, managers not only shape the present but also chart the course for the future of the company.   

Good managers are made, not born

Continuous training and development are indispensable for managers to keep pace with evolving requirements and successfully guide their companies. While empathy and a commitment to personal development are prerequisites for managerial suitability. Further, essential leadership skills can be honed through targeted training, encompassing active listening, adept handling of resistance and conflicts, and the ability to make tough decisions.  

As challenges confronting management teams multiply, skills such as decision-making under uncertainty and maintaining operational effectiveness gain prominence. Shifting away from the mere „management“ of existing standards and plans, managers must rethink strategies for the future. Regular self-reflection on leadership performance becomes imperative for continuity and personal growth.  

At Berylls, we offer support to management teams and executives in identifying strategic areas of focus and deriving actionable insights for leadership aspirations and performance. Our commitment extends to providing comprehensive assistance through team development, coaching, and capability building. 

Discover how Berylls can elevate your leadership team through tailored development programs. Navigate to our website for more information on our comprehensive support. 

Authors
Laura Kronen

Partner

Peter Eltze

Partner

Peter Nuck

Project Manager

Laura Kronen

Laura Kronen (1980) is a partner at Berylls by AlixPartners (formerly Berylls Strategy Advisors) with a focus on transformation. She is passionate about moving people and organizations forward. With over 18 years of industry and consulting experience, her focus is on transformative challenges in the operations context – from executives to individual employees, at manufacturers and suppliers. She helps her clients align strategy, structure, and culture in their respective market environments to build resilience.

Prior to joining Berylls, Laura Kronen worked at PwC Strategy&, Volkswagen AG and Audi. She holds a diploma degree in industrial engineering from the Karlsruhe Institute of Technology (KIT).

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Peter Eltze

Peter Eltze (1964) joined Berylls by AlixPartners (formerly Berylls Strategy Advisors) as a Partner in November 2015. He began his career in the medical technology division of an integrated technology corporation, and became a project manager at Malik Management Zentrum St. Gallen in 1996 before being appointed Partner in 2001. From 2003, in his role as member of the executive board, he was in charge of Management Education & Development. Since the end of the 1990s, Peter Eltze has advised companies in the automotive and mechanical engineering industries. At Berylls, his consulting activities focus on integrated organizational development (strategy, structure, culture), transformation management, and executive development.
Education in wholesale and international trade; administrative sciences at the University of Constance, Germany.

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Exceptional onboarding: creating an outstanding employee experience from the start

Munich, December 2023

Exceptional onboarding: creating an outstanding employee experience from the start Munich, December 2023

G

reat onboarding makes a difference to any employee journey. As the automotive industry’s transformation process accelerates, attracting, integrating, developing, and retaining employees with mission-critical capabilities is key to assuring future readiness and successful transformation.

Here we outline a “but different” recommendation for onboarding programs.

The automotive industry finds itself at a crossroads, navigating the challenging transformation driven by connected, autonomous, shared, and electric (CASE) vehicles in a digitized world. In this era of unprecedented change, future readiness and successful transformation inevitably require the ability to reshape capability and skill portfolios efficiently. It’s not just about staying competitive – it goes beyond simple competitiveness – it’s about dynamically redesigning the capability portfolio that serves as a foundation for driving future innovation and delivering greater customer value.

To address this challenge, the industry recognizes the pivotal role of onboarding both new hires and existing staff coming from upskilling and reskilling programs. The effectiveness of this process is crucial for closing strategic skill gaps and ensuring the rapid integration of new arrivals. At the same time, it acts as a protective measure and validates the substantial investments made in acquiring talent, fostering leadership, and developing functional skills. Against the backdrop of a shrinking average employee tenure, onboarding has emerged as a critical success factor. It acts as the driving force behind boosting commitment, strengthening retention rates, speeding up time-to-productivity, and ultimately cutting down on staff turnover. Onboarding goes beyond its usual role, becoming a strategic necessity that not only tackles challenges but turns them into chances to improve organizational effectiveness.

Facts tell a different story, but underline the importance of exceptional onboarding:

  • In 2021, a mere 12% of employees in the United States felt that their onboarding experience was satisfactory, according to Gallup (2021)
  • 1 out of 3 HR professionals reported that the quality of their company’s onboarding processes was not up to standard (ClickBoarding, 2020)
  • 35% of companies have zero spend on onboarding (Enboarder, 2019)

On the flip side, a remarkable 70% of those who underwent exceptional onboarding describe their job as “the best possible job” (Gallup 2021). This contrast highlights the immense potential onboarding holds for shaping employees’ perception of their roles and, by extension, their commitment to the organization.

What is onboarding?

Onboarding is more than just setting up your laptop and finding your seat; it’s about grasping your responsibilities, understanding the context of your role, familiarizing yourself with processes and tools, and immersing yourself in the company’s unique culture. And it should be even more than that. As we see it, onboarding is the art of forging connections and relationships, instilling purpose, weaving the company’s DNA, and nurturing commitment.

In essence, onboarding must create those legendary moments that can define the trajectory of high-performing employees. The efforts put into recruitment and people development should come to fruition during the onboarding process. However, the reality looks different. Onboarding processes often seem to be “undermanaged,” leading to a disconnect between potential and performance.

So, what concrete results can be achieved by an exceptional onboarding process?

  • Improved retention rates by more than 50% (Harvard Business Review, 2018) 
  • Increased productivity by over 60% (Harvard Business Review, 2018)
  • Strengthened employee commitment by almost 90% (BambooHR, 2023)

By the way, we are not simply talking about the onboarding of a few individuals in this article. Reviewing our actual project landscape, we conclude that it regularly involves hundreds, and even up to several thousand new team members annually at certain clients. This large-scale onboarding requirement and its significance cannot be overstated. In practical terms, it goes hand in hand with CASE-related upskilling/reskilling initiatives, venture building projects, the setting up of new hubs and plants, post-merger integration situations, and lots more.

The four phases of onboarding

Source: Berylls Strategy Advisors

Onboarding – cornerstones of a “but different” concept

The onboarding journey is far more than just a day-one matter. It begins long before the new employee walks through the office doors or embarks on an upskilling journey. A strong focus is usually on the first 6–12 weeks, but it extends well up to the end of the first year in a new position. Effective onboarding is a journey, not a destination where the mutual understanding that you are an exceptional employer is established.

To ensure an outstanding onboarding process, our latest project experience indicates that a holistic approach that combines a fluid offering of collective and individual nuggets, action orientation, culture alignment, and networking is key. For organizations dealing with a significant number of new employees, especially in a global environment, the digitization of onboarding is essential in order to meet the efficiency requirement.

So lastly, here are the “but different” key factors that can help secure the ultimate success of your onboarding program:

1. Modular and flexible learning journeys: Firstly, onboarding is based on purpose and outcome. Tailor onboarding content to specific roles and requirements, allowing for individualized learning. Offer a mix of mandatory and optional content and provide additional nuggets for those who want to explore further and deeper. Onboarding should be focused on action, with a “learn, apply, experience” approach. Avoid overwhelming employees with information and ensure that the content aligns closely with daily business needs.

2. Purpose and culture integration: Instill a sense of belonging by aligning onboarding with your organization’s purpose and culture. Create emotionally empowering experiences and moments that foster a shared mindset, e.g. a special brand, product, or cultural experiences. Make new employees or team members feel like essential teammates and celebrate their unique talents, contribution, and perspectives in meetings and all-hands situations. Always provide equal and transparent access to information. Help employees to feel like they can be their true self and make sure you find ways to openly value differences.

3. Fostering networks and relationships: Building relationships within the organization is key. Invest time in fostering cross-functional relationships, whether in digital, blended, or physical formats, to encourage interaction with colleagues from across the organization. Build and grow communities as a forum for commitment, interaction, and communication around onboarding-related topics (e.g. special use cases, processes, methods, tools, etc.), and empower participants by providing peer support. 

4. Put digital learning at the center: When it comes to scalability, flexibility, online and offline availability, effectiveness, and efficiency the advantages of digital learning in onboarding situations outweigh the disadvantages of higher initial costs and longer preparation times before programs can be launched. Live online formats can be a significant part of blended learning and make highly personalized learning journeys possible. This transition is not confined to individual organizations, but is essential on a larger scale in a world that has become smaller thanks to digitization.

5. AI-based learning journeys: The use of AI in onboarding boosts efficiency when it comes to creating relevant nuggets and enables the deep-search and auto-tagging of content across your databases. Truly personalized training and virtual coaches that provide real-time feedback, answer common questions, and offer guidance on company policies and procedures are no longer a utopia. Finally, AI can leverage large amounts of data to identify trends and patterns in the onboarding process. This data can be used to continually refine and improve the onboarding experience, leading to higher employee satisfaction and improved retention rates.

Conclusion

By following these five key factors, your onboarding program can ensure that every employee, whether new to the organization or coming from an upskilling and/or reskilling program, has the best possible start. Give your employees the time they need to acclimatize to their roles, understanding that the investment will pay off over time.
Remember, you’re not starting from scratch. Many intelligent and engaging applications and experiences are already in place. They might only need to be adapted to form a holistic approach and enriched by some new but already proven pieces of technology. By doing so, your onboarding can become a transformative and rewarding journey for both employees and the organization as a whole.

We are happy to support you in the conception, design, and implementation of effective onboarding programs that strongly engage your workforce and thus dynamically develop your capability landscape for driving future innovation and increasing customer value. Please contact us to find out more. We look forward to hearing from you.

Authors
Dr. Frank Heines

Associate Partner

Wiktoria Bulka

Consultant

Dr. Frank Heines
Dr. Frank Heines (1967) joined Berylls Strategy Advisors as Principal in September 2016, and is based at Berylls’ Swiss office. He started his career at the postal automation division of Siemens AG before changing to a medium-sized electrical and electronics company where, in his position as responsible for the technical department, he soon became member of the board. In 2003, he began his consulting career at the Malik Management Zentrum St. Gallen, becoming Partner and member of the group management board in 2007. The focus of his consulting work lies in strategy development, organizational design, productivity increase as well as in integrated organizational development and transformational management.
Economics at the University of Constance, Germany; business administration at the University of Zurich; Ph.D. at the University of St. Gallen, Switzerland.
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Beyond Agile … oder wie man ein Unternehmen im Sturm steuert

München, Dezember 2023

Beyond Agile … oder wie man ein Unternehmen im Sturm steuert München, Dezember 2023

W

ie erhalten wir die Steuerbarkeit des Unternehmens, wenn es grösser und komplexer wird? Wie können wir navigieren, wenn Prognosen kaum mehr möglich sind und sich Kundenbedürfnisse, Märkte und Technologien rasend schnell verändern? 

Die deutsche Automotive Industrie befasst sich intensiv mit dieser Frage. Die Qualität des Steuerungssystems der Firma ist zum neuen Top-Management Thema geworden.

«If the rate of change outside exceeds the rate of change inside – the end is in sight» soll die amerikanische Management Legende Jack Welch bereits in den 70er Jahren gesagt haben.  Das Unternehmen muss anpassungsfähig sein an eine sich immer schneller verändernde Umwelt. Aber wie geht das heute bei der schwindelerregenden Veränderungsrate, mit der Unternehmen in Form von Multikrisen, neuen Technologien und neuen Geschäftsmodellen konfrontiert sind? Wenn wir uns auf keine Prognose mehr verlassen können: Wie kommen wir trotz Nebel und Sturm am Ziel an? Viele Unternehmen sind diesbezüglich schwerfällig geworden und stellen ihre Steuerungsorganisation auf den Prüfstand. Sie hat trotz der Einführung agiler Methoden noch immer Mühe im Umgang mit Dynamik. Sie gibt nicht die nötige Orientierung, vergeudet zu viel Management-Kapazität auf das Kompensieren von Schwächen in der Entscheidungsstruktur, die Kommunikation funktioniert nicht und die Traktion ist nicht da.

 

Kybernetik: Die Wissenschaft vom Steuern

Es wird derweil in vielen Bereichen nach Lösungen gesucht: Man startet Initiativen zur Verbesserung der Leadership, der Kultur, der Agilität oder gar der Abschaffung von Hierarchie. Aber nichts scheint wirklich zu funktionieren. Man hat halt am Ende doch nur die Leader die man eben hat, die Kultur ändert sich sehr langsam – wenn überhaupt. Agil sind zwar nun die Prozesse aber nicht das Unternehmen als Ganzes, und wo Hierarchien abgeschafft werden, entstehen neue informelle. In der Firma heisst es, dass die falschen Leute die falschen Dinge entscheiden und das Unternehmen fährt derweil langsam gegen den Eisberg. Die gute Nachricht ist, dass es eine Wissenschaft gibt, die uns lehrt, wie man steuert. Sie hat die Welt bereits einmal revolutioniert, indem sie die Automation, den Computer und letztlich die KI hervorge- bracht hat, und sie könnte es nun ein zweites Mal tun, wenn wir sie nicht nur auf die technischen, sondern auch auf die sozialen Systeme anwenden: Die Kybernetik. «Kybernetes» ist das griechische Wort für Steuermann, und Wörter wie der Gouverneur oder Corporate Governance sind daraus abgeleitet. Sie hält überraschend interessante und robuste Lösungen für das Steuern im Sturm bereit, die derzeit insbesondere in der Automotive Industrie diskutiert und aufgegriffen werden. 
 

Steuern was steuert

Als erstes müssen die Voraussetzungen für das Funktionieren von Steuerung geschaffen werden.

Dazu verlagert sich der Fokus des Top-Managements für einen Moment von der Arbeit im System auf die Arbeit am System, also an den Strukturen. Die Kernfrage lautet: Welche Entscheidungsstruktur erlaubt es uns, das Unternehmen sicher durch die aktuellen und zukünftigen Stürme zu steuern? Mit der Entscheidungsstruktur untrennbar verbunden ist die Kommunikationsstruktur. Wie bei der Maschine: Wenn man das Kabel kappt, funktioniert die Steuerung nicht mehr. Aber wie macht man das? Benchmarking liefert keine relevanten Hinweise, weil die meisten Unternehmen hier gerade ihre eigene Baustelle haben. Woran kann man sich also orientieren? Bereits in den 70er Jahren schlägt der britische Kybernetiker Stafford Beer ein Modell für die Diagnose und das Design der Entscheidungs- und Kommunikationsstruktur einer Firma vor. Dies zu einer Zeit allerdings, in der weder das Problem relevant noch die Technologie verfügbar war. Dennoch wird damit in allen Arten von Organisationen wie NGOs, KMUs, Konzerne und ganzen Staaten experimentiert. Inzwischen ist neben der Lösung auch die Technologie da und vor allem der Bedarf.

 

Die Lösung liegt nicht in „Reinventing Organizations“

Die Lösung die Beer vorschlägt, liegt nicht in neuen Organisationsformen oder alternativen Organisationsmodellen. Die Suche nach hierarchiefreien, soziokratischen, purpose-driven oder andersartigen Organisationen ist eine Suche am falschen Ort. Beer führt unseren Blick in eine andere Richtung, nämlich in eine dritte Dimension des Organisierensneben der Aufbau- und der Ablauforganisation. Man hat sie bisher übersehen und deshalb nur schwer steuerbare Organisationen gebaut. Wie in der Fliegerei: Man konnte schon lange geradeaus fliegen, bevor die Gebrüder Wright mit dem Seitenruder eine dritte Dimension unter Kontrolle gebracht haben. In unseren Firmen optimieren wir seit 100 Jahren die ersten beiden Dimensionen, die Aufbau- und Ablauforganisation. Was fehlt also? Orientierung gibt das beste Steuerungssystem, das wir kennen: Unser eigener Organismus. Er besitzt neben seiner Anatomie (Struktur) und seiner Physiologie (Prozesse) eine Neurologie, die ihn dazu befähigt, mit Komplexität umzugehen. Die spannende Frage ist nun, welche Lösung die Natur in vier Milliarden Jahren für das Steuerungsproblem wohl entwickelt hat, und was wir daraus für unsere Firmen lernen können. Diesen Problemlösungsansatz kennen wir auch aus der Bionik, die sich mit dem Lernen von Lösungen der Natur für technische Probleme befasst. Unsere Unternehmen brauchen neben der richtigen Aufbaustruktur und neben funktionierenden Prozessen also eine dritte Dimension, die Steuerungsorganisation, damit das Unternehmen fliegt.

 

Viability als oberstes Ziel

Die von Beer vorgeschlagene Struktur ist das Viable System Model (VSM).  Es enthält alle notwendigen und hinreichenden Bedingungen für die Lebensfähigkeit eines Unternehmens. Lebensfähig oder eben «viable» heisst, seine eigene Existenz auf Dauer aufrechterhalten zu können – also nicht nur «Geschäfte machen», sondern im Geschäft bleiben. Es gibt eine allgemeingültige Steuerungsstruktur vor. Diese gilt über alle Ebenen des Unternehmens (Prinzip der Rekursivität) und sieht die Entscheidung auf möglichst niedriger Ebene vor (Prinzip der Subsidiarität), nämlich dort, wo die sachnahe Information ist. Durch diese Prinzipien entsteht Agilität, die eine Teilmenge der Viabilität ist.

Quelle: Berylls Strategy Advisors, MP Consulting

Kundennutzen als Ausgangspunkt

Die erste Vorsteuergrösse für Lebensfähigkeit fängt beim Kunden an. «Stellt die Struktur das ins Zentrum der Aufmerksamkeit, wofür unsere Kunden bezahlen?» Mit dieser Frage beginnt der Designprozess und hieraus wird die Aufbauorganisation des Unternehmens abgeleitet. Es werden die Einheiten festgelegt, die sich weitgehend selber steuern sollen und dem Prinzip der Rekursivität folgend wiederum ein eigenständiges Viable System repräsentieren. Diese Entscheidung mag in Unternehmen mit wenigen, gut abgegrenzten Produkten und einer überschaubaren Anzahl an Kunden und Märkten einfach erscheinen. Die Realität zeigt aber, dass durch die zunehmende Vernetzung von Produkten zu Ökosystemen, die Unterschiedlichkeit der globalen Märkte und dem Streben der Kunden nach möglichst individuellen Produkten die Komplexität bei der Beantwortung der Frage nach dem Kundennutzen steigt. Ein guter organisatorischer Schnitt durch die Etablierung von E2E-Verantwortung in der Aufbauorganisation auf allen Ebenen reduziert diese Komplexität, löst sie aber nicht auf.

 

Komplexität als Massstab

Wir erinnern uns: Es geht um das Steuern im Sturm. Die verbleibende Komplexität muss übergeordnet gesteuert werden. Das ist die zentrale Aufgabe des Top-Managements, und wie gut sie erfüllt wird, ist heute der wohl wichtigste Qualitätsmassstab. Komplexitätsmanagement bedeutet dabei die Vermeidung unnötiger, aber auch die Beherrschung notwendiger Komplexität. Die vielleicht wichtigste Lektion, die uns die Kybernetik lehrt, basiert auf «Ashby’s Law»: Nur Komplexität kann Komplexität bewältigen. Mit anderen Worten: Je komplexer ein System und seine Umwelten sind, desto stärker muss die Steuerung sein – das erinnert uns an Jack Welch! Das VSM stellt uns das Werkzeug zur Verfügung, um diese Passgenauigkeit herzustellen.

 

Die Klaviatur der Steuerung

In einem lebensfähigen System umfassen die Aufgaben der Steuerung: Koordination (System 2), Steuerung des operativen Erfolgs (System 3), Steuerung der Zukunftsfähigkeit (System 4) sowie das Schaffen von Identität und Normen (System 5). Dieses Zusammenspiel aus operativem, strategischem und normativem Management ist von herausragender Bedeutung. Insbesondere das rechtzeitige Umsteuern der personellen und finanziellen Ressourcen vom bestehenden auf neues Geschäft, ist wohl die schwierigste und zugleich wichtigste Aufgabe des Senior Managements. Wann und wie viele Ressourcen sollen, z.B. in den Ausbau der Elektromobilität, der Konnektivität oder in die ADAS-Entwicklung investiert werden? Diesen Fragen kann sich ein Senior Management nur dann ausreichend widmen, wenn das operative Geschäft nicht seine ganze Aufmerksamkeit absorbiert. Zur Integration dieser Aufgaben stehen dem Management aus dem VSM ein Prinzip und zwei Steuerungsachsen zur Verfügung, die situativ gespielt werden müssen.

Quelle: Berylls Strategy Advisors, MP Consulting

Das Prinzip der Selbststeuerung

Gerade in zunehmen volatilen Zeiten, gewinnt das Prinzip der Selbststeuerung über alle Hierarchieebenen hinweg an Bedeutung. Kann das Unternehmen auf kurzfristige Veränderungen interner und externer Faktoren reagieren und rechtzeitig den Kurs anpassen, um trotzdem die gesteckten Ziele zu erreichen? Auch hier steht uns die Kybernetik als Wissenschaft Pate. Wie bei technischen Systemen müssen im Unternehmen durchgängige Regelkreise mit regelmäßigen Rückmeldungen (Feedbackloops) installiert und gelebt werden, um die Leistung und das Verhalten des Unternehmens im richtigen Moment anzupassen. Dies gelingt nur, wenn klare Entscheidungsstrukturen und -kompetenzen vorhanden sind. Dann kann Feedback, also die Auswirkung von Entscheidungen, zurück auf den Entscheider fallen. Das sorgt für Selbstregulation.

Zwei Achsen der Steuerung

Wir streben mit dem VSM also möglichst autonome und sich selbst regulierende Einheiten an. Auch bei technischen Systemen ist das so. Das Antriebssystem im Automobil kümmert sich auf gerader, freier Strecke autonom um den Vortrieb und muss sich dazu nicht groß mit dem Brems- und Lenkungssystem abstimmen. Es besitzt eine Schlupfregelung, die kontinuierlich die Straßenbedingungen (Umwelt) erfasst und bei Bedarf regelnd eingreift. Im VSM wird diese autonome Steuerung über die horizontale Steuerungsachse beschrieben. Wird es hingegen kurvig, benötigt es eine übergreifende Steuerung, um sicher am Ziel anzukommen: Die vertikale Steuerungsachse. Sie sorgt dafür, dass mehrere Systeme sinnvoll zusammenwirken. Über sie wird im Unternehmen koordiniert, Informationen gesammelt, Ressourcen und Budgets verteilt, oder im Notfall regelnd in die Autonomie der Subsysteme eingegriffen. Im genannten Beispiel würde diese Achse über den Fahrer oder das automatisiert fahrende Fahrzeug gesteuert, um rechtzeitig vom Gas zu gehen, zu bremsen und zu lenken. Durch diese Steuerung wird das Gesamtoptimum des Systems sichergestellt: Schnell aber sicher am Ziel anzukommen. Hierarchie ist in diesem Sinne besser als ihr Ruf.  Steuerungssystem erfordern eine logische Hierarchie, die sich aus der Relevanz von Information ergibt, nicht aus Status und Macht. Diese Art von Hierarchie ist für das Funktionieren notwendig.

 

Keine Steuerung ohne Kommunikation

Kommunikation ist schon zwischen zwei Menschen nicht einfach. Hier könnten gegebenenfalls Paul Watzlawick oder Schulz von Thun helfen. Noch anspruchsvoller ist die Gestaltung der Kommunikationskanäle, die für eine wirksame Steuerung funktionieren müssen. Welche das sind, und wie leistungsfähig sie sein müssen, können wir wiederum am VSM ablesen. So stellen wir sicher, dass die Verkabelung funktioniert. In der Praxis bedeutet das die Gestaltung der Input-/Output Verbindungen zwischen Organen, Gremien, Meetings und Abteilungen verschiedener Management-Ebenen. Hier kann man sich leicht verlieren, wenn man keine Orientierung hat. Diese Orientierung fehlt üblicherweise, und so entsteht eine ungesunde Art der Selbstorganisation, in der sich die Dinge zurecht rütteln und dabei weniger die Steuerungslogik sondern mehr persönliche Interessen und historische Gründe das Design bestimmen. Die meisten Steuerungsorganisationen sind demzufolge nicht aktiv designt, sondern einfach irgendwie entstanden. Hierin liegt das vermutlich grösste Leistungsreservepotential in unseren Firmen.

 

Beyond agile

Qualität ist nicht erst seit «Total Quality Management» von grösster Bedeutung für ein Unternehmen. Sie verlagert sich aber nun zunehmen von Produkten, Services und Prozessen auf die Qualität der Entscheidungs- und Kommunikationsstruktur des Unternehmens, also auf seine Steuerung. Es wird im zunehmenden Nebel und bei zunehmender Dynamik schwierig, ohne ein hochqualitatives Steuerungs- und Kommunikationssystem auszukommen, das diesen Herausforderungen gewachsen ist. Agilität alleine genügt nicht, selbst wenn sie gut skaliert ist, weil sie sich auf die Prozesse beschränkt, und nicht auf die Steuerung des Unternehmens als Ganzes. Der Aufbau einer starken Neurologie des Unternehmens ist im Sinne der «Total Management Quality» deshalb die aktuell wichtigste Aufgabe des Top-Managements. Viability ist der nächste Schritt nach der Agility.

Hinweis: Erstveröffentlichung 11/23 im Q-Magazin

Autoren
Laura Kronen

Partner

Peter Eltze

Partner

Sebastian Bräuer

Associate Partner

Dr. Martin Pfiffner

CEO MP Consulting

Laura Kronen

Laura Kronen (1980) ist Partner bei Berylls by AlixPartners (ehemals Berylls Strategy Advisors) mit Schwerpunkt Transformation. Menschen zu bewegen und Organisationen voranzubringen begeistert sie. Mit über 18 Jahren Industrie- und Beratungserfahrung liegt ihr Fokus auf transformativen Fragestellungen im Operations Umfeld – vom Executive bis zum einzelnen Mitarbeiter, bei Herstellern und Zulieferern. Sie unterstützt ihre Kunden dabei, Strategie, Struktur und Kultur in ihrem jeweiligen Marktumfeld in Einklang zu bringen und somit ihre Resilienz zu stärken.

Bevor Laura Kronen zu Berylls kam, arbeitete sie bei PwC Strategy&, Volkswagen AG und Audi. Sie hat einen Diplomabschluss in Wirtschaftsingenieurwesen vom Karlsruher Institut für Technologie (KIT).

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Peter Eltze

Peter Eltze (1964) ist seit November 2015 als Partner bei Berylls by AlixPartners (ehemals Berylls Strategy Advisors) tätig, einer internationalen und auf die Automobilitätsindustrie spezialisierten Strategieberatung. Er ist Experte für ganzheitliche Transformationsprozesse und kann auf eine langjährige Erfahrung im Vertriebs- / Marketing- und Operations-Umfeld zurückschauen.
Peter Eltze berät seit 1994 Automobilhersteller und -zulieferer im globalen Kontext. Er verfügt über ein fundiertes Expertenwissen in den Bereichen Strategie- und Organisationsentwicklung. Zu seinen weiteren fachlichen Schwerpunkten zählen unter anderem Top Executive Coaching und der Themenkomplex rund um die Gestaltung von Führungsstrukturen und -konzepten.
Vor seinem Einstieg bei Berylls Strategy Advisors war er für MP und Malik als Mitglied der Geschäftsführung.
Im Anschluss an seine kaufmännische Ausbildung bei Siemens studierte er Verwaltungswissenschaften mit dem Schwerpunkt Managementlehre an der Uni Konstanz.

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Sebastian Bräuer
Sebastian Bräuer (1982) verstärkt Berylls by AlixPartners (ehemals Berylls Strategy Advisors) seit 2022 als Associate Partner. Er startete seine Karriere in der Beratung und leitete Projekte mit Fokus auf Operational Excellence vorrangig in der Automobilindustrie, jedoch auch im Konsumgüterbereich und der Chemieindustrie. Anschließend wechselte er zu einem deutschen OEM, bei dem er Führungsrollen in der Organisationsentwicklung, der Entwicklung digitaler Produkte und Services sowie der digitalen Produktstrategie übernahm. Bei Berylls fokussiert er auf Themen rund um das Digital Car.
Sebastian schloss den Diplom-Wirtschaftsingenieur an der Technischen Universität Dresden ab.
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AI as your strategic ally: customer-centric portfolio decisions, powered by Artificial Intelligence

Munich, December 2023

Portfolio decisions have become increasingly complex as the pace of transformation in the automotive industry accelerates. Munich, December 2023

P

ortfolio decisions have become increasingly complex as the pace of transformation in the automotive industry accelerates. Driven by intensifying competition to meet customer demands ever more accurately, we expect this trend to continue for the foreseeable future.

A well-crafted portfolio strategy has never been more essential. Selecting the right products, services, and features that meet customer desires and needs is the key challenge in portfolio decision making, alongside managing risk, allocating resources, and maintaining agility. Could AI be the new standard for how portfolios can be managed more effectively? Think of AI as your most versatile team member, equipped for a variety of tasks. Much like a Swiss Army knife in the field of portfolio strategy. The only question is: how do you best use it?

Our approach offers a practical roadmap for using AI effectively. Not all pieces of the puzzle allowing usage of AI’s full potential are yet in place. However, the picture of how these pieces will fall into place is becoming increasingly clear. By integrating AI into portfolio strategy, a variety of benefits, such as time efficiency, deeper analytical insights, and easier scenario analyses, can be expected. The urgency to integrate AI into strategic planning cannot be emphasized enough. By doing so, not only present work and decision making can be improved, but also the groundwork for significantly greater benefits in the future is laid. Now is a crucial time to consider AI integration; those who act promptly are likely to be better positioned in an increasingly competitive market.

This article is the first in a series of articles exploring the potential of AI for portfolio strategy. As this technology is evolving fast, we look at AI with a dual lens: assessing what is already possible, and subsequently, identifying the next steps for leading automotive players towards (even more) customer-centric portfolios. In this context, this first article will focus primarily on the opportunities for automotive OEMs.

Artificial Intelligence (AI) is a broad field of computer science dedicated to developing systems capable of performing tasks that normally require human intelligence; for example, solving problems, recognizing patterns, and understanding natural language.

Large Language Models (LLMs), such as the latest GPT models, are advanced AI models trained on vast datasets and designed to understand, process, and generate human-like text based on the context provided. LLMs have revolutionized the way we interact with AI systems and bear great potential for applications like customer segmentation and – when done right – can even support strategy work.

 

Unleashing AI’s potential for customer-centric portfolio decision making:

The transformative power of AI for customer and portfolio strategy lays in the ability to inform human decision making. If complemented by a robust framework and accurate data, AI has the potential to enhance customer understanding and increase the speed of analysis in portfolio decision-making. This can lead to greater cost-efficiency compared to traditional data science methods such as advanced analytics or big data. By harnessing vast amounts of structured and unstructured data from different sources, such as customer demographics, purchasing behaviour, vehicle usage, and online interactions, AI can unlock nuanced insights about customer preferences and behaviour enabling a dynamic, real-world understanding of customers. From the discerned patterns, AI can then offer a glimpse into the future, fundamentally informing human decision-making. This granular understanding empowers automotive companies to refine their portfolio strategy with timely insights and tailor their products and services in a way that resonates with customer preferences fostering competitiveness as well as strategic, sustainable growth.

We believe that harnessing the potential of AI for portfolio strategy offers distinct key benefits for OEMs:

Authors
Malte Broxtermann

Partner

Timo Krall

Project Manager

Philipp Brandner

Consultant

Claudius Feldmann

Thesis Student

Benefit 1
Benefit 2
Benefit 3
Benefit 1

1. Dynamic customer understanding & sensing tomorrow’s landscape:

The utilization of AI as a tool for dynamic understanding of customers and market prediction can be a strategic advantage for OEMs. AI can provide timely updates on customer behaviors and needs as required, coupled with detailed segment and persona analysis. This allows automotive companies to gain comprehensive insights into their target audience's preferences and anticipate shifting demands. Through pattern identification AI can anticipate emerging market changes before they fully emerge. By this proactive detection of trends, risks, and opportunities as they emerge, OEMs are better equipped to make more effective decisions in an industry that is more dynamic than ever before.

Benefit 2
2. Aligning strategy with real-world insights:

In today’s rapidly evolving market, aligning strategy with real-world insights is crucial, and AI can play a pivotal role in this process. By comparing strategic concepts with a wealth of internal and external data, AI performs well in scenario recognition. It detects inconsistencies effectively and allows the refinement of strategies to ensure they align more closely with actual market conditions. With actionable insights, these strategies are not only robust on paper but also practical in real-world conditions.
Benefit 3

3. Empowering decision-making through human-AI synergy:
Consider AI as the perfect partner for complex analysis. When humans and AI collaborate, decision-making is supercharged. While humans bring empathy, ethical considerations, and intricate practical understanding to the table, AI ensures precision, speed, and vast data handling. Together, this partnership takes decision-making to the next level, combining the depth of human intuition with the efficiency and analytical capabilities of AI.

After exploring the key benefits and understanding the significant impact of AI, we recognize that the automotive industry is on the threshold of a new era. This leads us to these key questions:

“How do we transition from recognizing these advantages to implementing them in tangible, actionable strategies for portfolio management? How can OEMs leverage AI to not only understand but also anticipate the dynamic demands of the market?”

Operationalizing AI insights: A strategic approach to portfolio management

A clear roadmap is essential to harness the AI benefits and close the gap between AI’s potential and its practical application in portfolio management. This is where we, at Berylls, step in. As agents of operationalization, our goal is to transform abstract concepts into tangible, actionable strategies.

Our approach follows five clear steps for portfolio strategy development:

A) It begins with summarizing existing relevant aspects of corporate, functional, and business unit strategies into key statements, forming a comprehensive understanding of the strategic landscape. Where feasible, key statements are also quantified to enable more meaningful data-based analysis.

B) This is followed by a detailed analysis of the portfolio environment, which is crucial for understanding the stakeholder landscape and market trends. This analysis especially highlights the significant role of understanding customers and competitors to ensure a thorough evaluation of latest leading portfolio strategies and best practices. There is no one-size-fits-all solution, and we pay close attention to what makes a portfolio work (or fail) depending on each specific company and its target customers.

C) We then conduct a comprehensive review of the current internal portfolio in question, analyzing its composition, performance, and enablers.

D) These initial steps lay the foundation for the subsequent phases, where we identify and evaluate potential improvements to the portfolio, focusing on portfolio gaps, key levers, and scenarios for enhancement.

E) Finally, the analysis concludes with defining or updating the portfolio strategy, the setting of specific objectives, and the development of a detailed implementation roadmap.

This approach is optimized for AI integration with the first three steps (A-C) providing the relevant input for AI-supported decision making and the last two steps (D-E) involving AI to analyze all available information effectively.

Figure 1: Berylls framework for portfolio strategy

Source: Berylls Strategy Advisors

The integration of AI into portfolio strategy development represents a significant improvement. Already in the initial stages where qualitative and quantitative data is made accessible for AI, it can play a key role in improving the speed, cost efficiency and quality of strategic decision making. In later steps, AI supports in identifying data patterns, pinpointing missing data points, suggesting portfolio improvements, and preparing meetings by decision makers effectively. Moreover, this integration of AI enables a more dynamic, agile, and efficient iteration of the entire process that closely aligns with evolving market needs.

Figure 2: Berylls framework for AI-enabled portfolio strategy

Source: Berylls Strategy Advisors

An example ChatGPT case illustrating the framework can be found by clicking on this box

Output generated by current LLMs should never be used without diligent review and confidential data must not be submitted to ChatGPT. The example ChatGPT output above was created based on dummy data for a hypothetical company. This output was left completely unchanged by Berylls to help provide a realistic example of the type of quality that is feasible with the latest generation of LLMs. Replicating similar results for real applications requires access to the latest generation of LLMs that guarantees full data security. A real-world project covers drastically more inputs, analytical steps, and output than is included in the simplified example case.
Click here

An example ChatGPT case illustrating the framework can be found by clicking on this box

Output generated by current LLMs should never be used without diligent review and confidential data must not be submitted to ChatGPT. The example ChatGPT output above was created based on dummy data for a hypothetical company. This output was left completely unchanged by Berylls to help provide a realistic example of the type of quality that is feasible with the latest generation of LLMs. Replicating similar results for real applications requires access to the latest generation of LLMs that guarantees full data security. A real-world project covers drastically more inputs, analytical steps, and output than is included in the simplified example case.
Click here

Using AI to take smarter portfolio decisions by putting the customer front-and-center:

AI can demonstrate significant advantages over traditional analyzing methods. AI’s speed and adaptability enable quicker decisions, eliminate human biases, reduce manual research, and efficiently process large datasets for precise market alignment and feedback analysis. Correctly providing relevant input and gradually refining the prompts provided to AI does require expertise and AI currently cannot (and in our assessment: should not) replace human decision making in matters of strategic importance. Latest AI models, nevertheless, can be applied to accelerate analysis and help human experts take more informed and well-prepared decisions. Remarkably, an analysis as outlined in the example case linked above can be replicated in minutes, assuming an AI model comparable to the latest version used by ChatGPT has access to all essential data in a way that ensures data security. While the case linked above is hypothetical, AI indeed can create significant value once it has access to an organization’s real-world data and strategies. Berylls invites you to start working towards unlocking the full potential of AI to achieve a deep, data-driven understanding of customer behaviors, preferences, and expectations.

Engage several powerful AI flywheels and lay the foundation for success by embracing the following enablers:
1. Redefine data for AI

Quality data is the backbone of meaningful AI insights. Ensure your data is well-structured, high-quality, meaningful, and thus prepared to enable accurate results.

2. Forge AI-ready strategies

Condense your core business strategies into written key statements to make them AI-readable. This allows AI to analyze data and generate insights with your strategy in mind.

3. Human-AI decision synergy

Use AI to support your decision-making process, ensuring faster responses to dynamic market demands. Combining the strengths of human intuition with AI’s analytical capabilities creates a powerful duo, maximizing both precision and efficiency.

4. Tailor your AI approach

Different needs require different solutions. Whether it's leveraging AI for specific tasks or a more comprehensive AI-driven strategy, aligning the technology with individual organizational goals and capabilities is crucial.

Engage several powerful AI flywheels and lay the foundation for success by embracing the following enablers:

1. Redefine Data for AI

Quality data is the backbone of meaningful AI insights. Ensure your data is well-structured, high-quality, meaningful, and thus prepared to enable accurate results.

2. Forge AI-ready strategies

Condense your core business strategies into written key statements to make them AI-readable. This allows AI to analyze data and generate insights with your strategy in mind.

3. Human-AI decision synergy

Use AI to support your decision-making process, ensuring faster responses to dynamic market demands. Combining the strengths of human intuition with AI’s analytical capabilities creates a powerful duo, maximizing both precision and efficiency.

4. Tailor your AI approach

Different needs require different solutions. Whether it's leveraging AI for specific tasks or a more comprehensive AI-driven strategy, aligning the technology with individual organizational goals and capabilities is crucial.

We expect a broad spectrum of relevant use cases for applying AI to portfolio strategy. The role of AI can range from fundamental applications in stakeholder analysis to more complex scenarios in business case preparation and continuous improvement. Each use case demonstrates a specific aspect of AI’s contribution to portfolio strategy and has unique requirements for successful implementation. Different use cases can be rolled out sequentially, gradually creating more value with AI over time.

Four types of portfolio strategy use cases for AI

Low Complexity
Low Output

High Complexity
High Output

Use case I: stakeholder analysis

LLMs enable detailed profiling and analysis of customers, competitors, and other key stakeholders. Profiles can also cover the portfolios and best practices by key competitors. LLMs can provide frequent, cost-effective updates based on AI-assisted research, competitor monitoring, and customer data analysis. This speed and breadth of analysis helps improving the understanding of market expectations, behaviors, and dynamics.

Main requirements:

- Introduce LLM integration: Provide access to a Large Language Model (LLM) like ChatGPT for analyzing anonymized stakeholder data.

- Establish customer data platform: Develop a platform to manage and analyze customer data across relevant data sources, ensuring it is structured and AI-ready.

- Ensure data updates: Implement a system for continuous updating of stakeholder data to keep the analysis relevant.

Use case II: Identification of decision option

AI aids in broadening decision options for portfolio adjustments. It helps in generating new ideas for portfolio changes by using pattern recognition in the available data to identify potential opportunities and optimize strategic choices. Specifically, AI can help identify untapped potential by highlighting discrepancies of the current portfolio with available customer insights, market trends and best practices by comparable competitors.

Main requirements:

- Ensure available strategies are AI-readable: Turn the relevant aspects of current strategies into AI-readable (quantified) key statements.

- Integrate results of stakeholder analysis: Grant AI access to all relevant analysis of the impact and interests of various key stakeholders, especially by customers and competitors. This should include a thorough review of competitor portfolio best practices and strategies with clear assessments of the company-specifics that make each competitor succeed (or fail).

- Describe portfolio for AI Analysis: Submit a comprehensive documentation of the current product and service portfolio, the portfolio performance, and any relevant information about company-specific portfolio enablers. The information about enablers should include the main resources allocated across the portfolio as well as key competitive strengths (or weaknesses). Examples for relevant competitive attributes would be information about the sales and manufacturing model used, describing (e.g.) the resulting supported production speed, variety, flexibility, and cost-efficiency

Use case III: Assessment of decision options

Latest generation LLMs can assist in qualitative comparison of decision alternatives, for example by suggesting (dis)advantages for each available decision. For quantitative decisions, they can also support in preparing business cases. At the current technological level of LLMs, business cases are mainly manually crafted, with LLMs offering suggestions that require in-depth expert review for accuracy. Over time and as LLMs evolve, their suggestions become more reliable, allowing them to draft business cases based on company data. This evolution will gradually shift the human role towards systematically refining these AI-generated suggestions, leading to more complex and accurate business cases that account for scenarios involving competitor responses, thus enhancing strategic decision-making

Main requirements:

- Integrate results of stakeholder and decision option analysis: Inform the AI about the final set of decision alternatives and the related scenario assumptions. This will be the starting point for the business case. In addition, provide input to the AI with any relevant findings from the stakeholder analysis such as identified customer preferences. This information, for example, can help the AI to suggest meaningful parameters and assumptions for the business case.

- Provide access to operational and financial data: Both internal and market-related operational and financial data must be accessible for a comprehensive AI analysis. Where available, validated forecast data will improve the accuracy of the analysis.

- Involve industry experts: From start to finish, AI prompts should be engineered by industry experts to ensure relevant and accurate AI outputs.

Use case IV: Continuous improvement

AI supports the ongoing refinement of a portfolio strategy. It quickly identifies opportunities for action based on current data and ensures that the portfolio remains aligned with market trends and customer preferences, leading to continuous improvement and strategic agility.

Main requirements:

- Establish regular data updates: Ensure AI analyses are based on most current information.

- Implement feedback mechanisms: Integrate customer feedback and performance data into ongoing AI analysis.

- Develop a dynamic strategy adaption process: Allow regular strategy refinements based on AI insights.

Leveraging AI for your company… but different

The potential of AI to drastically change the way strategy work is done grows rapidly. Berylls stands ready to help you transform successfully and in a way that is uniquely centered around your customers, company, situation, and aspirations. Stepping into the AI realm can be complex; however, having a dedicated partner ensures that you harness its full potential efficiently and strategically. Berylls provides a tailored way to make AI work best for your needs, from initial assessment to final implementation. Backed by a team of experts and collaboration partners with custom software solutions at the forefront of AI advancements. Embrace the AI-driven future of automotive customer strategy and gain the AI advantage with us. The automotive industry faces a time of accelerating change, and those who can sense and adapt to these changes first as AI matures will have the upper hand.

Malte Broxtermann

Malte is an expert in the development and implementation of automotive digitization strategies.

He focuses on helping clients scale (generative) artificial intelligence to improve their bottom line across the entire automotive value chain. His primary customers are automotive manufacturers and their suppliers, especially those active in the Software-Defined-Vehicle space.

Before his time at Berylls by AlixPartners (formerly Berylls Strategy Advisors), he advised leading North American utility companies. Prior to that, he saved lives as emergency medical technician. Malte holds master’s degrees in economics from Maastricht University and Queen’s University in Canada.

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