BMW has been implementing next-level production processes throughout its plants around the world for quite some time now. From robots carrying parts around the compound, to exoskeletons for the workers, a number of innovative features have been added to the mix. Today, the Bavarian manufacturer announced the publishing of its AI algorithms used in production to be used by anyone interested. They are now available on the popular GitHub platform.

The algorithms published are mainly part of various AI applications, in particular in automated image recognition and image tagging. Making these publicly available allows software developers all over the world to view, change, use and improve the source code. “With the algorithms we are now publishing, the BMW Group has significantly reduced the development time for neural networks for autonomous transport systems and robots,” says Dirk Dreher, Head of Logistics Planning.

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Neural networks independently compare live images in production and logistics with image databases to detect any deviations from the target state. The open source approach benefits both interested software developers and the BMW Group. “We provide elements of our innovative digital image tagging software, which has proven effective in multiple AI applications; in turn, we receive support in taking our AI software to the next level of development. Also, this allows us to focus more strongly on advancing specific AI applications in production and logistics,” comments Christian Patron, Head of Innovation, Digitalization, Smart Data Analytics.

“We are making major investments in artificial intelligence. By sharing our algorithms with the global developer community, we want to do our part and make AI accessible to a broad group of users. We expect the further open source development to lead to a rapid and agile advancement of the software,” adds Kai Demtröder, Head of Artificial Intelligence, Data Platforms at BMW Group IT.

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In keeping with the open source approach, all users of the algorithms are guaranteed anonymity. Any flaws in the algorithms can be identified quickly; in this process, automated functions provided by the platform operators can also be used, if needed. For quality assurance purposes, the BMW Group checks all incoming user suggestions before they are put into productive use or shared. The model – in other words, the actual AI application being developed with these algorithms – always remains protected. All users are free to decide whether they want to make their models accessible to partners, such as suppliers.


Algorithms have come to define a significant chunk of modern marketing strategies. Google’s search engine algorithm dictates which websites appear highest in relevant search results, driving search engine optimization and related strategies. Facebook and other social media sites use algorithms to determine which types of content their users prefer, driving social media and content marketing strategies. And dozens of other algorithms, like recommendation engines in platforms like Amazon, can have secondary effects on the visibility of your products. 

As time passes, these algorithms become more complex; the tech companies implementing them gather more data, they refine their processes and they intentionally make things harder to “game” with proprietary and novel engineering tactics. And while it’s possible to try and break down these new updates by running your own experiments, it’s hard to tell for sure how they function at a base level. 

In the future, algorithms are going to become even more complex. So how should you prepare to adjust your strategies to accommodate this? 

The future of marketing algorithms

Let’s start by working to understand how algorithms will likely develop from here. There are several key areas of development to keep in mind, most of which are driven by more advanced artificial intelligence, better and higher volumes of data, or both. 

  • Elimination of human supervision. To date, most advanced marketing-adjacent algorithms have relied on supervised learning, or the process of using human beings to oversee and direct how machine learning systems improve over time. However, next-generation algorithms will likely rely on themselves for ongoing improvement. For example, Google’s RankBrain update self-adapts to improve its ability to “understand” the meaning behind complex user queries. Accordingly, it’s going to be harder to guess which factors the algorithm is considering, since even the human beings that designed it won’t have directorial control over how it develops. 
  • Manipulation prevention. Marketers love to figure out ways to improve their chances of being favored by commonly accessed algorithms. SEO and all its related sub-strategies are focused on optimizing content, page titles, inbound links and dozens of other factors to make web pages seem more authoritative in Google’s eyes. Of course, Google condemns outright ranking manipulation and has many built-in algorithmic factors to check for and punish these types of schemes. But there’s only so much their algorithms can do in their current state. Future versions may be so effective at detecting manipulative or unnatural content that human beings can scarcely tell the difference. 
  • Flexibility. Possibly the most important factor to consider is the flexibility and customizability of future algorithms. Currently, you’re used to using a version of Google search that may be very different from the “standard” version of Google search; Google incorporates many factors, including your location and search history, to customize your results. In the near future, algorithms everywhere will be heavily individualistic and may be difficult to predict high-level – or else, you’ll need to “pay to play,” spending advertising dollars on being able to target hyper-specific audiences. 

What this means for marketers

So what does this mean for your marketing campaigns? For starters, don’t start worrying that your current marketing efforts are going to become obsolete. Algorithms, like any technology, change gradually, and you’ll have plenty of opportunities to refine your approach and adapt to the new changes before they become unrecognizable. 

Beyond that, there are several steps you can take to prepare for the future: 

  • Follow tech blogs and individual developers. Many tech companies have an official blog (like Google), and at least a handful of developers who occasionally post teases of new and upcoming changes. Follow these content outlets so you can stay abreast of new developments and hear the new information straight from the source. You’ll get updates as they come, but you’ll also get a feel for their developmental patterns, so you can predict the general subjects and intentions of future updates. 
  • Avoid manipulation. Any effort you make to take advantage of an algorithm should be as natural and as valuable to users as possible. If you want your content and product listings to be evergreen, you can’t run the risk of being the victim of any future update designed to weed out manipulative content. Pay close attention to your phrasing, your formatting and your intentions as you develop new material. 
  • Refine your demographic targeting. Future algorithms will cater to individual preferences, so understanding your target audience as a lumped-together “group” isn’t going to be enough. Obviously, you can’t get to know each of your customers individually, but you can do more to understand the individual types of users that comprise your main groups, including what kind of content and products they need most.

Algorithms are already ridiculously complex and hidden by a shield of proprietary intentions. In the future, they’re going to be even more effective and even more obscured. However, if you’re paying close attention to the inevitable changes and you’re working hard to stay relevant, natural and focused on what your audience needs, you can stay ahead of the competition and make sure these next-generation algorithms favor your business.

Opinions expressed in this article are those of the guest author and not necessarily Marketing Land. Staff authors are listed here.

About The Author He has spent more than 20 years in the world of SEO and digital marketing leading, building and scaling sales operations, helping companies increase revenue efficiency and drive growth from websites and sales teams. When Timothy isn’t telling the world about the great work his company does, he’s planning his next trip to Hawaii while drinking some Kona coffee.