How machine learning contributes to corporate success in times of crisis
2021 follow-up study: The new 2021 “Machine Learning” study from IDG

We have been aware of the potential for process optimization or the development of new products and services using machine learning (ML) and artificial intelligence (AI) for some time now. The opportunities seem to be limitless. However, even though machine learning is now used almost everywhere, there are still some companies that shy away from making larger investments and that run the risk of falling behind as a result.

Even the uncertainties of the last few months can’t hold machine learning down

73 percent of large companies (with more than 10,000 employees) already rely on machine learning. Indeed, even among smaller companies, more than half are benefiting from the advantages of automation technology. The use cases range from IT problems and production improvements to logistics and sales. In view of rising customer expectations, growing economic pressure and an increasingly complex IT infrastructure, companies need the support that self-learning systems can provide if they want to continue to survive in the market.

Plus, the past few months of the COVID-19 pandemic have shown clearly that digitalization guarantees stability and resilience, particularly in these confusing times. True digital transformation can only come about with ML and AI. The study shows that companies are beginning to realize this too. During the COVID-19 pandemic, spending on ML projects actually increased at more than half of the companies surveyed.

The study outlines the challenges that many companies are still facing when using ML and AI and why there is still no alternative in the long term.

Download: 2021 IDG study: “Machine Learning”

Read the study and learn about how high-ranking decision-makers are assessing the current developments in machine learning and the challenges that smaller companies in particular are facing.

You can download the entire “Machine Learning” IDG study 2021 free of charge in PDF format.

The study contains:

  • Detailed figures on how decision-makers and IT experts from more than 360 companies assess the developments surrounding machine learning
  • An overview of the reasons and goals for introducing machine learning within companies
  • AI project success rates
  • Insights into which AI/ML methods are increasingly being used in companies
  • Explanations of the challenges and problem areas in the field of machine learning
  • Background on why the corporate organization needs to be adapted to support AI and ML

Download: 2021 IDG study: “Machine Learning”

Read the study and learn about how high-ranking decision-makers are assessing the current developments in machine learning and the challenges that smaller companies in particular are facing.

The study was carried out by IDG Research Services in partnership with Lufthansa Industry Solutions in April/May 2021, as a follow up to IDG’s 2020 “Machine Learning” study. 367 interviews were conducted; the companies surveyed came from various industries and varied in size and annual turnover.

The full results, as well as further assessments by and interviews with various experts, can be read in the complete study. The study is available to you free of charge.

The following is a brief summary of some important findings and figures.

The ML status of companies broken down by IT budget.
Are machine learning technologies being used in your company? Which of the following descriptions comes closest to the ML status in your company?

Machine learning has become essential, especially for large companies

73 percent of large companies with more than 10,000 employees already use ML technology. The figure remains at 59 percent among smaller companies.

The financial aspect seems to be the decisive factor. More than 60 percent of companies with an IT budget of more than ten million euro are already using at least one example of ML technology, with many using a wider range. Only 41 percent of companies with smaller IT budgets can claim this.

The biggest hurdles for implementing machine learning
What do you see as the biggest hurdles for implementing machine learning in your company?

The shortage of skilled workers remains a familiar problem

Machine learning is associated with all sorts of opportunities and potential. However, there are still some hurdles to overcome. The shortage of specialists and skilled workers has been one of the biggest problems for years. This has hardly changed this year (37 percent). Insufficient programming skills (25 percent) and a lack of expertise within the company (24 percent) come next.

As 19 percent of respondents also complain about the lack of budget for the further development of their own employees, bringing in competent external service providers remains necessary.

The most important criteria when selecting a machine learning solution
What are the key criteria for your company when selecting a suitable machine learning solution?

The price is the decisive factor

Selecting the correct piece of ML software for any particular company can depend on a range of criteria. For 37 percent of the companies surveyed, however, price remains the most important factor.

Other important factors are traceability (34 percent) and adaptability to individual company requirements (32 percent). Comprehensive support plays an equally important role across all company sizes (30 percent).

The metrics for successful machine learning projects.
How do you measure the success of your machine learning projects?

The added value of machine learning becomes apparent sooner than you think

Of course, those who initiate machine learning projects want to know how soon their effort and investment will pay off.

The study shows that this occurs faster than you might expect. 62 percent of the companies were able to benefit from ML technologies after a maximum of three months. In total, 84 percent were able to record noticeable added value after one year at the latest.

This is particularly noticeable in the form of increased productivity (49 percent), reduced costs (47 percent) and increased efficiency (43 percent).

Further study results on this and other topics can be found in the “Machine Learning” IDG study 2021. This can be downloaded here, free of charge, in PDF format (only in German).