Earlier this year, Gartner published its new hype cycle for Artificial Intelligence [1]. Business adoption of AI is experiencing significant growth – thus, according to Gartner’s 2019 CIO Agenda survey, organizations that have deployed artificial intelligence (AI) grew from 4% to 14% between 2018 and 2019 [2]. However, until now, only two AI technologies – GPU Accelerators and Speech Recognition – have reached the plateau of productivity. The greater majority is situated in the first half, with a lot of excitement and experimentation, but also disillusionment and steep learning curves to be expected in the years to come.

To dive into the details of AI adoption, we mined a large-scale dataset of Web data for industries and use cases which are affected by each trend. The following overview shows the amount of discussion for the top 15 trends in our dataset in 2019 (excluding umbrella terms such as Machine Learning and Deep Learning):


Figure 1: Amount of discussion per technology

As differentiated in this chart, our analysis integrates three types of Web resources with different degrees of specialization on AI:

  • General news are business and economic news without an explicit specialization, such as businessinsider.com and independent.co.uk.
  • Technology blogs are focused on technological and digital topics, both from the technology and the business perspective. Examples are techcrunch.com and theverge.com.
  • Blogs with AI focus are specialized AI and machine learning resources for practitioners, such as machinelearningmastery.com and aitrends.com.

As can be seen from the chart, the bulk of discussion goes on in technology blogs and blogs with AI focus. Topics with significant ethical, infrastructural and regulatory stakes, such as Autonomous Driving, Quantum Computing and Artificial General Intelligence, also attract considerable attention in general news.

It is worth noting that the considered trends belong to different conceptual classes and levels of AI. Some of them, for example Computer Vision and Natural Language Processing, are whole subdomains of AI that can be relevant to a multitude of use cases and industries. Others, like AutoML, simplify and scale the integration of AI in the enterprise context. Autonomous Vehicles, Chatbots, Virtual Assistants and Conversational UIs are situated at the application level. Finally, concepts such as Augmented Intelligence relate to the human-machine interface and are mainly meant to spur the user acceptance of new AI technologies.

Artificial Intelligence across industries

The following chart shows how tightly the considered trends are associated with various B2B and B2C industries:

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Figure 2: AI trends by industry

Let’s look at some highlights:

  • Computer Vision is highly relevant in Automotive since it is an important component of Autonomous and Assisted Driving technology.
  • Autonomous Vehicles are tightly associated with Automotive – not really a surprise. In our analysis, Autonomous Vehicles are not necessarily cars – they also include Aerospace devices such as drones and space robots. Finally, the Construction industry is developing highly specialized autonomous utility vehicles. Operating on a well-delimited, well-understood terrain, the adoption of these narrow-focus vehicles faces lower hurdles in terms of infrastructure, ethics and regulation.
  • Quantum Computing is gaining attention as traditional computers approach their limits in terms of processing power. Quantum Computing is widely researched in the domain of Aerospace, where it is used to address the complex challenges of flight physics. Its high relevance to the Energy industry is due less to the possible applications, but to the fact that it is itself a solution to a major anticipated energy problem: by 2040, energy needs for classical computational computing will exceed capacity that can be delivered by the worldwide grid [3]. Alternatives such as the quantum computer are set out to prevent this bottleneck.
  • Robotic Process Automation (RPA) is performed by virtual “software” robots as opposed to physical robots which are used in manufacturing etc. RPA is essential in the Finance industry, where it enhances and automates routine tasks such as verification, credit scoring and fraud detection.
  • Conversational UIs are especially relevant to B2C industries such as Entertainment, Fashion and Retail. Their distribution in our data roughly correlates with Chatbots, Virtual Assistants and Speech Recognition, a core technology behind voice interfaces. Most Big Data companies are pushing their own conversational solutions such as Amazon Alexa, Google Assistant etc. According to Gartner, the conversational interface is one of the technologies with the biggest transformational impact in a short-term perspective.

Artificial Intelligence across use cases

The following chart illustrates the relevance of the considered trends to industry-independent use cases and business functions:

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Figure 3: AI trends by use case

Let’s consider some of the data points in detail:

  • As most of us have experienced, AI experiments are particularly popular in the domain of Customer Service. Especially in B2C, most customer requests can be roughly classified into a finite number of buckets, thus providing a fertile ground for training data creation and automation. Most customer interactions use language, and all trends related to language processing – NLP, Chatbots, Speech Recognition, Virtual Assistants and Conversational UIs – are highly salient in this domain. To a smaller degree, the relevance of these technologies is also visible for Customer Acquisitionand Marketing in general.
  • Quantum Computing is strongly associated with Manufacturing. According to IBM, Manufacturing is one of the most promising early beneficiaries of this technology. Quantum Computing can significantly scale up and optimize tasks such as chemical discovery, simulations for product development and supply chain optimization [4].
  • Augmented Intelligence is strongly present across most of the use cases. It should be kept in mind that Augmented Intelligence is a rather abstract concept, mainly used to communicate and also reassure that in the foreseeable future, AI will be “enhancing” (rather than substituting) the intelligence of human beings. The concept clearly demonstrates and helps to build awareness of the limits of AI on the application level. On the opposite end, Artificial General Intelligence – the intelligence of machines that can learn to perform any intellectual tasks formerly performed by humans – shows a loose association to most of the considered use cases.

Summing up

A great majority of the considered trends aim to increase the efficiency of existing tasks by reproducing fundamental functions of the human brain such as language and vision. They are necessary building blocks of the overarching vision behind AI, as reflected in the concept of Artificial General Intelligence. For the present moment, the popularity of Augmented Intelligence shows that AI has passed a “reality check” and deployment is smoothened by more realistic expectations about the cooperation between humans and machines. Finally, Quantum Computing is an active area of research which could allow to harness the combined potential of ever-growing data quantities and sophisticated algorithms, thus allowing for a “quantum leap” towards the general application of AI.


[1] Gartner (2019). Hype Cycle for Artificial Intelligence, 2019. Retrieved from https://www.gartner.com/en/documents/3953603/hype-cycle-for-artificial-intelligence-2019.

[2] Gartner (2018). Gartner Survey of More Than 3,000 CIOs Reveals That Enterprises Are Entering the Third Era of IT. Retrieved from https://www.gartner.com/en/newsroom/press-releases/2018-10-16-gartner-survey-of-more-than-3000-cios-reveals-that-enterprises-are-entering-the-third-era-of-it.

[3] SIA / SRA (2015). Rebooting the IT Revolution: A Call to Action. Retrieved from https://www.semiconductors.org/wp-content/uploads/2018/06/RITR-WEB-version-FINAL.pdf.

[4] IBM (2019). Exploring quantum computing use cases for manufacturing. Retrieved from https://www.ibm.com/thought-leadership/institute-business-value/report/quantum-manufacturing.


The automotive industry is undergoing exciting developments. To become “future-proof”, manufacturers face a double challenge: on the one hand, they need to satisfy high standards on sustainability, emissions and environment protection. On the other hand, they are now competing not only with traditional car manufacturers, but also with tech giants such as Google, Apple and Baidu. While these digital leaders are unlikely to dominate anytime soon, they are constantly coming up with alternative technologies and business models for mobility which show potential to disrupt existing patterns in the automotive industry.

The following chart shows how tightly top-selling auto brands are associated with the concepts of Sustainability and Digitalization in online media for the timespan 2018-2019:

Toyota, Jaguar and BMW score highest in terms of Sustainability, whereas Audi, Nissan and BMW are tightly associated with Digitalization. It is important to note that the positions are given not by the actual efforts and results of the companies in these areas, but by the image reflected in online media. The size of the bubbles indicates the proportional amount of discussion for each brand – thus, Ford, Toyota, Nissan and BMW are the most-discussed brands.


The analysis is based on articles from the following English-language Web blogs and portals:

  • www.autoexpress.co.uk
  • www.thecarexpert.co.uk
  • www.carmagazine.co.uk
  • www.autocar.co.uk
  • www.cars.com
  • www.edmunds.com
  • www.autoblog.com
  • www.autonews.com

The analysis is conducted using a concept embeddings, an algorithm of Natural Language Processing which allows to compute the latent association strength between various concepts. The algorithm has been developed by Anacode as an extension to traditional word embeddings.[1]

Would you like to learn more about our analytics capabilities for the automotive industry? Get in touch and let’s talk!


[1] Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J. (2013). Distributed Representations of Words and Phrases and their Compositionality. In: Proceedings of NIPS 2013.

When it comes to the adoption of new technologies, the construction industry is on the conservative end of the spectrum. However, in the past years, buzzwords such as 3D printing, Augmented Reality and Big Data have also penetrated architecture and construction. Technologies which have been rather perceived as toys or entertainment some years ago now show serious global potential to disrupt construction and, thus, our urban landscape.

From inflated expectations to implementation

The following chart displays the amount of online discussion for four key technological trends in construction since the beginning of 2016:


All curves show relatively high values until mid-2017, which remind us of the phase of inflated expectations according to Gartner’s Hype Cycle [1]. This enthusiasm is followed overall slump around the end of 2017. Starting in 2018, all trends have a growing tendency and are frequently discussed in the context of implementation and production use. Drones have the most pronounced curve, which might be an indicator of their huge transformative potential.


The analysis is based on a range of major acknowledged English-language Web blogs and portals on construction and architecture. The following chart lists the resources and shows the average number of daily articles analysed for each resource:

Mention numbers are normalised by the quantity of data available for each time span. The analysis is conducted with Concept Extraction, an algorithm of Natural Language Processing. Anacode’s Concept Extraction uses a self-learning ontology which is updated daily from a continuous stream of new Web data.

Would you like to learn more about our analytics capabilities for the construction industry? Get in touch and let’s talk!


[1] Jackie Fenn , Marcus Blosch. Understanding Gartner’s Hype Cycles. Gartner, 2018.


This report presents a snapshot on AI in Chinese social media Dec 2018 – Jan 2019, focussing on related technologies, use cases and startups.


Download the report sample here.

This report presents consumer feedback on connected car brands and features, with a special focus on Audi, BMW, Tesla, Geely, Chery and BYD.


Download the report sample China Connected Car Overview-5

With over 860,000 new-energy vehicles (NEVs) sold in the first 10 months of 2018, China is currently on the forefront of electrification.[1] Made in China 2025, China’s strategic plan tracing the energy transition and the internal development into a tech superpower, includes a significant increase of the EV proportion until 2025. The government is generously incentivizing producers and consumers to reach this goal.

Taking a qualitative perspective, how does supply match demand in this highly regulated segment? In this article, we analyze the main players in the industry and shed light on awareness, acceptance and confidence on the side of real-world consumers. The provided data was collected from Chinese social media in 2018 and analysed using Anacode’s text analytics technology.[2,3]

A vivid playing field for automotive producers

Both Chinese and international OEMs are motivated to compete for market share and pioneering technology in the EV race. The following chart shows the frequently mentioned players along with their sentiment:

Frequencies and sentiments of OEMs in e-mobility discussions

As expected for an industry with a strong vision and a favourable funding environment, startups were fast to pick up on the NEV wave. In terms of media attention and awareness, these dynamic lightweights compete on a par with the OEM incumbents:

Frequencies and sentiments of startups in e-mobility discussions

Sophisticated PR strategies, fancy concept cars and huge funding rounds generate a lot of buzz around startups. However, when it comes to actual products on the market, the discussion is dominated by NIO along with a range of OEM-produced models:

Frequencies and sentiments of NEV models

The ambivalent perception of Chinese consumers

Putting aside the famous Chinese entrepreneurial spirit, where are down-to-earth consumers on their journey of acceptance for the new technology and its long-term benefits? Are they willing to serve as test bed for technological experiments, pay higher prices and buy into – even temporary – trade-offs in terms of quality and convenience? And, most important, do they actually have trust or sense another bubble coming? To dig into these topics, we created and mined a comparative dataset of random samples of equal sizes (50k posts) relating to NEVs and internal combustion engine (ICE) vehicles. The following chart depicts the general image of NEVs and ICE vehicles:

Comparison of image attributes for NEVs and ICE vehicles

Product quality is the main concern for NEVs, as opposed to ICE vehicles where design is more prominent. In terms of sentiment, NEVs score lower on central aspects such as quality, design and price. These trade-offs can still be acceptable if there is high awareness for the long-term environmental benefits of NEVs. The following charts shows the discussion quantities and sentiments for environmental aspects on the comparative dataset:

Comparison of environment-related discussions for NEVs and ICE vehicles


Clearly, environment topics are more relevant to the NEV discussion. The opinions are not always optimistic and, more often than not, critical towards the domestic providers:

你说讽刺不讽刺,宣传“节能”的玩具车,还能呼叫“污染”的燃油车过来给它充电这是传说中的 #蔚来# 产品的移动燃油车充电宝吗? ​

Isn’t it funny that, in order to push their NEV toys, NIO offers a charging service where a non-electric car comes by to charge your “environment-friendly” NEV?




I will not buy a domestic NEV. The two options I consider are a Toyota PHEV or a petrol car. Domestic OEMs jumped on the NEV train since they failed to produce high-quality gasoline engines and didn’t really have a choice.  The actual benefits of NEVs for the environment are currently far below expectation. They are just cheating on subsidies and consumers to move the money around.


Finally, consumer trust is also undermined on the financial level – the topics of excessive subsidies, subsidy fraud and the “burning” of large funding amounts are common topics in the discussions:


Domestic OEMs are not able to develop high-quality internal combustion engines and transmissions, so they had to switch to electric cars. But after many subsidies, consumers realized that the top technologies for NEV batteries, engines and electronic controls are still not from China.

– @喜欢吉普-男人帮



This country has no future for NEVs. The policy has failed – it has subsidized a bunch of so-called environment-friendly NEVs that will have no market after three years.




10 billion RMB of funding is still not enough for these manufacturers! Can the NEV startup Xpeng win the battle against NIO?


China has set highly ambitious goals for the energy transition and its internal technological development which are highly stimulating for players in the automotive industry. However, to create a sustainable business environment, consumer trust and acceptance have to match up to these ambitions. Once government subsidies decrease and gradually turn into “soft”, non-financial incentives, industry players should be prepared to assume responsibility for product-market fit and convince their customers based on reputation, quality and long-term trust and loyalty.



[1] CAAM (2018). 2018年10月汽车工业经济运行情况. Retrieved from http://www.caam.org.cn/xiehuidongtai/20181109/1505220056.html

[2] Weibo data 2018 on e-mobility topic. Retrieved from https://www.weibo.com

[3] Anacode GmbH (2018). Anacode MarketMiner: Web-based Text Analytics for International Market Intelligence. Retrieved from http://anacode.de/wordpress/wp-content/uploads/2017/11/Anacode_Technology_Whitepaper_v1.pdf