Gartner 2020 Hype Cycle for Emerging Technologies. What’s in It for AI Leaders?
Gartner’s 2020 Hype Cycle for Emerging Technologies is out, so it is a good moment to take a deep look at the report and reflect on our AI strategy as a company. You can find a brief summary of the complete report here.
It has been already a year since I published a similar article on the same Gartner’s report for 2019 that you can find here. Which AI related technologies have been excluded from the report? Which ones should be, according to Gartner, focus areas for companies AI leaders?
First, a quick reminder of an important background to understand how Gartner’s Hype Cycles are presented. As Gartner explains in its research, its Hype Cycle covers a very broad spectrum of topics, so if a specific technology is not featured it does not necessarily imply that they are not important, quite the opposite. One reason for some technologies to disappear from the Hype Cycle might be that they are no longer “emerging” but key for business and IT. Please also note that Gartner mentions explicitly that given the vast amount of emerging technologies to be covered, some of them are going to be covered in dedicated Hype Cycles.
So let’s start by actually reviewing foundational AI related technologies that have been excluded from this year’s report:
- Augmented Intelligence
- Edge AI
- AI Cloud Services
- Emotion AI
- Transfer Learning
Both Augmented Intelligence and Edge AI where considered focus areas for companies AI leaders in 2019, while Emotion AI and Transfer Learning were considered new emerging AI technologies last year.
Which ones should be, according to Gartner, the focus areas for companies AI leaders? Based on its Priority Matrix for Emerging Technologies, 2020, those should be:
- Composite AI. Described as combining different techniques of Artificial Intelligence to improve the way it learns (Gartner actually defines it in a way that can Composite AI can be viewed as the path of AI to achieve “common sense” and to become AGI — Artificial General Intelligence).
- Generative AI. Described as a subset of algorithms that can generate new content (e.g. text, images) that preserve some relationship to previous learnt representations of training data. These subset of algorithms have become extremely popular recently due to Open AI’s GPT-3.
Finally, which are the new emerging technologies related to AI in 2020 Hype Cycle:
- AI-Augmented Design. Defined as using Artificial Intelligence to aid the process of creating or updating user workflows, interfaces, and contents.
- Self-Supervised Learning. Defined as a special case of Unsupervised Learning in which the algorithm creates its own labeled data. This subset of Machine Learning algorithms can prove very useful in those scenarios in which accessing large amounts of labeled data is not possible.
- AI-Augmented Development. Defined as using Artificial Intelligence to aid the process of delivering software applications faster and with better quality.
Gartner’s 2020 Hype Cycle also includes Adaptive ML and Responsible AI as emerging technologies. They are not covered in this article as they were also included in the 2019 edition. You can find more details about them in my last year summary.
Finally, it is interesting to note that Gartner’s groups the 2020 emerging technologies into five clear trends, two of them being directly related to AI:
- Algorithmic Trust, including set of technologies that must ensure the privacy and security of data being used for training the algorithms, as well as technologies such as Responsible and Explainable AI.
- Formative Artificial Intelligence (AI). Gartner describes this trend as a set of emerging AI technologies capable of responding to variances in the situational context, enabling the creation of tools to aid in the process of design, software development, or even AI models that can adapt over time (or even create their own new models to solve newly faced problems).
A third trend, which Gartner calls Composite Architecture, it also highly linked to AI. Gartner’s definition of a Composite Architecture is a set of software components that deliver business capabilities in a way that it is design to rapidly adapt to business changes and needs. Gartner explicitly mentions Embedded AI as one of the technologies that should be included in this kind of Enterprise Architectures.
As a recap, and based on my personal experience, AI leaders should:
- Embrace AI not as an innovation initiative, but as part of the core strategy for the company. I also mentioned this one last year, and I think there is still a lot to do in this specific area. Companies should be moving from lab PoCs to scalable AI approaches, including how to organize AI talent, apply methodology and implement common platforms across the company. A good example of this is the adoption of AI Cloud Services. While Gartner is not considering it emerging anymore in its 2020 Hype Cycle, a lot of companies still lack a clear strategy and adoption of AI solutions in the Cloud.
- Consider how AI and Composite Architectures can work together to help their companies solve business challenges. And this is a two way-challenge: Composite Architectures need AI to better adapt to business changes, but also AI needs to be integrated in a well-designed architecture (including development and deployment tools, technical and functional monitoring, etc.) to scale.
- Finally, focus on the human side of the technology. From how to make our digital talent to benefit from automation, to considering AI impact on their employee’s jobs, customer experience, and society in general. I kept this paragraph exactly as the one I wrote last year, as I think the level of maturity towards this specific challenge in big organizations is still low.
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