Gartner’s Top 10 Strategic Technology Trends for 2018
Each year, Gartner’s top 10 technology trends for the year ahead are an interesting guideline. A couple of weeks ago, at the Gartner Symposium in Orlando, Gartner Fellow David Cearley presented the “strategic” technology trends for the upcoming year. Gartner defines “strategic” as the technologies that have the potential to be significantly disruptive in the next five years, with a focus on budgeting as well.
Cearley underlined the interest in a number of intelligent services, the need for a mesh, how digital information is more and more needed to connect to the real world and data.
Here’s a summary of the discussed trends:
Intelligent
1. AI Foundation
AI has the potential to enhance decision making, reinvent models and ecosystems and remake the customer experience. These are all facts that businesses have already taken notice of. A recent Gartner survey showed that 59% of companies are still in the process of gathering information to build AI strategies, while the rest are already trialling/adopting AI programs.
Big digital business payoffs will result is AI is used correctly, however the promise that AI perform any intellectual task that humans can do is completely speculative. AI techniques are quickly evolving and it’s recommended that businesses focus on narrow AI. This is an AI consisting of highly scoped machine-learning solutions that focus on a specific task (e.g. driving vehicles in controlled environments, etc.).
Gartner estimates that 30% of CIOs will include AI into their top 5 investment priorities by 2020. It appears that now is the time to invest in integration, data preparation, algorithm and training methodology selection and model creation.
2. Intelligent Apps and Analytics
AI is expected to be incorporated at some level in every app, service and application over the next few years. Gartner underlines augmented analytics, which automates data preparation by using machine learning, insight sharing and insight discovery as areas of strategic importance. AI is now the major battleground in a very wide range of service markets and software, including areas of ERP.
Cearley states “Challenge your packaged software and service providers to outline how they’ll be using AI to add business value in new versions in the form of advanced analytics, intelligent processes and advanced user experiences.”
A new intelligent intermediary layer between systems and people is created by intelligent apps. It has the potential to change the nature of work and also the structure of the workplace (e.g. enterprise advisors and assistants, virtual customer assistants)
Cearley said “Explore intelligent apps as a way of augmenting human activity, and not simply as a way of replacing people”. A particular strategic growing area is augmented analytics, it used machine learning for automating data preparation, insight sharing and insight discovery for a wide range of business users, operational workers and citizen data scientists.
3. Intelligent Things
AI and machine learning are used by Intelligent things to interact in a smarter way with surroundings and people. As the IoT grows to include an ever-growing number of things, AI and machine learning will help these operate independently or semi-independently. There are some intelligent things that already exist (e.g. a camera) which AI will make intelligent (e.g. smart camera) and there are some intelligent things that wouldn’t exist without AI.
As intelligent things multiply, you should expect to see a shift from solitary intelligent things to a horde of collaborative intelligent things. In this model, several devices will work together, either with human input or independently. The area in which it’s used most currently is the military, which is researching and studying the use of drone swarms to either defend or attack targets.
Digital
4. Digital Twin
Digital Twins are digital representations of real-world systems that offer information/data on the status of their real-world twin. Cearley said though, that it isn’t the only function of the model that counts, but also how it drives business value through operation, observation and optimization. He gave an example of GE monitoring aircraft engines, some airlines reporting reducing downtime by 30%-50%.
Furthermore, he also talked about digital twins in use for optimization on a wind farm, where they are used to determine how best to adjust blade orientation.
While most examples of digital twins exist mainly in the IoT space, there is a growing potential for them to exist for objects that aren’t smart objects (e.g. a digital twin for a human that offers medical data to doctors, etc.).
5. Cloud to the Edge
Edge computing is a computing topology in which data processing and content collection/delivery are placed closer to sources of this information. Latency and connectivity challenges, bandwidth limits and greater functionality embedded at the edge favours distributed models. Edge design patterns should start being used by enterprises in their infrastructure architectures- especially for those with significant IoT elements. Colocation and edge-specific networking capabilities could be a good starting point.
A trend in this area is using cloud as a point of coordination or control for the edge. Some good examples of this would be Office 365 which is managed in the cloud but has traditional desktop Office software on the edge. Cearley expects to see more of a balance in the long-run.
6. Conversational Platforms
Conversational platforms are changing the way people interact with the digital world. According to Cearley, the important thing here is “flipping the paradigm” so instead of people having to adapt to technology, technology adapts to people. So rather than a person having to learn the interface, conversational platforms will enable users to relay their intent using natural language. Conversational interfaces are thought to become the primary design goal for user interaction over the next few years, and may also become the primary way users communicate with the online sphere.
7. Immersive Experiences
By 2020 Gartner expects that the market for head-mounted augmented and virtual reality devices will generate $75 billion revenue and exceed 35 million devices. VR places the user in a digitally created environment and AR overlays digital information on the real world, the both of them are erasing the boundaries between the physical and digital realm. Cearley discussed the concept of “mixed reality”, a combination and extension of AR and VR. He suggested that from now until 2022 companies should focus on more specific solutions and that more all-encompassing platforms should result after that.
Mesh
8. Blockchain
The blockchain is a shared and distributed mesh, which usurps business friction due to it being independent of singular applications and participants. It’s mainly been used up to now just by financial services and government. Cearly said that supply change management will make up 40% of the total value of blockchain. Gartner predicts that by 2022, 30% of higher education bodies will leverage blockchain for academic credentials. However, Cearly has admitted that there are barriers to the broader use of blockchain.
9. Event-Driven Model
Digital business will be driven by digital business moments. These are a mixture of business events that reflect the discovery of notable states or state changes. A simple example of this would be a signal that a PO has been completed, but as IoT and other tech emerges, complex events will be detected faster and analysed in greater detail. Cearley said that by 2020, participation in 80% of new business environments will need support for event processing. He also discussed about changes in the model, like serverless development, and said that both structured and event-based applications will be around for a while.
10. Continuous Adaptive Risk & Trust
Threats and threat protection evolves and thus the security world keeps changing. Continuous adaptive risk and trust assessment (CARTA) to security-enables digital business through real-time, risk and trust-based decision making with adaptive responses. In order for CARTA to become a reality, businesses need to explore integrating security into their DevOps to deliver a constant SevSecOps process and explore deception technologies.