Explore the latest: Your Detailed Guide to Gartner Top 10 Strategic Technology Trends 2023
How do designers make cars safer? They treat them like a school of fish. Safe Swarm, recently unveiled by Honda, uses vehicle-to-vehicle communication to allow cars to pass information on to other cars in the vicinity. For example, alerts about an accident miles up the road could be relayed to cars miles back, enabling them to operate collaboratively and intelligently to avoid accidents and mitigate traffic.
The evolution of intelligent things, such as collective thinking car swarms, is one of 10 strategic trends with broad industry impact and significant potential for disruption.
“The continuing digital business evolution exploits new digital models to align more closely the physical and digital worlds for employees, partners and customers,” says David Cearley, vice president and Gartner Fellow, at Gartner 2017 Symposium/ITxpo in Orlando, Florida. “Technology will be embedded in everything in the digital business of the future.”
The intelligent digital mesh
Gartner calls the entwining of people, devices, content and services the intelligent digital mesh. It's enabled by digital models, business platforms and a rich, intelligent set of services to support digital business.
Intelligent: How AI is seeping into virtually every technology and with a defined, well-scoped focus can allow more dynamic, flexible and potentially autonomous systems.
Digital: Blending the virtual and real worlds to create an immersive digitally enhanced and connected environment.
Mesh: The connections between an expanding set of people, business, devices, content and services to deliver digital outcomes.
Trend No. 1: AI foundation
The ability to use AI to enhance decision making, reinvent business models and ecosystems, and remake the customer experience will drive the payoff for digital initiatives through 2025.
Given the steady increase in inquiry calls, it’s clear that interest is growing. A recent Gartner survey showed that 59% of organizations are still gathering information to build their AI strategies, while the remainder have already made progress in piloting or adopting AI solutions.
Although using AI correctly will result in a big digital business payoff, the promise (and pitfalls) of general AI where systems magically perform any intellectual task that a human can do and dynamically learn much as humans do is speculative at best. Narrow AI, consisting of highly scoped machine-learning solutions that target a specific task (such as understanding language or driving a vehicle in a controlled environment) with algorithms chosen that are optimized for that task, is where the action is today. “Enterprises should focus on business results enabled by applications that exploit narrow AI technologies and leave general AI to the researchers and science fiction writers,” says Cearley.
Trend No. 2: Intelligent apps and analytics
Over the next few years every app, application and service will incorporate AI at some level. AI will run unobtrusively in the background of many familiar application categories while giving rise to entirely new ones. AI has become the next major battleground in a wide range of software and service markets, including aspects of ERP. “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,” notes Cearley.
Intelligent apps also create a new intelligent intermediary layer between people and systems and have the potential to transform the nature of work and the structure of the workplace, as seen in virtual customer assistants and enterprise advisors and assistants.
“Explore intelligent apps as a way of augmenting human activity, and not simply as a way of replacing people,” says Cearley. Augmented analytics is a particularly strategic growing area that uses machine learning for automating data preparation, insight discovery and insight sharing for a broad range of business users, operational workers and citizen data scientists.
Trend No. 3: Intelligent things
Intelligent things use AI and machine learning to interact in a more intelligent way with people and surroundings. Some intelligent things wouldn’t exist without AI, but others are existing things (i.e., a camera) that AI makes intelligent (i.e., a smart camera.) These things operate semiautonomously or autonomously in an unsupervised environment for a set amount of time to complete a particular task. Examples include a self-directing vacuum or autonomous farming vehicle. As the technology develops, AI and machine learning will increasingly appear in a variety of objects ranging from smart healthcare equipment to autonomous harvesting robots for farms.
As intelligent things proliferate, expect a shift from stand-alone intelligent things to a swarm of collaborative intelligent things. In this model, multiple devices will work together, either independently or with human input. The leading edge of this area is being used by the military, which is studying the use of drone swarms to attack or defend military targets. It’s evident in the consumer world in the opening example showcased at CES, the consumer electronics event.
Trend No. 4: Digital twins
A digital twin is a digital representation of a real-world entity or system. In the context of IoT, digital twins are linked to real-world objects and offer information on the state of the counterparts, respond to changes, improve operations and add value. With an estimated 21 billion connected sensors and endpoints by 2020, digital twins will exist for billions of things in the near future. Potentially billions of dollars of savings in maintenance repair and operation (MRO) and optimized IoT asset performance are on the table, says Cearley.
In the short term, digital twins offer help with asset management, but will eventually offer value in operational efficiency and insights into how products are used and how they can be improved.
Outside of the IoT, there is a growing potential to link digital twins to entities that are not simply “things.” “Over time, digital representations of virtually every aspect of our world will be connected dynamically with their real-world counterparts and with one another and infused with AI-based capabilities to enable advanced simulation, operation and analysis,” says Cearley. “City planners, digital marketers, healthcare professionals and industrial planners will all benefit from this long-term shift to the integrated digital twin world.” For example, future models of humans could offer biometric and medical data, and digital twins for entire cities will allow for advanced simulations.
Trend No. 5: Cloud to the edge
Edge computing describes a computing topology in which information processing and content collection and delivery are placed closer to the sources of this information. Connectivity and latency challenges, bandwidth constraints and greater functionality embedded at the edge favors distributed models. Enterprises should begin using edge design patterns in their infrastructure architectures — particularly for those with significant IoT elements. A good starting point could be using colocation and edge-specific networking capabilities.
While it’s common to assume that cloud and edge computing are competing approaches, it’s a fundamental misunderstanding of the concepts. Edge computing speaks to a computing topology that places content, computing and processing closer to the user/things or “edge” of the networking. Cloud is a system where technology services are delivered using internet technologies, but it does not dictate centralized or decentralized service delivering services. When implemented together, cloud is used to create the service-oriented model and edge computing offers a delivery style that allows for executions of disconnected aspects of cloud service.