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Game Changers—Technologies Poised to Disrupt Industries : AI, General-purpose Autonomous Robotics, 3D Printing, Drones, and the Blockchain will Reshape Vertical Markets

  • March 2015
  • 105 pages
  • Frost & Sullivan
Report ID: 2768519

Summary

Table of Contents

Key Findings

•Technological progress is not linear, and when thinking about the future, humans tend to use the past as a guide—this is a mistake. Exponential growth over the next x years will drive technological advancement faster than at any other time in history.
•General-purpose autonomous robotics, artificial intelligence (AI), the Blockchain, 3D printing, and drones will be the most disruptive technologies over the next 10 years, whilst augmented reality (AR) and virtual reality (VR) will revolutionize the way people interact with the digital and physical worlds.
•On a macro-level, these technologies will reduce the production and distribution costs for all businesses, similar to how the Internet reduced the cost of communication and distribution of digital goods to near-zero. 3D printing, the Blockchain, AI, general-purpose autonomous robotics, and drones will do the same for physical goods.
•This change has implications for any organization with a business model predicated on generating revenue from the production or distribution of physical and digital products.
•Incumbents will use these technologies to reduce costs and offer new services. New market entrants will compete with incumbents by using new business models only possible through the use of these new technologies.
•Legislation and powerful value chains will slow disruption in some industries, but over the next 10 years, agriculture, education, energy, financial services, healthcare, manufacturing, retail and hospitality, and transportation will all be fundamentally reshaped.

Identification of Key Technologies

•The ICT team brainstormed to produce a list of all ICT trends.
•This list was refined and x technologies were consolidated based on applicability before 2025.
•Many vertical-specific technologies were omitted based on their domain specificity including: nuclear fission, stem cells, and synthetic biology.

Scoring Technology Trends by Disruptive Potential

•Secondary research was conducted to score each of the 30 trends, 1 to 5, by its disruptive impact on 8 vertical markets by 2025.
•Disruptive impact reflects the technology’s impact on the existing value chain and its ability to reduce costs beyond incremental improvements as well as the ability of the new technology to enable new service offerings by existing participants or new entrants.
•In addition, each technology was scored 1 to 5 by its current diffusion in 2015. This criteria was used to give less weight to technologies that are already diffusing through industry.
•Another criteria ‘market adoption by 2025’ provided a lower weighting for technologies that are unlikely to be disruptive in the next 10 years.

Choosing Game Changing Technologies

•The ICT team decided to explore the top 5 disruptive technologies as identified by the research.
•However, due to the extensive coverage of the Internet of Things by existing Frost & Sullivan publications, the 6th most disruptive technology is used instead: drones.
•The following technologies are explored in greater depth:
oGeneral-purpose autonomous robotics
oAI
oThe Blockchain
o3D printing
oDrones
•In addition, this study offers a close took at augmented reality and virtual reality. Whilst not game changing, these technologies will be extremely important in industries that will not be completely disrupted by AI and general-purpose autonomous robotics. Further, these technologies will augment and advance human capabilities providing valuable enhancements to human capabilities and enabling them to compete with AI/robotics.

General-purpose Autonomous Robotics

•General-purpose autonomous robotics describes a machine that is the mechanical manifestation of advanced AI.
•These new versions of robots are only possible with improved machine-learning techniques. Existing robots lack basic sensorimotor skills and the ability to navigate the environment. They are therefore typically static and focus on single-tasks (e.g., ATMs or industrial robots).
•The application of machine-learning techniques such as deep neural networks and reinforcement learning enable robots to learn from experience. They can make decisions in real-time without having been programmed by a human engineer in advance.
•Machine learning algorithms are applied to computer vision and natural language processing imbuing robots with the ability to more effectively navigate the real world and converse with humans.
•All of these software advances will be combined with the continued advances in processing speeds, storage capacity, and data storage allowing on-board intelligence as well as access to vast amounts of combined cloud intelligence.
•Post 2020, materials such as graphene and carbon nanotubes, nano-solar cell coatings, and electronic fabrics will allow robots to be self-powering, flexible, and, in some instances, self-healing.
•Robots will continue to replace low-skill work, but the real disruption will be the combination of low costs, AI, and advanced materials that move robots into high-skill human environments.

Progress
Advances in elastic actuators allow robots to generate smooth movements.
Rethink Robotics has commercialized Baxter, the first machine-learning robot.
Google and others have tested and proved viability of fully autonomous vehicles.

Challenges
Huge loss of low-skill and high-skill jobs will come from widespread adoption of robots.
Developing countries will not be able to benefit from lower wages to attract manufacturing jobs.
Requirements for robotic OSs will standardize developments and ensure interoperability.

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