Wind River Research Finds An Intelligent Systems Approach On Track To Becoming a Predominant Business Model

• 62% of tech leaders say their organizations have embarked on a journey to become intelligent systems companies; 16% of these leaders stand to realize four times higher ROI than their peers.
• Blueprints for seven industries show which characteristics are most critical and the optimal timelines for investing in each.
• The characteristics identified as key for initial infrastructure and/or foundational needs were far edge compute capabilities and a common workflow platform across an intelligent system’s entire lifecycle.

Wind River, a global leader in delivering software for intelligent systems, has issued new research, “13 Characteristics of an Intelligent Systems Future,” which examines the technology characteristics roadmap for a mission-critical intelligent systems world. The study predicts that success depends on properly timing the implementation of 13 key intelligent system characteristics that support a rapidly evolving intelligent systems machine economy.

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“Wind River is at the forefront of helping companies address the complexities surrounding the secure development, deployment, operations, and servicing of mission-critical intelligent systems for the next generation.”

An Intelligent Systems Future

By 2030, $7 trillion of the U.S. economy will be driven by the machine economy, in which systems and business models increasingly engage in unlocking the power of data and new technology platforms. Intelligent systems are helping to drive the machine economy and more fully realize IoT. Further fueling this future is the growth of 5G, AI, automation, and cloud native technologies, as well as an increasing intersection of IoT and the edge. This potent combination has opened up new possibilities for far edge applications such as robotics, drones, telemedicine, and autonomous vehicles. Realizing this promise requires a new approach to building systems that can compute, sense, learn, and adjust in near-latency-free real time on the edge. Executives across the automotive, energy and utilities, medical, telecommunications, industrial manufacturing, and aerospace and defense sectors share a common belief that this future is going to require a new blueprint.

The report surveyed technology executives across various mission-critical industries and revealed the 13 requirements of the intelligent systems world for which industry leaders must prepare. The research found that 80% of these technology leaders desire intelligent systems success within the next five years.

Success in this machine economy will be dictated by the technology approaches and capabilities that are built now. 62% of technology leaders are putting into place strategies to move to an intelligent systems future, and 16% are already committed, investing, and performing strongly. It’s estimated that this 16% could realize at least four times higher ROI than their peers who are equally committed but not organized for success in the same way. Additionally, those who are succeeding have prioritized overcoming challenges posed by skill shortages and ongoing cyberthreats, which were two of the most commonly cited barriers in the study.

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“In a growing machine economy driven by intelligent systems, humans and machines are on a path to a future where data will exponentially increase the volume, type, and quality of work that is possible, across all industries. These new use cases will demand more complex computing workloads, data, and analytics, and often that must happen in real time,” said Michael Gale, chief marketing officer at Wind River. “Wind River is at the forefront of helping companies address the complexities surrounding the secure development, deployment, operations, and servicing of mission-critical intelligent systems for the next generation.”

Blueprints Leverage 13 Key Intelligent Systems Characteristics

The research surfaced 13 key characteristics that are critical markers for successful intelligent systems. It also highlighted the importance of investing the right elements at the right time, or “blueprinting.” In the case of building intelligent systems, three concepts are key: prepare the right infrastructure first, next work on foundational needs, and then address longer-term capabilities.

The 13 key characteristics are:

• Ability to simulate and emulate in near real time
• Automated learning and machine learning functionalities
• Digital feedback loops that influence product development
• Action based on sensory data and algorithms
• Customized device experience in the cloud
• True compute on the far edge
• Adapting tasks based on reprogramming via cloud
• Ability to predict stresses and failures
• Detection and resolution of events
• Total automation
• Near-real-time, seamless connections across multiple ecosystems
• Real-time collaborative workflow platform
• Experimenting as a learning system

The study also indicates that just four of the 13 characteristics create the most possible impact: true compute on the edge, a common workflow platform, AL/ML capabilities, and ecosystems of real-time applications. These characteristics are core needs on which longer-term success is dependent. Without these, it would not be possible to build all the remaining characteristics and achieve success.

Insights by Industry

The report also highlights key findings by vertical industry, providing a blueprint that isolates the key characteristics most critical to that market and pinpoints when to invest in them. Input was sourced from companies in the aerospace and defense, automotive, energy and utilities, industrial, medical, and telecommunications markets. Leaders in these markets shared their perspectives in the following areas:

• Key components for mission-critical success in intelligent systems
• Barriers and drivers in the adoption of intelligent systems
• Factors that would accelerate the adoption of intelligent systems in each sector
• The importance of each intelligent systems characteristic as it pertains to investments
• The importance of 5G, AL, ML, and cybersecurity in their decision-making
• What the future of embedded devices and solutions looks like in an intelligent systems world
• Where digital feedback loops are most crucial for success
• Which key metrics define success
• Where intelligent systems could have extensive value in addressing wider societal issues

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