01
Sixty-eight percent of leaders land in the mainstream-to-positive range concerning digital transformation; only 9% see themselves as already fully moved to an intelligent systems future.
Digital transformation has reached a mainstream or better state inside these organizations. The ideas and practices behind intelligent systems are at the same stage digital transformation was in by 2015. Current giants of digital business models—Amazon, Google, Netflix, Facebook—have grown by at least two and half times in the six years since then because the world has evolved to where they were going. In other words, in a mere half dozen years, companies committed to a dominant new business model were significantly rewarded in terms of size, growth and profitability. A commitment to intelligent systems will show similar rewards by 2030. The patterns are being set now.
02
This is the next wave of the digital economy. Already, 62% of leaders are moving toward their own intelligent systems futures.
9%
of leaders and executives see their organizations as fully moved to an intelligent systems digital business model.
of leaders and executives are just talking about the idea of intelligent systems success in the next five years.
38%
53%
of leaders and executives see their organizations putting strategy, experiments and plans into place right now for intelligent systems success in the next five years.
Fewer than 3% of executives and leaders believe that one or some of these 13 meta-trends are not important or relevant. Ideas about AI and machine learning, the power of real-time data, agile methodologies, 5G, connected systems, cybersecurity in the cloud, cloud-based design and the mission-critical importance of embedded devices have all permeated the decision-making process.
03
13 interwoven meta-trends form the components of intelligent systems.
Behind Peers
In Line With Peers
Ahead of Most Peers
Not applicable to our business right now
17%
25%
2%
55%
17%
26%
2%
55%
18%
27%
1%
54%
18%
27%
2%
53%
19%
27%
3%
52%
19%
27%
3%
51%
19%
31%
1%
49%
22%
27%
2%
49%
23%
28%
1%
49%
21%
30%
1%
48%
21%
30%
3%
47%
20%
31%
2%
46%
22%
29%
3%
46%
The idea that AI and machine learning are or will be critical for future success
The idea that using real-time data to drive decision-making will be key for success in our products and services
The idea that agile versus waterfall development ideas are critical for how we will develop and deploy embedded devices and services in the near future
The ability to deliver services and products inside other business sectors we have not worked in traditionally
The idea that embedded devices are mission critical for the future of our company
The idea that all the systems from customer to supplier, product and services used are highly connected in real time
The idea that the intelligent edge and 5G is the future
The idea that cloud-based design and delivery is the dominant way our business will work moving forward
The idea that business models are at
our core
Cybersecurity in our products and services are critical as building blocks for all business success
The ideas that real-time digital and data feedback loops will drive our ongoing decision-making
The idea that our embedded systems can run much of themselves autonomously
The idea that companies are increasingly becoming software companies at their core
Only 12% of these leaders believe that technology leads their overall strategy. Yet 61% of them are leaning into the processes to build intelligent systems for themselves. This is a business imperative and shows an awareness of meta-trends. Software-led enterprises, the edge and data-centric decision-making are driving leaders’ desire to push their organizations to become intelligent systems companies.
04
There is a clear business imperative served by the idea of intelligent systems technologies.
Technology leads strategy
Building intelligent systems
12%
61%
When these executives and leaders talk to us about where they are and where the potential could lie for intelligent systems inside their industry, we see that there are different patterns. But leaders in up to 27% of sectors (such as manufacturing) and 10% of industries (such as energy) believe they will be following a predominantly intelligent system business model. Using current (2020) GDP contributions for these sectors, we see that a combined $1.1T+ of GDP in these sectors could be affected by intelligent systems.
05
The depth of buy-in and actions varies by industry, but the race is on now, with $1.1T in play in the six industries we researched.
Aerospace & Defense
27%
6%
Could be
the most dominant business model
Some core IS changes already
13%
19%
Automotive
Energy
Manufacturing
Medical Technologies
Telecom & Technology
17%
10%
19%
27%
14%
22%
32%
20%
After using discrete choice models to simulate 4,000 possible combinations of characteristics for intelligent systems success, we were able to rank what matters (priorities) and when it matters in terms of timing (now, the near and/or further future state). Forbes researched the opinions of 506 executives and leaders in the following industries: automotive, medical technologies, aerospace and defense, energy and utilities, telecommunications, technology hardware and industrial manufacturing. In six of the seven industries, the ability to compute at the far edge is the No. 1 priority. Their systems would be sensing, predicting, computing, creating and connecting using digital feedback loops to run autonomously or with machine learning and automation. The capacity to do this 24/7/365 on the far edge will be essential for the machine economy that will be working in the same 24/7/365 ways in thousands or maybe millions of different ways.
06
4,000 simulations of the 13 characteristics show the power of one particular characteristic:
true compute on the far edge.
If this transformation were easy, every organization would be there now. But currently, 57% of leaders believe that integrating the necessary workflows will be challenging; 37% believe that needed skill sets and common approaches are not fully available. Thirty-six percent believe that their industries are so highly regulated that adoption of intelligent systems is restricted. Only 15% believe that their industry will not easily embrace moving business to the cloud from the intelligent edge and devices to the customer.
07
Barriers and drivers to success are at times obvious, and some are very human.
57%
37%
36%
15%
Workflow integration is a challenge
Skills and frameworks are lacking
Industry regulations are restrictive
The industry does not embrace the concept of moving business to the cloud and devices to the customer on the edge
08
Mission-critical capabilities dominate the pathway for success.
Mission criticality is defined in a number of ways, from certification of codes to safe, secure, cyber-protected and near-latency-free compute times. One in 5.5 of the embedded devices on that far edge will need to be mission-critical capable. 18% to 24% of all embedded products and services will need to be mission-critical capable across seven dimensions for success.
Executives told us how they rank the 13 key characteristics in importance over the next three to five years. Viewing the ranking, we see that 18% of their focus is on infrastructure needed now, 41% is on foundational elements that build for longer-term success and 22% is on characteristics they will need in five years’ time. The rest of the focus is on nice-to-haves, not essentials.
09
Building a foundation is an absolutely vital steppingstone for measurable success.
Two of the three tier-one characteristics from the list of 13 are the ability to utilize real-time ecosystems of applications and AL/ML learning. In effect, embedded devices and the digital feedback loops they are creating or interacting through in near-latency-free time can be infused with AI/ML as well as a wider array of application capabilities. We might call this a sign of true intelligent systems.
10
In five years’ time, two of the most important characteristics overall will come into play.
Each industrial sector places a different emphasis on each of the 13 intelligent-systems characteristics in order to build a solid infrastructure, lay a foundation for growth and ultimately show growth by the end of five years’ time.
11
Industry-specific road maps are important
for success.
When we modeled commitment to the idea of intelligent systems plus a clear acceptance that a level of highly authentic reengineering of certain business processes is required, and then correlated that across 15 ROI metrics models, we found that only 16% of leaders interviewed are committed and succeeding. They outperform their committed peers by 4:1 across the combined metrics.
To see why they are succeeding in the creation of their own intelligent systems future, we defined 200 distinctive flags that highlight the way they think, design, measure and orchestrate each of the characteristics for intelligent systems success.
12
Your next steps will drive where you end up. Can you be part of the 16% who are committed and succeeding?
Ability to predict stresses/failures
Detection of events/resolution
Automates basic or even comples tasks
Near real-time seamless multiple ecosystems connections
Real-time collaborative workflow platform
Experiments as
a learning system
Adapts tasks based on reprograming via cloud
True compute on the far edge
Customized device experience in the cloud
Digital feedback loops into product development
Automated learning ML functionality
Simulate and emulate in near real time
True compute on the far edge
Acts based on sensory data and algorithms
18%
Nascent And Succeeding
23%
Experimenting And Succeeding
16%
Committed And Succeeding
11%
Nascent And Unsure
22%
Experimenting And Stalling
10%
Committed And Stalling
LOW
HIGH
HIGH
Level of intelligent systems commitment
Level of ROI from intelligent systems state
4
2
3
Intelligent Systems Overview
Aerospace & Defense
Automotive
Energy & Utilities
Manufacturing
Medical
Telecommunications
To learn more about how to become a leader in intelligent systems, download the overview and industry-specific booklets below: