INDEX

  • Page numbers followed by f refer to figures.
  • A
  • Accenture, 93
  • Accountability:
    • in human‐centered transformation, 170
    • for return on investments, 151
    • tied to system maintenance, 48
  • Ackoff, Russell, 65–66, 81, 155
  • Advanced Robotics for Manufacturing Institute, 9
  • Agent for change, storytelling as, 176–177, 177f
  • Agricultural science (agriscience), 62–63
  • Airline industry, 15–16, 48–49
  • AlphaGo, 116, 118–119, 199
  • Altitudes of inputs and outputs, 136–140, 137f, 139f, 204
  • Altruism, profit vs., 6–8, 8f
  • Amazon:
    • creation by, 48, 49
    • organizational culture of, 174
    • profitability of, 151
    • in Seattle, 130
    • written communications at, 176
  • Amazon Go, 16
  • Analog Transformation, 22
  • Analysis:
    • as automatic response, 39
    • data‐driven, 144
    • in diagnostic analytics, 39
    • by machines, 116
    • in statistics, 194
    • steps in, 81, 81f
    • in systems thinking, 81–83, 81f
    • in Taylorism, 38
  • Analytics, 192–194, 192f
  • Analytical Engine, 18
  • Apollo missions, 18, 76
  • Appeal to authority fallacy, 75, 116–118
  • Apple:
    • creation by, 48
    • iPhone, 18, 74, 163
    • Jobs' leadership of, 75
    • systemic orchestration by, 163
  • Apple Watch, 163
  • Aristotle, 144
  • Artificial general intelligence, 38, 117–118
  • Artificial intelligence, 1, 191–206
    • analytics in, 192–194, 192f
    • automation vs., 201–202
    • and automation vs. autonomy, 202–203
    • autonomous, 192f, 200
    • in Autonomous Transformation, 183
    • being human in era of, 25–32
    • and capability of humans vs. machines, 28–31, 29f
    • clearing fog around, 131
    • components of, 191–192, 192f
    • concerns about, 191
    • connected circles of, 89–93, 90f–92f
    • and consciousness, 31–32
    • data science in, 192f, 195–196
    • decision science in, 192f, 196–197
    • deep learning in, 192f, 194
    • definitions of, 201
    • evaluating person's expertise in, 203–204
    • generative, 197–198
    • in global logistics, 187
    • Hawking on, 116–118
    • in health care, 188
    • machine learning in, 192f, 194, 197
    • machine teaching in, 200–201
    • in a more human future, 4. See also specific topics
    • in product development, 185
    • quiz about, 204–206
    • reinforcement learning in, 192f, 194, 198–199
    • return on investment in, 38
    • uncertainty in era of, 27–28
  • Artists:
    • images generated by, 198
    • paradigm shift for, 54
  • Athinarayanan, Ragu, 77
  • Augmented reality, see Virtual and augmented (mixed) reality
  • Automation:
    • artificial intelligence vs., 201–202
    • autonomy vs., 202–203
    • decision tree–based, 201, 202
    • defined, 202
    • reduction of jobs due to, 20
  • Automobile industry, 87–88
  • Autonomous (term), 13
  • Autonomous artificial intelligence, 192f, 200
  • Autonomous Reformation:
    • at Bell Flight, 16
    • defined, 15
    • in Transformation and Reformation Matrix, 15f, 16
  • Autonomous technologies, 14, 16
  • Autonomous Transformation, 4f, 13–24
    • in creating a more human future, 1–9, 4f
    • and cycles of reformation, creation, and transformation, 18–19. See also Cycles of reformation, transformation, and creation
    • defined, 13
    • Digital Transformation in, 14–16, 15f
    • examples of, 16–18
    • human‐centered, 4f, 14, 170–171, 171f
    • opportunity presented by, 74
    • process of, 4f. See also individual steps
    • Profitable Good in, 5–9
    • for social purpose corporations, 9
    • in Transformation and Reformation Matrix, 15f, 16
  • Autonomous Transformation technologies, 181
    • artificial intelligence, 191–206. See also Artificial intelligence
    • and blockchain, 179–180
    • for global logistics, 186–187
    • for health care, 187–189
    • leader's guide to, 183–190
    • for product development, 184–186
  • Autonomy:
    • automation vs., 202–203
    • human and machine, 13
    • human connection vs., 21
  • B
  • Babbage, Charles, 18
  • Bach, Johann Sebastian, 53–54
  • Banking industry:
    • analysis and synthesis in, 82–83
    • machine capabilities in, 28
  • BBC (British Broadcasting Corporation), 116–118
  • Beckett, Samuel, 165
  • Behavioral sciences, 192f, 196–197
  • Being human. See also Human(s)
    • in era of artificial intelligence, 25–32
    • meaning of, 25–26
  • Bell Flight, 16
  • Bell Labs, 57–58, 105
  • “The bench,” 106–107
  • Benz, Carl, 88
  • Bias:
  • Bing, 152
  • Blockbuster, 49, 50, 155, 156
  • Blockchain, 179–180
  • Boeing, 130
  • Bons.ai, 68
  • Boorstin, Daniel J., 113
  • British Broadcasting Corporation (BBC), 116–118
  • Broken systems, 103–112
    • and default problem solving process, 103–105
    • reimagining product teams in internal technology organizations, 110–112
    • reimagining value creation in internal technology organizations, 105–109
    • using Hedberg strategy with, 103
  • Bughouse chess, 160–161
  • Buonarroti, Michelangelo, 118
  • Business function, 123–124, 125f
  • Business unit leaders, 108, 109
  • Byrne, Jennifer, 50
  • C
  • Capability:
    • building‐vs.‐buying, 124
    • in data science, 196
    • of humans vs. machines, 28–31, 29f
    • linear vs. exponential value of, 152–154, 153f, 154f
  • Carbon emissions strategies, 147–148
  • Chaos, 115–118
  • ChatGPT, 29–30, 198
  • Chess, 85–86, 115–116, 160–161
  • Chicago World's Fair, 174
  • Children, teaching skills to, 197
  • Child labor, 36
  • China, 101
  • Chinese Room Argument, 31
  • Clear the digital fog, 4f, 113
    • chaos, noise, and logical fallacies, 115–118
    • and current degree of division, 123–130
    • epistemology in the digital age, 118–122, 119f–121f
    • by multiplying expertise, 131–140
  • Cloud providers, 195
  • Clover Imaging Group, 88–89
  • Command‐and‐control leadership, 96
  • Commission:
    • errors of, 156
    • reformational economics of, 155–158, 158f
  • Communication:
    • across generational or cultural lines, 176–177
    • altitudes of inputs and outputs in, 136–140, 137f, 139f
    • in logistics, 186
    • semantic satiation in, 131, 191
    • shared language for, 132–135, 132f
    • through storytelling, 173. See also Storytelling
    • of values and vision, 175–176
  • Computer programming, foundation for, 18
  • Computers:
  • Computing:
    • foundation for, 18
    • parallel, 199
  • Consciousness, 31–32, 115–116
  • Corteva Agriscience, 78
  • COVID‐19 pandemic, 69, 73, 80, 175, 177
  • Creating a more human future, 1–9, 4f, 165, 209–210
    • in Autonomous Transformation process, 4f
    • beyond pilots, 167–171, 171f
    • defining goals for, 2–3
    • with more human organizations, 179–180
    • Profitable Good in, 5–9
    • with storytelling, 173–177, 177f
    • through reformation, transformation, and creation, 21–22, 23f
  • Creation, 17–18
  • Customer service, 21
  • Cycles of reformation, transformation, and creation, 18–24, 23f
    • creating more human future through, 21–23, 23f
    • examples of, 18–19
    • and human work experiences, 21
    • and machines taking over human jobs, 19–21
    • and survival paradigm, 23–24
  • D
  • Dali, Salvador, 198
  • DALL·E 2, 29–30, 198
  • Data‐driven decision making, 143–149
    • Aristotle's conundrum in, 144–145
    • justification matrix for, 145–146, 145f, 146f
    • moving to reason‐driven decision making from, 147–148, 149f
    • and organizational empiricism, 146–147
  • Data science, 192f, 195–196
    • defined, 195
    • elements of, 192f
    • pilots in, 167–168
  • Data Science Taylorism, 40–41, 168
  • Data scientists, 47, 195, 196, 201
  • Dearborn Independent, 88
  • Decentralized World Wide Web (Web 3.0), 189
  • Decision making:
    • acts of commission and omission in, 156–157
    • data‐driven, 143–149
    • organizational empiricism in, 144, 146–147
    • reason‐driven, 147–148, 149f
  • Decision science, 192f, 196–197
  • Decision tree–based automation, 201, 202
  • Deep Blue, 115–116, 118
  • Deep learning, 192f, 194, 203
  • DeepMind, 116, 118–119
  • Delacroix, Eugene, 211
  • Descartes, René, 31
  • Descriptive analytics, 192–193, 192f
  • Design for inevitability, 4f, 141
    • with ecosystem of partnerships, 159–163
    • and linear vs. exponential value, 151–154, 152f–154f
    • by moving from data‐driven to reason‐driven, 143–149
  • Diagnostic analytics, 39, 192f, 193
  • Diffusion technique, 198
  • Digital contracts, 61–62
  • Digital Darwinism, 23–24
  • Digital divide, 129–130
  • Digital Reformation, 14–16, 15f
    • in airline industry, 15–16
    • defined, 14
    • Digital Transformation vs., 14–16, 15f
    • in product development, 184
    • in Transformation and Reformation Matrix, 15f
  • Digital Transformation, 14–16, 15f
    • challenge throughout era of, 74
    • defined, 14
    • Digital Reformation vs., 14–16, 15f
    • in health care, 187
    • investments in, 77
    • in logistics, 186
    • at Netflix, 15
    • in product development, 184
    • in Transformation and Reformation Matrix, 15f
    • value of, 21–22
  • Digital twins/simulations:
    • in Autonomous Transformation, 4, 183
    • clearing fog around, 131
    • in global logistics, 186, 187
    • in health care, 17, 187–188
    • investment for initiatives focused on, 38
    • of machinery, 22
    • in product development, 184, 185
    • return on investment in, 38
  • Dimensions of systems, 78–80
  • Directional milestones, 75–76
  • Discover and rediscover, 4f, 71
    • broken systems, 103–112
    • organizational systems, 95–101
    • seeing subsets vs. whole systems, 85–94
    • systemic design and synthesis in, 71–83
  • Disneyland, 95
  • Division, 123–130
    • across technology consulting and technology industries, 125–129, 127f, 128f
    • within organizations, 123–124, 125f
    • between social and organizational systems, 35–36
    • societal and systemic, 129–130
  • Doing the wrong thing right, 65–66
  • Domain experts, 168–169, 201
  • Duarte, Nancy, 51, 177, 177f
  • Dycam, 49
  • E
  • Economic incentives, 120–122, 151
  • Economic Incentive Test, 126–129, 127f, 128f, 204
  • Ecosystem(s):
    • of analytics, 194
    • building an, 160–161
    • and designing for inevitability, 150
    • of industrial sectors, 100
    • maintaining vs. sustaining, 161–163
    • of partnerships, 159–163
    • and partnerships within organizations, 163
    • in reimagining internal technology organizations, 105–106
  • Edison, Thomas, 75, 101, 174
  • Educational system, 95
  • Eliot, T. S., 71
  • Elkind, Edith, 31
  • Emerging technological initiatives:
    • failure of, 39–40
    • investment in, 45, 73–74
    • lack of decision‐making data with, 144
  • Empiricism, 146
  • Environmental responsibility, 5–6
  • Envisioning your future, 4f, 51
    • developing skill of, 67–70
    • ecosystem dynamics in, 160. See also Ecosystem(s)
    • functional reimagining in, 68–69
    • future solving in, 57–66
    • multiverse reimagining in, 69–70
    • rehumanizing work in, 53–55
  • Epistemology:
    • defined, 122
    • in the digital age, 118–122, 119f–121f
  • European manufacturing, 101
  • Examining inbound information:
    • through economic incentive lens, 120–122
    • by zooming out to the whole picture, 118–122, 119f–121f
  • Executive Committee for Human‐Centered Transformation, 170–171, 171f
  • Expedia, 130
  • Experiments:
    • framing changes as, 175
    • riskless, pilots as, 168–169
  • Expertise:
    • and appeal to authority fallacy, 116–118
    • in artificial intelligence, evaluating, 204
    • “data science‐adjacent,” 196
    • division of, 123–124, 125f
    • domain, 168–169
    • multiplying, see Multiplying expertise
  • Exponential value, linear value vs., 151–154, 152f–154f
  • F
  • Failure:
    • of artificial intelligence and emerging technological initiatives, 39–40
    • outcome bias in face of, 157–158, 158f
    • of pilots, 167
    • types of, 157
  • Fear, 96
  • Fishing industry, 9
  • Ford, Henry, 44, 87–88, 101
  • Fordlandia, 88
  • Fosslien, Liz, 173, 175–177
  • Freeman, Brian, 78
  • Full artificial intelligence, 117–118
  • Functional reimagining, 68–69
  • Future:
  • Future solving, 57–66
    • in agriculture use case, 62–63
    • doing the wrong thing right in, 65–66
    • problem solving vs., 59–62, 60f
    • questions to ask in, 60
    • shared understanding in, 175
    • in Use Case Battleship, 64–65
  • G
  • General Electric, 195
  • Generation Z, 5
  • Generative artificial intelligence (generative AI), 197–198
  • Gigi's Playhouse, 89
  • Global logistics:
    • Autonomous Transformation technologies for, 186–187
    • blockchain in, 190
  • Goals:
    • changing means of moving toward, 74
    • for more human future, 2–3
  • Google:
    • artificial intelligence definition by, 201–202
    • creation by, 48
    • domain registered for, 123
    • in Seattle, 130
  • Greed, profit vs., 6–8, 8f
  • H
  • Hamilton, Alexander, 100–101
  • Hammond, Mark, 68
  • Harvard Business Review, 179–180
  • Hawking, Stephen, 116–118
  • Hayashi, Chikio, 195
  • Health care:
    • autonomous paradigm for, 16–17
    • Autonomous Transformation technologies for, 187–189
    • blockchain in, 190
  • Hedberg, Mitch, 103
  • Hedberg strategy, 103
  • “Hero's Journey,” 177
  • Hindsight bias, 152
  • The Honest Company, 6
  • Human(s):
    • capability of machines vs., 28–31, 29f
    • challenges inherited by, 35–41
    • connection needed by, 21
    • consciousness in, 31–32
    • directional milestones of, 75–76
    • meaning of being, 25–26
    • social nature of, 179
  • Human autonomy, 13
  • Human‐centered Autonomous Transformation, 4f, 14, 170–171, 171f
  • Human experience:
    • finding meaning in, 25–26
    • at work, 14, 21
  • Human future, 1–9
    • better and more human, 3–4, 4f, 209–210
    • created through reformation, transformation, and creation, 21–22, 23f. See also Creating a more human future
    • defining goals for, 2–3
    • foundation for creating, 26
    • Profitable Good in, 5–9
  • I
  • IBM, 115–116, 118
  • Image generation, 198
  • Imagination, as fundamental human characteristic, 29
  • Incentives:
    • in broken systems, 104
    • for consulting firms, 106
    • economic, 120–122, 151
    • in interorganizational situations, 98–99
    • tied to system maintenance, 48
  • Individual contributor impacts:
    • in maintenance mode, 50
    • on seeing subsets vs. whole systems, 93–94
  • Industrial Revolution:
    • breaking away from minutiae of, 209
    • challenges inherited from, 36–38
    • evolutionary trajectory of, 54
    • expansion of, 101
    • job protectionism following, 20
    • systems built during, 43–44
  • Industry 4.0, 54
  • Industry function, 123–124, 125f
  • Industry‐specific organizational systems, 99–101
  • Industry x.0, 37, 54
  • Information Age, 35
  • Information technology organizations:
    • in broken systems, 103–104
    • in manufacturing sector, 100
    • and pilots, 168
    • reimagining, 105–112
  • Inherited challenges, 35–41
    • from Data Science Taylorism, 40–41
    • from Industrial Revolution, 36–38
    • from Taylorism (scientific management), 38–40
  • Inputs, altitudes of, 136–140, 137f, 139f, 204
  • Interconnectedness, of system parts, 76–78
  • Internal technology organizations, 104–105
    • reimagining product teams in, 110–112
    • reimagining value creation in, 105–109
  • Internet of Things, 16
    • in Autonomous Transformation, 4, 183
    • clearing fog around, 131
    • in global logistics, 186
    • in health care, 188–189
    • investment for initiatives focused on, 38
    • in product development, 184, 185
  • Interorganizational systems, 98–99
  • Intraorganizational systems, 96–97
  • Investment(s):
    • in artificial intelligence, return on, 38
    • in Digital Transformation, 77
    • in emerging technological initiatives, 45, 73–74
    • path to return on, 151
    • short‐ and long‐term value of, 152–154, 153f, 154f
    • in technology pilots, 167
  • iPhone, 18, 74, 163
  • J
  • Jacquard, Jacques Marie, 18, 19
  • Jacquard loom, 18–19
  • Jassy, Andy, 174
  • Jeopardy, 118
  • Jobs:
    • aligned with employee values, 5
    • alternate, training for, 20
    • machines taking, 2, 19–21
    • and work experiences for humans, 21
  • Job fatalism, 20
  • Job pragmatism, 20
  • Job protectionism, 20
  • Jobs, Steve, 74, 75
  • K
  • Kasparov, Garry, 115–116, 118
  • Kodak, 49
  • L
  • Language, shared, 132–135, 132f
  • Large Language Models (LLMs), 198
  • Leaders and leadership:
    • acts of commission and omission by, 156–157
    • and appeal to authority fallacy, 75
    • communication of values and vision by, 175–176
    • future solving vs. problem solving by, 64
    • in interorganizational systems, 98–99
    • in intraorganizational systems, 96–97
    • need for new form of, 96
    • pilots as means of promotion for, 169–170
    • reporting to, 68–69
    • to respond to advanced technologies, 46–47
    • seeing the whole system in, 87
    • social systems style for, 180
    • storytelling by, 176
    • during uncertainty, 27–28
  • Leader's guide to Autonomous Transformation technologies, 183–190
  • Learning:
  • Linear value, exponential value vs., 151–154, 152f–154f
  • Linear value staircase, 154, 154f
  • Linsenmeyer, Tim, 88–89
  • LLMs (Large Language Models), 198
  • Llorens, Ashley, 181
  • Logical fallacies, 115–118
    • appeal to authority, 75, 116–118
    • nonsequitur, 116
    • organizational empiricism, 145–147
    • slippery slope, 115–116
  • Lovelace, Ada, 18
  • M
  • Machines:
    • capability of humans vs., 28–31, 29f
    • human jobs taken over by, 2, 19–21
    • logical fallacies related to intelligence of, 115–118
    • relationship between humans and, 3
    • risk of consciousness in, 31–32
    • view of organizations as, 179
  • Machine autonomy, 13, 20, 202–203
  • Machine learning, 38–40, 192f, 194, 197
  • Machine teaching, 189, 192f, 200–201
  • Maintaining ecosystems, 161–163
  • Maintenance mode, 43–50
    • and adoption of advanced technologies, 46–47
    • with centuries‐old systems, 43–44
    • creation coexisting with, 49–50
    • data‐driven analysis in, 144
    • defined, 44
    • focus on, 43
    • individuals' impact on, 50
    • and return on investments, 151
    • signs for assessing, 44–45
    • for sustaining scaffolded systems, 45–46
    • and technology as problem or solution, 47–49
  • Manufacturing:
    • centuries‐old systems in, 43–44
    • core competencies in, 88–89
    • machine capabilities in, 28
    • organizational systems in, 100
    • potato chip, 28, 29
    • sense of cultural history in, 100–101
    • Shadow IT in, 109
  • Mario (video game), 199
  • Market(s):
    • and ecosystem of partnerships, 159
    • and preference for doing good, 5
    • technology, 105–106
    • understanding sweeping change in, 105
  • Marketing analytics platforms, 104
  • Martin, Roger, 143
  • Matrix (term), 78–79
  • Meaning, search for, 25–26
  • Mechanistic worldview, 44, 147, 152, 167, 179–180
  • Merck, 187
  • Meta, 130
  • Metrics:
    • in business reviews, 39
    • as milestones toward market impact, 50
    • in partnerships, 161–162
    • related to maintenance mode, 48
  • Michelangelo, 118
  • Microsoft:
    • artificial intelligence definition by, 201
    • Bing, 152
    • cost of new releases for, 110–111
    • creation by, 48
    • Nadella at, 75
    • organizational culture of, 174–175
    • in Seattle, 130
    • transformation of, 27
    • visionary goals of, 74
  • Microsoft Research, 200
  • Milestones:
    • directional, 75–75
    • metrics as, 50
  • Mixed reality, see Virtual and augmented (mixed) reality
  • Multiplexing, 18
  • Multiplying expertise, 131–140
    • and altitudes of inputs and outputs, 136–140, 137f, 139f
    • shared language for, 132–135, 132f
  • Multiverse reimagining, 69–70
  • Music, 53–54, 95, 96
  • N
  • Nadella, Satya, 27, 74, 75
  • National Science Foundation, 9
  • Nelson, Harold, 74, 141
  • Netflix:
    • and Blockbuster's streaming service, 155
    • creation by, 48, 49
    • Digital Transformation of, 15
    • founding of, 123
  • No Hard Feelings (Fosslien), 175–177
  • Noise, 115–118
  • Nonprofit organizations, 7
  • Nonsequitur fallacy, 116
  • Nordstrom, 130
  • Norris, Ron, 155
  • O
  • Omission:
    • errors of, 156
    • reformational economics of, 155–158, 158f
  • OpenAI, 152
  • Operational technology organizations:
    • in broken systems, 103–104
    • in manufacturing sector, 100
  • Organizations:
    • aligned with people's values, 5
    • during COVID‐19 pandemic, 73
    • division within, 123–124, 125f
    • as machines, 179
    • maintenance mode in, 44. See also Maintenance mode
    • market and societal functions of, 8
    • more human, 179–180
    • nourished by profit, 8
    • partnerships within, 163
    • social impact of initiatives of, 35–36
    • social purpose corporations, 9
    • as social systems, 179–180
    • story as foundational in, 173–175
    • subsets of systems in, see Seeing subsets vs. whole systems
  • Organizational empiricism, 144, 146–147
  • Organizational reasoning, 147–148, 149f, 175
  • Organizational systems, 95–101
    • divide between social systems and, 35–36
    • industry‐specific, 99–101
    • interorganizational, 98–99
    • intraorganizational, 96–97
  • Outcome bias, 157–158, 158f
  • Outputs, altitudes of, 136–140, 137f, 139f, 204
  • P
  • Palace of Electricity, 174
  • Pall, Gurdeep, 159
  • Parallel computing, 199
  • Partnerships:
    • being open to, 86
    • in building solutions, 112
    • ecosystem of, 159–163
    • in global logistics, 186
    • within organizations, 163
    • preferences for doing good in, 5–6
  • PepsiCo, 201, 203
  • The Persistence of Memory (Dali), 198
  • Pilots:
    • failed model of, 46
    • overcoming purgatory of, 170–171, 171f
    • as riskless experiments, 168–169
    • for securing promotions in organizations, 169–170
    • within social systems, 167–171
  • Pilot purgatory, 46, 170–171, 171f
  • Political systems, 176
  • Potato chip manufacturing, 28, 29
  • Predictive analytics, 40, 192f, 193, 194
  • Prescriptive analytics, 40, 192f, 193–194
  • Problem solving:
    • context for, 36–37
    • default process for, 103–105
    • future solving vs., 59–62, 60f
    • replacing our approach to, 3–5
  • Product companies:
    • as competitors to internal technology organizations, 110–112
    • types of, 110
  • Product development:
    • Autonomous Transformation technologies for, 184–186
    • machine and human capabilities in, 30–31
  • Product ecosystems, 163
  • Product teams, reimagining, 110–112
  • Profit:
    • definition of, 7
    • greed or altruism vs., 6–8, 8f
    • in Profitable Good equation, 5
    • short‐ vs. long‐term, 151–152, 152f
    • technologies for generating, 3
  • Profitable Good, 5–9
    • chess programs for, 86
    • at Clover Imaging Group, 89
    • defined, 5
    • as differentiator, 6
    • and greed or altruism, 6–8, 8f
    • market signals of need for, 5–6
    • in the real world, 9
    • and social purpose corporations, 9
    • in workforce transformations, 21
  • Project teams, 79
  • Promotions:
    • through problem solving, 66
    • using pilots to secure, 169–170
  • Purdue University Smart Manufacturing Innovation Center, 77
  • Purgatory:
  • Q
  • Quiz, about artificial intelligence, 204–206
  • R
  • Reason‐driven decision making, 147–148, 149f
  • Reasoning:
  • Recruiting, 174
  • Reductionist thinking, 162
  • Reformation:
  • Reformational economics:
    • of linear and exponential value, 151–154, 152f–154f
    • of omission and commission, 155–158, 158f
  • Rehumanizing work, 53–55
  • Reimagining:
    • functional, 68–69
    • multiverse, 69–70
    • of product teams in internal technology organizations, 110–112
    • of value creation in internal technology organizations, 105–109
  • Reinforcement learning, 192f, 194, 198–199, 201, 203
  • Reinforcement Learning with Human Feedback, 198
  • Report on the Subject of Manufactures (Hamilton), 101
  • Reports to leaders, 68–69
  • Research life cycle, 119–120
  • Riskless experiments, pilots as, 168–169
  • Robotics:
    • in agricultural science, 62
    • in Autonomous Transformation, 4, 183
    • clearing fog around, 131
    • in fishing industry, 9
    • in global logistics, 187
    • in health care, 189
    • investment for initiatives focused on, 38
    • in product development, 185–185
    • reinforcement learning paired with, 199
  • S
  • SalesForce, 130
  • SAP Concur, 130
  • Scaffolded systems:
    • complexity of problems in, 35
    • sustained in maintenance mode, 45–46
  • Scientific management, see Taylorism
  • Scientific method, 38–39, 144–145
  • Searle, John, 31
  • Seattle, 130
  • “The Secret Structures of Great Talks” (Duarte), 177, 177f
  • Seeing subsets vs. whole systems, 85–94
    • assessing whether you are, 87–93, 90f–92f
    • individuals' impact on, 93–94
  • Semantic satiation, 131, 191
  • Shadow IT, 109, 124
  • Shared language, 132–135, 132f
  • Shared values and identity, 174
  • Simulations. See also Digital twins/simulations
    • of electric grid, 138
    • in machine teaching, 201
  • Six Sigma, 195
  • Skill of envisioning, developing, 67–70
  • Slippery slope fallacy, 115–116
  • Smartphones, 18, 74, 163
  • Social purpose corporations, 9
  • Social responsibility, 5–6
  • Social systems:
    • for clothing people, 69
    • divide between organizational systems and, 35–36
    • envisioning the future of, 67
    • organizational ecosystems as, 161–163
    • organizations as, 179–180
    • pilots within, 167–168
    • storytelling as core means of leading, 173
  • Social systems worldview, 147, 179–180
  • Societal division, 129–130
  • Software engineering, 77–78
  • Statistics, 192f, 194–195
  • Steinbeck, John, 11, 25
  • “Stephen Hawking Warns Artificial Intelligence Could End Mankind,” 116–118
  • Story circle, 175–176
  • Storytelling, 173–177, 177f
    • as agent for change, 176–177, 177f
    • closing the story circle, 175–176
    • as strategic organizational imperative, 173–175
  • Strategic partners, preferences for doing good among, 5–6
  • Subscription revenue, 111
  • Supply chains, hardening, 14
  • Survivalism, 23–24
  • Sustaining ecosystems, 161–163
  • Sustaining scaffolded systems, 45–46
  • Symbols:
    • for shared values and identity, 174
    • for sharing ideas, 176
  • Synthesis:
    • in diagnostic analytics, 39, 193
    • in statistics, 194
    • steps in, 81f, 82
    • in systems thinking, 81–83, 81f
  • Synthesis thinking, 63, 110
  • Systems:
  • Systemic design, 71–83
    • analysis and synthesis in, 81–83, 81f
    • of Apple products, 163
    • dimensions of systems in, 78–80
    • and interconnectedness of parts, 76–78
    • passive and active, 73–76
  • Systemic division, 129–130
  • Systems thinking:
    • accounting for complex dynamics in, 162
    • analysis and synthesis in, 81–83, 81f
    • defined, 78
    • systemic problem with, 58
  • T
  • Tableau, 130
  • Talent, doing good preferred by, 5
  • Taxes, 7
  • Taylor, Frederick Winslow, 33, 38, 54–55, 143
  • Taylorism, 38
    • challenges inherited from, 38–40
    • Data Science, 40–41, 168
  • Teaching, machine, 189, 192f, 200–201
  • Technologies, 1, 3
  • Technology advisors, 126–129
  • Technology companies, 47–48
    • informational and operational perspectives in, 100
    • investment in pilots by, 167
    • product companies, 110–112
    • stories at, 174
  • Technology consulting:
    • as competitor to internal technology organizations, 106–108
    • division across technology industries and, 125–129, 127f, 128f
    • investment in pilots in, 167
    • “the bench” in, 106–107
  • Technology function, 123–124, 125f
  • Technology industries, division across technology consulting and, 125–129, 127f, 128f
  • Technology initiatives, categories of, 145–146, 145f, 146f
  • Technology market, 105–106
  • Telecommunications, 57–59, 143–144
  • Teledoc, 49
  • Telephones, 18, 57–58
  • Transformation:
  • Transformation and Reformation Matrix, 15f
  • Travelocity, 49
  • U
  • UL (Underwriters Laboratories), 108, 174
  • Uncertainty:
    • in artificial intelligence era, 27–28
    • physiological impacts of, 27
    • pushing through, 131
    • when leadership is silent, 175
  • Underwriters Laboratories (UL), 108, 174
  • United States Department of Commerce, 129
  • United States National Open Chess Tournament, 85
  • Use cases:
    • agriculture, 62–63
    • in broken systems, 103
    • framing applications within, 64
    • reinforcement learning approach to, 199
  • Use Case Battleship, 64–65
  • Utilities sector:
    • critical function of, 45
    • deregulation in, 43, 86–87
  • V
  • Values:
    • defining and communicating, 175–176
    • organizational alignment with, 5
    • in political systems, 176
  • Value creation:
    • in internal technology organizations, reimagining, 105–109
    • in technology organizations, 48
  • Virtual and augmented (mixed) reality:
    • in Autonomous Transformation, 4, 183
    • clearing fog around, 131
    • in global logistics, 187
    • in health care, 189
    • investment for initiatives focused on, 38
    • in product development, 185
    • return on investment in, 38
  • Vision, 70
    • comfort with lack of, 103
    • defining and communicating, 175–176
    • of leaders, 156
    • in political systems, 176
    • realizing, 153
  • Visualizations, 192
  • W
  • Web 3.0 (decentralized World Wide Web), 189
  • Westinghouse, 174
  • Whittinghill, Joe, 27, 74
  • Work:
    • elements of, 37
    • experiences for humans at, 14, 21
    • flexibility policies for, 80
  • World Wide Web, 189
  • Wrong things, done right, 65–66
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