6 Ways to Reimagine Business Strategy
This is a chapter from a new book I am writing titled:
See the Invisible to Do the Impossible: Transformative Business Models and Strategies
“Every company is a software company. The strategy, value proposition, and progress of a software business need to be communicated consistently. One challenge is to do so in a way that prioritizes the software business while keeping the core business and its people performing and feeling valued.” — McKinsey Quarterly, December 2022[1]
Since the late 18th century, the world has been shaped for better or worse by four industrial revolutions where new technology paradigms transform every part of our lives. As you may know, we are at the beginning of the Fourth Industrial Revolution (4IR). With that philosophy in mind, a 21st century business strategy must be “digital first” and built on the transformative power of the new technology paradigm — AI-Cloud-IOT — and the emerging paradigm of Augmented Collective Intelligence (ACI). We can now take a problem, beginning at the atomic scale, identify the multifactorial characteristics, adjust for uncertain interaction over multiple scales of time and space and produce new insights into science, engineering and medicine that benefit humanity in tangible ways that improve human life, the environment and society. The NIH describes this scenario beautifully as “transdisciplinary, translational, and network-centric”.[2]
· Transdisciplinary — transcends existing disciplines to create a new field (e.g., nanotechnology)
· Translational — research advances far enough to tangibly benefit humanity (not science for science sake)
· Network-centric — harnessing the power of the system for bi-directional flow of information between stakeholders (e.g., outsourcing)
I think we need to define and develop each company — “digital first” — with the company built and the entire customer experience design provided digitally. You may make trucks, but the entire experience from designing the trucks, creating the regenerative materials for the truck bodies to onboarding the customer and after-sale service is an online, network-centric, digital experience — “digital first”. As Nobel economist Joseph Stiglitz put it, “Every industry and every business today has a digital future.” We have reached the point with Nvidia GPUs and cloud services where computational capacity even for the most advanced AI is much less of a constraint.
Thomas Philbeck (WEF) advised the National Academy of Sciences, Engineering and Medicine[3] in 2017 about the importance of values in the 4IR. He proposed three values that must shape our thinking:
1) preserving the common good,
2) delivering multigenerational environmental stewardship
3) holding the primacy of human dignity.”
“Values are embedded in technological systems,” but since the first industrial revolution we have put shareholder returns and political power ahead of all our generational responsibilities.
To do a better job at managing these ethical responsibilities, we must first recognize the increasingly complex operating environment that will shape how business will be done in the 4IR. The complexity of this operating environment is explained well by the legendary Nobel physicist Richard Feynman, who said, “Scientific knowledge is a body of statements of varying degrees of certainty — some most unsure, some nearly sure, but none absolutely certain.”[4] If we think about Feynman’s “uncertainty”, we realize that the 20th and early 21st centuries have been shaped by tools to quantify the uncertainty, such as quantum physics, Shannon’s information theory, Black-Scholes[5] option pricing model and artificial intelligence. Each tool is a variation on the combination of pattern recognition and probability. Each tool had a profound effect on economic development and business model, and today is AI’s turn. As Nobel Laureate Joseph Stiglitz put it, “to respond to the growing demand for information, the supply of statistics has also increased considerably, covering new domains and phenomena.”[6] AI is the only tool today to handle the increasing size of the relevant datasets, that may include one thousand factors and petabytes of data.
As is the case, at the beginning of every industrial revolution, economic, technological and cultural factors will shape new business strategy and prompt new business models. Consideration of the new 21st century business strategy is shaped by six such metaconcepts:
1. Design
2. Systems/Process
3. Network
4. Cyber-physical
5. Collective Intelligence
6. Adaptability.
1. Design
Much of human discovery, creativity and even invention will be replaced by combinatorial synthesis modeled and automated by AI. Much of science has become an “information science”, from materials science to chemistry and biology. Design will become the creative force, not in the sense of craftsmanship but rather in Herbert Simon’s concept of design as problem solving and decision-making. The creativity will come in picking the problem to be solved or the solution from the choices and then applying the horsepower of AI/ML to model and synthetically produce the component combinations for validation of the possible solution(s). We will still need to validate the synthetic combinations in a lab or real world setting, but the nature and scope of the solutions will be synthetically developed more quickly in a wider range of applications. Moderna’s rapid development of a COVID vaccine illustrates such a synthetic process using information science.
When quantum computing based on the ever versatile qubit (instead of the 0–1 logic of current computers) reaches commercial feasibility, the range of solvable problems will increase exponentially. Quantum computing will increase the processing speed to collapse time for solutions to previously unsolvable problems. Whenever man collapses time, there is great economic value created and we start to provide aspirational solutions. (For example, every iPhone model has an improved camera, which illustrates the value of the camera changing the time to create and capture art.[7])
Note: As environmental problems increase in scale and magnitude, speed to develop new solutions will be even more critical. The speed of AI to develop predictive and prescriptive alternative scenarios will be one of several critical applications.
2. Systems/Process
If we were to debate the two concepts most underappreciated in intellectual history, I might propose “system” and “network”, which are quite closely related. The serious problems humanity faces in the 21st century are all at the intersection of natural and manmade systems. A fundamental feature of all systems is the network which allows for the transfer of matter, information and energy. As we mentioned earlier, another key feature of a system is the process — input-process-output. Porter's “value chain”, the process of value creation, value delivery and value capture, has shaped business strategy for over forty years. As AI becomes a more integral part of the value chain, the importance of approaching a business model as a process will become even more important. As information increasingly becomes the raw material to create economic and social value, the process will be information processing to create competitive advantage and economic value. Singularity University, founded by futurist Ray Kurzweil, makes the insightful point that we are now data-agnostic, the AI algorithms have progressed to deal with all modalities of data for almost any problem, which permits an even richer understanding of process.
This logic further explains the special power of configuring Augmented Collective Intelligence (ACI) systems. For example, ACI now links product design all the way through to customer experience in an end-to-end process, which is detailed in the next chapter. Another example is cybersecurity where we need security for each data infrastructure component but also for the integrated process of data, infrastructure, algorithms and apps as a whole system.
Another great example of process comes from Google. Nathan Benaich, a noted AI commentator, describes Google Deep Mind research, “Using iterative rounds of material generation, property prediction and DFT (Density Functional Theory), Gnome discovered over 2.2 million stable structures, many of which escaped previous human chemical intuition and would have taken 800 years to discover. Of these, 380,000 are the most stable and should be amenable to synthesis in the lab.” 800 years saved!!! TIME!!!
3. Network
Networks are a two-edged sword. Today they foster the flow of information instantly from anywhere in the world. They also provide the option to build any platform business not only from in-house expertise and functionality but to find other resources just a chat or API away. Managed properly, a network facilitates “evolutionary”, iterative and transformative innovation, perhaps the greatest benefit of a network. And, the network effect, popularized by the venture capital firm nFx, explains much of the success of social media platforms like Facebook, Twitter and even LinkedIn. As network effects make clear, their popularity as a business model comes from the support for low-cost self-organizing behavior, one of the key characteristics in defining complex systems. The frequency of self-organizing behavior for social and economic purposes will only increase as connectivity improves, more people become self-employed or contract workers and smaller and smaller market slices become viable businesses through their availability via the Internet. This increase in the benefits of a network provides easy access to optionality, alternative resources and additional people. This flexibility of access, if built into the design, will reduce risk for the business.
The downside of the network is that it increases the complexity of the business model by adding in possibly unpredictable or unreliable “partners” and expands relevant information exponentially which increases the risks from infrastructure integrity and reliability (e.g., cybersecurity) for a business. These two factors may produce what Naseem Taleb calls a “black swan” event. A black swan is a rare but catastrophic event. Given that every business model in the future will employ features of a network, risk management must address the increasing possibility of black swans that threaten the survival of the business. There is also predicted an increased threat of black swan natural disasters due to continued environmental degradation which will only complicate the operating environment.
And in the end, we should remember RPI professor Eben Bayer’s point, “When you link many predictable systems together, you get a complex system that is unpredictable.”[8] As we continue to network and connect the world, we are fundamentally making it more unpredictable.
4. Cyber-physical
The history of man and wealth creation can be traced to the productive integration of matter and labor. From early agriculture to the pyramids and on to the railroads and the Bessemer Process for steel production and beyond, man’s energy and ingenuity has been devoted to purposeful shaping of matter. Beginning with the Third Industrial Revolution in the 1960s, the wealth creation model shifted more toward computing, information and capital.
Jensen Huang, CEO of Nvidia, takes the idea further and explains the Fourth Industrial Revolution (4IR ). He talks about the industrial company of the future, “Every car company in the future will have a factory that builds the cars — the actual goods, the atoms — and a factory that builds the AI for the cars, the electrons.” [9] Cyber-physical systems will be the bridge between the two factories and the future of wealth creation.
Note:
“The FDA is looking to the skies and taking a page out of the FAA’s approach to aviation safety — rigorous, yet adaptive — recently launching its new Office of Therapeutics Products and piloting Operation Warp Speed for rare disease to create more transparent and flexible processes for evaluating and approving programmable medicines.[10] This is an important “first” step in a policy change for regulators to recognize programmable AI-driven medicine and adopt a new approach to uncertainty. Cyberphysical approaches will play a critical role in this revised FDA approach.
5. Collective Intelligence
Augmented Collective Intelligence (ACI) was popularized at MIT to explain the increasing number of connected networks linking people, AI resources, sensors and Internet of Things (IOT). Intelligence has to a large degree increasingly become the interconnectedness of the humans and information processing resources (cyberphysical systems). Brian Arthur would say that this model emerged to solve the increasingly complex problems being tackled by scientists and encountered by the management of all types of organizations. Such a model of collective intelligence offers two valuable benefits — real-time data and near instant collaboration with people and/or computing resources. If hierarchy and consequently management approval is reduced or better yet eliminated, the ACI model also informs the people closest to the customer to provide better real-time customer experience. This focus on real-time customer experience is increasingly becoming the standard everywhere — business, government, universities, …and collective intelligence is the ideal process to support it.
We are approaching the point where ACI will dictate the strategy and strategic alternatives for a company, what Jensen Huang sees as the future of industrial companies. Without state-of-the-art computing, data science and design capabilities, companies will not be able to quickly change direction and pursue the largest opportunities. This point is made clear by the success of venture-backed “unicorn” companies (mentioned in the Introduction to the book) and certain standalone forms of corporate venture capital where no pre-existing configuration of human, physical or computational resources limits alternatives, adaptability and response time. Let me say that again, “where no existing configuration of human, physical or computational resources limits alternatives, adaptability and response time”. Jessica Flack at Santa Fe Institute describes a future with “emergent engineering”[11] to deal with more and more complexity from the increasingly networked resources. Engineering is transitioning from forecasting, control and limited product iterations to a new model that embraces uncertainty, modeling, scenario planning and real-time adaptability…made possible by augmented collective intelligence.
6. Adaptability
Flack defines adaptability as “systems that can be creative and responsive when faced with an array of possible scenarios”.[12] Others call it “execution under uncertainty”, a robustness that survives the current situation of black swans and non-equilibrium. Ray Kurzweil writes about rapid trial-and-error learning where change becomes exponential through the Law of Accelerating Returns. “The more advanced a system that improves through iterative learning becomes, the faster it can progress.”[13]
Brian Arthur describes our lives whether in business, government, NGOs or at home as:
“These very actions of agents’ exploring, changing, adapting, and experimenting further change the outcome, and they’d have to then re-adapt and re-adjust. So, they are always re-adapting and re-adjusting to the situation they create.”[14]
The “re-adapt and re-adjust” behavior, which I refer to as explore-exploit in previous writings, is a fundamental principle of complexity science, but we must also remember that we are a social species. Cesar Hidalgo, following up on the seminal work of Mark Granovetter, describes how social institutions affect adaptability.
“The firms of Route 128, through their distrust of their employees and other firms, promoted structures that were more hierarchical and less porous, giving rise to a regional cluster that was less adaptable. This lack of adaptability, in turn, translated into a difference in size, since over the long run the less adaptable Route 128 cluster shrank with respect to Silicon Valley. So social institutions affect not only the size of the networks that people form but also their adaptability, and this helped Silicon Valley leave Route 128 in the dust.”[15]
Note: Route 128 runs through Massachusetts near Boston and Cambridge (home of MIT and Harvard) and was the home of well-known early computer companies such as Wang and Digital Equipment. Route 128 was also an early center for venture capital investing.
When we step back and consider the six metaconcepts above, we realize Feynman’s profound insight into the relationship between uncertainty and knowledge.
“All scientific knowledge is uncertain — this is a very important point. There is no knowledge that is absolutely certain. Every scientific statement is a statement of probability, even the laws of physics are only so probable.”
Each metaconcept recognizes the uncertainty of the increasing complexity of our world, the voluminous amount of new information each day and the need to consider the increased uncertainty from a networked model for the exchange of this information whether by humans or through computer infrastructure.
We now turn in the next chapter to the two tangible technology paradigms that are shaping the 21st century.
[1] https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/every-company-is-a-software-company-six-must-dos-to-succeed
[2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3940421/
[3] National Academies of Sciences, Engineering, and Medicine. 2017. The Fourth Industrial Revolution: Proceedings of a Workshop–in Brief. Washington, DC: The National Academies Press. https://doi.org/10.17226/24699.
[4] https://www.capecodtimes.com/story/opinion/columns/2019/06/26/exploring-value-science-to-humankind/4824243007/
[5] https://en.wikipedia.org/wiki/Black%E2%80%93Scholes_model
[6] Stiglitz Commission, 2009: Executive Summary, para.2
[7] Nicholas Mirzoeff, How to See the World
[8] https://roberthhacker.medium.com/what-evolution-teaches-us-about-how-to-live-your-life-5cb5ca631605
[9] https://leading.business.columbia.edu/main-pillar-digital-future/digital-future/ai-nvidia-ceo-jensen-huang
[10] https://a16z.com/big-ideas-in-tech-2024/
[11] https://aeon.co/essays/complex-systems-science-allows-us-to-see-new-paths-forward
[12] https://aeon.co/essays/complex-systems-science-allows-us-to-see-new-paths-forward
[13] https://www.linkedin.com/pulse/our-lizard-lateral-brains-exponential-world-were-rita-mcgrath-tyrke?utm_source=share&utm_medium=member_ios&utm_campaign=share_via
[14] W. Brian Arthur, Eric D. Beinhocker, and Allison Stanger, Complexity Economics
[15] Cesar Hidalgo, How Information Grows
