The Seven Stages of AI Acceptance
By Julian Waters-Lynch
A Guide to Understanding and embracing your Organisations Journey of AI Adoption.
10-Second Summary
The co-evolution of humans and technology suggests we are on the cusp of substantial AI advancements - understand that AI tools like ChatGPT could rapidly reshape your industry.
We propose a 'seven-stage model' of AI adoption in organisations - take note and identify where your company is on this journey to guide your next steps.
Success in this new era will come to those fostering cultures of curiosity, adaptability, and continuous learning - embrace this mindset to maximise the potential of human-machine collaborations.
Encountering Exponentiation
Some concepts, once encountered, forever change the way we perceive the world. I’m certain you’ve had this experience with a handful of ideas in your own life. I can still recall where I was when the implications of machine intelligence dawned on me. I was 20 years old, halfway through a year’s leave of absence from my commerce/arts degree, and temporarily sleeping on a friend’s floor in Vancouver. Matt, an aspiring jazz singer, was in music school. We had connected at an open mic night at a local club, where I was playing piano.
Matt, like me at the time, was a student and a musician, so neither of us had much. The apartment he rented was just a single room - there wasn’t even space for a couch - so I slept on a rug spread over the concrete floor. I was grateful for his kindness, someone without much being willing to share with someone he recently met. But hard floors don’t encourage sleeping in, and I found myself waking up early and browsing the books on his single shelf while he was still asleep.
One of the titles that caught my eye was ‘The Age of Spiritual Machines’, by Ray Kurzweil. Kurzweil, a computer scientist and inventor, had led important advances in machines reading printed text (1) and, in one of the cooler moves for a computer scientist, built early synthesisers for Stevie Wonder (2). In an earlier work (3), he had predicted that a computer would beat the world’s chess grandmaster before 1998. His claim was met with considerable scepticism at the time, not least because similar predictions had been made by computer scientists decades earlier, and all had fallen short (4).
The central theme of this book was the astonishing implications of exponential growth in computational power, a trend first observed by Gordon Moore in 1965 and subsequently known as Moore’s Law (see figure 1). Moore’s Law refers to the doubling of transistor density on microchips approximately every two years, a result of ongoing advances in technology and manufacturing processes. To give a sense of this impact, this explosion in processing power is why the smartphones we now carry in our pockets possess more computational power than the room-sized computers that NASA used to send the first astronauts to the moon.
Exponentiation can be a challenging concept to fully grasp because most of our typical experiences and intuitive understanding of growth are linear. Exponential changes often begin as barely noticeable, but even the mere act of repeatedly doubling a small number soon results in seemingly astounding increases. For instance, if you start with the number 1 and double it five times, you end up with 32. But continue this process 25 more times, and you end up with 1 billion. Or, more vividly, if you fold a piece of paper in half, you double its thickness - but the difference is negligible. If you could fold that same paper in half 41 more times, its thickness would stretch all the way to the moon (5). The calculations are straightforward, yet the outcomes are startlingly counterintuitive.
Nestled among many examples of technological leaps in history, was Kurzwell’s proposition around the exponential explosion of artificial intelligence. The pinnacle of this evolution, often described as the singularity, would be the moment when machine intelligence suprasses human intelligence. Beyond this threshold, superintelligent machines would begin to independently take control of their own future developments, enhance their capabilities, and chart unknown paths. While many regard this prospect as an existential threat to humanity, Kurzweil, a relentless optimist, envisions a civilisational synthesis of biological and non-biological intelligence, a new epoch birthed from the transformative convergence of human and machine.
This prospect, needless to say, blew my 20 year old mind.
The Coevolution of Humans and Technology
For many, the prospect of exponential explosions in artificial intelligence and impending singularities may still sound more like fiction than science. But reflecting on the longer, intertwined history of our species with technology - particularly its entanglement with work and labour - can help challenge some of this instinctive scepticism.
When we take a moment to reflect, it becomes clear that even our simplest tasks are the products of collaboration with technology. Ordinary tools, the ones we use for farming the land, preparing meals, or cleaning our homes, were all once pivotal innovations of their time, but are now so ubiquitous we barely recognise them as technologies. Our lives are now so intertwined with our forebears' creations that we can no longer survive without them.
Our relationship with technology transcends mere tools and utility, it embodies a more profound symbiosis. Our inventions have etched permanent changes in our very biology: the advent of cooked food altered our digestive systems; the creation of clothing spurred the loss of body hair; the invention of writing, now compounded by smartphones, has progressively externalised portions of our memories.
The notion that our present-day machines might evolve into superintelligent entities, or even gain subjective experience, might seem far-fetched when considering the current capabilities of our computers and smartphones. However, we should remember that the notion of intelligence, creativity and subjectivity emerging amongst life forms would have seemed equally unlikely when viewed from the perspective of the archaebacteria of 3 billion years ago. Predicting that the descendents of these primitive microbes would one day compose symphonies and engineer space stations would have appeared utterly outlandish.
Overcoming Scepticism
For the better part of the last decade, I’ve engaged in discussions with business leaders and conducted workshops with their teams, focusing on the impact of emerging technologies including AI, on their value propositions, business models, operating structures, and strategic plans. This work has been interesting and rewarding, and sometimes helped shape strategies or inspire new projects or products.
However, each time I touched upon the transformative potential of AI, supported by forecasts detailed in numerous credible reports and books, I noticed that my audience often regarded these scenarios more as infotainment than as actionable intelligence. This was the case despite the ubiquity of cautionary tales about organisations that overlooked major digital technology revolutions - the eclipse of Netflix over Blockbuster, or Instagram over Kodak, for instance. But the warnings still seemed too speculative. The sense of peril wasn’t imminent. The startup barbarians weren’t quite visible at the gates.
ChatGPT dispelled this illusion.
We are now eight months into the era where a new wave of generative AI tools has begun to reshape the business ecosystem. In an article earlier this year (6), we proposed that generative AI would not just be the year’s most significant technology story, but likely that of several years to come. You didn’t have to be Nostradamus to see this. Twitter took 2 years to reach 1 million users. Instagram took 2.5 months. ChatGPT took 5 days. It is the fastest publicly deployed technology in history.
However, after witnessing a myriad of organisations wrestling with understanding and adapting to this new technological wave, we have developed a ‘seven-stage model’ of AI adoption within organisations (7). Our goal is to stimulate reflection on how your organisation might be responding to this major technological shift.
1. Scepticism - AI Will Never be Able to Do What We Do.
This was the most common intuitive response that I encountered over the past 10 years. Some leaders reflexively dismissed the possibility of machine intelligence, treating it as a distant scenario rather than tangible threat. Others enjoyed the presentations and discussions about AI as a kind of infotainment, yet failed to recognise its potential impact on their operations, or believed it wasn’t a concern warranting serious strategic consideration.
2. Shock - AI Can Suddenly Do Tasks That We Pay People to Do.
This initial scepticism was understandable, given the opaque nature of AI capabilities at the time. But the first encounter with ChatGPT and other large language model-based applications has ushered in moments of shock for many. I’ve personally witnessed this among academic colleagues when they’ve input their subject’s assessment questions and asked it to write an essay in response. They get even more concerned once they learn how to tweak the prompts to make the responses more difficult to detect, such as sounding like a second-year international business student. The eerily accurate imitation of human-like responses leaves many shocked.
3. Shit! - How Did We Miss How Fast AI’s Capabilities are Advancing?
The irony of this stage isn’t lost on me, given what I covered in my introduction. The initial shock at AI’s capabilities can quickly morph into dismay, even indignation among leaders. They feel blindsided, at risk of being left behind by a technological step change. How did we miss this? Why wasn’t anyone ringing alarm bells? How did we not see this coming? This phase often sparks a newfound urgency to act swiftly - to grasp at anything that might help them catch up.
4. Scramble - A Frantic Rush to Appear Proactive, but a Lack of Strategy
In this phase, project task forces are assembled hastily, leaders rush to make announcements about AI initiatives, and superficial AI projects, like chatbots, get the green light with little consideration. This stage has echoes of the late 1990s dotcom bubble or the 2021 cryptomania, where the appearance of riding the next techwave eclipses crafting sound value propositions and sustainable business models
5. Strategise - Thoughtfully Exploring and Learning Where AI Might Fit Within the Business
On the other side of the scramble comes a more thoughtful and considered analysis of the capabilities of the technology. Companies study and experiment with AI, learning about its strengths and weaknesses, and its potential impact on their operations and industry. This is an iterative process of trial and error, as companies progressively discover a more nuanced approach to integration, rather than the frantic dash of the scramble phase.
6. Strengthen - Integrating AI to Fortify the Existing Value Proposition
This phase translates the insights discovered from the exploration of the strategise phase into implementation. Armed with a more refined understanding, companies start integrating AI into their processes and offerings, fortifying their core value propositions. Rather than hastily stamping AI labels on their products or services, nor seeing the technology as a simple substitute for human roles, these businesses recognise how AI can bolster their existing strengths. Yes, some jobs will become redundant (8), but more frequently, it’s the tasks and workflows within roles that will change. The focus of human labour will be on tasks that are more valuable, creative, and strategic. A more symbiotic relationship emerges between human and machine intelligence.
7. Stabilise - A New Generation of Business Models Emerge
As companies solidify their value propositions through novel human-machine collaborations, a new era of robust business and operational models will emerge. In the past, the first forays with a substantially new generation of technology have rarely landed on the dominant business model. Take, for instance, the progression from printed encyclopaedias like Britannica to digitised versions on CD-ROMs such as Microsoft's Encarta. This intermediate step, while crucial, was ultimately short-lived. With the maturation of the technological ecosystem—marked by the advent of Web 2.0— a fundamentally different model emerged: Wikipedia, which introduced an operational approach starkly different from its predecessors.
The evolution of music distribution followed a similar path, transitioning from physical records and CDs to downloadable MP3 files, and finally to today's streaming subscription services. In each case, the consolidation of a new technological ecosystem precipitated a step change in the business and operating model.
The models that eventually become dominant often sound preposterous at their outset - imagine explaining Wikipedia to an encyclopaedia salesman in 1991, or Airbnb to the Marriott International in 2001. Yet, they become obvious in retrospect. As such, we anticipate that as the interplay between human and AI matures, similarly transformative business models will come to the fore, revolutionising industries and resetting the norms of business operations.
How is your organisation responding to this next generation of AI tools? Leaders of organisations that emerge on the other side of this technological transition will have fostered cultures of curiosity, adaptability and continuous learning in order to discover new combinations of machine-human intelligence that will enable their organisations to strengthen their value propositions and successfully navigate this transition.
Leaders of organisations that successfully navigate this technological transition will be those who foster cultures of curiosity, adaptability, and continuous learning. Their focus will be on discovering new combinations of machine-human intelligence that strengthen their value propositions and ensure their organisations thrive in this evolving landscape. How is your organisation responding to this next wave of AI?
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REFERENCES >
(1) https://www.invent.org/inductees/raymond-kurzweil
(2) https://www.youtube.com/watch?v=Bf2pSfItFag
(3) Kurzweil, R., Richter, R., Kurzweil, R., & Schneider, M. L. (1990). The age of intelligent machines (Vol. 580). Cambridge: MIT press.
(4) The most notable of these was made by AI pioneer Herbert Simon, who claimed that by 1967 computers would beat the world champion of chess, write music that critics would value, discover new mathematical theorems and soon be capable of “doing any work a man can do”. Others continued to make such predictions. For example at the 1982 North American Chess Championship, several experts made predictions that a computer would soon become world chess champion, such as Monroe Newborn (within five years); Michael Valvo (within ten years); Dan and Kathe Spracklen (within 15); and Ken Thompson (within twenty years). The most widely held view however was that this would happen before the year 2000 Softline Issue 1.3 (cgw museum.org)
(5) https://bigthink.com/starts-with-a-bang/paper-folding-to-the-moon/
(6) What impact will Generative AI have on our work, education and society? — Neu21
(7) We offer this 7-stage idea with a healthy nod to Kevin Kelly’s 7 stages of robot acceptance, itself a playful riff on the classic 5-7 stages model of grief.
(8) Does Sam Altman Know What He’s Creating? - The Atlantic (publicservicesalliance.org)