Artificial intelligence, generative AI. large language models and ChatGPT and Bard are the latest examples of new technologies most observers believe will have a measurable positive impact in many industries and processes.
On the other hand, maybe we should stop confusing the matter by insisting that applied AI is going to have outcomes different in quantity, if not quality, from the earlier waves of "digital transformation" and all computing innovations of the last 50 years.
Which is to say, applied AI is going to take some time to massively transform processes, even if we believe it will do so quickly. And we need to be ready for as much as 70 percent of efforts to fail.
That is only because 70 percent or more of all information technology programs fail.
“74 percent of cloud-related transformations fail to capture expected savings or business value,” say McKinsey consultants Matthias Kässer, Wolf Richter, Gundbert Scherf, and Christoph Schrey.
Those results would not be unfamiliar to anyone who follows success rates of information technology initiatives, where the rule of thumb is that 70 percent of projects fail in some way.
Of the $1.3 trillion that was spent on digital transformation--using digital technologies to create new or modify existing business processes--in 2018, it is estimated that $900 billion went to waste, say Ed Lam, Li & Fung CFO, Kirk Girard is former Director of Planning and Development in Santa Clara County and Vernon Irvin Lumen Technologies president of Government, Education, and Mid & Small Business.
That should not come as a surprise, as historically, most big information technology projects fail. BCG research suggests that 70 percent of digital transformations fall short of their objectives.
From 2003 to 2012, only 6.4 percent of federal IT projects with $10 million or more in labor costs were successful, according to a study by Standish, noted by Brookings.
IT project success rates range between 28 percent and 30 percent, Standish also notes. The World Bank has estimated that large-scale information and communication projects (each worth over U.S. $6 million) fail or partially fail at a rate of 71 percent.
McKinsey says that big IT projects also often run over budget. Roughly half of all large IT projects—defined as those with initial price tags exceeding $15 million—run over budget. On average, large IT projects run 45 percent over budget and seven percent over time, while delivering 56 percent less value than predicted, McKinsey says.
Beyond IT, virtually all efforts at organizational change arguably also fail. The rule of thumb is that 70 percent of organizational change programs fail, in part or completely.
There is a reason for that experience. Assume you propose some change that requires just two approvals to proceed, with the odds of approval at 50 percent for each step. The odds of getting “yes” decisions in a two-step process are about 25 percent (.5x.5=.25).
In other words, if only two approvals are required to make any change, and the odds of success are 50-50 for each stage, the odds of success are one in four.
The odds of success get longer for any change process that actually requires multiple approvals.
Assume there are five sets of approvals. Assume your odds of success are high--about 66 percent--at each stage. In that case, your odds of success are about one in eight for any change that requires five key approvals (.66x.66x.66x.66x.66=82/243).
In a more realistic scenario where odds of approval at any key chokepoint are 50 percent, and there are 15 such approval gates, the odds of success are about 0.0000305.
So it is not digital transformation or AI specifically which tends to fail. Most big IT projects fail.
If one defines AI as integrated “ into all areas of a business resulting in fundamental changes to how businesses operate and how they deliver value to customers,” you can see why it is so hard to get early progress.
Massive AI adoption affecting “all” of the business processes will take longer than we think.
The e-conomy 2022 report produced by Bain, Google and Temasek provides an example of why massive new technology benefits are so hard to realize and measure. Literally “all” of a business, all processes and economic or social outcomes must be changed to take advantage of a truly-significant new technology.
We should not expect people and organizations to stop talking about “AI” impact. But maybe we shouldn’t listen quite so much to claims that important outcomes will happen quite soon.
“Most people overestimate what they can achieve in a year and underestimate what they can achieve in ten years” is a quote whose provenance is unknown, though some attribute it to Stanford computer scientist Roy Amara. Some people call it the “Gate’s Law.”
It will prove useful to keep that in mind as the hype over artificial intelligence, ChatGPT, large language models and generative AI eventually cools. It will. Outcomes will likely prove less than we expect early on.
The expectation for virtually all technology forecasts is that actual adoption tends to resemble an S curve, with slow adoption at first, then eventually rapid adoption by users and finally market saturation.
That sigmoid curve describes product life cycles, suggests how business strategy changes depending on where on any single S curve a product happens to be, and has implications for innovation and start-up strategy as well.
Some say S curves explain overall market development, customer adoption, product usage by individual customers, sales productivity, developer productivity and sometimes investor interest. It often is used to describe adoption rates of new services and technologies, including the notion of non-linear change rates and inflection points in the adoption of consumer products and technologies.
In mathematics, the S curve is a sigmoid function. It is the basis for the Gompertz function which can be used to predict new technology adoption and is related to the Bass Model.
Another key observation is that some products or technologies can take decades to reach mass adoption.
It also can take decades before a successful innovation actually reaches commercialization. The next big thing will have first been talked about roughly 30 years ago, says technologist Greg Satell. IBM coined the term machine learning in 1959, for example, and machine learning is only now in use.
Many times, reaping the full benefits of a major new technology can take 20 to 30 years. Alexander Fleming discovered penicillin in 1928, it didn’t arrive on the market until 1945, nearly 20 years later.
Electricity did not have a measurable impact on the economy until the early 1920s, 40 years after Edison’s plant, it can be argued.
It wasn’t until the late 1990’s, or about 30 years after 1968, that computers had a measurable effect on the US economy, many would note.
The S curve is related to the product life cycle, as well.
Another key principle is that successive product S curves are the pattern. A firm or an industry has to begin work on the next generation of products while existing products are still near peak levels.
There are other useful predictions one can make when using S curves. Suppliers in new markets often want to know “when” an innovation will “cross the chasm” and be adopted by the mass market. The S curve helps there as well.
Innovations reach an adoption inflection point at around 10 percent. For those of you familiar with the notion of “crossing the chasm,” the inflection point happens when “early adopters” drive the market. The chasm is crossed at perhaps 15 percent of persons, according to technology theorist Geoffrey Moore.
For most consumer technology products, the chasm gets crossed at about 10 percent household adoption. Professor Geoffrey Moore does not use a household definition, but focuses on individuals.
And that is why the saying “most people overestimate what they can achieve in a year and underestimate what they can achieve in ten years” is so relevant for technology products. Linear demand is not the pattern.
One has to assume some form of exponential or non-linear growth. And we tend to underestimate the gestation time required for some innovations, such as machine learning or artificial intelligence.
Other processes, such as computing power, bandwidth prices or end user bandwidth consumption, are more linear. But the impact of those linear functions also tends to be non-linear.
Each deployed use case, capability or function creates a greater surface for additional innovations. Futurist Ray Kurzweil called this the law of accelerating returns. Rates of change are not linear because positive feedback loops exist.
Each innovation leads to further innovations and the cumulative effect is exponential.
Think about ecosystems and network effects. Each new applied innovation becomes a new participant in an ecosystem. And as the number of participants grows, so do the possible interconnections between the discrete nodes.
Think of that as analogous to the way people can use one particular innovation to create another adjacent innovation. When A exists, then B can be created. When A and B exist, then C and D and E and F are possible, as existing things become the basis for creating yet other new things.
So we often find that progress is slower than we expect, at first. But later, change seems much faster. And that is because non-linear change is the norm for technology products.
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