An Assessment of McKinsey’s Forecast for Artificial Intelligence

An Assessment of McKinsey’s Forecast for Artificial Intelligence
By
Jeffrey Funk
Retired Professor and Independent Consultant
jeffreyleefunk@gmail.com

1. Introduction
This report assesses an analysis by the McKinsey Global Institute on the future of artificial
intelligence (AI), with a focus on the impact of AI on productivity and economic growth. It
focuses on productivity and economic growth because thought leaders such as Erik
Brynjolfsson, Andrew McAfee, Martin Ford, Peter Diamandis, and Ray Kurzweil claim that
there are endless opportunities arising from AI and related technologies such as robots and
drones. Many of them argue that the main challenge for policy makers is to prevent mass
unemployment in the face of rapid improvements in productivity that will also disrupt many
incumbents.
Others have made bolder claims. For example, Accenture, Frontier Economics and Allianz
Global Artificial Intelligence claim that artificial intelligence-enabled technologies could
double the economic growth rates of many advanced countries by 20351
.
PricewaterhouseCoopers predicts that AI will drive global Gross Domestic Product (GDP)
gains of US$15.7 trillion by 20302
. But these reports provide few details3
and optimistic
predictions continue to be made even though productivity improvements remain flat and even
as two precursors to AI, the Internet of Things and Big Data, have grown rapidly over the last
10 years with combined markets of over $300 Billion4
.
This report uses a June 2017 report by the McKinsey Global Institute to assess these bold
forecasts, as it also critiques the report from the McKinsey Global Institute. It focuses on the
McKinsey Global Institute because it is a think tank for McKinsey and Co., the world’s leading
managerial consulting firm, and because the report provides many more details than do other
reports such as one from PwC cited in the previous paragraph. McKinsey’s report (Artificial
Intelligence: The New Digital Frontier?) assesses AI’s impact on four categories of activities
and for five industries. The four activities are: 1) enabling companies to better project and
forecast to anticipate demand, optimize R&D, and improve sourcing; 2) increasing companies’
ability to produce goods and services at lower cost and higher quality; 3) helping promote
offerings at the right price, with the right message, and the right target customers; and 4)
allowing them to provide rich, personal, and convenient user experiences. The five industries
are retail, electric utilities, manufacturing, health care, and education.
My assessment relies on a 40 year-career in academic and professional positions related to
new technologies, on information available from the online media, and on several books about
AI. Many searches were done to check the veracity of specific events or forecasts in the
McKinsey Report, often checking whether a success story has been replicated in other
companies. The books include the AI Advantage by Thomas Davenport, Prediction Machines

by Ajay Agrawal, Avi Goldfarb, and Joshua Gans, The Rise of the Robots by Martin Ford, The
Second Machine Age and Machine Platform Crowd by Erik Brynjolfsson and Andrew McAfee,
The Future of the Professions by Daniel and Richard Susskind, and AI Super-Powers by Kai-
Fu Lee. Davenport’s book is referenced the most partly because it is the most recent, published
in September, 2018.
2. Assessing the Categories and Industries
I begin with an assessment of the four categories and five industries. Clearly numbers two
and four will have more impact on productivity than will the other two. The lower cost and
higher quality of number 2 and the rich, personal, and convenient user experiences of number
4 are much more relevant to productivity than are better forecasting or better promotions
(which are emphasized by many books including Prediction Machines). Although categories 2
and 4 will likely have a large impact on competition between firms and the best strategies for
them, they will likely have a small impact on cost and quality in most industries (see next
section for most details). Exceptions include industries where fixed assets have a huge impact
on costs and thus increased asset utilization through better price promotions and targeting can
lead to dramatically lower costs, perhaps for example in hotels and airplanes. As for the five
industries chosen to analyze, they represent a good mix of economic sectors, although the
largest consumer expenditure (housing) is not considered.
Exhibit 1 is taken directly from the McKinsey report (Exhibit 6 in the report). It summarizes
the impact of AI on the four categories and five industries. This includes the general impact of
AI on better forecasts, more efficient operations, better price offerings, and richer and more
personal experiences for each of the five industries. The first question concerns the magnitude:
how much will AI improve forecasts, automate operations, optimize pricing, or personalize
products and services? It is easy to argue that AI will have some impact on all four categories
in each of the five industries, but the real question is how much of an impact and when.
A second question to ask is: how important are these four categories to the cost and quality
of products and services, particularly from the five industries listed in the exhibit? We have
already noted that categories 2 and 4 probably have more impact on cost and quality than the
other two categories. Nevertheless, we would like to know more than details than this; for
example, what percent of costs are represented by those that will be impacted by AI? What
aspects of quality are important to customers and which of them can be improved by AI?
Without knowing these types of answers, it is difficult to reach conclusions about AI’s impact
on productivity.

A third question, one related to the second question, is what are the current levels of
inefficiencies in specific industries? Some of AI’s purported benefits will come from lower
inventories, less equipment downtime, and higher quality and yields. Thus, a key question is
what are these levels in specific industries? Are there currently high inventories, large
downtimes, and low yields that can be improved by AI? Similarly, other benefits include better
back-office operations including development processes. What percent of costs do these
operations represent?
Exhibit 1: Artificial intelligence can create value across the value chain in four ways

A fourth question to ask is: what are the historical trends in each of the four categories and
five industries? Improvements have been occurring in inventories, downtime, and yields for
many decades, largely driven by improvements in previous generations of information
technology (IT), and thus we would expect some further improvements even without AI.
Knowing the historical trends would help us estimate the magnitude of the likely effect from
AI, also in comparison to past technologies. For example, will AI have a greater impact on
retail than did bar-code scanners, which reduced the cost of groceries for consumers by an
estimated 1.4% according to a McKinsey study5
. Or will the impact of AI be another example
of Solow’s paradox, "You can see the computer age everywhere but in the productivity
statistics.”
Exhibit 2 is also taken directly from the McKinsey report (Exhibit 7). Based on analyses of
the five industries, it summarizes the likely impact of AI on the four categories for each of the
five industries. These summaries reference claims made by incumbents and startups on what
they believe their AI products and services can do. A first comment is that firms often
exaggerate the benefits of their products and services and furthermore only a small number of
startups succeed, so we must be careful about believing their claims and extrapolating from
them. History is littered with failed startups and with over-hyped technologies such as nuclear
fusion, solar water heaters, synthetic fuels, hydrogen vehicles, cellulosic ethanol, the Strategic
Defense Initiative (a.k.a., Star Wars), microfinance, the aerospace hysteria of the 1960s, and
magnetic levitating trains.
These historical examples should be kept in mind as we look for evidence for the claims
made byAI startups and incumbents.Are the claims consistent with claims made by other firms
and by users of these technologies? Are they consistent with answers to the four questions
raised above? The larger the number of startups and incumbents making similar claims and the
larger the number of users confirming these claims, the more believable the claims become.
The subsequent sections focus on each box in Exhibit 2, beginning with those for retail, and
ending with back-office operations, a category not emphasized by the McKinsey and not in
Exhibit 2, but one receiving much emphasis by others.
3. Retail
Exhibit 2 says that better projections through AI will lead to lower inventories (20%
reduction), fewer product returns (2 million), and higher profits (1-2% EBIT). But why 20%
and 20% of what, floor space or warehouse inventory? Also, how high are inventories as a

percent of sales; how much has this dropped over the last 40 years; and how much further can
we expect them to drop? We can also ask similar questions about product returns. Two million
is what percentage of purchases, how has this percentage increased or fallen over the last 40
years and how might AI impact on this in the future?
Exhibit 2. AI can help capture significant gains, across the value chain

Exhibit 2 says that more efficient production through AI-based AVs will lead to a “30%
reduction of stocking time” in warehouses. But is stocking time in warehouses important? For
example, how much retail costs are represented by stocking time in warehouses, or even in
stores? Or are other things such as cost of floor or warehouse space, damaged goods, or delivery
activities more important drivers of cost and how might AI impact on them?
The text in the report provides more details than do the two exhibits, and sometimes they
address the estimates made in the exhibits for retail. For instance, the text says these types of
things: “AI-based approaches to demand forecasting are expected to reduce forecasting errors
by 30 to 50 percent” and reduce “lost sales due to product unavailability by up to 65 percent.”
The report continues: “Costs related to transportation and warehousing and to supply chain
administration are expected to decrease by 5 to 10 percent and 25 to 40 percent, respectively.
With AI, overall inventory reductions of 20 to 50 percent are feasible.” These estimates are
plausible, although more details would be nice. Furthermore, without a discussion of cost
structure or references to historical trends (as noted in Section 2), it is hard to understand the
plausibility of these estimates or their importance to productivity.
The text in the report also emphasizes increasing online sales, consistent with other analyses
and with all historical trends. It mentions the use of digital assistants and voice recognition for
placing orders, and drones for delivery. While digital assistants and voice recognition may be
plausible, the report does not mention the problems with drones making landings at their final
destinations or even the need for high energy density batteries to carry objects for any distance
and instead emphasizes the impact of deep learning on the feasibility of drones. This is
unfortunate because better batteries are needed to carry objects any reasonable distance and
even to enable accurate and stable landings. Given the slow diffusion of electric vehicles (and
thus poor cost-performance of batteries), it is likely that drones will be limited to high value
items. And even for these applications, it will probably take many years before batteries have
sufficiently high energy densities to enable long-range services or precision landings in
crowded areas.
Neither the text nor the table mention another likely outcome for retail, the elimination of
check-out. Many stores have experimented with self-checkout and some are now
experimenting with automated checkouts. For example, Thomas Davenport describes
Amazon’s pilot Go stores in Seattle in his book The AI Advantage. Amazon’s “Just Walk Out
Technology automatically detects when products are taken from or returned to the shelves and
keeps track of them in a virtual cart. When you’re done shopping, you can just leave the store.

Shortly after, we’ll charge your Amazon account and send you a receipt.” Nevertheless, the
impact on costs will likely be small because humans will still work in these stores, accepting
deliveries, stocking shelves, and changing prices.
4. Electric Utilities
Exhibit 2 says that deep learning “to predict power demand and supply” can “cut national
electricity usage by 10%,” an ambitious goal. But how does a better match between supply and
demand lead to less energy usage? A cut in peak energy usage would be more believable, but
the exhibit and the text in the report emphasize the ability of deep learning to cut energy usage.
Exhibit 2 also says that machine learning and smart sensors can increase energy production
by 20% and improve profits (EBIT) by 10-20%. The former purportedly comes from better
optimization of assets and the latter from enhanced predictive maintenance, automated fault
prediction, and increased capital productivity. But the 20% figure for increased energy
production assumes that assets are down for at least 20% of the time; is this true? Base load
electricity plants (coal and nuclear) are running a high percentage of the time due to the high
costs of shutting them down while gas, oil, and solar are used to meet peak demand. And the
shutdowns are largely planned, such as for reloading nuclear fuel. Better data on the amount of
unplanned downtime would be useful to understand the potential impact of AI on energy costs.
Exhibit 2 also says that machine learning can help users automatically switch between
electricity providers thus giving them $10-$30 savings on monthly bills. Do these different
prices reflect dramatically different supplier costs or just that different suppliers will offer
different time-of day prices because they have different cost structures? If it is the latter, then
the impact on productivity will be small.
The text in the report provides details, but little of it provides justification for the estimates
in Exhibit 2. For example, the text cites an academic paper and the success of DeepMind, a
Google owned-startup, to justify the impact of AI-based forecasting to reduce energy usage.
The academic paper (Jaime Buitrago and Shihab Asfour, Short-term forecasting of electric
loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs,
Energies, vol 10, no 1, 2017) only discusses forecasting techniques and does not say anything
about the possible cost reductions from better forecasts.
The success of DeepMind might be a bigger reason for optimism about AI. It purportedly
reduced energy usage at a Google data center by 20% and Brynjolfsson’s and McAfee’s 2017
book, Machine, Platform, Crowd: Harnessing Our Digital Future, provides more details on

this example, apparently one of many successes that were achieved by Google’s DeepMind by
2015. DeepMind’s algorithms used many years of historical data from thermometers (inside
and outside), pressure gauges, hydrometers, and other sensors to suggest better settings for
pumps, coolers, cooling towers, and other equipment, admittedly a complex task that might
benefit from AI. On the other hand, McKinsey’s extrapolation from data centers to DeepMind’s
claims are less believable. Can DeepMind really reduce the UK’s energy usage by 10% and
maximize renewable energy? The claims would be more believable if information about other
data centers and other energy applications existed. My online search for such examples found
very little evidence of this6
, certainly not proof that they don’t exist, but evidence that it might
it might not occur, or it might take a while before large economy wide energy reductions (and
thus improvements in energy productivity) are achieved through AI.
The report also emphasizes smart meters and thus presumably some of the estimates in
Exhibit 2 assume that smart meters will provide some if not most of the purported reductions
in energy usage. However, the text mostly emphasizes the large number of smart meters that
have been installed, driven largely by government subsidies, and does not mention, much less
demonstrate falling energy usage from the installation of smart meters. My search for
independent analyses of smart meters also found no evidence of a linkage between them and
lower energy usage. Instead my search found that the advertised potential 2020 energy savings
figure for the UK has been more than halved since the campaign began, dropping from £26 to
just £11 a year for duel-fuel bills7
and the cost of smart meters and their installation has risen,
not good news for smart metes8
. Google’s financial reports also do not support the idea that
smart meters will have a large impact on energy efficiency. Google reports revenues of $726
million with losses of $621 million in 2017, results that do not suggest smart meters are
reducing energy usage9
.
The text uses a claim from GE’s wind turbine business and a startup to justify more energy
output through AI. For example, GE says AI could boost a wind farm’s energy production by
as much as 20 percent and create $100 million in extra value over the lifetime of a 100-
megawatt farm. Upside Energy, a startup that received a UK government grant to use AI to
manage a portfolio of batteries and other storage assets, estimates that machine learning could
be used to help unlock up to six gigawatts of demand-side flexibility that can be shifted during
the evening peak without affecting end users. The report uses these examples to “estimate that
optimizing preventive maintenance, automating fault prediction, and increasing capital
productivity through AI applications could increase power generation earnings before interest,

taxes, depreciation, and amortization (EBITDA) by 10 to 20 percent,” estimates that assume
much larger reductions in cost than 10 to 20%. But the text provides few details, and thus how
generalizable are the claims made by GE and Upside Energy? My independent search for
similar examples did not find anything noteworthy.
5. Manufacturing
Exhibit 2 says that AI will improve forecasts, thus enabling R&D to experience a “10%
yield improvement for integrated circuit-products” and a “39% IT staff reduction” for
procurement. But why do better forecasts lead to better yields or even to fewer staff needed in
procurement? And why does Exhibit 2 focus on ICs and not other products.
Exhibit 2 says that AI will improve production, thus increasing (typo?) material delivery
time and improving yields. The report probably means that machine learning will reduce
material delivery time, but one wonders why the exhibit focuses on this issue and not something
else. Material delivery time is not a key performance indicator for manufacturing, certainly not
to the extent that yields are. And for yields, why 3-5% and not something different? Without
more details, it is difficult to judge the impact on yields, which will clearly vary by industry.
Exhibit 2 also says that “AI-based customer promotions and providing” will lead to a
“13% EBIT improvement “and a “12% fuel savings for manufacturers’ customers, airlines.”
The former is expected to come through better predicting sources of servicing revenues and
optimizing sales efforts and the latter from airlines using machines to optimize flight routes.
Both depends on the type of product being manufactured with some products having higher
service revenues and delivery expenses than others. These kinds of details would be useful to
understanding the plausibility of the claims, but they are not provided.
The text in the report emphasizes the potential impact ofAI on better yields, lower downtime,
and better procurement. It cites a presentation by Siemens10
and examples from Intel, an
aerospace manufacturer, and GE. Intel purportedly used AI to achieve higher yields than other
suppliers of ICs, even though higher yields depend on many factors. An aerospace
manufacturer gained 35 million in Euro savings through using advanced analytics for process
planning, machine learning algorithms, collaborative robots, and self-driving vehicles. GE is
applying AI to aircraft engines and to the data that it has been collecting on their performance
for decades. Other examples include drones for inspecting aircraft.
These types of examples provide evidence that AI can contribute to improvements, but they
do not justify any estimates. Manufacturers have been trying to increase yields, reduce
inventory, and reduce downtime using various techniques in combination with IT for the last

40 years. How much room for further improvement is there and how much further can AI take
us down this road? For example, the story about GE aircraft engines has been told many times
in the business press11
and one wonders how many further improvements AI can achieve with
these engines. Along a similar line of argument, will AI have the same effect that CAD and
CAM had on manufacturing? Or will the impact even be as large as the impact from just
machining centers, which enabled more operations to be done before parts were moved on to
other machines?
More generally speaking, how low are yields or how high are downtime and inventories?
Without these types of data, it is hard to understand the extent to which AI can make
improvements. Ideally, historical data would show the general trends in yields, downtimes, and
inventories to understand the potential for improving these indicators. Then, the case for AI
could be made within a better context
6. Health Care
Exhibit 2 shows that $300 billion and 3.3 billion pounds in savings are possible in the U.S.
and UK from better projections with AI than from current approaches. In the U.S., machine
learning for population health forecasting and in the UK, AI for preventative care and non-
elective hospital admissions are the purported methods of cost saving. Presumably, the different
examples used for the US and UK are just accidental, merely to demonstrate the different ways
in which AI can reduce the high costs of this sector. But why would $300 billion be possible
from better population forecasting?
Exhibit 2 shows that 30-50% productivity improvements for nurses and up to 2% GDP
savings are possible in developed countries. The former is purportedly possible through AI
tools for nurses and the latter from higher operational efficiencies. Presumably, these AI tools
involve less hand-written notes and a greater use of IT while the higher operational efficiencies
involve a better organization of people, materials, and equipment. But does the 2% GDP figure
represent a reduction in the percent of the economy devoted to health care or a reduction in 2%
of health care expenditures. Assuming the former, a 2% percentage point reduction in health
care representation means a 15% reduction in costs overall, not a huge number, but still
meaningful. But any improvement in operational efficiencies assumes there are currently large
inefficiencies; how large are these inefficiencies?
Exhibit 2 also shows a 5-9% health expenditure reduction from using machine learning to
tailor treatments and keep patients engaged. How might machine learning enable more tailored
treatments? IBM’s Watson is the most discussed method of machine learning for health care.

Unfortunately, it has been reported as a failure in many hospitals and thus is probably at least
5 to 10 years away from providing better diagnoses than humans.
For example, Davenport writes in The AI Advantage, “IBM claims, for example, to have
already mastered the treatment of six types of cancer, but the actual results as evaluated by
cancer-oriented researchers and institutions are far more equivocal. No objective, rigorous
research articles have evaluated its healthcare projects. And in healthcare and other industries,
when Watson does deliver results, it’s usually only with the aid of large numbers of IBM’s (or
other firms’) consultants. Watson was criticized for this in a 2017 investor analysis report by
Jeffries & Co.” Clearly health care is a difficult application for AI.
Exhibit 2 also shows a $2 trillion to $10 trillion savings globally by tailoring drugs and
treatments and a 0.2 to 1.3 additional years of average life expectancy. Tailoring drugs sounds
like personalized drugs, something that drug manufacturers have been trying for decades
without successes. In reality, drug development costs have risen between eight and 80 times
over the last 40 years with some improvements in the last decade12
. In fairness, the report might
be referring to better prescriptions for patients, but this assumes large amounts of data for
multiple patients, something that is many years in the future, as is IBM’s Watson.
The text in the report also emphasizes the potential of AI “to draw inferences and recognize
patterns in large volumes of patient histories, medical images, epidemiological statistics, and
other data. AI has the potential to help doctors improve their diagnoses, forecast the spread of
diseases, and customize treatments. Artificial intelligence combined with health care
digitization can allow providers to monitor or diagnose patients remotely as well as transform
the way we treat the chronic diseases that account for a large share of health-care budgets.”
On the positive side, AI-based image recognition and machine learning can see far more
detail in MRI and X-ray images than human eyes can register, thus suggesting that AI will be
able to interpret these images better than can humans. And since these imaging tests “account
for about 10 percent of all healthcare costs in the United States,” better diagnostic accuracy,
“which seems likely with deep learning–based image recognition,” might lead to lower costs
and better outcomes (The AI Advantage).
On the other hand, most doctors are pessimistic about AI reducing human involvement in
reading medical images in the short term. According to Davenport’s The AI Advantage, it is
difficult to determine whether AI provides similar results to human interpretations, and how
the results compared to each other because the tests don’t use the same output measures.
According to one medical group’s director, “some focused on the probability of a lesion, others
the probability of cancer. Some would describe the features inside a nodule, some would give

its location. So we concluded we needed to define the inputs and outputs for the vendors of
these machines. We need to be able to verify the algorithms before and after they are taken to
market in terms of their effectiveness and value. We need to develop some initial processes for
radiologists to use. We will need to have a saliency map for why the system says cancer, and
ideally we’ll have things like reason codes to aid with transparency.” The bottom line is that it
will be a while before AI reduces the cost and improves the performance of medical imaging,
and yet this may be the most promising application for AI.
Others are trying to develop a Watson-type AI approach for specific types of cancer. The
report cites examples of AI being used to model “cell biology on the molecular level and being
used to identify the best drug to use for specific tumors. [AI] can also identify complex
biomarkers and search for combination therapies by performing millions of simulated
experiments each day.” For example, “Mindmaze uses AI to optimize rehabilitation for stroke
patients. Ginger.io uses it to recommend the best time to take medication based each patient’s
metabolism and other factors. A startup called Turbine uses AI to design personalized cancer
treatment regimens.” But do these products currently add value, or is their performance like
that of Watson, and thus many years away? Not enough evidence is presented to address this
question, but since few startups succeed, the more likely answer is that these approaches are
many years in the future.
The text also emphasizes the potential of machine learning to analyze the data in millions
of medical histories and to forecast health risks at the population level. Here the problem is the
lack of adequate data, although this may be easier to solve than making Watson the know-it-all
doctor.
The fourth area the report emphasizes for AI in health care is the use of “AI solutions to
optimize many ordinary business tasks. Virtual agents could automate routine patient
interactions. Speech recognition software has been used in client services, where it has reduced
the expense of processing patients by handling routine tasks such as scheduling appointments
and registering people when they enter a hospital.” Here the question is the extent to which
costs depend on these activities and the ease of implementing these systems. As mentioned
earlier, data on the cost structure of hospitals could help us better understand the percentage of
costs represented by ordinary business tasks, something that is important for many industries
(addressed in Section 8, back-office operations). On the implementation side, how easy will it
be to customize AI solutions for different hospitals? The regulations that pervade hospitals and
that often depend on country, state, and even cities will take require many years of
implementation work, even after AI is ready, which it is not.

7. Education
Exhibit 2 make three claims about the impact of AI on education, none of which seem
important. The column on production says that virtual teaching assistants can answer 40% of
student’s routine questions, thus leading to lower costs for educational institutions. But what
percent of current education costs involve teachers answering questions? Other activities such
as research, administration, and facilities are probably higher. The column on “promotions”
says that virtual assistants can increase enrollment by 1% and this also seems like an
unimportant solution for education. The column on “providing” says that machine learning and
predictive modeling can do grading, with an 85% match found with human grading.
The text in the report discusses a much broader set of AI techniques along with the already
heavy emphasis on IT by educational facilities; these facilities spent $160 billion on education
technology in 2016, and spending is expected to rise 17 percent annually through 2020. For AI,
the text cites the potential to use personal, academic, and professional data to ensure that
students benefit from the courses they choose. As with other industries, the report uses
examples from startups to judge the future of AI. This includes adaptive-learning systems from
Knewton and DreamBox Learning, computer vision and machine learning from GradeScope,
and online courses and machine learning from Coursera. But is this the key issue in university
or even secondary education, or is something else more important?
In general, the text does not acknowledge the problems and concerns with education and
the many books on how to fix it. No one seems to like the current methods of teaching, except
the teachers and professors who are responsible for most of the work. Books on education cite
a long list of problems from high costs to endless years of study, lack of learning, irrelevant
courses, and coddled students. With titles such as Rethinking School, Weapons of Mass
Instruction, Designing the New American University, Fail U, and Higher Education?, it is clear
that large changes in university and secondary education are advocated by many. For example,
these problems are clearly evident in the title of Bryan Caplan’s book, The Case Against
Education. Caplan is a professor of economics at George Mason University. By referring to
analyses of salaries and educational levels, his book argues that graduates have higher salaries
primarily because the degrees signal intelligence and hard work to employers, and not the
learning of useful skills. How can AI improve education when the problems run so deep?
8. Back-Office Operations
AI also has the potential to automate many back-office operations. The McKinsey report
only emphasizes these applications in health care, but other sources consider this application

to be the biggest one for AI in the short term. For example, Davenport’s book, The AI
Advantage, discusses the integration of different enterprise systems in order to facilitate a long
list of processes including “requests for address changes or new service additions, replacing
lost credit or ATM cards without human intervention, reaching into multiple systems to update
records and handle customer communications, reconciling failures to charge for services across
different bank billing systems by extracting information from multiple document types, reading
legal and contractual documents to extract contract provisions using natural language
processing, producing automated investment content (a few paragraphs about how customer
investments have performed over the last period) for wealth management customers at
insurance companies.”
Davenport also discusses how AI can improve development processes including software,
mechanical and electrical ones. For example, AI is “being used to automate or partially
automate certain aspects of the software development process—that is, the creation of IT
products—beyond basic programming. Software testing and quality assurance, for example,
have been labor-intensive processes. But automated testing software can create thousands of
test scripts in a few seconds that test many different uses of the software being tested.” In other
part of the book, he describes AI’s impact on mechanical and electrical design. For example,
“generative design, a new approach to computer-aided design, employs machine learning
algorithms to translate high-level design goals and constraints into thousands of possible
designs—most of which the human designer didn’t anticipate. Autodesk is the most aggressive
advocate of generative design, and is using it, for example, to help Airbus design a new
lightweight cabin partition.”
These types of applications are also discussed by many others including Martin Ford in the
Rise of the Robots, Daniela and Richard Susskind in The Future of the Professions, or in my
presentations on the impact of AI on the professions13
. The expected success of these
applications is also why some are concerned about jobs for lawyers, accountants, and other
white-collar workers in the future. Thus, it is surprising that McKinsey’s report only mentions
back-office applications for health care, and not for other industries such as manufacturing or
electric utilities.
When AI applications for back-office operations become widely used is a different question,
one that will depend on both the state of the technology and the difficulties of implementation.
The AI Advantage discusses these difficulties and emphasizes the need to embed new AI
applications in an existing system. This book claims that “it is often much easier to develop a

model than to deploy it” and that “vendors of major transaction systems—including enterprise
resource planning, customer relationship management, and HR systems—” will likely embed
these models in their applications, something that will likely take years, but not decades.
Another problem emphasized in the AI Advantage is the language must be heavily
structured “before a machine can do much with it.” Davenport claims “there is no AI without
IA” (information architecture)” and this is one of the reasons for the poor performance of IBM
Watson projects in a number of applications, not just health care. He claims that “if you are the
first to adopt the technology in your industry, you will have to teach Watson the language of
your industry and find a way to structure the knowledge you want it to absorb.” This will also
likely take many years.
9. Conclusions
Assessing the potential for AI is highly problematic. But it can be done better than what has
been done by leading institutions such as Accenture, Frontier Institute, and PricewatersCooper.
This report’s assessment of McKinsey’s report shows no evidence that economic growth rates
will double or that multi-trillion-dollar gains in global economic size will occur from AI. It also
does not show evidence for a large impact on productivity and thus employment, that have
been projected by thought leaders such as Erik Brynjolfsson, Andrew McAfee, Martin Ford,
Peter Diamandis, and Ray Kurzweil.
This report also concludes that AI can be assessed better than what has been done by the
McKinsey Global Institute. Startups are a good place to begin for an analysis of AI, but such
analyses should be careful about extrapolating from a small number of startups, particularly
when so many of them make unrealistic claims and so few of them survive. Furthermore, recent
evidence suggests that their chances of success have fallen. For example, the time to
profitability for startups have increased and the percentage of profitable startups at IPO time
has fallen, even as valuations have risen14
. Thus, even high-valued startups have become a less
reliable indicator for predicting the technologies that will diffuse.
This report recommends that more attention be placed on the economics of the relevant
industries in order to improve our understanding of AI’s potential impact. For example, what
are the cost structures of them including what drives the costs and the quality of an industry’s
products and services? Second, what are the historical trends in these industries? How have
costs and quality changed over the last 40 years and what were the drivers of these changes,
particularly those that are associated with IT? Understanding the extent to which previous
waves of IT impacted on cost and quality provides insights into what AI might do. Third, what

are the current levels of inefficiencies in specific systems and to what extent can AI solve these
inefficiencies? The third point is much easier to address once we understand the first two points.
Fourth, we should carefully examine the claims being made by the AI suppliers and the
experiments occurring with the new AI products and services.
Fifth and most importantly, how is AI getting better? This question is largely ignored by all
the reports cited in this assessment. Some reports do mention improvements in the ability of
computers to play Chess, Go, and Jeopardy, but these games have few similarities with tasks
in retail, electric utilities, manufacturing, health care, and education, tasks that require a much
broader set of capabilities. Evidence for the trivial skills needed for games is seen in the
difficulties of computers adapting to even minor changes in the rules of games15
.
One exception is improvements in image recognition algorithms through machine learning.
First noted in the early 2010s by academics from various universities (ImageNet Classification
with Deep Convolutional Networks), improvements in image recognition have continued to
occur, often made public through the ImageNet Challenge that was first started in 2010. These
improvements are a big reason why many including this assessment’s author are somewhat
optimistic about AI applications for magnetic resonance imaging, computer tomography, and
other medical imaging, which are mentioned in the section on health care. But outside of these
medical applications and the games mentioned above, it is difficult to find examples of
improvements in algorithms.
Furthermore, Moore’s Law is slowing even though it though it was a big reason for the
success of neural networks over the rule-based approaches to AI, which dominated AI in the
1980s when the author was a PhD student at Carnegie Mellon. Neural networks require more
information processing than do rule-based approaches and thus Moore’s Law has made a large
contribution to the current success of neural networks16
. But if Moore’s Law continues to slow
and if improvements in algorithms are difficult to find, what will drive the economics of AI
applications? Moore’s Law has driven the introduction of new electronic products over the last
70 years17
and along with improvements in lasers, LEDs, and glass fiber has driven
improvements in Internet speed and cost18
, thus enabling a continuous stream of innovations
in Internet content, services, and commerce. But what will drive the emergence of new AI
applications? This is the question that analysts should be asking of AI.

1
https://www.accenture.com/us-en/insight-ai-industry-growth https://hk.allianzgi.com/zh-
cn/retail/our-products/fund-in-focus-landing/allianz-global-artificial-intelligence
2
www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-
study.html
3
www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf
4
Bain argues the market for IoT will grow to about $520B in 2021, more than double the $235B
spent in 2017. Discussed in Forbes by Louis Columbus, August 16, 2018. IDC predicts revenue
from the sales of big data and business analytics applications, tools, and services will increase
more than 50%, from nearly $122 billion in 2015 to more than $187 billion in 2019. Jessica
Davis, Information Week, Big Data, Analytics Sales Will Reach $187 Billion By 2019, May
24, 2016
5
http://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Digital/
Our%20Insights/Where%20machines%20could%20replace%20humans%20and%20where%
20they%20cant/SVGZ-Sector-Automation-ex3.ashx
6
The title of this June 11, 2018 FastCompany article (Microsoft is using AI to cut the cloud’s
electric bill) suggests that AI is having an impact on energy costs but the text of the article
focuses more on the benefits of outsourcing data processing to the cloud than to AI, which can
also be seen in the sub-title (Microsoft’s cloud is far more energy-efficient and carbon-efficient
than traditional on-site data centers).
7
Energy firms are running out of time to meet 2020 smart meter rollout deadline, Which?, Alex
Geraghty, 19 November 2018.
8
Smart meter benefits cut by old technology and rising costs: Energy companies may end up
spending billions unless they can install smart meters more cheaply, Cliff Saran, 23 Nov 2018,
ComputerWeekly.com.
9
https://www.recode.net/2018/4/23/17272756/google-alphabet-nest-q1-earnings-2018-
revenue-operating-loss
10
Welcome to Electronics Works Amberg (EWA),” presentation to analysts, September 29,
2015; Massimo Barbato, “Inside Amberg: Industry 4.0 in action,” Chartered Management
Institute, November 4, 2015),
11
Source: Economist, January 8, 2009. Britain's lonely high-flier
http://productserviceinnovation.com/home/blog/ (August 20, 2014)
12
Jack Scannell, Alex Blanckley, Helen Boldon & Brian Warrington, Diagnosing the Decline
in Pharmaceutical R&D Efficiency, Nature Reviews Drug Discovery, Nature Reviews Drug
Discovery 11: 191–200, 2012. Nicholas Bloom, Charles I. Jones, John Van Reenen, Michael
Webb, Are Ideas Getting Harder to Find, Working Paper. Anne-Marie Knott, The Record on
R&D Outsourcing, US Census Bureau Center for Economic Studies Paper No. CES-WP- 16-
19
13
Funk J, AI and the Future of the Professions, https://www.slideshare.net/Funk98/ai-and-
future-of-professions
14
Copeland R and Brown E, Palantir Has a $20 Billion Valuation and a Bigger Problem: It
Keeps Losing Money, Wall Street Journal, 12 November 2018. Kenney M and Zysman J,
Unicorns, Cheshire cats, and the new dilemmas of entrepreneurial finance, Venture Capital
2018. IPO Market Has Never Been This Forgiving to Money-Losing Firms, Corrie Driebusch
and Maureen Farrell Oct. 1, 2018.
15
How to Teach Artificial Intelligence some Common Sense, Clive Thompson, November 13,
2018 https://www.wired.com/story/how-to-teach-artificial-intelligence-common-sense/
16 Brynjolfsson E and McAfee A 2017. Machine, Platform, Crowd: Harnessing Our Digital

Future, W. W. Norton & Co. Kai-Fu Lee, AI Superpowers: China, Silicon Valley, and the Rest
of the World. Houghton, Mifflin, Harcourt, 2018
17
Funk J 2018. Technology Change, Economic Feasibility and Creative Destruction: The Case
of New Electronic Products and Services, Industrial and Corporate Change 27(1): Pages 65–
82.
18
Funk J 2013. What Drives Exponential Improvements, California Management Review,
Spring. Funk J and Magee C 2015. Rapid Improvements with No Commercial Production:
How do the improvements occur? Research Policy 44(3): 777-788

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