AI SPECIAL A
Strategist’s Guide to Artificial Intelligence
As the
conceptual side of computer science becomes practical and relevant to
business, companies must decide what type of AI role they should play.
Jeff Heepke knows where to plant corn on
his 4,500-acre farm in Illinois because of artificial intelligence (AI).
He uses a smartphone app called Climate Basic, which divides Heepke’s
farmland (and, in fact, the entire continental U.S.) into plots that are 10
meters square. The app draws on local temperature and erosion records, expected
precipitation, soil quality, and other agricultural data to determine how to
maximize yields for each plot. If a rainy cold front is expected to pass by,
Heepke knows which areas to avoid watering or irrigating that afternoon. As the
U.S. Department of Agriculture noted, this use of artificial intelligence
across the industry has produced the largest crops in the country’s history.
Climate Corporation, the Silicon Valley–based
developer of Climate Basic, also offers a more advanced AI app that operates
autonomously. If a storm hits a region, or a drought occurs, it adjusts local
yield numbers downward. Farmers who have bought insurance to supplement their
government coverage get a check; no questions asked, no paper filing necessary.
The insurance companies and farmers both benefit from having a much less
labor-intensive, more streamlined, and less expensive automated claims process.
Monsanto
paid nearly US$1 billion to buy
Climate Corporation in 2013, giving the company’s
models added
legitimacy. Since then, Monsanto has continued to
upgrade the AI models, integrating data from farm equipment and sensors planted
in the fields so that they improve their accuracy and insight as more data is
fed into them. One result is a better understanding of climate change and its
effects — for example, the northward migration of arable land for corn, or the
increasing frequency of severe storms.
Applications like this are typical of the new
wave of artificial intelligence in business. AI is generating new approaches to
business models, operations, and the deployment of people that are likely to
fundamentally change the way business operates. And if it can transform an
earthbound industry like agriculture, how long will it be before your company
is affected?
An Unavoidable Opportunity
Many
business leaders are keenly aware of the potential value of artificial
intelligence, but are not yet poised to take advantage of it. In PwC’s
2017 Digital IQ survey of senior executives
worldwide, 54 percent of the respondents said they were making substantial
investments in AI today. But only 20 percent said their organizations had the
skills necessary to succeed with this technology (see “Winning with
Digital Confidence,” by Chris Curran and Tom Puthiyamadam).
Reports on artificial intelligence tend to
portray it as either a servant, making all technology more responsive, or an
overlord, eliminating jobs and destroying privacy. But for business decision
makers, AI is primarily an enabler of productivity. It will eliminate jobs, to
be sure, but it will also fundamentally change work processes and might create
jobs in the long run. The nature of decision making, collaboration, creative
art, and scientific research will all be affected; so will enterprise
structures. Technological systems, including potentially your products and
services, as well as your office and factory equipment, will respond to people
(and one another) in ways that feel as if they are coming to life.
In
their book Artificial
Intelligence: A Modern Approach (Pearson,
1995), Stuart Russell and Peter Norvig define
AI as “the designing and building of
intelligent agents that receive percepts from the environment and take actions
that affect that environment.” The most critical difference between AI and
general-purpose software is in the phrase “take actions.” AI enables machines
to respond on their own to signals from the world at large, signals that
programmers do not directly control and therefore can’t anticipate.
The fastest-growing category of AI is machine
learning, or the ability of software to improve its own activity by analyzing
interactions with the world at large (see “The Road to Deep Learning,” below).
This technology, which has been a continual force in the history of computing
since the 1940s, has grown dramatically in sophistication during the last few
years.
The
Road to Deep Learning
This
may be the first moment in AI’s history when a majority of experts agree the
technology has practical value. From its conceptual beginnings in the 1950s,
led by legendary computer scientists such as Marvin Minsky and John McCarthy,
its future viability has been the subject of fierce debate. As recently as
2000, the most proficient AI system was roughly comparable, in complexity, to
the brain of a worm. Then, as high-bandwidth networking, cloud computing, and
high-powered graphics-enabled microprocessors emerged, researchers began
building multilayered neural networks — still extremely slow and limited in
comparison with natural brains, but useful in practical ways.
The
best-known AI triumphs — in which software systems beat expert human players
in Jeopardy, chess, Go, poker, and soccer — differ from most
day-to-day business applications. These games have prescribed rules and
well-defined outcomes; every game ends in a win, loss, or tie. The games are
also closed-loop systems: They affect only the players, not outsiders. The
software can be trained through multiple failures with no serious risks. You
can’t say the same of an autonomous vehicle crash, a factory failure, or a
mistranslation.
There
are currently two main schools of thought on how to develop the inference
capabilities necessary for AI programs to navigate through the complexities of
everyday life. In both, programs learn from experience — that is, the responses
and reactions they get influence the way the programs act thereafter. The first
approach uses conditional instructions (also known as heuristics) to accomplish
this. For instance, an AI bot would interpret the emotions in a conversation by
following a program that instructed it to start by checking for emotions that
were evident in the recent past.
The
second approach is known as machine learning. The machine is taught, using
specific examples, to make inferences about the world around it. It then builds
its understanding through this inference-making ability, without
following specific instructions to do so. The Google search engine’s
“next-word completion” feature is a good example of machine learning. Type in
the word artificial, and several suggestions for the next word will
appear, perhaps intelligence, selection, and insemination.
No one has programmed it to seek those complements. Google chose the strategy
of looking for the three words most frequently typed after artificial.
With huge amounts of data available, machine learning can provide uncanny
accuracy about patterns of behavior.
The
type of machine learning called deep learning has become increasingly
important. A deep learning system is a multilayered neural network that learns
representations of the world and stores them as a nested hierarchy of concepts
many layers deep. For example, when processing thousands of images,
it recognizes objects based on a hierarchy of simpler building blocks:
straight lines and curved lines at the basic level, then eyes, mouths, and
noses, and then faces, and then specific facial features. Besides image
recognition, deep learning appears to be a promising way to approach complex
challenges such as speech comprehension, human–machine conversation, language
translation, and vehicle navigation.
Though it is the closest machine to a human
brain, a deep learning neural network is not suitable for all problems. It
requires multiple processors with enormous computing power, far beyond
conventional IT architecture; it will learn only by processing enormous amounts
of data; and its decision processes are not transparent.
News
aggregation software, for example, had long relied on rudimentary AI to curate
articles based on people’s requests. Then it evolved to analyze behavior,
tracking the way people clicked on articles and the time they spent reading,
and adjusting the selections accordingly. Next it aggregated individual users’
behavior with the larger population, particularly those who had similar media
habits. Now it is incorporating broader data about the way readers’ interests
change over time, to anticipate what people are likely to want to see next,
even if they have never clicked on that topic before. Tomorrow’s AI aggregators
will be able to detect
and counter “fake news” by scanning for
inconsistencies and routing people to alternative perspectives.
AI
applications in daily use include all smartphone digital assistants, email
programs that sort entries by importance, voice recognition systems, image
recognition apps such as Facebook Picture Search, digital assistants such as
Amazon Echo and Google Home, and much of the emerging Industrial Internet. Some
AI apps are targeted at minor frustrations — DoNotPay, an online legal bot, has
reversed thousands of parking tickets — and others, such as connected car and
language translation technologies, represent fundamental shifts in the way
people live. A growing number are aimed at improving human behavior; for
instance, GM’s 2016 Chevrolet Malibu feeds data from sensors into a backseat
driver–like guidance system for teenagers at the
wheel.
Despite
all this activity, the market for AI is still small. Market research firm
Tractica estimated
2016 revenues at just $644 million. But it expects
hockey stick–style growth, reaching $15 billion by 2022 and accelerating
thereafter. In late 2016, there were about 1,500 AI-related startups in the
U.S. alone, and total
funding in 2016 reached a record $5 billion. Google, Facebook,
Microsoft, Salesforce.com, and other tech companies are snapping up AI software
companies, and large, established companies are recruiting deep learning talent
and, like Monsanto, buying AI companies specializing in their markets. To make
the most of this technology in your enterprise, consider the three main ways
that businesses can or will use AI:
• Assisted intelligence, now widely
available, improves what people and organizations are already doing.
• Augmented intelligence, emerging today,
enables organizations and people to do things they couldn’t otherwise do.
• Autonomous intelligence, being developed
for the future, creates and deploys machines that act on their own.
Many
companies will make investments in all three during the next few years, drawing
from a wide variety of applications (see Exhibit 1). They complement one
another, but require different types of investment, different staffing
considerations, and different business models.
Assisted Intelligence
Assisted intelligence amplifies the value of
existing activity. For example, Google’s Gmail sorts incoming email into
“Primary,” “Social,” and “Promotion” default tabs. The algorithm, trained with
data from millions of other users’ emails, makes people more efficient without
changing the way they use email or altering the value it provides.
Assisted intelligence tends to involve
clearly defined, rules-based, repeatable tasks. These include automated
assembly lines and other uses of physical robots; robotic process automation,
in which software-based agents simulate the online activities of a human being;
and back-office functions such as billing, finance, and regulatory compliance.
This form of AI can be used to verify and cross-check data — for example, when
paper checks are read and verified by a bank’s ATM. Assisted intelligence has
already become common in some enterprise software processes. In “opportunity to
order” (basic sales) and “order to cash” (receiving and processing customer
orders), the software offers guidance and direction that was formerly available
only from people.
The Oscar W. Larson Company used assisted
intelligence to improve its field service operations. This is a
70-plus-year-old family-owned general contractor, which among other services to
the oil and gas industry, provides maintenance and repair for point-of-sales
systems and fuel dispensers at gas stations. One costly and irritating problem
is “truck rerolls”: service calls that have to be rescheduled because the
technician lacks the tools, parts, or expertise for a particular issue. After
analyzing data on service calls, the AI software showed how to reduce truck
rerolls by 20 percent, a rate that should continue to improve as the software
learns to recognize more patterns.
Assisted intelligence apps often involve
computer models of complex realities that allow businesses to test decisions
with less risk. For example, one auto manufacturer has developed a simulation
of consumer behavior, incorporating data about the types of trips people make,
the ways those affect supply and demand for motor vehicles, and the variations
in those patterns for different city topologies, marketing approaches, and
vehicle price ranges. The model spells out more than 200,000 variations for the
automaker to consider and simulates the potential success of any tested
variation, thus assisting in the design of car launches. As the automaker
introduces new cars and the simulator incorporates the data on outcomes from
each launch, the model’s predictions will become ever more accurate.
AI-based packages of this sort are available
on more and more enterprise software platforms. Success with assisted
intelligence should lead to improvements in conventional business metrics such
as labor productivity, revenues or margins per employee, and average time to
completion for processes. Much of the cost involved is in the staff you hire,
who must be skilled at marshaling and interpreting data. To evaluate where to
deploy assisted intelligence, consider two questions: What products or services
could you easily make more marketable if they were more automatically
responsive to your customers? Which of your current processes and practices,
including your decision-making practices, would be more powerful with more
intelligence?
Augmented Intelligence
Augmented intelligence software lends new
capability to human activity, permitting enterprises to do things they couldn’t
do before. Unlike assisted intelligence, it fundamentally alters the nature of
the task, and business models change accordingly.
For example, Netflix uses machine learning
algorithms to do something media has never done before: suggest choices
customers would probably not have found themselves, based not just on the
customer’s patterns of behavior, but on those of the audience at large. A Netflix
user, unlike a cable TV pay-per-view customer, can easily switch from one
premium video to another without penalty, after just a few minutes. This gives
consumers more control over their time. They use it to choose videos more
tailored to the way they feel at any given moment. Every time that happens, the
system records that observation and adjusts its recommendation list — and it
enables Netflix to tailor its next round of videos to user preferences more
accurately. This leads to reduced costs and higher profits per movie, and a
more enthusiastic audience, which then enables more investments in
personalization (and AI). Left outside this virtuous circle are conventional
advertising and television networks. No wonder other video channels, such as
HBO and Amazon, as well as recorded music channels such as Spotify, have moved
to similar models.
Over time, as algorithms grow more
sophisticated, the symbiotic relationship between human and AI will further
change entertainment industry practices. The unit of viewing decision will
probably become the scene, not the story; algorithms will link scenes to
audience emotions. A consumer might ask to see only scenes where a Meryl Streep
character is falling in love, or to trace a particular type of swordplay from
one action movie to another. Data accumulating from these choices will further
refine the ability of the entertainment industry to spark people’s emotions,
satisfy their curiosity, and gain their loyalty.
Another
current use of augmented intelligence is in legal research. Though most cases
are searchable online, finding relevant precedents still requires many hours of
sifting through past opinions. Luminance, a startup specializing in legal research, can run
through thousands of cases in a very short time, providing inferences about
their relevance to a current proceeding. Systems like these don’t yet replace
human legal research. But they dramatically reduce the rote work conducted by
associate attorneys, a job rated as the least
satisfying in the United States.
Similar applications are emerging for other types of data sifting, including
financial audits, interpreting regulations, finding patterns in epidemiological
data, and (as noted above) farming.
To develop applications like these, you’ll
need to marshal your own imagination to look for products, services, or
processes that would not be possible at all without AI. For example, an AI
system can track a wide number of product features, warranty costs, repeat
purchase rates, and more general purchasing metrics, bringing only unusual or
noteworthy correlations to your attention. Are a high number of repairs
associated with a particular region, material, or line of products? Could you
use this information to redesign your products, avoid recalls, or spark
innovation in some way?
The success of an augmented intelligence
effort depends on whether it has enabled your company to do new things. To
assess this capability, track your margins, innovation cycles, customer
experience, and revenue growth as potential proxies. Also watch your impact on
disruption: Are your new innovations doing to some part of the business
ecosystem what, say, ride-hailing services are doing to conventional taxi
companies?
You won’t find many off-the-shelf
applications for augmented intelligence. They involve advanced forms of machine
learning and natural language processing, plus specialized interfaces tailored
to your company and industry. However, you can build bespoke augmented
intelligence applications on cloud-based enterprise platforms, most of which
allow modifications in open source code. Given the unstructured nature of your
most critical decision processes, an augmented intelligence application would
require voluminous historical data from your own company, along with data from
the rest of your industry and related fields (such as demographics). This will
help the system distinguish external factors, such as competition and economic
conditions, from the impact of your own decisions.
The greatest change from augmented
intelligence may be felt by senior decision makers, as the new models often
give them new alternatives to consider that don’t match their past experience
or gut feelings. They should be open to those alternatives, but also skeptical.
AI systems are not infallible; just like any human guide, they must show
consistency, explain their decisions, and counter biases, or they will lose
their value.
Autonomous Intelligence
Very few autonomous intelligence systems —
systems that make decisions without direct human involvement or oversight — are
in widespread use today. Early examples include automated trading in the stock
market (about 75 percent of Nasdaq trading is conducted autonomously) and
facial recognition. In some circumstances, algorithms are better than people at
identifying other people. Other early examples include robots that dispose of
bombs, gather deep-sea data, maintain space stations, and perform other tasks
inherently unsafe for people.
The most eagerly anticipated forms of
autonomous intelligence — self-driving cars and full-fledged language
translation programs — are not yet ready for general use. The closest
autonomous service so far is Tencent’s messaging and social media platform
WeChat, which has close to 800 million daily active users, most of them in
China. The program, which was designed primarily for use on smartphones, offers
relatively sophisticated voice recognition, Chinese-to-English language
translation, facial recognition (including suggestions of celebrities who look
like the person holding the phone), and virtual bot friends that can play guessing
games. Notwithstanding their cleverness and their pioneering use of natural
language processing, these are still niche applications, and still very limited
by technology. Some of the most popular AI apps, for example, are small, menu-
and rule-driven programs, which conduct fairly rudimentary conversations around
a limited group of options.
Despite
the lead time required to bring the technology further along, any business
prepared to base a strategy on advanced digital technology should be thinking
seriously about autonomous intelligence now. The Internet of Things will
generate vast amounts of information, more than humans can reasonably
interpret. In commercial aircraft, for example, so much flight data is gathered
that engineers can’t process it all; thus, Boeing has announced a $7.5
million partnership
with Carnegie Mellon University,
along with other efforts to develop AI systems that can, for example, predict
when airplanes will need maintenance. Autonomous intelligence’s greatest
challenge may not be technological at all — it may be companies’ ability to
build in enough transparency for people to trust these systems to act in their
best interest.
First
Steps
As you contemplate the introduction of
artificial intelligence, articulate what mix of the three approaches works best
for you.
• Are you primarily interested in upgrading
your existing processes, reducing costs, and improving productivity? If so,
then start with assisted intelligence, probably with a small group of services
from a cloud-based provider.
• Do you seek to build your business around
something new — responsive and self-driven products, or services and
experiences that incorporate AI? Then pursue an augmented intelligence
approach, probably with more complex AI applications resident on the cloud.
• Are you developing a genuinely new
technology? Most companies will be better off primarily using someone else’s AI
platforms, but if you can justify building your own, you may become one of the
leaders in your market.
The
transition among these forms of AI is not clean-cut; they sit on a continuum.
In developing their own AI strategy, many companies begin somewhere between
assisted and augmented, while expecting to move toward autonomous eventually.
Though investments in AI may seem expensive
now, the costs will decline over the next 10 years as the software becomes more
commoditized. “As this technology continues to mature,” writes Daniel Eckert, a
managing director in emerging technology services for PwC US, “we should see
the price adhere toward a utility model and flatten out. We expect a tiered
pricing model to be introduced: a free (or freemium model) for simple
activities, and a premium model for discrete, business-differentiating
services.”
AI is often sold on the premise that it will
replace human labor at lower cost — and the effect on employment could be
devastating, though no one knows for sure. Carl Benedikt Frey and Michael
Osborne of Oxford University’s engineering school have calculated that AI will
put 47 percent of the jobs in the U.S. at risk; a 2016 Forrester research
report estimated it at 6 percent, at least by 2025. On the other hand, Baidu
Research head (and deep learning pioneer) Andrew Ng recently said, “AI is the
new electricity,” meaning that it will be found everywhere and create new jobs
that weren’t imaginable before its appearance.
At the same time that AI threatens the loss
of an almost unimaginable number of jobs, it is also a hungry, unsatisfied
employer. The lack of capable talent — people skilled in deep learning
technology and analytics — may well turn out to be the biggest obstacle for
large companies. The greatest opportunities may thus be for independent
businesspeople, including farmers like Jeff Heepke, who no longer need scale to
compete with large companies, because AI has leveled the playing field.
It is still too early to say which types of
companies will be the most successful in this area — and we don’t yet have an
AI model to predict it for us. In the end, we cannot even say for sure that the
companies that enter the field first will be the most successful. The dominant
players will be those that, like Climate Corporation, Oscar W. Larson, Netflix,
and many other companies large and small, have taken AI to heart as a way to
become far more capable, in a far more relevant way, than they otherwise would
ever be.
https://www.strategy-business.com/article/A-Strategists-Guide-to-Artificial-Intelligence?gko=0abb5&utm_source=itw&utm_medium=20170523&utm_campaign=respA
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