A Strategist’s Guide to Artificial
Intelligence
Outlook
2017-21
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.
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 (see Exhibit A).
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 . 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 (see Exhibit 2).
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.
by Anand Rao
https://www.strategy-business.com/article/A-Strategists-Guide-to-Artificial-Intelligence?gko=0abb5&utm_source=itw&utm_medium=20170607&utm_campaign=resp
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