frontier: Applications
and value of deep
learning PART II
1. Mapping AI techniques to problem types
2. Insights from use cases
1. and 2. Covered in PART I
IN PART II FOLLOWING
COVERED.
3. Sizing the potential value of AI
4. The road to impact and value
3.
Sizing the potential value of AI
We estimate that the AI
techniques we cite in this briefing together have the potential to create
between $3.5 trillion and $5.8 trillion in value annually across nine business
functions in 19 industries. This constitutes about 40 percent of the overall
$9.5 trillion to $15.4 trillion annual impact that could potentially be enabled
by all analytical techniques.
Per industry, we
estimate that AI’s potential value amounts to between one and nine percent of
2016 revenue. The value as measured by percentage of industry revenue varies
significantly among industries, depending on the specific applicable use cases,
the availability of abundant and complex data, as well as on regulatory and
other constraints.
These figures are not
forecasts for a particular period, but they are indicative of the considerable
potential for the global economy that advanced analytics represents.
From the use cases we
have examined, we find that the greatest potential value impact from using AI
are both in top-line-oriented functions, such as in marketing and sales, and
bottom-line-oriented operational functions, including supply chain management
and manufacturing.
Consumer industries
such as retail and high tech will tend to see more potential from marketing and
sales AI applications because frequent and digital interactions between
business and customers generate larger data sets for AI techniques to tap into.
E-commerce platforms, in particular, stand to benefit. This is because of the
ease with which these platforms collect customer information such as click data
or time spent on a web page and can then customize promotions, prices, and
products for each customer dynamically and in real time.
Here is a snapshot of
three sectors where we have seen AI’s impact:
·
In retail, marketing
and sales is the area with the most significant potential value from AI, and
within that function, pricing and promotion and customer service management are
the main value areas. Our use cases show that using customer data to
personalize promotions, for example, including tailoring individual offers
every day, can lead to a one to two percent increase in incremental sales for
brick-and-mortar retailers alone.
·
In consumer goods,
supply-chain management is the key function that could benefit from AI
deployment. Among the examples in our use cases, we see how forecasting based
on underlying causal drivers of demand rather than prior outcomes can improve
forecasting accuracy by 10 to 20 percent, which translates into a potential
five percent reduction in inventory costs and revenue increases of two to three
percent.
·
In banking,
particularly retail banking, AI has significant value potential in marketing
and sales, much as it does in retail. However, because of the importance of
assessing and managing risk in banking, for example for loan underwriting and
fraud detection, AI has much higher value potential to improve performance in
risk in the banking sector than in many other industries.
4.
The road to impact and value
There have been many exciting
breakthroughs in AI recently—but significant challenges remain. Partner Michael
Chui explains five limitations to AI that must be overcome.
Artificial intelligence
is attracting growing amounts of corporate investment, and as the technologies
develop, the potential value that can be unlocked is likely to grow. So far,
however, only about 20 percent of AI-aware companies are currently using one or
more of its technologies in a core business process or at scale.
For all their promise,
AI technologies have plenty of limitations that will need to be overcome. They
include the onerous data requirements listed above, but also five other
limitations:
·
First is the challenge
of labeling training data, which often must be done manually and is necessary
for supervised learning. Promising new techniques are emerging to address this
challenge, such as reinforcement learning and in-stream supervision, in which
data can be labeled in the course of natural usage.
·
Second is the
difficulty of obtaining data sets that are sufficiently large and comprehensive
to be used for training; for many business use cases, creating or obtaining
such massive data sets can be difficult—for example, limited clinical-trial
data to predict healthcare treatment outcomes more accurately.
·
Third is the difficulty
of explaining in human terms results from large and complex models: why was a
certain decision reached? Product certifications in healthcare and in the
automotive and aerospace industries, for example, can be an obstacle; among
other constraints, regulators often want rules and choice criteria to be
clearly explainable.
·
Fourth is the
generalizability of learning: AI models continue to have difficulties in
carrying their experiences from one set of circumstances to another. That means
companies must commit resources to train new models even for use cases that are
similar to previous ones. Transfer learning—in which an AI model is trained to
accomplish a certain task and then quickly applies that learning to a similar
but distinct activity—is one promising response to this challenge.
·
The fifth limitation
concerns the risk of bias in data and algorithms. This issue touches on
concerns that are more social in nature and which could require broader steps
to resolve, such as understanding how the processes used to collect training
data can influence the behavior of models they are used to train. For example,
unintended biases can be introduced when training data is not representative of
the larger population to which an AI model is applied. Thus, facial recognition
models trained on a population of faces corresponding to the demographics of AI
developers could struggle when applied to populations with more diverse characteristics. A recent report on the malicious use of AI highlights
a range of security threats, from sophisticated automation of hacking to
hyper-personalized political disinformation campaigns.
Organizational challenges around technology,
processes, and people can slow or impede AI adoption
Organizations planning
to adopt significant deep learning efforts will need to consider a spectrum of
options about how to do so. The range of options includes building a complete
in-house AI capability, outsourcing these capabilities, or leveraging
AI-as-a-service offerings.
Based on the use cases
they plan to build, companies will need to create a data plan that produces
results and predictions, which can be fed either into designed interfaces for
humans to act on or into transaction systems. Key data engineering challenges
include data creation or acquisition, defining data ontology, and building
appropriate data “pipes.” Given the significant computational requirements of
deep learning, some organizations will maintain their own data centers, because
of regulations or security concerns, but the capital expenditures could be
considerable, particularly when using specialized hardware. Cloud vendors offer
another option.
Process can also become
an impediment to successful adoption unless organizations are digitally mature.
On the technical side, organizations will have to develop robust data
maintenance and governance processes, and implement modern software disciplines
such as Agile and DevOps. Even more challenging, in terms of scale, is
overcoming the “last mile” problem of making sure the superior insights
provided by AI are instantiated in the behavior of the people and processes of
an enterprise.
On the people front,
much of the construction and optimization of deep neural networks remains
something of an art requiring real experts to deliver step-change performance
increases. Demand for these skills far outstrips supply at present; according
to some estimates, fewer than 10,000
people have the skills necessary to tackle serious AI problems. and competition
for them is fierce among the tech giants.
AI can seem an elusive business case
Where AI techniques and
data are available and the value is clearly proven, organizations can already
pursue the opportunity. In some areas, the techniques today may be mature and
the data available, but the cost and complexity of deploying AI may simply not
be worthwhile, given the value that could be generated. For example, an airline
could use facial recognition and other biometric scanning technology to
streamline aircraft boarding, but the value of doing so may not justify the
cost and issues around privacy and personal identification.
Similarly, we can see
potential cases where the data and the techniques are maturing, but the value
is not yet clear. The most unpredictable scenario is where either the data
(both the types and volume) or the techniques are simply too new and untested
to know how much value they could unlock. For example, in healthcare, if AI
were able to build on the superhuman precision we are already starting to see
with X-ray analysis and broaden that to more accurate diagnoses and even
automated medical procedures, the economic value could be very significant. At
the same time, the complexities and costs of arriving at this frontier are also
daunting. Among other issues, it would require flawless technical execution and
resolving issues of malpractice insurance and other legal concerns.
Societal concerns and
regulations can also constrain AI use. Regulatory constraints are especially
prevalent in use cases related to personally identifiable information. This is
particularly relevant at a time of growing public debate about the use and
commercialization of individual data on some online platforms. Use and storage
of personal information is especially sensitive in sectors such as banking,
health care, and pharmaceutical and medical products, as well as in the public
and social sector. In addition to addressing these issues, businesses and other
users of data for AI will need to continue to evolve business models related to
data use in order to address societies’ concerns.. Furthermore, regulatory
requirements and restrictions can differ from country to country, as well from
sector to sector.
Implications for stakeholders
As we have seen, it is
a company’s ability to execute against AI models that creates value, rather
than the models themselves. In this final section, we sketch out some of the
high-level implications of our study of AI use cases for providers of AI
technology, appliers of AI technology, and policy makers, who set the context
for both.
·
For AI technology
provider companies: Many companies that develop or provide AI to others have
considerable strength in the technology itself and the data scientists needed
to make it work, but they can lack a deep understanding of end markets. Understanding
the value potential of AI across sectors and functions can help shape the
portfolios of these AI technology companies. That said, they shouldn’t
necessarily only prioritize the areas of highest potential value. Instead, they
can combine that data with complementary analyses of the competitor landscape,
of their own existing strengths, sector or function knowledge, and customer
relationships, to shape their investment portfolios. On the technical side, the
mapping of problem types and techniques to sectors and functions of potential
value can guide a company with specific areas of expertise on where to focus.
·
Many companies seeking
to adopt AI in their operations have started machine learning and AI
experiments across their business. Before launching more pilots or testing
solutions, it is useful to step back and take a holistic approach to the issue,
moving to create a prioritized portfolio of initiatives across the enterprise,
including AI and the wider analytic and digital techniques available. For a
business leader to create an appropriate portfolio, it is important to develop
an understanding about which use cases and domains have the potential to drive
the most value for a company, as well as which AI and other analytical
techniques will need to be deployed to capture that value. This portfolio ought
to be informed not only by where the theoretical value can be captured, but by
the question of how the techniques can be deployed at scale across the
enterprise. The question of how analytical techniques are scaling is driven
less by the techniques themselves and more by a company’s skills, capabilities,
and data. Companies will need to consider efforts on the “first mile,” that is,
how to acquire and organize data and efforts, as well as on the “last mile,” or
how to integrate the output of AI models into work flows ranging from clinical
trial managers and sales force managers to procurement officers. Previous MGI
research suggests that AI leaders invest heavily in these first- and last-mile
efforts.
·
Policy makers will need
to strike a balance between supporting the development of AI technologies and
managing any risks from bad actors. They have an interest in supporting broad
adoption, since AI can lead to higher labor productivity, economic growth, and societal
prosperity. Their tools include public investments in research and development
as well as support for a variety of training programs, which can help nurture
AI talent. On the issue of data, governments can spur the development of
training data directly through open data initiatives. Opening up public-sector
data can spur private-sector innovation. Setting common data standards can also
help. AI is also raising new questions for policy makers to grapple with for
which historical tools and frameworks may not be adequate. Therefore, some
policy innovations will likely be needed to cope with these rapidly evolving
technologies. But given the scale of the beneficial impact on business the
economy and society, the goal should not be to constrain the adoption and
application of AI, but rather to encourage its beneficial and safe use.
By Michael Chui, James Manyika, Mehdi Miremadi, Nicolaus Henke, Rita Chung, Pieter Nel, and Sankalp Malhotra
https://www.mckinsey.com/global-themes/artificial-intelligence/notes-from-the-ai-frontier-applications-and-value-of-deep-learning?cid=other-eml-alt-mgi-mgi-oth-1804&hlkid=32b48757dfe44926a4e5437094fbb6f1&hctky=1627601&hdpid=e82a3072-74cf-43e1-9db5-56cd6a71dbe1
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