Enterprise Uses for Artificial Intelligence




Enterprise Uses for Artificial Intelligence











PDF version of this report
You must have Adobe Acrobat reader to view, save, or print PDF files. The
reader is available for free
download.

Enterprise Uses for
Artificial Intelligence

by Geoff Keston

Copyright 2022, Faulkner Information Services. All
Rights Reserved.

Docid: 00021055

Publication Date: 2210

Report Type: MARKET

Preview

Artificial intelligence (or AI) is the simulation of human intelligence
processes, especially learning and adaptive behavior, by machines. Among
other uses, AI is employed by enterprises to power a wide variety of
business and consumer applications, such as sifting through mountains of
Big Data to extract precious business intelligence, or permitting a
vehicle to drive itself.

Report Contents:

Executive Summary

[return to top of this
report]

Artificial intelligence (or AI) is the simulation of human intelligence
processes, especially learning and adaptive behavior, by machines.1
Among other uses, AI is employed by enterprises to power a wide variety of
business and consumer applications, such as sifting through mountains of
Big Data to extract precious business intelligence, or permitting a
vehicle to drive itself.

Artificial Intelligence Tutorial

The most prominent of AI technologies is machine learning (ML), which
enables a system to enhance its awareness and capabilities – that is, to
learn – without being explicitly programmed to do so. In some cases, ML
systems learn by studying information contained in data warehouses. In
other cases, they learn by conducting thousands of data simulations,
detecting patterns, and drawing inferences.

As a source of competitive advantage, AI is becoming an indispensable
element in enterprise:

  • Sales, such as demand forecasting
  • Marketing, such as website personalization
  • Customer support, such as chatbots as front-line customer service
    agents
  • Finance, such as automating tedious accounting activities
  • Human resources, such as analyzing job candidate profiles
  • Operations, such as enabling digital transformation initiatives
  • Research and development, such as contributing to product design and
    manufacture2

As a consequence, the global artificial intelligence market is expected
to expand from an estimated $387.45 billion in 2022 to $1394.30 billion in
2029, reflecting a remarkable compound annual growth rate (CAGR) of 20.1
percent during the forecast period.3

Market Dynamics

[return to top of this
report]

Top Enterprise AI Applications

Analyst Hanna Kleinings reports that “Automation, data analytics, and
natural language processing (NLP) are among the top [enterprise]
applications of AI:”

“Automation – People are no
longer required to undertake repetitive activities as a result of
automation. It frees up employees’ time to focus on higher-value work by
completing monotonous or error-prone tasks.

“Data analytics – Data
analytics allows organizations to gain insights that were previously
inaccessible by discovering new patterns and correlations in data.

“Natural language processing
(NLP) – Natural language processing is beneficial because
it empowers search engines to be smarter, chatbots to be more helpful, and
boosts accessibility for those with disabilities, such as hearing
impairments.”4

Top Enterprise AI Technologies

According to data which likely reflects the experience of most
industrialized nations, Eurostat, the statistical office of the European
Union, reports that in 2021 eight percent of [the] enterprises in the EU
with ten or more employees and self-employed persons used at least one of
the following artificial intelligence technologies:

Technologies analyzing written language (text
mining).

Technologies converting spoken language into a
machine-readable format (speech recognition).

Technologies generating written or spoken
language (natural language generation).

Technologies identifying objects or people
based on images (image recognition, image
processing).

Machine learning (e.g. deep learning) for data
analysis.

Technologies automating different workflows or
assisting in decision-making (AI-based software robotic process
automation).

Technologies enabling machines to physically
move by observing their surroundings and taking autonomous decisions.

Four percent of enterprises used at least two of the above-mentioned AI
technologies and two percent used at least three of these technologies.

Not unexpectedly, large enterprises used AI more than small and medium
enterprises. In 2021, six percent of small enterprises, 13 percent of
medium enterprises, and 28 percent of large enterprises used AI. This
difference might be explained by the complexity of implementing AI
technologies in an enterprise, economies of scale (i.e., enterprises with
larger economies of scale can benefit more from AI), or costs (i.e.,
investment in AI may be more affordable for large enterprises).5

Market Leaders

[return to top of this
report]

In the field of artificial intelligence, leadership is not determined
simply by sales or market share in a particular segment. AI technology is
often used as part of a service or woven into the operation of a piece
of software rather than being marketed to customers as a product or
service. The following are some of the major companies that are already
shaping the market and steering the thinking relative to AI and the
enterprise.

  • Amazon
  • IBM
  • Microsoft
  • Salesforce.com
  • Alphabet (Google LLC)
  • NVIDIA
  • Baidu
  • SAP
  • Oracle
  • Hewlett Packard Enterprise
  • SAS Institute 6

[return to top of this
report]

Projected Market Growth

According to Fortune Business Insights, the global artificial
intelligence market is expected to expand from an estimated $387.45
billion in 2022 to $1394.30 billion in 2029, reflecting a remarkable
compound annual growth rate (CAGR) of 20.1 percent during the forecast
period.7

Hot AI Topics

Figure 1 is a network visualization of AI in Business.
The size of each node “is proportional to the relative presence of the
topic in current literature while the width of each edge shows the level
of inter-topic distance.” A quick read of the graphic reveals current high
interest in:

  • AI and social applications (as opposed to industrial applications)
  • AI and predictive methods (as AI is viewed as a window into the
    future).

Figure 1. What’s Hot in the Latest Research

Figure 1. What's Hot in the Latest Research

Source: Wikimedia Commons | Adapted from: A. Sestino, A.
De Mauro (2021), “Leveraging Artificial Intelligence in Business:
Implications, Applications and Methods,” Technology Analysis &
Strategic Management, DOI: 10.1080/09537325.2021.1883583

Machine Learning Adoption

Among their various AI options, enterprise planners are expected to
embrace more machine learning as a method of coping with today’s
information overload. Among the major motivations for implementing ML are:

The exponential growth in Big Data; in
particular, data produced by Internet of Things (IoT) platforms, systems,
applications, and sensors.

The increasing generation of “synthetic” data
through data extrapolation and simulation.

The steady advancements in machine learning
algorithms, making machines smarter.

The collapsing costs of storage
infrastructure, making ML affordable.

The transformative effects of machine learning
on business processes, helping enterprises realize the goals of 90s-era
business process reengineering theory.

The influence of machine learning on robotics
and other allied AI fields.

The ability to displace expensive blue- and
white-collar personnel as executives recognize that technology, not
globalization, is the real engine of enterprise cost-cutting and
profitability.

As a Service

Along with machine learning, expect greater adoption of AI as a Service
(AIaaS). As analyst Pete Peranzo observes, a major inducement is the
availability of high-tech infrastructure. “With AIaaS, it is now easier to
access strong and fast GPUs needed to implement AI and ML models. Access
to high-tech infrastructure is welcome, especially as most SMEs
(small-to-medium-sized businesses) don’t have the necessary resources and
time to develop solutions in-house. Moreover, with AIaaS being
customizable, businesses get the opportunity to build a specific
task-oriented model.”8

Strategic Planning Implications

[return to top of this
report]

Summing up the case for artificial intelligence in the enterprise,
analyst Hanna Kleinings reminds us that “AI and Machine Learning have
revolutionized and will continue to revolutionize businesses for many
years to come. From IT operations to sales, implementing AI into business
environments cuts down on time spent on repetitive tasks, improves
employee productivity, and enhances the overall customer experience. It
also helps avoid mistakes and detect potential crises at a level
unattainable to humans.

“No wonder organizations are leveraging it to improve a number of
business areas, from logistics all the way through to recruiting and
employment. It’s our conviction that companies at the forefront of AI will
reap the financial advantages and dominate the competition in the future.”9

Unfortunately, one of the challenges of adopting AI is assessing what
other organizations – including competitors – are doing or attempting to
do. “It’s hard to gauge the proportion of businesses that are effectively
using artificial intelligence today, and to what extent,” writes reporter
Daphne Leprince-Ringuet.10 Without clear models to follow or
benchmarks to target, organizations are less likely to try more advanced
applications, or to put the technology to use for mission-critical systems
and services.

As with other recent innovations – edge computing, fog computing,
Internet of Things (IoT), etc. – the prospect of on-the-job training is
neither pleasant or prudent. Enterprise officials, especially those
serving small-to-medium-sized enterprises should engage an experienced
consulting company to help:

  • Draft artificial intelligence policies,
  • Develop artificial intelligence plans, and
  • Manage the incorporation of artificial intelligence into the
    enterprise information infrastructure.

References

    1 TechTarget.

    2 Hanna Kleinings. “Seven Applications of Artificial
    Intelligence in Business.” Levity AI GmbH. September 20, 2022.

    3 “Artificial Intelligence (AI) Market Forecast, 2022-2029.”
    Fortune Business Insights. 2022.

    4 Hanna Kleinings. “Seven Applications of Artificial
    Intelligence in Business.” Levity AI GmbH. September 20, 2022.

    5 “Use of Artificial Intelligence in Enterprises.” Eurostat.
    June 22, 2022.

    6 “Artificial Intelligence (AI) Market Forecast, 2022-2029.”
    Fortune Business Insights. 2022.

    7 Ibid.

    8 Pete Peranzo. “What Is AIaaS? The Ultimate Guide to AI as
    a Service.” Imaginovation.net. April 19, 2022.

    9 Hanna Kleinings. “Seven Applications of Artificial
    Intelligence in Business.” Levity AI GmbH. September 20, 2022.

    10 Daphne Leprince-Ringuet. “AI for Business: What’s Going
    Wrong, and How to Get It Right.” ZDNet. April 1, 2020.

[return to top of this
report]

About the Author

[return to top of this
report]

Geoff Keston is the author of more than 250 articles
that help organizations find opportunities in business trends and
technology. He also works directly with clients to develop communications
strategies that improve processes and customer relationships. Mr. Keston
has worked as a project manager for a major technology consulting and
services company and is a Microsoft Certified Systems Engineer and a
Certified Novell Administrator.

[return to top of this
report]