Enterprise Uses for Artificial Intelligence
Executive Summary
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.
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
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
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
Market Trends
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
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
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.
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