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Germinating in R&D labs since the 1940s, artificial intelligence (AI) is slowly but surely moving into the mainstream across the consumer world. But in the enterprise space, AI remains bound by concerns about balancing its responsible development, deployment and usage with its ability to deliver business value. And while there’s widespread recognition of AI’s immense potential, many organizations are still working to determine how AI can move the needle where it makes the most sense: controlling costs, unleashing new customer experiences and offering an intelligent foundation for creating products and services that drive topline growth.

Given the hype, it’s no wonder that the AI market is expected to grow at a strong 36% CAGR to reach $191 billion by 2025. And according to Gartner, global business value created from AI is projected to total $3.9 trillion in 2022.

To gauge executive perceptions of and achievement with AI, we recently surveyed 975 business leaders from organizations in the U.S. and Europe. While our study uncovered widespread enthusiasm and optimism about AI, it also revealed AI’s nascent stage of adoption. For instance, the vast majority of AI projects (78%) remain in experimental stages (i.e., proofs of concept and prototypes).

The state of the state of AI in business

The following are the key takeaways gleaned from our study:

  • Most respondents consider AI to be vital to business success. The vast majority of respondents across industries view AI as extremely or very important to their business. Not surprisingly, respondents were also optimistic about AI’s ability to generate benefits, including cost efficiency, revenues and new products and services.

  • Moreover, most respondents expect major or significant benefits in terms of revenue growth from their use of AI. In fact, almost all expect value to increase significantly within three years, with financial services and technology industry companies leading the pack.

  • AI is infiltrating multiple business functions. Among all business functions, customer service appears to be a prime target for AI use across industries. This is understandable, since customer satisfaction, engagement and buy-in is critical to ensuring business success and justification of an AI-led transformation agenda.

  • Organizations are also focusing their AI efforts on areas that are core to the business, such as operations in the healthcare industry, production in manufac- turing and R&D in technology.

  • Choice of AI technology is influenced by functional area and associated processes. Respondents reported using all five of the AI technologies included in our study at a fairly similar rate (see Figure 1).

  • Organizations seem to attach equal importance to the various technologies that can power an AI strategy. However, virtual agents (conversational AI) and computer vision (machine intelligence algorithms that recognize patterns, among other things) led other AI technologies by a small margin. Respondents said their companies are selectivelydeploying technologies tailored to specific functional areas, such as virtual agents for customer service and bots in production.

Base: 975 senior leaders in the U.S. & Europe Note: Multiple responses allowed
Source: Cognizant


  • Faster-growing organizations appear to be more optimistic about AI and more aggressive in their AI adoption. Roughly 85% of respondents at faster- growing organizations expect AI to provide a major or significant impact on revenues, compared with 71% of slower- growing businesses. A higher percentage of faster-growth organization respondents (89%) also expect AI to provide a major or significant benefit in terms of efficiencies that translate into cost reduction vs. slower growth businesses (77%). While a significant majority of faster-growth companies (66%) said AI will increase jobs, only 38% of respondents at businesses with slower growth rates said they believed this to be true.
  • AI adoption challenges span talent acquisition, business cases and ethics. Respondents expressed a similar level of concern regarding challenges on the path to AI, with 40% of executives considering each of the 13 challenges listed to be extremely or very challenging. When that data is combined with the finding that only 15% of respondents were aware of a fully implemented AI project at their organization, it becomes clear that most organizations have yet to hone a clear-cut AI strategy.
  • Further, given that top challenges related to senior management commitment, business buy-in, adequate budget and lack of preparedness, it’s apparent that many companies are still struggling to define AI’s central role in advancing business objectives.

Interestingly, technology industry respondents were more apt than respondents in other industries to be aware of ethical considerations playing a role in AI deployments. This could be the result of increased scrutiny of the FAANG (Facebook, Apple, Amazon, Netflix and Google) companies relative to their use of data- and algorithmic-enabled analytical decision making, as well as the issues they’ve had to contend with regarding user privacy. Sustainable and successful AI deployments will need to be built on a foundation that ensures ethical and responsible outcomes.

The road ahead: strategy, governance and ethics imperatives

To successfully move from the nascent stages of AI into full business value realization, we believe organizations should focus on three key areas: AI strategy, governance and ethics. Addressing gaps in these areas can place AI on a sustainable path to delivering desired results. We recommend businesses take the following actions when planning their path to AI:

1 Embrace a human-centric strategy.

In addition to focusing on measurable business value, an effective AI strategy should be geared around solving a human problem and factor in the right combination of machines and human talent – from devel- opment and deployment, through usage.

2 Enact an effective governance structure.

Businesses need to engage teams in defining standards, best practices and investment strategies to get the most value from AI. The governance model should ensure that AI-led decisions are reached in a transparent and auditable way while obviating the influence of biases (unintended or otherwise) that may creep into the fabric of AI designs.

3 Build an ethical foundation – and continually maintain it.

For AI to take hold, businesses need to embed processes that ensure integration of ethical considerations into the development, deployment and ongoing usage of AI, both inside the organization’s four walls and with customers and partners.

This article was adapted from our primary research-based report “Making AI Responsible – and Effective.” To learn more, visit


Rajeshwer Chigullapalli is an Associate Director within Cognizant’s thought leadership program. He has over 25 years of experience in the areas of business research and publishing. Previously, he was the Head of ICFAI University Press and Chief Editor, SPG Media, India. He can be reached at


The author would like to thank Cognizant Digital Business’s AI and Analytics Practice for their contributions to this article, including Poornima Ramaswamy, Vice President, James Jeude, Vice President and Practice Leader, Jerry A. Smith, Vice President, Data Sciences, and Bret Greenstein, Global Vice President and Head of the AI Practice.

To learn more, read the full Cognizanti journal, Navigating the digital age: What senior leaders worldwide have learned from pushing the boundaries of change, or contact us.