New Report Shows Cautious Optimism Among Enterprises Adopting AI

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Enterprises are investing in AI, but cautious optimism drives current market adoption of generative AI across various industries and geographies. According to Cognizant, which has released a companion analysis to its comprehensive 2023 study with Oxford Economics and the new report titled New Work, New World: Quantifying Global Gen AI Momentum, enterprises are cautious about scaling AI, with only 26% having implemented cross-enterprise use cases.

Key findings from the survey reveal that enhancing productivity is the greatest strategic priority for generative AI adoption. Additionally, 76% of businesses want to leverage the technology to create new revenue streams, while 58% incorporate revenue increases into their business cases.

In terms of readiness and business cases, the companies surveyed indicated that they plan to invest an average of $47.5 million in generative AI this financial year. Most funding is expected to come from IT and technology budgets, with contributions also from marketing and R&D. Furthermore, the survey highlights a commitment to workforce transformation, with 54% of companies planning to upskill workers to address skills gaps and 44% seeking to transition displaced workers to new roles.

Despite the enthusiasm for adopting generative AI, businesses also recognize the challenges of scaling the technology. Only 26% of companies have implemented cross-enterprise use cases, and there is widespread concern that delays in adoption could give competitors an advantage. Globally, 70% of companies say they are not moving fast enough, while 82% suggest that the same delay in execution could place them at a competitive disadvantage. The data also points to the need for outside expertise to help with AI adoption, with 43% of companies indicating they plan to work with external consultants to develop a plan for generative AI.

Challenges Inhibiting GenAI Momentum

The report found that most respondents (70%) believe their organization is not moving fast enough to keep pace with industry peers, and a large majority (82%) feel these delays could place them at a competitive disadvantage.

On average, respondents believe it will take four to five years to see a significant impact from generative AI. However, with only 26% of respondents saying they’d implemented cross-enterprise use cases of generative AI, there is still much room for scaling and maturing these implementations.

Globally, the top three inhibitors are:

1) Cost and availability of talent. Talent scarcity is truly a global issue. The technology requires deep expertise in machine learning, data science, and neural networks, so the demand for such talent far exceeds the current supply.

This talent gap threatens to slow down the development and deployment of AI solutions. It also drives up the costs associated with hiring and retaining qualified personnel, a fact lamented by respondents in our survey.

While 54% are investing in training and development programs to build the necessary expertise in-house, 37% plan to hire externally—with no clear idea of where that talent could be found (see Figure 2).

2) Consumer perceptions. Many consumers harbor concerns about privacy, security, and the ethical use of AI-generated content. Mistrust stems from three key concerns: economic security, uncertainty about technology’s work, and fear of its societal impacts. Shoring up consumer trust is essential to optimizing generative AI strategies.

3) Perceived immaturity of current generative AI solutions. Despite impressive advancements, many AI models struggle with reliability, accuracy, and scalability. Current generative systems can produce impressive outputs in certain contexts but fall short in others, often requiring human oversight to ensure quality and relevance.

Moreover, integrating these solutions into existing business processes can be complex and resource-intensive. The path to maturity involves extensive research and development and best practices for deployment and integration.