To assess the thoughts of business decision-makers at this juncture, MIT Technology Review Insights conducted a survey of 1,000 executives regarding their current and anticipated use cases for generative AI, implementation challenges, technology strategies, and workforce planning. Alongside insights from an expert interview panel, this survey provides insights into the key strategic considerations for generative AI today, assisting executives in making important decisions.
The key findings from the survey and interviews are as follows:
Executives acknowledge the transformative potential of generative AI but are proceeding cautiously with its deployment. Almost all companies believe that generative AI will impact their business, with only 4% stating that it will not affect them. However, currently, only 9% have fully implemented a generative AI use case within their organization. This figure drops as low as 2% in the government sector, while financial services (17%) and IT (28%) are the most likely to have implemented a use case. The main hurdle to deployment is understanding the risks associated with generative AI, which 59% of respondents selected as one of the top three challenges.
Companies will not go through this process alone: Partnerships with startups and Big Tech are crucial for successful scaling. The majority of executives (75%) plan to collaborate with partners to implement generative AI at scale within their organization, and only a small percentage (10%) consider partnering to be a significant implementation challenge. This suggests that a robust ecosystem of providers and services is available for collaboration and co-creation. While Big Tech, as developers of generative AI models and providers of AI-enabled software, have an advantage in the ecosystem, startups excel in various specialized niches. Executives are somewhat more inclined to partner with small AI-focused companies (43%) than large tech firms (32%).
Access to generative AI will be democratized across all sectors of the economy. According to our survey, company size does not determine the likelihood of experimenting with generative AI. Small companies (with annual revenue below $500 million) are three times more likely than mid-sized firms ($500 million to $1 billion) to have already implemented a generative AI use case (13% versus 4%). In fact, these small companies have deployment and experimentation rates similar to those of the largest companies (with revenue over $10 billion). Affordable generative AI tools can empower smaller businesses in the same way that cloud computing has provided access to tools and computational resources that would have previously required substantial financial investments in hardware and technical expertise.
One-quarter of respondents expect the primary effect of generative AI to be a reduction in their workforce. This figure is higher in industrial sectors such as energy and utilities (43%), manufacturing (34%), and transport and logistics (31%). The lowest figure is in IT and telecommunications (7%). Overall, this is a moderate figure compared to the more pessimistic scenarios of job replacement that are circulating. There is an increasing demand for skills in technical fields focused on operationalizing AI models and in organizational and management positions addressing ethical and risk-related challenges. AI is democratizing technical skills across the workforce, potentially leading to new job opportunities and increased employee satisfaction. However, experts caution that if generative AI is deployed poorly and without meaningful consultation, it could undermine the quality of human work.
Regulation is on the horizon, but uncertainty is the biggest challenge at present. Generative AI has prompted a flurry of activity as lawmakers attempt to grapple with the risks, but truly significant regulation will progress at the pace of government. In the meantime, many business leaders (40%) consider engaging with regulation or regulatory uncertainty to be a primary challenge in adopting generative AI. This varies significantly by industry, ranging from a high of 54% in government to a low of 20% in IT and telecommunications.
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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.
