Giddy predictions about AI, from its contributions to economic growth to the onset of mass automation, are now as frequent as the release of powerful new generative AI models. The consultancy PwC, for example, predicts that AI could boost global gross domestic product (GDP) 14% by 2030, generating US $15.7 trillion.
Forty percent of our mundane tasks could be automated by then, claim researchers at the University of Oxford, while Goldman Sachs forecasts US $200 billion in AI investment by 2025. “No job, no function will remain untouched by AI,” says SP Singh, senior vice president and global head, enterprise application integration and services, at technology company Infosys.
While these prognostications may prove true, today’s businesses are finding major hurdles when they seek to graduate from pilots and experiments to enterprise-wide AI deployment. Just 5.4% of US businesses, for example, were using AI to produce a product or service in 2024.
Moving from initial forays into AI use, such as code generation and customer service, to firm-wide integration depends on strategic and organizational transitions in infrastructure, data governance, and supplier ecosystems. As well, organizations must weigh uncertainties about developments in AI performance and how to measure return on investment.
If organizations seek to scale AI across the business in coming years, however, now is the time to act. This report explores the current state of enterprise AI adoption and offers a playbook for crafting an AI strategy, helping business leaders bridge the chasm between ambition and execution. Key findings include the following:
AI ambitions are substantial, but few have scaled beyond pilots. Fully 95% of companies surveyed are already using AI and 99% expect to in the future. But few organizations have graduated beyond pilot projects: 76% have deployed AI in just one to three use cases. But because half of companies expect to fully deploy AI across all business functions within two years, this year is key to establishing foundations for enterprise-wide AI.
AI readiness spending is slated to rise significantly. Overall, AI spending in 2022 and 2023 was modest or flat for most companies, with only one in four increasing their spending by more than a quarter. That is set to change in 2024, with nine in ten respondents expecting to increase AI spending on data readiness (including platform modernization, cloud migration, and data quality) and in adjacent areas like strategy, cultural change, and business models. Four in ten expect to increase spending by 10 to 24%, and one-third expect to increase spending by 25 to 49%.
Data liquidity is one of the most important attributes for AI deployment. The ability to seamlessly access, combine, and analyze data from various sources enables firms to extract relevant information and apply it effectively to specific business scenarios. It also eliminates the need to sift through vast data repositories, as the data is already curated and tailored to the task at hand.
Data quality is a major limitation for AI deployment. Half of respondents cite data quality as the most limiting data issue in deployment. This is especially true for larger firms with more data and substantial investments in legacy IT infrastructure. Companies with revenues of over US $10 billion are the most likely to cite both data quality and data infrastructure as limiters, suggesting that organizations presiding over larger data repositories find the problem substantially harder.
Companies are not rushing into AI. Nearly all organizations (98%) say they are willing to forgo being the first to use AI if that ensures they deliver it safely and securely. Governance, security, and privacy are the biggest brake on the speed of AI deployment, cited by 45% of respondents (and a full 65% of respondents from the largest companies).
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.
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