Business leaders have high hopes that artificial intelligence (AI) investments can drive market-changing innovations to transform everything from customer satisfaction to product innovation. However, a poor data foundation is holding 78% of organisations back from achieving these goals.

A new report from MIT Technology Review Insights, in partnership with Snowflake, the AI Data Cloud company, titled ‘Data Strategies for AI Leaders’ found that while businesses have big ambitions for generative AI — with 72% looking to increase efficiency or productivity, 55% betting on increased market competitiveness, and 47% aiming to see more innovation in products and services — the foundational data strategy needs to be improved to maximise AI’s potential.

Businesses need strong data foundations, powered by modern cloud data platforms, to enable them to harness their own stores of data, and also vast volumes of previously inaccessible data, largely from unstructured data such as videos and images. According to the report, just 22% of business leaders say they are ‘very ready’ to engage with AI, while 53% are ‘somewhat ready’. Higher readiness correlates with fewer challenges related to accessing scalable computing power, data silos and integration issues, and data governance. Despite many business leaders’ confidence in the results AI can deliver, they are realising that data is key to how quickly and effectively they can unlock AI’s value.

Another challenge facing organisations is deploying AI at scale. 95% of those surveyed reported facing hurdles when implementing AI. 59% of respondents cited data governance, security, or privacy as their most prevalent challenge, followed by data quality and timeliness (53%), and costs of resources or investment (48%). Spending and resourcing decisions, including those needed to improve data foundations, are a challenge when it comes to any technology investment. But the cost of generative AI itself is decreasing, with enterprises having begun to build smaller large language models (LLMs) that remain equally capable, and less expensive.

“Many of today’s organisations have big ambitions for generative AI: they are looking to reshape how they operate and what they sell,” says Baris Gultekin, Head of AI, Snowflake. “Our joint research shows that as organisations feel increasing urgency to deploy AI applications, they are realising that their data can help them deliver insights from previously untapped sources of information. A strong data foundation is at the core of generative AI capabilities, and business leaders need to move quickly to deal with concerns such as data security and cost, and establish the foundation they need to deliver on the promise of AI.”

Generative AI’s benefits are becoming visible to those companies farther along their data journey, because they invested heavily in data foundations and are now being rewarded by bringing AI onto that data. For any business looking to capitalise on AI, they must first establish a robust data foundation, which covers a broad collection of processes and assets involved in the gathering, aggregation, storage, and accessibility of organisational data. Investment in data foundations across an organisation will enable much more powerful generative AI users, while also reducing governance and security concerns.

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Generative AI is the buzzword of the moment for the technology world. But, how are enterprises preparing themselves to unleash the potential of this technology? MIT Technology Review Insights polled 1,000 business leaders about their organizations’ approaches to implementing generative AI technologies. 

Executives have endured several years of heavily hyped tech fads—including Web3, blockchain, and the metaverse—which have yet to find significant use cases and gain, but Generative AI could be different, highlights the study. 

Respondents were distributed among 11 industries, including consumer goods and retail, financial services, manufacturing, and pharma and health care. The report also draws on in-depth interviews with academics, data leaders, and AI experts.

The key findings are as follows:

• Executives recognize the transformational potential of generative AI, but they are moving cautiously to deploy. Nearly all firms–96%–believe generative AI will affect their business. However, only 9% have fully deployed a generative AI use case in their organization, which indicates a wait-and-see posture.
  
  
• Companies will not go it alone: Partnerships with both startups and Big Tech will be critical to smooth scaling. Most executives (75%) plan to work with partners to bring generative AI to their organization at scale, but only 10% consider partnering to be a top implementation challenge, suggesting that a strong ecosystem of providers and services is available for collaboration and co-creation.
  
  
• Access to generative AI will benefit small and large companies alike. Company size has no bearing on a firm’s likelihood to be experimenting with generative AI, our poll found. Small companies (those with annual revenue less than $500 million) were three times more likely than mid-sized firms ($500 million to $1 billion) to have already deployed a generative AI use case (13% versus 4%).
  
  
• One-quarter of respondents expect generative AI’s primary effect to be to reduce their workforce. The figure was higher in industrial sectors like energy and utilities (43%), manufacturing (34%), and transport and logistics (31%). It was lowest in IT and telecommunications (7%). Overall, this is a modest figure compared to the more dystopian job replacement scenarios.
  
  
• Regulation looms, but uncertainty is today’s greatest challenge. Generative AI has spurred a flurry of activity as legislators try to understand risks, but truly impactful regulation will move at the speed of government.

“Business leaders are cautiously pursuing the transformative potential of generative AI, with nearly all respondents acknowledging its impact on their businesses,” says Laurel Ruma, global editorial director, MIT Technology Review Insights. “However, only a small fraction have fully embraced generative AI use cases, highlighting a measured approach to implementation. Workforce implications vary across industries, and while regulation looms, uncertainty stands as today’s primary challenge.”

Produced by MIT Technology Review Insights in partnership with Adobe, EY, and Owkin, “Generative AI deployment: Strategies for smooth scaling” explores global leaders’ key decision points for putting AI to use in the enterprise. 

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