New Economist Impact Study Finds Only 22 Percent of Enterprises Believe Their IT Infrastructure is Ready for AI

85% of global enterprises are actively using or testing generative AI in at least one function

Databricks, the Data and AI company, today unveiled a new Economist Impact report, “Unlocking Enterprise AI: Opportunities and Strategies,” which examines the challenges businesses face in adopting and scaling AI, and the techniques they are using to drive greater value from these investments. The report found the vast majority of enterprises (85%) are using or testing generative AI (GenAI) in at least one function. But few (22%) feel confident that their current IT architecture could effectively support new AI applications moving forward.

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As demand for data intelligence grows worldwide, AI continues to be a major focus area for companies. According to Goldman Sachs, global AI spend is expected to reach $1 trillion in the next few years. While more companies are investing in AI than ever before, struggles related to delivering business-specific, highly accurate, and well-governed results at a reasonable cost are preventing organizations from scaling their AI efforts and achieving more transformational results. Today, only 37% of executives believe their GenAI applications are production-ready. This figure falls to just 29% among practitioners, who cite key hurdles including cost (41%), skills (40%), quality (37%) and governance (33%).

“It’s clear that AI is becoming an integral part of every business, but leaders still have concerns about quality and cost when it comes to GenAI. They’re seeking solutions tailored to their organizations, and they realize they need a platform that prioritizes data, centralizes governance and delivers efficient TCO at scale,” said Andy Kofoid, President of Global Field Operations at Databricks. “At Databricks, we’re bringing together data, analytics and GenAI that understands our customers’ unique businesses to deliver data intelligence. This report from Economist Impact showcases why data intelligence is essential, and why the winners in each industry will be those who take a holistic approach that encompasses data management, governance and domain-specific expertise.”

Whether streamlining clinical trials in the pharmaceutical industry or identifying potential vehicle issues before they occur in the automotive sector, many enterprises are already using AI to improve efficiency and productivity. With the growth of ‘Agentic AI’ — artificial agents with a natural language interface that can plan and execute tasks on behalf of a user — companies can spread these benefits to more of the workforce. In fact, nearly 60% of respondents expect that, within the next three years, natural language will be the primary or only way non-technical staff will interact with complex datasets. Increasingly, organizations are also using AI to improve customer service, fraud detection and patient care, among the many other use cases, highlighting the long-term potential of the technology to accelerate overall business success.

“AI can lead to gains in productivity across the workforce. And for businesses just starting out on their AI journeys, it’s a logical way to measure initial progress,” said Senthil Ramani, Global Lead, Data and AI at Accenture. “However, organizations aiming to become the AI leaders of tomorrow will need to capitalize on the use of the technology to drive growth, enhance customer experience, manage risk and unleash enterprise knowledge. This holistic approach will not only boost efficiency but also open new business opportunities and can attract and retain talent.”

The Economist Impact report surveyed 1,100 technical executives and technologists from 19 countries across Asia, Europe and the Americas and included additional insights from 28 C-Suite executives from 11 industries. Among the organizations represented are Accenture, CJ CheilJedang, Condé Nast, Dream Sports, Fanatics B****** & Gaming, Flo Health, Frontier, General Motors, HP, JetBlue, Mahindra Group, Mastercard, Molson Coors, Novartis, NTT Docomo, Opendoor, Providence, Rakuten Group, Repsol, Rivian, Seven West Media, Shell, Siam Commercial Bank, TD Bank Group, Thermo Fisher Scientific, Unilever, UPS and the United States Army.

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Additional key findings include:

• Only 18% of respondents believe AI is overhyped. In fact, 73% see the technology as crucial to their long-term goals. Despite the momentum, only one in five believe investment across technical and non-technical domains is sufficient.
• Large organizations are flocking to GenAI, with 97% of companies with over $10 billion in revenue now using the technology in at least one internal business function. By 2027, 99% of all respondents expect GenAI adoption across both internal and external use cases.
• Nearly half of data scientists (45%) are still using a general-purpose large language model (LLM) without contextual enterprise data. Those models often struggle to provide the necessary quality, governance and the ability to evaluate outputs. 58% of data scientists have begun to augment their LLMs with proprietary data through retrieval augmented generation (RAG), and two-thirds of organizations see significant potential in combining LLMs with enterprise data to build data intelligence.
• Organizations expect to mix and match different models and tools in their Agent Systems, spanning open source and proprietary technologies, to drive better performance. By 2027, 96% plan to deploy open source AI models.
• Just one in six respondents are confident their organization can secure enough AI talent.
• 40% of respondents acknowledge their organization’s data and AI governance is insufficient. Half of data engineers say governance takes up more time than anything else, with many practitioners and executives pointing toward unified governance as the key to unlocking enterprise AI.

“From classic machine learning to generative AI, the business world’s obsession with AI isn’t letting up. But our findings show that, for many organizations, the real value comes when the technology is unleashed on their own proprietary data to develop data intelligence,” said Tamzin Booth, Editorial Director of Economist Impact. “That data intelligence is even more valuable in an increasingly unpredictable world. To drive the algorithm advantage they’re seeking, it’s clear enterprises must address significant challenges with producing high-quality outputs, identify ways to evaluate performance and governance with large AI models, and work out how to effectively connect AI to the workforce.”

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