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Exasol Survey Finds AI Underinvestment Leads to Business Failure

Data challenges such as poor data quality stall rapid AI adoption; report reveals nearly 3 in 4 decision-makers believe not investing in AI will put business viability at risk.

Note: TDWI's editors carefully choose vendor-issued press releases about new or interesting research and services. We have edited and/or condensed this release to highlight key study results or service features but make no claims as to the accuracy of the vendor's statements.

Senior decision-makers know that AI is critical for their business’ viability, yet despite growing stakeholder pressure to implement the technology quickly, regulatory and technological challenges are slowing the process, according to a new report from Exasol, a provider of high-performance analytics databases. Exasol’s 2024 AI and Analytics Report investigates the current state of AI implementation, top data analytics challenges, and the future of the C-suite given the explosive growth of data and adoption of emerging technologies.

In partnership with Vanson Bourne, an independent research firm, Exasol surveyed 800 senior decision-makers as well as data scientists and analysts across the U.S., U.K., and Germany to assess enterprises’ data and analytics initiatives, including their top challenges and how they are planning to address those challenges in the short-term (within two years). 

Investment Perspectives and Implementation Barriers

Nearly all (91%) respondents agree that AI is one of the most important topics for organizations in the next two years, with 72% admitting that not investing in AI today will put future business viability at risk. Stakeholder pressure is also a factor in greater AI adoption, with 45% claiming they are experiencing increased pressure from stakeholders to embrace the technology. Top reasons cited for the belief in the importance of AI include creating new businesses or sources of revenue (50%); changing workforce roles and responsibilities (47%); accelerating competitiveness in the market (46%); and automating processes (43%).

However, despite understanding how critical the technology is for future success, there are barriers to its seamless implementation, with almost 9 in 10 (88%) stating evolving bureaucratic requirements and regulations for AI require more clarity. Additionally, lack of implementation strategy (44%), poor data quality and insufficient data volume (43%), and integration with existing systems (38%) are hindering widespread AI adoption. Organizations must find ways to overcome these obstacles, as more than a third (38%) of businesses plan to increase AI infrastructure in the next few years.

The Latency Problem

Organizations are struggling to progress their data analytics and AI projects, with a staggering 78% of decision-makers reporting gaps in at least one area of their data science and machine learning (ML) models. Nearly half (47%) cite speed to implement new data requirements as a challenge.

Despite most (96%) using BI acceleration engines to speed up queries directly in their tools, 69% of BI users admit they continue to struggle with slow reporting performance. An additional 79% claim new business analysis requirements take too long to be implemented by their data teams, meaning latency continues to hamper organizations’ innovation capabilities, data analytics projects, and AI potential.

The Evolving Role of Chief Data Officer

The role of the chief data officer (CDO) will evolve in response to the integration of AI, including infrastructure development, AI-driven automation, and AI-driven insights. In fact, more than half (52%) of respondents believe the CDO role will need to work more closely with other C-suite members, and 44% believe it will merge with the chief AI officer while ethical and compliance issues continue to be a focus.

In terms of business operations forecasting, 90% of enterprises believe they will increase their investment in headcount and/or budget over the next two years to support expected data growth. The roles anticipated to increase most over this period include BI/analytics developers and engineers (both 48%), data analysts (46%), and data architects/modelers (45%). Despite the anticipated increased headcount, 47% of survey respondents report concerns that generative AI will threaten their role.

“AI has become critical to business success, but it’s only as effective as the tools, technology, and people powering it on the backend. Our study further proves there is a significant gap between current BI tools and their output -- more tools does not necessarily mean faster performance or better insights,” said Joerg Tewes, CEO of Exasol. “As CDOs prepare for more complexity and are tasked to do more with less, they must evaluate their data analytics stack to ensure productivity, speed, and flexibility -- all at a reasonable cost.”

To help close this gap for the enterprise, Exasol has enhanced its versatile query engine, Exasol Espresso, with the launch of Espresso AI, a new suite of integrated AI features and ML tools designed to help customers approach data analytics in a faster, more cost-efficient, and flexible manner. To learn more about Espresso AI, visit:

You can download the full 2024 AI and Analytics Report here (short registration required).

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