Exasol Finds AI Underinvestment Leads to Business Failure, But Data Challenges Stall Rapid Adoption

Inaugural report reveals nearly 3 in 4 decision-makers believe not investing in AI will put business viability at risk, yet poor data quality, regulation complexity and integration create barriers to success

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 our friends over at Exasol, the high-performance analytics database provider. 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). 

Key findings include:

Decision-makers and technical analysts believe that not investing in AI today will lead to business failure, but there are still significant barriers to broader implementation 

Nearly all (91%) respondents agree that AI is one of the most important topics for organizations in the next two years, with a whopping 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 cited reasons 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 nine in ten (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.

Latency continues to hamper organizations’ data analytics and AI initiatives

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 accelerate queries directly in their tools, an astounding 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.

Given increased data volumes and AI acceleration, the role of Chief Data Officer will evolve to become more integrated, impactful, and challenging

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 time 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 the data analytics stack to ensure productivity, speed, and flexibility – all at a reasonable cost.”

For more information, please download the full 2024 “AI and Analytics Report” HERE.

Methodology

Exasol commissioned independent market research agency Vanson Bourne to conduct research into data, analytics, and AI. The study surveyed 800 senior decision-makers in IT and non-IT roles, as well as data scientists/analysts, in November 2023. Respondents were from the US, UK and Germany, and all had some responsibility or knowledge of their organizations’ data science and analytics strategy or program.

Respondents were from organizations with 1,000 or more employees, across the following sectors: financial services, healthcare (public and private), retail and telecommunications. All interviews were conducted using a rigorous multi-level screening process to ensure that only suitable candidates were given the opportunity to participate.

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