Key Takeaways:
– Over 800 IT and non-IT decision-makers consider AI as an integral backup for business sustainability.
– 91% of survey respondents believe AI will dominate organizational agendas in the next two years.
– Despite the excitement around AI, adoption rates are relatively low due to implementation latency.
– Data preparation eats up significant time in building AI models, leading to monetary losses.
– Data quality lies at the core of the drawbacks in AI programs.
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AI: Vital for Business Sustainability
A recent report by Exasol, an analytics database provider, suggests that the majority of business leaders and technical experts view artificial intelligence (AI) as a crucial device needed for business sustainability. Nearly three in four decision-makers fear that the failure to invest in AI could risk their business. The study was based on Exasol-commissioned research from an independent tech sector market research firm, Vanson Bourne. More than 800 IT and non-IT senior decision-makers acted as respondents for the study.
The Hindrance to AI Adoption
Although the importance of AI is well understood by business leaders, the hurdles of technological barriers and regulatory mandates delay the process. These obstacles persist even in the face of increasing stakeholder pressure to incorporate AI. This proves that stakeholder pressure alone cannot substantially speed up the adoption of AI.
The Future of AI in Businesses
Exasol’s report delves into the current status of AI implementation while unearthing the primary challenges, opportunities, and upcoming trends in the context of emergent technologies. An impressive 91% of survey respondents anticipate that AI will top organizational strategies within the next two years. Their optimism stems from AI’s potential to uncover new revenue streams (50%), the evolving nature of roles and responsibilities (47%), and the escalating market competition (46%).
Challenges Encountered in AI Integration
Despite the enthusiasm encircling the transformative potential of AI, the adoption rates do not match up. Latency issues regarding the pace of fulfilling new data requirements are a major impediment. Almost half of the respondents from the Exasol study (47%) expressed that the time taken to adapt to fluctuating data landscapes and integrating new data sources discourages the implementation of AI. Other significant challenges include a sluggish reporting performance and increased data volumes.
Inefficiencies in AI Model Performance
Another study by Vanson Bourne for Fivetran, a global leader in data movement, reveals that while companies strive to embrace AI, they incur extensive annual losses due to underperforming AI models. The focus here is the considerable time spent on data preparation as opposed to the actual construction of AI models.
Based on the report, companies lose on average 6% of global annual revenues, which sums up to approximately $406 million. Particularly, AI models built using inaccurate or poor-quality data yield misleading decisions. Consequently, organizations in the U.S are reportedly suffering inaccuracies at a startling rate of 50%.
Data Governance: The Foundation of AI Success
Despite the under-delivery of anticipated results, nearly 90% of organizations continue to use AI/machine learning (ML) techniques for autonomous decision-making. A promising 97% plan to maintain or initiate investments in generative AI within the next one to two years. A top obstacle to AI adoption, as pointed out by senior executives, is the paucity of AI skills while those in junior roles are more concerned about cyberinfrastructure.
The key to successful AI deployment hinges on the quality of data in terms of its availability, reliability, and accuracy. Ensuring robust data governance foundations and adhering to optimal data practices could pave the way for overcoming the current setbacks and realizing the maximum potential of AI.
Related Reads:
– Why Digital Transformations Failed and AI Implementations are likely to.
– Altair Survey Unpacks the Friction Surrounding AI and Data Projects.
– The Three Approaches to AI Implementation.
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