- Fivetran presented data from a survey that showed 86% of companies struggle to trust AI completely, showing that they have low AI maturity to make all business decisions without human intervention.
- The research investigates how, even though 87% of businesses identify AI as the future of business and aim to expand their investment in it, a lack of trust in machine-led decision-making is a significant obstacle caused by technical challenges and a lack of education.
- Furthermore, 71% of those polled had difficulty accessing all the data required to execute AI algorithms, workloads, and models.
- According to Fivetran, data scientists will spend less time on manual activities in the future, allowing them to focus on creating AI models and launching more data and AI projects.
According to study results by Fivetran, 86% of companies struggle to trust AI to make all business decisions without human participation. In contrast, 90% of enterprises rely on manual data procedures.
Most employees think their company lacks AI maturity
The companion paper, “Achieving AI: A Study of AI Opportunities and Obstacles,” explains the problems businesses confront in today’s AI ecosystem. The paper investigates how, even though 87% of businesses identify AI as the future of business and aim to expand their investment in it, a lack of trust in machine-led decision-making is a significant obstacle caused by technical challenges and a lack of education. Only 14% of respondents believe their companies are “advanced” in AI maturity.
Almost all of the companies polled acquire and use data from operational systems, yet data challenges continue. According to the findings, technical data pipelines are a big cause of frustration, with 73% indicating that extracting, loading, and processing data from various sources into separate warehouses is a significant difficulty. Furthermore, 71% of those polled had difficulty accessing all the data required to execute AI algorithms, workloads, and models.
This leads to 73% of respondents having less trust in converting data insights into practical guidance for decision-makers, which pushes them to rely on human-led judgments 71% of the time.
According to the findings, data scientists spend more time working with data than constructing AI models to enhance business results through forecasting and decision-making insights. When asked how much time they spend preparing data vs constructing AI models, data scientists said it takes up an average of 70% of their time, and 87% said they felt underused in their company as a result.
Data governance issues are also a concern for organizations, with 64% of surveyed U.S. organizations admitting there is room for significant improvement in their adherence to data governance roles, policies, and standards to ensure data is used effectively, securely, and in accordance with government regulations.
Fivetran sees data automation and AI pipelines as solutions to AI maturity problems. “With greater automation, organizations can achieve greater scale and cost-efficiencies while saving time. More importantly, more automation allows data scientists to focus on solving complex problems that matter to the business rather than keeping data pipelines working, ” Brenner Heintz from Fivetran stated in a blog post.
He also mentioned that teaching business stakeholders to build trust in AI and improve their AI maturity may be a solution. “Stakeholders and business users must be aware of AI processes to fully understand how these decisions are made. But it’s also important that human involvement is focused on the right areas — such as improving data quality and the performance of AI models, which will lead to greater trust.”
ML engineers build the bridge between data and AI
Fivetran says that its automated data pipelines react to schema changes, allowing customers to entirely automate the ingest of numerous data sources into a single cloud-based data warehouse or data lake for data transformation, resulting in significant time savings. Fivetran further claims that its consumption-based pricing strategy enables enterprises to reduce expenses by reproducing just the data that is required. Finally, the business claims that data scientists would spend less time on manual activities, freeing them up to focus on developing AI models and launching new data and AI projects.
George Fraser, CEO of Fivetran, stated, “This study highlights significant gaps inefficient data movement and access across organizations. A successful AI program depends on a solid data foundation, starting with a cloud data warehouse or lake as its base. Analytic teams that utilize a modern data stack can more readily extend the value of their data and maximize their investments in AI and data science.”
Vanson Bourne conducted the online poll of 550 senior IT and data science workers from the United States, the United Kingdom, Ireland, France, and Germany. You may obtain a copy of the report here.
