Most businesses don't trust AI to make business decisions autonomously

Most companies do not believe that artificial intelligence can make business decisions independently

1. A survey data released by Fivetran shows that 86% of companies cannot fully trust it Artificial intelligence, which indicates a lower level of AI maturity and the inability to make all business decisions without human intervention.
2. Although 87% of enterprises see AI as the future of business and intend to expand investment in AI, there is a significant lack of trust in machine-led decision-making due to technical challenges and lack of education. obstacle.
3. Additionally, 71% of respondents have difficulty accessing all data needed to execute AI algorithms, workloads, and models.
4. According to Fivetran, in the future data scientists will spend less time on manual activities, thus focusing on creating AI models and launching more data and AI projects.

Most employees believe their companies are not mature enough

Paper "Realizing Artificial Intelligence: A Study of Opportunities and Barriers to Artificial Intelligence" Explains the issues businesses face in today’s AI ecosystem. The paper investigates how while 87% of businesses see AI as the future of business and intend to expand investment in it, a lack of trust in machine-led decision-making is a significant barrier due to technical challenges and lack of education. Only 14% of respondents believe their organization is “advanced” in terms of AI maturity.

Nearly all businesses surveyed obtain and use data from operational systems, but data challenges continue. According to the survey results, technical data pipelines are a major cause of frustration, with 73% of respondents stating that extracting, loading and processing data from disparate sources into separate warehouses is a significant difficulty. Additionally, 71% of respondents reported difficulty accessing all data needed to execute AI algorithms, workloads, and models.

This resulted in 73% of respondents being less trusting in translating data insights into practical guidance for decision makers, forcing them to rely on human-led judgment in 71% of cases.

According to research findings, data scientists spend more time crunching data instead of building AI models to improve business outcomes through prediction and decision-making insights. When asked how much time they spend preparing data and building AI models, data scientists said it takes up an average of 70% of their time. 87% of respondents said they felt they were underutilized in their company.

Data governance issues are also a concern for organizations. 64% of U.S. organizations surveyed acknowledged that there is still significant room for improvement in compliance with data governance roles, policies, and standards to ensure data is used effectively, securely, and in compliance with government regulations.

Fivetran believes that data automation and AI pipelines are solutions to AI’s maturing problems. “With increased automation, businesses can achieve greater scale and cost efficiencies while saving time. What’s more, more automation allows data scientists to focus on solving complex problems that matter to the business, rather than maintaining The data pipeline is working properly." - Brenner Heintz from Fivetran said in a blog post.

Fivetran also mentioned that teaching business stakeholders to build trust in AI and increase their AI maturity could be a solution. “Stakeholders and business users must understand the AI ​​process to fully understand how these decisions are made. But it is equally important that human involvement should be focused on the right areas, such as improving data quality and AI model performance, This will lead to greater trust."

Fivetran said its automated data pipelines react to schema changes, allowing customers to fully automate the feeding of massive data sources into a cloud-based Save a lot of time by transforming data into your own data warehouse or data lake. Fivetran further claims that its consumption-based pricing strategy enables businesses to reduce expenses by replicating only the data they need. Finally, the company claims that data scientists will spend less time on manual activities, allowing them to focus on developing AI models and launching new data and AI projects.

Fivetran CEO George Fraser said: “This research highlights significant gaps in inefficiencies in data movement and access across organizations. A successful AI program relies on a solid data foundation, anchored by a cloud data warehouse or lake. Analytics teams leveraging a modern data stack can more easily extend the value of their data and maximize their investments in artificial intelligence and data science.”

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