Businesses Say They Want AI, But Their Data Isn’t ReadyBusinesses Say They Want AI, But Their Data Isn’t Ready

Dive Brief:

Dive Insight:

AI models need a good foundation to work well for businesses, and that foundation comes from data. In terms of AI readiness and adoption, the financial services industry (FSI) is an important industry to watch because they have a wealth of high-quality, well-structured data that is complex and highly regulated. FSI data requires accuracy, confidentiality and explainability, making this industry ahead of others in AI readiness. Indicium and PureSpectrum’s report states that 67% of FSI firms run AI across departments, 59% use AI for back-office automation and 56% use AI to manage data quality and governance.

Despite the finance industry’s AI use being ahead of other industries, 52% of respondents in their field said that their data infrastructure is outdated or aging. In addition to this, only 8% of respondents in FSI consider their data infrastructure to be state-of-the-art, while 40% say it’s modern or scalable. In other industries, the percentage of those with outdated infrastructure was slightly lower, coming in at 44%.

Since data is the foundation of AI readiness and adoption, these numbers show that even industries that are ahead in this area still face significant challenges, slowing progress. 46% of FSI respondents – and 40% of respondents in other industries – also admitted they were unprepared to leverage data for AI because of quality and governance issues before they started data platform modernization.

Going forward, FSI respondents say their leading objectives are: scaling AI initiatives (74%), focusing on select business functions (68%) and evaluating AI for potential use cases (46%). Other industry professionals reported similar numbers – 66%, 62% and 45% respectively. 72% of FSI professionals also say AI enablement and data integration are primary objectives for the near future. This shows that businesses plan on preparing their businesses to have better AI readiness so they can implement it in their processes.

Respondents also note that internal training (70%) and stronger collaboration with external partners (40%) will be crucial to accelerating AI and data strategies.

The overall consensus is that a well-maintained data infrastructure is vital for effective AI adoption, and this is what many IT professionals across industries are planning to work on in the near future. However, there are still challenges in terms of scalability that need to be faces, as well as the difficulties that come with managing a well-maintained data infrastructure.