The Future of Enterprise Data & AI 2023
Corinium collaborated with WNS Triange to survey 100 data, analytics, and AI leaders from industries including Manufacturing, Retail, Consumer Packaged Goods, Banking and Financial Services, Insurance, and Healthcare. Of these, 40% were from The United States, 40% were from Europe, and 20% were from Asia Pacific. Respondents were selected from global enterprises with at least $500 million USD in annual revenues.
Participants in the research hold senior decision-making roles, such as Chief Data Officers, Chief Data and Analytics Officers, and Heads of Data, Analytics, Innovation, and AI in major international enterprises. Respondents answered 16 questions about how they are overcoming challenges related to managing their data to enable the success of advanced analytics and AI initiatives.
Key Findings Include:
- - 47% of respondents say that security and privacy concerns top the list of challenges in hosting or implementing generative AI
- - 54% of respondents are implementing phased integration to bridge the gap between legacy systems and intelligence cloud and data systems
- - 76% are either planning or are currently involved in generative AI projects
- - 60% of respondents say that the integrity and quality of the data to be used in AI and analytics initiatives is the most crucial aspect
- - 57% of respondents have deployed data integration platforms or tools to address issues related to siloed and fragmented data
- - 72% of respondents are extremely concerned about the ethical implications of AI decision-making in their organizations
Corinium collaborated with WNS Triange to survey 100 data, analytics, and AI leaders from industries including Manufacturing, Retail, Consumer Packaged Goods, Banking and Financial Services, Insurance, and Healthcare. Of these, 40% were from The United States, 40% were from Europe, and 20% were from Asia Pacific. Respondents were selected from global enterprises with at least $500 million USD in annual revenues.
Participants in the research hold senior decision-making roles, such as Chief Data Officers, Chief Data and Analytics Officers, and Heads of Data, Analytics, Innovation, and AI in major international enterprises. Respondents answered 16 questions about how they are overcoming challenges related to managing their data to enable the success of advanced analytics and AI initiatives.
Key Findings Include:
- - 47% of respondents say that security and privacy concerns top the list of challenges in hosting or implementing generative AI
- - 54% of respondents are implementing phased integration to bridge the gap between legacy systems and intelligence cloud and data systems
- - 76% are either planning or are currently involved in generative AI projects
- - 60% of respondents say that the integrity and quality of the data to be used in AI and analytics initiatives is the most crucial aspect
- - 57% of respondents have deployed data integration platforms or tools to address issues related to siloed and fragmented data
- - 72% of respondents are extremely concerned about the ethical implications of AI decision-making in their organizations
