AI adoption in various departments is still lagging in support teams, possibly due to concerns about accuracy and data security. Trust in AI output is moderate even among those actively using AI tools. Companies are prioritizing internal AI use cases before external applications to ensure accuracy and address potential risks. The future of AI adoption will involve a greater balance between internal and external uses. Businesses envision using AI for problem-solving and high-level conceptual challenges. Customization of AI models is common, with fine-tuning and retrieval augmented generation being popular methods. Accuracy and bias mitigation are top priorities, with regular audits and reviews being the most common approach.

Keywords

AI adoption, support teams, trust in AI, internal AI use cases, external AI use cases, customization of AI models, accuracy, bias mitigation

Takeaways

• Support teams are lagging in AI adoption due to concerns about accuracy and data security.
• Trust in AI output is moderate even among active users of AI tools.
• Companies prioritize internal AI use cases before external applications to ensure accuracy and address potential risks.
• The future of AI adoption will involve a greater balance between internal and external uses.
• Businesses envision using AI for problem-solving and high-level conceptual challenges.
• Customization of AI models is common, with fine-tuning and retrieval augmented generation being popular methods.
• Accuracy and bias mitigation are top priorities, with regular audits and reviews being the most common approach.

Links:

https://retool.com/reports/state-of-ai-h1-2024#these-teams-use-ai