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Hayley Sutherland
Research Manager, Conversational AI & Knowledge Discovery at IDC
Hayley Sutherland is a Research Manager for Conversational AI and Intelligent Knowledge Discovery within IDC's Software market research and advisory group.1 With nearly 15 years of experience in market research, she primarily focuses on the tech industry.3
Areas of Expertise
Sutherland's key areas of coverage include:
- Cognitive/artificial intelligence
- Conversational AI
- Text and speech AI
- Search and knowledge discovery15
Role and Responsibilities
As a Research Manager at IDC, Sutherland:
- Analyzes market trends and provides insights on conversational AI and intelligent knowledge discovery
- Authors reports on various aspects of AI technology, including market forecasts and vendor market shares1
- Offers expert commentary on industry developments, such as acquisitions and emerging technologies in the AI space
Industry Recognition
Sutherland is recognized as an expert in her field. She frequently contributes to industry discussions and events:
- Participates as a guest speaker in webinars focused on AI and customer service4
- Provides quotes and insights for industry reports and analyses2
Professional Presence
Hayley Sutherland maintains an active professional presence:
- LinkedIn: Her profile can be found under the username hayley-sutherland-a91410153
- Twitter: She tweets about AI-related topics under the handle @HSutherlandAI5
Sutherland's expertise and analysis are valued in the AI industry, particularly in the areas of conversational AI, knowledge discovery, and the application of generative AI in enterprise settings.
Highlights
Delighted to have published this IDC TechBrief today on Retrieval-Augmented Generation! There's been a lot of client demand for a deep dive on this topic, and there will be more to come in the near future. Thanks to my colleagues Stewart Bond, Carl Olofson, and Shari Lava for their collaboration on this piece!
This IDC TechBrief describes the factors involved in leveraging retrieval-augmented generation (RAG) to get the most out of generative AI in the enterprise, including the various technology aspects and associated use cases, benefits, risks, and other considerations for and effects of implementing RAG.
Retrieval-augmented generation is making enterprise adoption of generative AI more feasible and practical by augmenting LLMs with enterprise data using search techniques. Forward-looking enterprises see the value of connecting their proprietary data to generative AI systems and should understand the RAG approach for doing this, as well as other methods of specializing and improving the accuracy of LLM output.
#RAG #AI #GenAI #LLMs #data
Verint showing how #AI is infused throughout the #contactcenter lifecycle:
#conversationalai #conversationalintelligence #verintanalystdays
