1、Behind the AI HypeThe 2024 State of LLMs in Business ProcesseswithIContents01 Overview02 Top Findings03 Chapter 1:Whats Driving AI Adoption05 Chapter 2:Activating LLMs in Business Processes10 Chapter 3:The Outcomes of Generative AI in Business Processes15 Chapter 4:Governance of AI in Business Proce

2、sses18 Chapter 5:The Role of People in a World of Autonomous AI and Agents23 Chapter 6:AI Agents&The Future of Business Processes28 Conclusion29 Methodology&Demographics32 About UserEvidence01OverviewMarket sentiment about generative AI has entered a new era.Early hype is fading.Business leaders hav

3、e a clearer view of its strengths and challenges.Major press,such as the Wall Street Journal,The Economist,and others have wondered if the AI hype over-promised and under-delivered.But very few have considered a key value driver for companies:Business processes and operations.As legendary business t

4、hinker Tom Davenport wrote in HBR:“Most AI applications to date seek to improve a given task.But this is missing the larger picture;smart companies are viewing the introduction of AI as the rationale for a new look at end-to-end processes.”Several firms,such as McDonalds,Air Canada,and IBM have expe

5、rienced high profile failures with early generative AI projects.Conventional wisdom says gen AI issues are model challenges:Ways that large language models(LLMs)need improvement.However,McKinsey notes that it is“important to recognize that the model itself makes up only 15 percent of the success of

6、an AI project.”There is much more to an AI strategy than training data,model weights,and prompt engineering.To create consistent business value with AI,companies must also address process challenges.These include:How gen AI is activated in business processes.How AI accesses the latest data.How AI ri