Jia Anya
Copilot Product Leader, Shangtang
The Product Lead of Copilot at ShangTech, responsible for the ShangTech Little Raccoon series of products, has more than 7 years of product experience. Prior to joining ShangTech, Anja worked as a product manager at EverQuote and Charles River Development in Boston, where she was responsible for the product from concept to market. After joining ShangTech, she has experienced the past innovation and landing of AI products in To G and To B. She has also fully launched and responsible for the ShangTech Little Raccoon series products - Big Model Native Productivity Tools product line from day 1 concept, model, product to the current commercial closed-loop validation. She received her undergraduate degree in Mathematics and Economics and her Master's degree in Financial Engineering from Boston University, and her Master's degree in International Relations and Business Administration from the Fletcher School of Law and Diplomacy at Tufts University.
Topic
Mindstream and Innovation - Development, Value and Business Scenario Landing of AI Native Productivity Tools
Synopsis: The AI Native Productivity Tools product has undergone significant growth and change over the past year. These changes are mainly reflected in the following aspects: 1. Scenario changes: gradually expanding from single function to full chain solutions, and then to closed loop for vertical segmentation scenarios. 2. Competition changes: the market is highly competitive, with big model vendors, cloud service providers, and startups all participating in the productivity tool competition, from model roll-up to application. 3. User changes: users' attitude towards big models has gradually changed from initial excitement to dispelling, paying more attention to the actual user experience. AI Native productivity tools, while not yet a full replacement for human work, have high value in reducing repetitive and paradigmatic workloads and help people focus on creative work. These tools can free up human energy on low-value work and facilitate workflow optimisation. In this sharing, I will combine these three changes, as well as the experience & lessons learnt from the product landing and delivery in the past year, and discuss and exchange ideas around the dimensions of market development and user attitudes, the value and application of AI tool products, and the business model and scenario application landing of big model products. Outline: 1. Market Development and User Attitude - Outline the development of AI Native productivity tools over the past year, including changes in scenarios and competition. - Discuss the evolution of user attitudes, big model excitement -> big model dispelling -> user experience priority 2. Value and application of AI tool products - Explain the role of AI Native productivity tools in reducing the burden of repetitive work and promoting creative work. - Explore the integration of B-side and C-side services, as well as the three-tier product design landing point of personal efficiency, team collaboration and enterprise management. 3. Business Model and Functional Innovation - Analyse the commercialisation strategy of AI Native productivity tools. - Introduce product and technology updates, such as Prompt management, multimodal access and solving latency issues. 4. Scenario Application and Future Trends - Demonstrate the application of AI Native productivity tools in different scenarios through concrete cases. - Predict the development trend of AI Native productivity tools and discuss potential innovations and challenges.
