Reshaping 2B Creative Productivity in the Wave of AIGC

Generative AI is transforming productivity relations across various sectors. Utilizing the most advanced artificial intelligence technologies, including natural language processing, computer vision, and machine learning, it offers content creators robust and flexible solutions. It undeniably acts as a powerful driver for the advancement of the creative industry. However, following successive waves, the depth of application of AIGC in actual production relationships remains rather superficial. This session will focus on this pain point.

For institutional clients faced with building AIGC productivity tools, how do they differ from interest-driven platforms in consumer communities? Supported by the top algorithmic strength of Damo Academy, in what form should it surface to the market? As the number of AIGC platforms increases, how can we differentiate from platform homogenization? These methodological principles, precipitated from thinking and analysis, practice and correction, and centered around efficiency and reverse-engineering behavioral norms, help us provide institutional clients with practical product functionalities.

In this workshop, we will explore the practical experiences of AIGC application platforms in B-end scenarios. Through real case analysis and interactive experience, participants will deeply comprehend how AI aids in content creation and how to leverage this platform to achieve more personalized, innovative content design. Through the workshop, participants will capture the forefront trends of generative AI technology and apply them effectively in their daily work.

Core Content of the Workshop:

1. Experience Design Background of B-end AIGC Productivity Tools
Through field research with creative design groups from three authoritative B-end institutions, we identified the common demands of "efficiency (effectiveness, efficiency), and controllability." Based on these shared pain points, we proceed with design work around principles of feasibility, abstracting into three dimensions of thought:
1.1 Ascertain the core demands of the target users.
1.2 Maximize the return on resources (team resources).
1.3 Metrics for the new paradigm of AI tools.

2. Implementing the Above Mindset, Detailed Explanation of How to Upgrade the Experience of Productivity Tools
2.1 Enter the field to introduce the operation model of field research.
2.2 Pose questions to demonstrate true expectations from real clients regarding the effects, efficiency, and controllability at the AI productivity level.
2.3 Introduce solutions that revolve around "Alibaba AIGC Industry Application Platform," detailing the type of considerations designers need in the AIGC wave, the abilities they should master, and how to utilize design-driven technology innovations.
· Platform background introduction.
· The first step (establishing the AIGC needs hierarchy model for the target group – clarifying scene priorities).
· The second step (finding the most cost-efficient solution – the cost-partner principle).
· The third step (building the optimal task model – a quantifiable new interaction paradigm).

3. First Step: Establishing the AIGC Needs Hierarchy for the Audience
3.1 Identify the overlap between mature technologies and audience demands.
3.2 Determine the frequency data for demand scenarios.
3.3 Construct the pyramid model based on the information above.

4. Second Step: Finding the Most Cost-Efficient Solution
4.1 Clarify the team's internal capability mapping.
4.2 Maintain keen industry foresight at the forefront of AIGC.
4.3 Find the balance between technology (feasibility and implementation cycle) and computing power (cost, speed).
4.4 Apply design-driven practical solutions – technological breakthroughs via the tech stack of reverse engineering.

5. Third Step: Building the Optimal Task Model – A Quantifiable New Interaction Paradigm
5.1 Follow the principle of efficiency first.
5.2 Help users shorten the journey of the smallest task unit.
5.3 Build an evaluative model for the new paradigm to establish a closed loop of value verification.

1、Workshop Introduction: Content Summary
2、Theoretical and Methodological Explanation: The Reshaping of AI Toolchains from Underlying Drivers to Reverse Engineering Approach
3、Case Analysis: The Construction and Implementation of Alibaba's AIGC Industrial Application Platform
4、Practical Interaction: Groups use systematic thinking to complete scheme designs based on thematic scenarios.
5、Interactive Exchange: Presentation of Results and Q&A Session
6、Recap

1、Entry/Mid-level Interaction Designer
2、Entry/Mid-level Visual Designer
3、Product Manager with 3-5 years of experience
4、AI industry practitioners and those interested in the field of AI.

1、Understand the use of systematic thinking in AIGC applications
2、Learn how to drive technology to achieve practical engineering design and implementation
3、Practice hands-on in real project reenactments and get answers to doubts on the spot.