Traditional product managers’ self-salvation strategies in the AI ​​era

With the advancement of technology and the development of AI technology to this day, product managers will also face a changing destiny. So what should product managers do? Let’s take a look at what good suggestions the author in the article below has!

Today, with the continuous development of artificial intelligence technology, product managers are facing unprecedented challenges. Intelligence, automation and data drive are changing the product life cycle and the way the market operates. So, in this era of change, how should product managers adapt to and take advantage of these changes to create truly competitive products?

1. New role positioning in the AI ​​era In the AI ​​era, new role positioning has become crucial. With the rapid development and application of artificial intelligence technology, many traditional roles are changing, and many new roles have emerged. First, AI engineers have become a new role that has attracted much attention. They are responsible for developing and maintaining artificial intelligence systems, ensuring they function properly and are continuously optimized. AI engineers need to have deep technical background and professional knowledge, and be able to understand and apply algorithms such as machine learning and deep learning. Secondly, data scientists have also become an important role. They are responsible for collecting, cleaning and analyzing large amounts of data to extract valuable information and patterns. Data scientists need to have knowledge in statistics, mathematics, and programming, and be able to use various tools and techniques to solve practical problems. In addition, AI ethicists are also an emerging role. They pay attention to the ethical and moral issues of artificial intelligence technology and ensure that its application complies with ethical standards and laws and regulations. AI ethicists need to have an in-depth understanding of ethics and law and be able to make reasonable suggestions and regulations. Finally, the AI ​​product manager also plays an important role. They are responsible for determining the functions and features of artificial intelligence products from market demand and user feedback, and working with the development team to implement them. AI product managers need to have market analysis, project management and technical understanding capabilities, and be able to balance business goals and technical feasibility. In short, in the AI ​​era, new roles are constantly emerging, requiring talents from different fields to work together to promote the development and application of artificial intelligence technology

Under the wave of AI, the role of product managers is undergoing a profound change. This is not only about technological changes, but also about the comprehensive upgrade of thinking, methods and strategies. In this section, we will delve into the three key dimensions of the new role positioning of product managers in the AI ​​era: technology-driven decision-making, user experience transformation, and business model innovation.

1. Technology-driven decision-making

In the AI ​​era, data and algorithms have become the core of product decision-making. Product managers no longer just rely on intuition and experience, but need to dig deep into the insights behind the data and use algorithms to optimize every aspect of the product.

Data Insight: For example, by analyzing user behavior data, product managers can more accurately understand user needs and pain points, thereby optimizing product design and functionality. This may involve using data analytics tools, such as Google Analytics or Mixpanel, to track and analyze user behavior and preferences.

Algorithm Application: Algorithms play a vital role in product recommendation, sorting, search, etc. For example, e-commerce platforms may optimize product recommendation logic through machine learning algorithms, thereby improving conversion rates and user satisfaction.

2. Changes in user experience Changes in user experience are an important trend. As technology continues to advance, users' expectations for products and services are also increasing. Therefore, enterprises need to continuously improve user experience to meet user needs and expectations. This change needs to be carried out from many aspects, including product design, interface optimization, interaction methods, etc. By continuously improving user experience, companies can increase user satisfaction and loyalty, thereby gaining more market share and competitive advantages

The application of AI technology has greatly enriched and changed the user experience. Product managers need to rethink how to incorporate AI elements into product design and interaction to bring users a smarter and more convenient experience.

INTELLIGENT INTERACTION: For example, by introducing voice assistants and chatbots, product managers can provide users with a more natural and convenient interactive experience. This may involve collaboration with NLP (natural language processing) experts to understand and optimize the interaction logic of speech and text.

Personalized Experience: By leveraging machine learning algorithms to analyze user behavior and preferences, product managers can personalize product experiences. For example, the music streaming service Spotify uses algorithms to analyze users’ music listening behavior and recommend personalized playlists to users.

3. Business model innovation

AI technology not only changes the function and experience of products, but also creates new value and business models for products. Product managers need to explore how to transform AI technology into business value

New value provision: For example, through AI technology, product managers can provide users with more accurate information and services. In the financial field, robo-advisory platforms analyze market data through algorithms to provide users with personalized investment advice.

Business model innovation: AI technology also provides product managers with the possibility of innovative business models. For example, as data is a kind of value, product managers can explore how to transform data into business value through data exchange, data market, etc.

2. New skill tree for product managers As technology continues to develop and markets change, the role of the product manager continues to evolve. In order to adapt to this changing environment, product managers need to master some new skills. Here is the new skill tree for product managers: 1. Data analysis capabilities: In the digital era, data has become an important basis for decision-making. Product managers need to have the ability to analyze data to understand user behavior, market trends, and competitor dynamics. Through data analysis, product managers can make more accurate decisions and optimize the product's functionality and user experience. 2. User research capabilities: The success of a product depends on user needs and feedback. Product managers need to have good user research capabilities and guide product design and improvement through in-depth understanding of user needs, behaviors and preferences. By interacting with users, product managers can better understand users’ pain points and provide targeted solutions. 3. Technical understanding: Product managers need to work closely with the development team, so they need to have certain technical understanding. Although product managers do not need to be development experts, they must have a certain understanding of the basic concepts and principles of technology in order to better communicate and collaborate with developers. 4. Application of agile development methods: Agile development methods have become the mainstream of modern software development. Product managers need to be familiar with the principles and processes of agile development in order to better collaborate with the development team and adjust product direction and strategy in a timely manner. 5. Marketing knowledge: Product managers need to understand the basic principles and strategies of marketing. By understanding the market's needs and competition, product managers can better position their products and develop effective marketing plans. 6. Innovative thinking ability: Product managers need to have innovative thinking ability to respond to changing market and user needs. By constantly thinking about and trying new ideas and solutions, product managers drive product innovation and development. The above is the new skill tree for product managers. I hope it can help product managers develop and grow better in the fiercely competitive market

Under the wave of AI, the skill tree of product managers (PM) is undergoing a revolutionary reshaping. Data, algorithms, cross-domain collaboration and continuous learning have become key skills for PMs in the new era. In this section, we’ll delve into the connotation and application of these new skills, and how product managers can master them through learning and practice.

1. Data and algorithm understanding

In the era of artificial intelligence, data and algorithms have become the basis for product decision-making. Product managers need to have certain data analysis and algorithm understanding capabilities in order to better cooperate with data scientists and engineers, and to more accurately grasp the direction and strategy of the product

Data Insight: For example, by in-depth analysis of user behavior data, PM can discover the core needs and potential problems of users. This might involve using A/B testing to validate hypotheses, or cluster analysis to discover distinct groups of users.

Algorithm Application: Understanding basic machine learning algorithms, such as decision trees, clustering and neural networks, can help PMs better understand the technical implementation of the product and can also communicate with the technical team. More handy.

2. Cross-disciplinary collaboration

The development of AI products often requires the collaboration of experts in multiple fields. Product managers need to have the ability to collaborate across disciplines to better communicate, coordinate resources, and promote project progress.

Technical Communication: For example, the PM may need to discuss the implementation details of the algorithm with engineers, or discuss with designers how to integrate AI technology into the user experience.

Project Coordination: In AI projects, PMs need to coordinate resources and work from multiple fields such as data science, engineering, design, and marketing to ensure the smooth progress of the project.

3. Continuous learning

In the rapidly developing AI era, product managers need to maintain the ability and enthusiasm for continuous learning in order to continuously update their knowledge and skills.

Learning resources: For example, PMs can learn new knowledge and skills through online courses, workshops, reading and other methods. This may include data analysis tools and methods, new AI technologies and applications, and product management best practices.

Practical Application: Applying the learned knowledge and skills to actual work is a key step in learning. PMs can try new tools and methods on projects, or conduct experiments and verification on a small scale.

3. Build an AI-driven product team

In the wave of artificial intelligence, a strong product team is a key factor for success. Product managers need to deeply explore and practice aspects such as team building, collaboration models, and innovation culture. In this part, we’ll discuss in detail how to build and grow an AI-powered product team

1. Team building: looking for all-round talents

In the development process of AI products, multi-disciplinary cross-cooperation is indispensable. Product managers need to build a team with different professional backgrounds and skills to explore and solve problems from multiple angles and dimensions.

Multidisciplinary background: For example, the team needs not only data scientists and engineers, but also designers, psychologists, and industry experts to more comprehensively understand and solve problems.

Collaboration mechanism: Build an open, collaborative team culture and mechanism to encourage communication and cooperation among team members to better integrate different knowledge and skills.

2. Collaboration mode: breaking down barriers

In a multidisciplinary team, how to collaborate effectively is a key issue. Product managers need to break down the barriers within the team and establish a smooth and efficient collaboration model.

Communication platform: For example, establish a shared communication and collaboration platform, such as Slack or Microsoft Teams, so that team members can easily exchange information and knowledge.

Iteration mechanism: Adopt an agile development and iteration mechanism to encourage teams to quickly trial, error and learn in order to find solutions to problems faster.

3. Innovation culture: encourage trying

In the AI ​​era, innovation is the key driving force for product and team development. Product managers need to build a team culture that encourages innovation and experimentation.

Innovation time: For example, you can provide some "innovation time" for team members to encourage them to explore new ideas and solutions, such as Google's "20% time" policy.

Failure Tolerance: Establish a culture that tolerates failure and encourages team members to try and take risks instead of fearing failure.

4. Practical case analysis

Practical case analysis is the touchstone for the application of theoretical knowledge. In this part, we will delve into the success and failure cases of some AI products, trying to extract valuable experiences and lessons from them, and provide reference and inspiration for the practice of product managers.

Success Stories: Behind the Scenes of AlphaGo AlphaGo, an artificial intelligence computer program developed by DeepMind, caused a global sensation when it defeated world champion Lee Sedol in the Go game. However, the story behind AlphaGo is not just about winning a game. Behind the success of AlphaGo is the unremitting efforts of the team and the huge breakthrough in deep learning technology. DeepMind scientists have spent years developing and training AlphaGo so that it can continuously improve itself by playing against itself and against human players. During the training process of AlphaGo, the team used a large amount of data and reinforcement learning algorithms. By analyzing data from millions of Go games, AlphaGo learned to extract key information and make the best decisions when playing chess. At the same time, the team continues to improve AlphaGo's strategies and techniques by playing against top players. The success of AlphaGo is not only a victory in a human-machine game, but also an important milestone in artificial intelligence technology. It shows the world the great potential of deep learning and reinforcement learning on complex problems and opens up a new path for the development of artificial intelligence. The story behind AlphaGo tells us that with enough effort and innovative spirit, artificial intelligence can achieve breakthroughs in various fields. It is not only a technological breakthrough, but also a combination of human wisdom and machine intelligence, bringing us a broader future

The success of AlphaGo is not only a victory of technology, but also a masterpiece of product management. It successfully transforms complex technologies into products with commercial value and social impact by collaborating with experts in multiple fields.

Cross-field collaboration: AlphaGo’s team includes multiple roles such as AI researchers, Go experts and product managers. They discussed the problem together, tested their hypotheses, and finally found a workable solution.

The combination of technology and market: AlphaGo not only pays attention to the development of technology, but also pays attention to market demand and feedback. It continuously optimizes its algorithm through games with professional Go players, and also increases market attention and recognition.

Lessons from Failure: IBM’s Watson Health Project IBM's Watson for Health project is a highly anticipated project that aims to use artificial intelligence technology to improve the field of healthcare. However, the project suffered a series of setbacks and failures. First, the project faced technical difficulties at the outset. Although Watson is considered a powerful artificial intelligence system, it has encountered many problems in processing medical data and understanding medical knowledge. This resulted in the project progressing slowly and failing to make breakthrough progress as expected. Secondly, IBM’s Watson health project also faces the challenges of complexity and confidentiality in the medical industry. The protection and privacy issues of medical data became a significant obstacle to the project. Many medical institutions and patients have expressed concerns about handing over their sensitive data to an external artificial intelligence system, which limits the development and scope of the project. In addition, market demand and user acceptance have also put certain pressure on the project. Although IBM's Watson health project has great potential, it has encountered many difficulties in practical application. The complexity and traditional workflows of the healthcare industry make doctors and patients less receptive to new technologies, which leads to difficulties in marketing and user adoption of the project. To sum up, the lesson learned from the failure of IBM’s Watson health project is that technical challenges, industry complexity and market demand are all key factors for project success. In future development, it is necessary to pay more attention to the feasibility and adaptability of technology, while also taking into account the particularity of the industry and the needs of users to ensure that the project can achieve better results

IBM’s Watson health project aimed to use artificial intelligence technology to revolutionize the medical and health field, but it ultimately failed to achieve its expected goals. We can summarize some reasons and lessons from failure

Excessive Expectations: The Watson Health Project set expectations that were too high at the beginning. It attempts to solve complex problems in the medical field through technology, but ignores the actual difficulty and complexity of implementation.

Disconnect between market and technology: Although Watson has certain advantages in technology, it does not understand and meet the actual needs of the market well. This resulted in a deviation between the direction of the project and market demand.

3. Inspiration: Learn from cases

By analyzing these cases, we can gain some enlightenment about artificial intelligence product management

Stay realistic and realistic: When setting goals and expectations, you need to be realistic and realistic, and fully consider the actual implementation difficulty and market acceptance.

Closely integrate with the market: During the product development process, it is necessary to closely integrate the needs and feedback of the market to ensure that the direction of the product is consistent with the market.

Strengthen cross-field collaboration: In the development process of AI products, strengthen cross-field collaboration and exchanges to ensure that knowledge and skills in different fields can be fully integrated and applied.

This article was originally published by @yancheng on Everyone is a Product Manager. Reprinting without permission is prohibited

Title picture comes from Unsplash, based on CC0 protocol

The above is the detailed content of Traditional product managers’ self-salvation strategies in the AI ​​era. For more information, please follow other related articles on the PHP Chinese website!