AI has revolutionized product management by increasing creativity, efficiency, and market relevance. AI is our greatest technological advancement. Modern technology relies on AI subsets DL and ML. Machines that learn and improve have enabled the technological revolution. AI is being used in many fields, including product management. Product Managers have developed AI-enabled products using AI technologies.
This theoretical framework discusses the relationship between AI and product management as it examines the product development process. This discourse integrates AI and promotes sustained growth by discussing its theoretical foundations and examples of its use throughout the four product development stages.
This paper describes the product management life cycle. AI, the ability of a machine to mimic human cognitive functions, has expanded globally across every industry, causing sweeping changes in product design, promotion, and delivery. The ability to process large data sets, identify trends, generate insights, and provide recommendations has given product managers latent tools that have transformed idea generation, market research, prototyping, metrics and data analysis, customer engagement strategies, and more. Conception lays the groundwork for product development.
AI acts as a catalyst, improving traditional ideation methods with advanced computational methods and engaging predictive analysis. AI-informed analysis allows product managers to identify new trends, consumer preferences, and market niches, which helps create new product management concepts. When most products go from idea to market, AI helps with market research and consumer analysis. System insights can determine customer preferences, attitudes, and product management decisions, reducing risks and improving product management.
AI-based product management
First, define the AI product manager’s role. Because AI isn’t always the best solution, an AI project manager must identify the most pressing business issues AI can solve. The second reason for AI project management is choosing the right challenges and their importance. Plan the steps and vision strategy. Finally, AI PM must ensure the plan is followed. AI product management can take many forms, each with its own duties.
| Type | Job Duties |
| Product | Develop artificial intelligence for use in products such as search engines, autonomous vehicles, and advertising opportunities. |
| Platform | Develop artificial intelligence by enhancing the tools and infrastructure available to developers. |
| Research | Works with AI scientists to commercialize research. These are usually product managers from larger, more prestigious companies whose departments prioritize research. |
| Responsible AI | Put your attention on developing AI in a responsible manner. |
Roles AI products manager
The following skills are needed to become an AI product manager: Product managers must define the output, delivery method, and product purpose before starting construction. Restarting a procedure costs money. Design professionals use UX mockups, wireframes, and user surveys to innovate quickly.
These tools should be available to AI product managers from the start. The product’s benefit must be stated. ML can solve ranking suggestion problems in classification, regression, grouping, and anomaly detection. The AI lifecycle begins with training and data. The ML algorithms mentioned above need data to learn.
After training, design and development build the product based on model predictions. Validation and testing are required before deployment. AI product managers must monitor an optimization after it is deployed to ensure changes. Thus, system quality must always be monitored.
Responsibilities of AI product manager
AI’s vast capabilities make creating AI-powered products more accountable. The Hippocratic oaths emphasize “first, not harm” in medicine. Users and customers must be protected when using AI. If not carefully developed and optimized, AI can produce unexpected results.
Ethical additional data enhances
AI has social and human biases. One example is facial recognition systems’ inability to identify darker skin tones. AI accidentally recommends men for higher-paying jobs. Major AI duties are listed below. AI product managers must ensure ethical, inclusive, prejudice-free, and transparent AI production.
Private and secure
Privacy must be prioritized in AI threat protection. New AI-deception methods are exciting. Picture recognition algorithms can be rendered useless by even minor image alteration. Therefore, when developing AI, consider the drawbacks.
Transparent
If AI is transparent, users should understand its decisions and actions. It becomes even more important when these use cases involve people’s lives and could change them. Automatic intelligence systems should be overseen by humans and have accountability safeguards.
Reliable
In order for models to maintain their consistency over time, artificial intelligence (AI) needs to be reliable enough to deliver consistently excellent performance. It is designed to identify issues that are present in the data. The importance of monitoring in that pipeline up there lies in the fact that it is located there.
Traditional market strategies
The traditional market strategies are an essential component of the many aspects that comprise business and commerce. According to Figure 3, marketers have the ability to engage in either business-to-business (B2B) commerce or business-to-consumer (B2C) marketing with their customers.
B2B: “A “business-to-consumer marketing” strategy or document targets businesses or organizations.
B2C: A company promotes its products and services to individual customers through “business-to-consumer marketing”.
Understanding the following ideas is crucial to marketing because customer needs are so important.
Needs: A stable, safe, and healthy life is essential for humans. Objective material needs include food, drink, and shelter. Family or social group membership is a subjective and psychological goal.
Wants: They are desired and aspirational. Basic survival does not require wants. Culture or peer influence usually affects them.
Demands: When someone’s payment covers their needs and wants, they may become economic demands.
The four types of marketing are:
- Cause-related marketing: This is advertising. This type of advertising links a business’s products to a social or political movement.
- Relationship marketing: Focusing on customer retention and satisfaction increases loyalty and strengthens relationships.
- Scarcity marketing: This strategy creates a sense of scarcity to encourage sales.
- Undercover marketing: The target audience is unaware of the advertising. This is stealth marketing.
Business, functional domains, and business operations are AI subfields. Marketing is one of these areas that drives every business. AI has changed and will continue to change marketing.
Artificial Intelligence with machine learning
Technological revolutions are happening worldwide this century. Customers need modern technology like AI, ML, cloud computing, the IoT, and blockchain for many digital solutions. New technologies also improve customer service, helping businesses thrive. AI and ML, the two most powerful technologies in this space, have transformed goods and services. AI occurs when machines that mimic human intelligence, such as computer systems, perform previously human-only tasks autonomously.
Unnoticed AI applications include email spam filters, phishing detectors, search engines, autonomous vehicles, and many others. Current AI is weak or nocturnal artificial narrow intelligence. Its capabilities are limited to certain tasks and environments.
AI products use these technologies to boost productivity. One study found that about 39% of all product managers worry about missing launch dates, which typically occurs because of slow decision-making when it comes to choosing product features and functionalities.
1) Artificial Intelligence
The public has focused on AI for years. In 1950, Allan Turning asked “Can a computer think?” and coined the term artificial intelligence. AI has filled the vast field of computer science that aims to give robots human intelligence. A machine’s ability to mimic human intelligence summarizes the AI debate.
AI is too vast to summarize. Automated cars, cloud computing, IoT, bioinformatics, material sciences, and all other sciences use AI. Product management stands out among new fields using AI to boost product production.
2) Machine learning
ML, the most famous AI subfield, is often considered the field. ML algorithms let machines automatically learn from data. There are many uses for ML algorithms. Classification and regression are the main ML algorithms. ML studies in Product Management are gaining importance. Machine learning helps product managers create, improve, and release practical solutions.
3) Deep learning
Deep Learning is a subfield of ML. Deep Learning algorithms try to mimic the human brain to give machines intelligence. Deep learning is slowing the ML revolution.
Deep Learning can be seen as a modern version of Neural Networks, which are layers of simple, trainable mathematical units that solve complex problems. Deep Learning can learn features from noisy, diverse, and raw input, which is great. The most famous neural networks are CNN, DNN, and ANN.
Literature work
Statistical methods and elementary ML models have been used to solve machine learning-based product management analysis.
This paper presents cutting-edge AI technology to boost businesses’ innovation capabilities and uses a mixed-method approach (literature review, case study, etc.) to develop a strategy. The technology revolution presents a strategic opportunity for AI, according to this paper. AI technology can boost production efficiency by 56% and create 30% more commercial opportunities, helping businesses innovate and revitalize.
TF-IDF extracts keywords, the deep learning LSTM model sentiment classifies commodity data, and the association rule algorithm classifies keywords to quickly and accurately recommend consumer-desired cross-border e-commerce commodities. The study also examines how businesses, including VPN deals, can use AI to identify customer needs, improve services, and grow their market share.
Finally, the extracted keywords are analyzed using association rules to determine consumer online shopping preferences. The cross-border e-commerce product selection method based on TF-IDF has the highest accuracy rate in cosmetics at 90.8%. TF-IDF in health care has the highest recall rate (82.6%). The model had the highest classification accuracy of 90.20%, average of 86.47%, and lowest of 83.20%.
AI applications have developed, but research gaps remain. One study showed that about 42% of all product managers find it challenging to add new features because of the customers’ demand to support old features. AI’s innovation impact research suggests more tools and long-term effects. AI stress studies need larger, more diverse samples. They argue that current research requires more comprehensive product categorization and better algorithms. These gaps require an extensive literature review and a variety of approaches to use AI to create enterprise value.
Conclusion and future work
The revolutionary use of AI in product management changes the paradigm and boosts productivity throughout the product life cycle. AI helps product managers leverage innovation strategies to identify market opportunities, consumer trends, and product improvement. It also means that AI and product management synchronously accelerate market trend and consumer preference discovery, improve decision-making, and reduce risks.
AI product managers manage sophisticated technologies to solve critical business issues in this ecosystem. They create tactical visions and directions, ensure AI is used properly, and ensure AI is clear and accurate. Traditional marketing methods are still used, but AI makes them more effective.
Future research should eliminate bias, make AI techniques more transparent, and improve product management with AI. AI should be studied alongside IoT and blockchain to improve product management. Thus, AI’s evolution and adaptation to these issues create new product management opportunities and promote sustainable development.
