| Technology | Building scalable IT infrastructure supporting AI | Model analysis (Chen et al., 2021) |
| Integration of generative AI technology with other systems used in new product development (ERP, CRM, etc.) | Model analysis (Sonntag et al., 2024) |
| Automation of generative AI model deployment | Model analysis (Sonntag et al., 2024) |
| Use of cloud for data storage and processing for generative AI | Model analysis (Chen et al., 2021; Sonntag et al., 2024) |
| Use of tools for managing the lifecycle of generative AI models | Reviewers’ suggestion |
| Infrastructure supporting the handling of large datasets | Model analysis (Chen et al., 2021; Sonntag et al., 2024) |
| Implementation of real-time processing technologies | Model analysis (Chen et al., 2021) |
| Computational power required for deployment and maintenance of generative AI solutions | Interviews, Model analysis (Chen et al., 2021) |
| Use of tools (internal or external) based on generative AI in daily work (e.g. ChatGPT, MS Copilot) | Model analysis (Chukhlomin, 2024) |
| Scalability of generative AI solutions in use | Model analysis (Sonntag et al., 2024) |
| Data | Building high-quality data sets | Interviews, Model analysis (Chen et al., 2021; Sonntag et al., 2024) |
| Automation of data analysis and processing | Interviews, Model analysis (Chen et al., 2021; Sonntag et al., 2024; Chukhlomin, 2024) |
| Centralization of data sets (a single data dictionary in the organization) | Interviews, Model analysis (Chen et al., 2021) |
| Use of advanced tools for data quality assessment | Model analysis (Chen et al., 2021; Sonntag et al., 2024) |
| Developing a data management strategy | Model analysis (Chen et al., 2021; Sonntag et al., 2024) |
| Automation of data collection and cleaning processes | Model analysis (Chen et al., 2021) |
| Identification and integration of data from internal and external sources with current datasets | Model analysis (Chen et al., 2021; Sonntag et al., 2024) |
| Existence of a standard data model and standard metadata set | Model analysis (Sonntag et al., 2024) |
| Using generative AI to support data visualization | Model analysis (Chen et al., 2021; Chukhlomin, 2024) |
| People and competencies | Developing awareness and understanding of generative AI solutions | Literature review, Interviews, Model analysis (Sonntag et al., 2024) |
| Training teams in programming (including prompt engineering) and data analysis | Model analysis (Chen et al., 2021; Sonntag et al., 2024; Chukhlomin, 2024) |
| Creating interdisciplinary AI teams | Interviews, Model analysis (Sonntag et al., 2024) |
| Introduction of external generative AI consultants to teams | Literature review |
| Training in project management using generative AI | Literature review, Model analysis (Chukhlomin, 2024) |
| Enhancing knowledge transfer in generative AI through knowledge management processes | Model analysis (Sonntag et al., 2024) |
| Organization and processes | Integrating AI with existing processes (AI solutions freeing specific process’ managers from non-core tasks) | Interviews |
| Automating (product development) processes using generative AI | Model analysis (Sonntag et al., 2024; Chukhlomin, 2024) |
| Using AI to support decision-making | Interviews, Model analysis (Chen et al., 2021) |
| Implementing tools supporting AI teamwork (daily tools aiding product managers) | Interviews |
| Introducing continuous improvement cycles in implementing generative AI solutions (learning from past and present implementations for future use) | Model analysis (Sonntag et al., 2024) |
| Defined lifecycle management process for software delivering generative AI solutions | Model analysis (Sonntag et al., 2024) |
| Generative AI-based product development guide | Interviews |
| Strategy and management | Developing a long-term strategy for investing in generative AI | Model analysis (Sonntag et al., 2024; Chukhlomin, 2024) |
| Implementing an AI strategy in the (product development) process (implementation methodology) | Literature review, Interviews |
| Defining strategic goals for generative AI | Model analysis (Sonntag et al., 2024) |
| Assessing the business impact and feasibility of generative AI-based solutions in advance | Interviews |
| Developing a system for monitoring AI implementation outcomes (metrics for assessing the impact of the solution on the process) | Literature review, Interviews |
| Competitive analysis in terms of generative AI deployment capabilities | Model analysis (Sonntag et al., 2024) |
| Budget | Long-term budget planning for development of solutions and infrastructure supporting generative AI | Literature review, Model analysis (Sonntag et al., 2024) |
| Allocating funds for developing employee competencies in generative AI | Literature review, Model analysis (Sonntag et al., 2024) |
| Funding pilot and innovative projects related to generative AI solutions | Model analysis (Sonntag et al., 2024) |
| Allocating funds for external generative AI consultations | Reviewers’ suggestion |
| Prioritizing projects generating high added value through generative AI | Reviewers’ suggestion |
| Products and services | Supporting/automating product design and manufacturing processes | Literature review |
| Using generative AI for product personalization | Reviewers’ suggestion |
| Generating product-related ideas (including sentiment analysis/review analysis) | Literature review, Model analysis (Chukhlomin, 2024) |
| Supporting information reduction (e.g. text summarization) | Literature review, Model analysis (Chukhlomin, 2024) |
| Supporting and improving concept evaluation processes (requirement analysis, risk assessment, task planning) | Literature review |
| Reducing product testing time | Literature review |
| Supporting product marketing (creating advertisements, keywords, promotional videos) | Literature review |
| Enhancing product recommendation systems | Literature review |
| Improving the functionality of product databases | Literature review, Model analysis (Chukhlomin, 2024) |
| Using generative AI solutions as a component in professional applications | Literature review, Model analysis (Chukhlomin, 2024) |
| Identifying new use cases for generative AI solutions | Model analysis (Sonntag et al., 2024) |
| Ethics and regulations | Adhering to ethical principles in generative AI design | Literature review |
| Data security and privacy in the product development process | Literature review, Interviews, Model analysis (Das et al., 2023; Sonntag et al., 2024) |
| Trust in data and explainability | Literature review, Interviews, Model analysis (Das et al., 2023; Sonntag et al., 2024) |
| Implementing data protection standards and backup mechanisms | Model analysis (Chen et al., 2021) |
| Counteracting biases and unfairness in generative AI algorithms | Literature review, Model analysis (Das et al., 2023) |
| Regularly evaluating compliance of generative AI algorithms and tools with legal regulations | Reviewers’ suggestion |
| Creating audit systems for decisions made by generative AI (accountability for decisions made by generative AI) | Literature review |
| Increasing awareness of data protection among employees | Model analysis (Chen et al., 2021) |
| Using cybersecurity-related technologies | Model analysis (Chen et al., 2021) |
| Maintaining documentation (generative AI use cases, system logs, tests, version control, etc.) to monitor the use of generative AI solutions (inconsistencies, unusual events) | Model analysis (Das et al., 2023) |