Applications, challenges, and success factors identified from the interviews (Source: Own study)
| Research area | Factors / examples identified |
|---|---|
| Applications | Everyday tools that support the work of product managers, which are based on AI solutions, enhancing work efficiency and speed (A4, A8, A10) |
| Solutions that relieve the product manager of all tasks not related to their core responsibilities for product development and management (A3, A12) | |
| An AI guide to the new product development process, which directs product managers on actions to take during specific phases of the process (A4, A6, A11) | |
| AI tools that aid in making product business decisions (A6, A9) | |
| Challenges | High volume of high-quality data and processing these data to make improved business decisions (A1–A12) |
| The approach of people in the organization to the use of AI solutions, primarily unrealistic expectations about what is achievable (A1, A3, A5, A6, A8, A9) | |
| Security and privacy related to product data, particularly when it involves the use of these data by external companies to develop tools that support the work of the product manager (A1, A4, A5, A7, A8, A11) | |
| Trust and explainability of the data presented to the product manager (A1, A2, A4, A5, A7, A8, A11) | |
| Computing power required to implement and maintain models for AI solutions (A9, A11) | |
| Lack of clear methodology on how to introduce AI solutions in the product development and management process (A1, A11, A12) | |
| Success factors | Appropriate metrics to evaluate how the AI solution has aided the process (e.g., financial or time-based) (A1, A4, A7, A8, A9, A11) |
| Ability to accurately assess the business impact and feasibility of the introduced AI solution in advance (A1, A3, A5) | |
Team quality and interdepartmental collaboration across the organization (A2, A3, A5, A7, A12) One general data dictionary across the organization (A2, A12) |
Implications for product managers and organizations and possible future directions for researchers (Source: Own study)
| Implications/future directions | Description |
|---|---|
| Implications for product managers and organizations | Pre-identifying key responsibilities of product managers and determining how to treat implementations of AI tools to support product development and management process can facilitate their implementation |
| Implementing AI solutions that can support product managers in everyday tasks, relieve from tedious workflows, support in making product business decisions or guide through product development process can enhance their speed and work efficiency | |
| Addressing data challenges (quantity, quality, security, explainability, computing power to process the data) in the organization can benefit the product development and management process, as the challenges are similar | |
| Providing training for stakeholders involved in the product process about the capabilities and costs of AI solutions can help prevent unrealistic expectations | |
| Employing a proper methodology (AI readiness, AI maturity, or similar frameworks) can help companies assess the necessary steps for introducing AI solutions into the product development and management process | |
| Implementing measure of success (financial, time-based, satisfaction-based, or other) is important to properly assess implemented AI solution in product development and management process | |
| Accurately pre-assessing project challenges, evaluating the extent to which an AI solution will impact the process, quality team, and cross-departmental collaboration can increase the chances of success | |
| Implementing a general data dictionary in the organization that can be utilized by all departments (e.g., using data mesh architecture) can improve work efficiency across the organization | |
| Future directions for researchers | Empirical study of organizations that use AI solutions in the product development and management process |
| Investigation into the impact of the Product Ops role on the AI-driven product development and management process | |
| Research into methodologies or models for AI-driven product development and management processes | |
| Identification of the impact of organizational AI maturity or AI readiness on the product development and management process | |
| Data mesh architectures as part of an organizational knowledge management framework and their relationship to AI-driven product development and management processes |
Profile of the interviewees (Source: Own study)
| Interview reference | Current position | Years of experience |
|---|---|---|
| A1 | AI engineer | 5 years |
| A2 | Head of Product Development | 11 years |
| A3 | Head of R&D / Researcher | 10 years |
| A4 | Product Owner / AI Product Line Manager | 5 years |
| A5 | VC Founder / Researcher | 15 years |
| A6 | Data Science Engineering Manager / Researcher | 6 years |
| A7 | Head of AI / Researcher | 7 years |
| A8 | VC Investor / Researcher | 6 years |
| A9 | CTO / Researcher | 9 years |
| A10 | Lead Data Scientist / Researcher | 7 years |
| A11 | ML Consultant / Researcher | 6 years |
| A12 | Senior Product Manager | 8 years |