References

Customer First. Value Next.
The Executive Playbook for AI-Driven Omnichannel Personalization and Customer-Centric Growth

by Mariusz Gromada

Sources & Further Reading

• Batra, I., Panjwani, R., Aggarwal, N., Mathur, A., & Donohue, A. (2025). Agentic Commerce: Shopping and Payments Re-(AI)magined. Boston Consulting Group. [Batra et al., 2025]
• Baek, T. H., & Morimoto, M. (2012). Stay Away From Me: Examining the Determinants of Consumer Avoidance of Personalized Advertising. Journal of Advertising, 41(1), 59–76. [Baek & Morimoto, 2012]
• Brynjolfsson, E., Li, D., & Raymond, L. (2023). Generative AI at Work. NBER Working Paper 31161. [Brynjolfsson et al., 2023]
• Christensen, C. M., Hall, T., Dillon, K., & Duncan, D. S. (2016). Competing Against Luck: The Story of Innovation and Customer Choice. Harper Business. [Christensen et al., 2016]
• Daugherty, P., & Wilson, H. (2018). Human + Machine: Reimagining Work in the Age of AI. Harvard Business Review Press. [Daugherty & Wilson, 2018]
• Deloitte Digital & Google. (2020). Milliseconds Make Millions. [Deloitte & Google, 2020]
• European Union. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council. Official Journal of the EU. [EU AI Act, 2024]
• Eurostat. (2025). Digital society statistics at regional level. Statistics Explained. [Eurostat, 2025]
• GSMA. (2024). The Mobile Economy 2024. [GSMA, 2024]
• Gartner. (2023). Gartner Survey Finds 62% of Customer Service Channel Transitions Are High-Effort. Press Release. [Gartner, 2023]
• Godin, S. (1999). Permission Marketing: Turning Strangers into Friends and Friends into Customers. Simon & Schuster. [Godin, 1999]
• Goodhart, C. (1975). Problems of Monetary Management: The U.K. Experience. In: Papers in Monetary Economics (Vol. 1). Reserve Bank of Australia. [Goodhart, 1975]
• DalleMule, L., & Davenport, T. H. (2017). What’s Your Data Strategy? Harvard Business Review (May–June). [DalleMule & Davenport, 2017]
• Halligan, B., & Shah, D. (2014). Inbound Marketing, Revised and Updated: Attract, Engage, and Delight Customers Online. Wiley. [Halligan & Shah, 2014]
• Schwab, K. (2016). The Fourth Industrial Revolution. World Economic Forum. [Schwab, 2016]
• Hand, D. J. (1998). Data mining: Statistics and more? The American Statistician, 52(2), 112–118. [Hand, 1998]
• Hand, D. J. (1999). Statistics and data mining: Intersecting disciplines. SIGKDD Explorations, 1(1), 16–19. [Hand, 1999]
• Hand, D. J., Mannila, H., & Smyth, P. (2001). Principles of Data Mining. MIT Press. [Hand et al., 2001]
• IBM Institute for Business Value. (2023). Augmented work for an automated, AI-driven world. [IBM, 2023]
• ISO/IEC. (2023). ISO/IEC 42001:2023 – Artificial intelligence – Management system (AIMS). [ISO-42001]
• Jaśkowski, M., & Jaroszewicz, S. (2012). Uplift modeling for clinical trial data. ICML 2012 Workshop on Machine Learning for Clinical Data Analysis. [Jaskowski & Jaroszewicz, 2012]
• Kleppmann, M. (2017). Designing Data-Intensive Applications. O’Reilly Media. [Kleppmann, 2017]
• Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. [Kohavi et al., 2020]
• Kreps, J. (2013). The Log: What Every Software Engineer Should Know About Real-Time Data’s Unifying Abstraction. LinkedIn Engineering Blog. [Kreps, 2013]
• Li, L., Chu, W., Langford, J., & Schapire, R. E. (2010). A Contextual-Bandit Approach to Personalized News Article Recommendation. WWW ’10. [Li et al., 2010]
• Lo, V. S. Y. (2002). The True Lift Model: A Novel Data Mining Approach to Response Modeling in Database Marketing. SIGKDD Explorations, 4(2), 78–86. [Lo, 2002]
• Microsoft. (2024). Work Trend Index Annual Report. AI at Work Is Here. Now Comes the Hard Part. [Microsoft, 2024]
• McKinsey & Company. (2021). The value of getting personalization right – or wrong – is multiplying. [McKinsey, 2021]
• Bijawat, A. (2022). Data Warehouse vs. Data Lake vs. Data Lakehouse vs. Data Mesh, LinkedIn. [Bijawat, 2022]
• McKinsey & Company. (2023). What is personalization? (Explainer). [McKinsey, 2023]
• NIST. (2023). AI Risk Management Framework 1.0 (AI RMF 1.0, NIST AI 100-1). [NIST, 2023]
• Nielsen, J. (1993, updated 2014). Response Times: The 3 Important Limits. Nielsen Norman Group. [Nielsen, 1993, 2014]
• Weinberger, M., Papas, P. (2016). Interview: IBM quietly built the world’s largest digital agency. Business Insider. [Weinberger & Papas, 2016]
• Provost, F., & Fawcett, T. (2013). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. O’Reilly Media. [Provost & Fawcett, 2013]
• Radcliffe, N. J., & Surry, P. D. (2011). Real-World Uplift Modelling with Significance-Based Uplift Trees. Stochastic Solutions. [Radcliffe & Surry, 2011]
• Speck, P. S., & Elliott, M. T. (1997). Predictors of Advertising Avoidance in Print and Broadcast Media. Journal of Advertising Research, 26(3), 61–76. [Speck & Elliott, 1997]
• Strathern, M. (1997). Improving ratings: audit in the British University system. European Review, 5(3), 305–321. [Strathern, 1997]
• Willink, J., & Babin, L. (2015). Extreme Ownership: How U.S. Navy SEALs Lead and Win. St. Martin’s Press. [Willink & Babin, 2015]
• Sensor Tower / data.ai (2024). State of Mobile 2024. [Sensor Tower, 2024]