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CONCERNS ABOUT
INNOVATION
THE CASES
STRATEGIES AND
CHALLENGES
THE DECISIONS
REFERENCES
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Publication date: February 11, 2025
Last review: February 11, 2025
2-SP90-31-047
The Race
for Personalization
This problem situation was written by Ján Rehák (janrehak@tec.mx) and translated by
Laura Elena Morales Garza of Global Words with the purpose of serving as discussion
material in the classroom; it does not intend to illustrate good or bad administrative
practices. This document was elaborated from secondary sources.
All Rights Reserved © Instituto Tecnológico y de Estudios Superiores de Monterrey;
Av. General Ramón Corona No. 2514 Col. Nuevo México, Zapopan, Jalisco 45138,
México. ITESM forbids any type of reproduction, storage, or transmission in whole or
in part of this work, without written consent.
In the frantic digital landscape, artificial
intelligence (AI) has become a backbone
in consumer-focused innovation. Com-
panies like Spotify and Netflix are using
advanced AI algorithms to create hyper
customized experiences that adapt to in-
dividual preferences. These innovations
do not only increase customer satisfac-
tion, but they promote higher participa-
tion and loyalty. Nevertheless, as com-
panies compete to personalize, they face
challenges such as concern over data
privacy, maintaining transparency in al-
gorithmic decision making, and, by all
means, the correct implementation of
technology in their processes. According
to a Deloitte survey, 47% of consumers are
worried about the use of their data: com-
panies must address these concerns to
Image. Rawpixel.com in Freepik, 2025.
Critics argue that the detailed collection of data poses questions about users’ consent and data protection, which
leads some to demand stricter regulations to manage AI’s increasing influence on personalization (Deloitte, 2024).
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guarantee trust and adoption (Arbanas, et
al., 2021).
For instance, Spotify uses AI to ana-
lyze listening habits, moods, and even
location habits to create personalized re-
production lists like “Discover Weekly”.
This approach has significantly increased
users’ participation; a 31% increase in dai-
ly active users after introducing recom-
mendations encouraged by AI (Cohen,
2023).
CONCERNS ABOUT INNOVATION
THE CASES
Netflix has become a pioneer in the use
of AI to recommend content based on the
display of the visualization history, time al-
lotted to certain genres, and even the time
of day in which users watch content. By
adapting their recommendations, Netflix
claims to avoid an estimated loss of one bil-
lion dollars per year in revenue due to cus-
tomer turnover (Factspan, 2024). Despite
these achievements, the company must
address the algorithmic bias and its poten-
tial to reinforce echo chambers, as well as
limits to exposure to diverse content. Re-
search from the University of the South of
California highlights how the algorithmic
bias can, inadvertently, perpetuate stereo-
types; in these cases, companies like Net-
flix are urged to adopt more inclusive AI
frameworks (USC AI Lab, 2024).
Image. Freepik, 2025.
Other companies, like Amazon, have
integrated AI to optimize customers’ jour-
neys. From predicting product preferences
to customizing prices strategies, Amazon’s
AI systems pursue maximizing suitability
while increasing sales (Patov, 2024). Nev-
ertheless, the ethical debate continues as
some researchers warn about the dangers
of prices discrimination based on consum-
ers’ profiles. A report of the World Econom-
ic Forum emphasizes the need for transpar-
ency in AI’s pricing mechanisms to avoid
eroding consumers’ trust (World Economic
Forum, 2025).
Companies must consider how AI can allow
for a scalable personalization without
sacrificing inclusion or ethical integrity.
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STRATEGIES AND
CHALLENGES
AI-driven personalization oers trans-
forming opportunities for the innovation of
business models by enabling companies to
create more adaptive, customer-centered
strategies. By capitalizing on AI, compa-
nies can develop dynamic business models
that closely align to consumers’ changing
needs to improve value propositions and
operating eciency. Nevertheless, achiev-
ing the latter requires overcoming signif-
icant challenges, including data security,
algorithmic transparency, and ethical use
(Sjödin, et al., 2021).
Adapting their models to include
AI-driven knowledge, entails companies
having to rethink customer acquisition and
retention strategies. Spotify, for instance,
has integrated features that are controlled
by the user, this enables adjustments in the
algorithmic recommendations to build up
trust while maintaining personalization
powers (Nagubandi, 2024). Similarly, Net-
flix’s personalized content delivery proves
AI’s potential to improve participation, al-
though it also highlights the need to ad-
dress biases that could restrict exposure to
several perspectives (Factspan, 2024).
Another innovation in business mod-
els includes services based on subscrip-
tion optimized by predictive AI, like Ama-
zon Prime. These systems use consumer
behavior analysis to oer personalized
benefits, encourage loyalty, and optimize
operational margins. McKinsey’s reports
purposefully highlight the need to main-
tain ethical standards to ensure that AI’s in-
novations do not compromise consumers
trust (Giovine y Roberts, 2024).
Eective decision making in corporate AI-driven innovation entails companies addressing critical issues about
the creation of value and risk management.
Image. Freepik, 2025.
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THE DECISIONS
Aligning AI’s capacities to long-term strate-
gic goals is essential to maintain competi-
tive advantages. For instance, the compa-
nies that adopt AI’s modular solutions can
iteratively refine their oers based on real
time feedback and enable adaptive innova-
tion. Spotify’s algorithmic transparency ini-
tiatives oer a convincing example of how
companies can increase users’ participation
while browsing through regulatory com-
plexities (Cohen, 2023). In parallel, strategies
based on Amazon data for predictive logis-
tics highlight how AI can optimize the sup-
ply chain eciency, oer cost savings, and
better customer experiences (Team, 2024).
As companies innovate, it remains es-
sential to address the algorithmic bias. The
integration of several data sets and building
inclusive AI contexts can mitigate risks and
create more equitable results. Companies
must also establish governance structures
that supervise AI’s deployment to ensure
that innovation aligns with ethical and so-
cial expectations.
In conclusion, corporate leaders must
assess how AI-driven personalization is
integrated into their general business
models and answer the following ques-
tions:
• Data Usage and Ethics: Which innova-
tive approaches can companies adopt
to collect, manage, and use consumers’
data ethically, balancing a higher per-
sonalization with the growing concerns
regarding privacy?
• Optimization of the Value Proposition:
How can companies redefine their value
propositions to benefit from AI-driven
personalization while they ensure inclu-
sion and avoid alienating neglected cus-
tomer segments?
• Transformation of the Income Model:
How can AI-driven knowledge enable
new income models, like dynamic sub-
scription prices or usage-based services
that align to customers’ preferences and
behaviors?
• Sustainability and Scalability: How can
companies scale AI-driven personaliza-
tion in such a way that they maintain
competitive advantage while ensuring
transparency and ethical governance
frameworks?
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REFERENCES
Arbanas, J., Silverglate, P. H., Hupfer, S., Loucks, J., Raman, P., y Steinhart, M. (6 de diciembre de 2024). “Earning trust as gen AI takes hold: 2024 Connected Consumer Survey”. Deloitte Insights:
https://www2.deloitte.com/us/en/insights/industry/telecommunications/connectivity-mobile-trends-survey.html
Cohen, B. (27 de septiembre de 2023). “How Spotify Uses AI to Create an Ultra-Personalized Customer Experience and What Distributors Can Learn from It”. Distribution Strategy Group:
https://distributionstrategy.com/how-spotify-uses-ai-to-create-an-ultra-personalized-customer-experience-and-what-distributors-can-learn-from-it/
Deloitte. (15 de enero de 2024). “New Deloitte survey finds expectations for Gen AI remain high, but many are feeling pressure to quickly realize value while managing risks”. Deloitte:
https://www.deloitte.com/global/en/about/press-room/gen-ai-survey.html
Factspan, Inc. (11 de junio de 2024). “How Netflix Save $1 Billion a Year with AI?”. Factspan:
https://www.factspan.com/blogs/how-netflix-save-1-billion-a-year-with-ai/
Giovine, C., y Roberts, R. (26 de noviembre de 2024). “Building AI trust: The key role of explainability”. McKinsey & Company:
https://www.mckinsey.com/capabilities/quantumblack/our-insights/building-ai-trust-the-key-role-of-explainability
Nagubandi, N. K. (2024). “Leveraging AI to revolutionize subscription business models”. International Journal of Scientific Research in Computer Science Engineering and Information Technology, 10(5), 649–660:
https://doi.org/10.32628/cseit241051052
Patov, A. (4 de septiembre de 2024). “How Amazon Redefines Customer Experience (CX) with Innovation and Convenience”. Renascense:
https://www.renascence.io/journal/how-amazon-redefines-customer-experience-cx-with-innovation-and-convenience
Sjödin, D., Parida, V., Palmié, M., y Wincent, J. (2021). “How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops”. Journal of Business Research, 134, 574–587:
https://doi.org/10.1016/j.jbusres.2021.05.009
Team, S. (2 de julio de 2024). “How Amazon is using AI to become the fastest supply chain in the world”. Sifted:
https://sifted.com/resources/how-amazon-is-using-ai-to-become-the-fastest-supply-chain-in-the-world/.
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