Privacy-first AI: a strategic advantage for modern strategy teams

Imagine your team has just developed an AI system that can predict customer churn with remarkable accuracy, potentially saving millions in revenue. But there's a problem: it requires combining sensitive data sets in ways your privacy team flags as concerning. Do you push forward with the innovative solution or rethink your approach?

This scenario plays out daily across organizations as companies wrestle with the tension between AI capabilities and privacy considerations. The good news? This isn't a zero-sum game. A privacy-by-design approach to AI implementation isn't just about compliance or risk avoidance; it's becoming a powerful strategic advantage in itself.

In this blog, we'll explore how forward-thinking companies are transforming privacy from a limitation into a competitive differentiator, building sustainable business value while respecting customer data, and examine the strategic value of privacy-enhanced AI and key implementation challenges.

Defining privacy in the AI context

Before diving into strategic considerations, it's important to clarify what we mean by "privacy" in the context of AI systems. Privacy encompasses several distinct but related concepts:

• Data protection and security: Preventing unauthorized access to data through technical safeguards
• Transparency and consent: Ensuring users understand and approve how their data is used
• Personal identifiable information (PII) protection: Preventing the identification of specific individuals through data use
• Purpose limitation: Using data only for the specific purposes for which it was collected
• Data minimization: Collecting and retaining only the data necessary for a specific purpose

These are the types of principles to keep in mind when building AI systems and features. “Privacy by design” is the approach that incorporates privacy protections and principles into system and process design from the get-go. “Privacy-enhacing technologies” is a term referring to specific technologies and tools that help with working with user data while respecting user privacy.

The strategic value of privacy-enhanced AI

1. Privacy as a competitive differentiator

In today's market, privacy has evolved from a compliance checkbox to a business differentiator. Before, companies thought of privacy mainly in the sense that improper data use could be strategically disastrous. A data misuse scandal (distinct from a security breach) can erode years of brand equity, with many consumers "voting with their feet" by leaving companies that mishandle their information.

But today, businesses now recognize strong data privacy practices as a competitive advantage in the marketplace, especially since studies have shown that as much as 71% of consumers would stop doing business with a company if they gave away sensitive data improperly.¹ Apple, especially with iPhones, is a notable example but far from the only one. This isn't just perception; it directly impacts consumers' and businesses' purchasing decisions.

It's important to note that while data security (protecting against unauthorized access) is foundational, true privacy goes beyond security to include transparency, control, and appropriate use of data. But, privacy concerns extend further: consumers increasingly want control over how their data is used, not just assurance that it's secure. A significant portion of consumers would consider switching to a competing brand if it offered better privacy controls and transparency. When customers feel in control of their data through clear consent mechanisms and usage options, their trust and preference for a brand significantly increase. In fact, 79% of customers are willing to share data about themselves if it leads to more customized and personalized experiences with the product.²

These trends reveal a fundamental shift: privacy has become a form of currency in the marketplace. Companies that prioritize privacy build what we might call a "trust reserve", a strategic asset that translates into customer loyalty, particularly in industries handling sensitive data like healthcare and financial services.

Privacy-enhancing practices unlock long-term business value that often outweighs short-term gains from aggressive data monetization. In fact, the return on investment for privacy initiatives is increasingly measurable. Organizations typically see benefits that exceed their spending on privacy programs, with studies indicating positive returns on privacy investments. Organizations centering their strategy on respecting customer data typically experience:

• Reduced sales delays, especially in B2B contexts
• Mitigated or avoided regulatory fines resulting from privacy violations
• Stronger brand perception and customer loyalty
• Even, in some cases, efficiency gains in innovation

Today, 96% of organizations acknowledge they have a responsibility to use data ethically and aligned with customer expectations,³ indicating a mainstream shift away from "grab all data" toward more principled approaches.

2. Examples of privacy as strategic advantage

Companies across industries are successfully leveraging privacy as part of their value proposition:

• Apple has made privacy a core brand promise and built features to give users control, boosting customer loyalty and differentiating from competitors.⁴ For example, their App Tracking Transparency feature allows users to control which apps can track their activity across other companies' apps and websites.
• Privacy-centric startups like DuckDuckGo and Brave have gained users by offering alternatives to data-hungry competitors, demonstrating market demand for privacy-enhanced services.⁵ DuckDuckGo has built a business model centered on not tracking users' search history, while Brave offers built-in ad and tracker blocking.
• European banks use privacy commitments to reassure clients when adopting digital channels, sometimes explicitly advertising that they won't misuse personal data beyond regulatory requirements.⁶ Monzo and Revolut have emphasized transparent privacy controls in their digital banking interfaces, helping them acquire customers concerned about data protection.
• Healthcare organizations are forming data-sharing consortia with strong de-identification and governance, turning smart privacy policies into an enabler for collaborative research.⁷ Some hospital groups use privacy-preserving federated learning to collectively train AI models on patient data without sharing raw records. Mayo Clinic and MIT have collaborated on federated learning for medical diagnostics, allowing algorithm improvement without violating patient confidentiality. This example demonstrates how privacy-enhancing technologies can enable AI innovation while protecting sensitive information.

Key privacy challenges in strategic AI implementation

1. Is there tension between a desired business outcome and privacy considerations?

Integrating AI into strategic planning introduces inherent tensions with privacy. AI systems thrive on data, with more detailed and comprehensive data providing better insights and predictions. Yet this appetite for data directly conflicts with privacy principles like data minimization and purpose limitation.

In recent surveys, most CEOs view AI positively while expressing caution about implementation, with privacy being a top concern regarding AI's unintended consequences.

For strategy teams considering new opportunities, this creates practical dilemmas:

• A retail strategy group might want granular customer behavior data to inform store strategy, but privacy teams may object if that data wasn't collected with that purpose in mind.
• Marketing units might eye AI tools for personalization, while legal teams warn about regulations restricting data use beyond its original purpose.

Building trust becomes essential to AI adoption, with many senior managers expressing concern that AI could expose customers to greater privacy risks. Strategy teams must navigate these tensions, ensuring AI initiatives don't outpace the organization's privacy comfort level.

2. What data is being collected?

The scope and granularity of data collected significantly influence strategic AI outcomes, yet determining the "right" scope under privacy constraints is challenging. Expansive datasets might produce more accurate models but heighten privacy risk and potential non-compliance. Conversely, limiting data collection could reduce model accuracy and lead to less reliable strategic insights, creating a tension between higher quality outcomes and privacy considerations.

For example:

• A healthcare company's strategy group might want patient data to forecast treatment outcomes, but privacy regulations require data minimization.
• An international bank thinking about their regional strategy might wish to aggregate customer financial data across regions, but differing national privacy laws could force data silos by country.

Leading organizations address these challenges by focusing on quality over quantity, identifying which specific data points yield the most strategic signal rather than hoarding all available data.

3. How is the AI model being trained?

AI models require training data, which creates unique privacy challenges. Models learn from various data sources including public datasets, company records, and user interactions. This data might include customer transactions, demographics, behavior patterns, or market information. The key challenge is using this data responsibly while respecting privacy principles.

To train effective AI models, organizations typically need large amounts of data. However, the more detailed and comprehensive this training data is, the greater the potential privacy risks. This creates a fundamental tension: better models need more data, but more data means increased privacy concerns.

Key considerations include:

• Real vs. synthetic data: Synthetic data (artificially generated data mimicking statistical properties of real data) allows model training without exposing real individuals' information. It can augment training data when collecting more sensitive information from users isn't feasible. Gartner predicted that by 2024, up to 60% of data used for AI development would be synthetically generated.⁹
• Data cleaning and anonymization: Data engineering and AI development teams need robust processes to strip out or mask personal identifiers in training data to prevent models from learning or potentially exposing unnecessary personal details.
• Preventing model memorization: There's a risk of AI models memorizing sensitive data, potentially exposing confidential business plans or personal information if the model later outputs this data.

To mitigate these risks, AI model developers have techniques such as differential privacy or rigorous testing to ensure models don't output sensitive fields.

4. What types of inferences or predictions is the AI model making?

Once deployed, AI can create new privacy concerns through its inferences. Predictive models might combine various data points to make predictions that inadvertently reveal sensitive information, essentially creating what some regulations consider to be personal data via inference.

For example:

• A model might infer health status from purchasing patterns - Target got some unpleasant PR from this as early as 2012¹⁰
• An AI could predict an individual's financial situation based on various behavioral signals

Many privacy regulations, particularly GDPR, treat inferences about identifiable individuals as personal data. However, not all predictions trigger heightened legal duties; whether one does depends on both the identifiability of the individuals and the sensitivity of the inferred information.

There's also the risk of re-identification: AI models working on ostensibly anonymized data can sometimes correlate outputs in a way that reveals identity. A strategic analysis might output a finding like "high-value customer segment in region X with trait Y," which could be narrow enough to identify specific individuals.

Consumer research indicates growing concern about how data is used in AI and automated decision-making. One study showed that as much as “70% [of U.S. adults] have little to no trust in companies to make responsible decisions about how they use it in their products”.¹¹ Strategy teams need to verify that their AI tools aren't crossing ethical or privacy lines in the insights they generate. Teams may even want to gauge their customers' sentiment around specific AI features and outputs.

5. Who from your team is working on these AI features?

New product offerings or internal strategy work often requires combining data from multiple departments to gain comprehensive insights. This raises challenges around how to enable cross-team data access without breaching privacy rules.

The tension is between the need-to-know principle (individuals should only access data necessary for their role) and holistic analysis (strategic analysis often seeks to merge data for broader insights). Organizations can address this through tools like:

• Controlled data sharing frameworks
• Data escrow or analytics sandboxes
• Role-based access controls
• Data classification systems

International privacy variations further complicate global strategy. Privacy regulations vary widely, from broad-based ones like the EU's GDPR to more sectoral frameworks in other countries. These differences constrain how data is collected and moved, directly affecting strategic analysis and planning.

Data localization laws, which are distinct from privacy laws, add another layer of complexity. These laws in some countries require certain data to remain in-country, which means a strategic AI project or analysis that requires cross-country data might have blind spots or must incorporate federated approaches. With most countries now having some form of privacy legislation, any global strategy must anticipate compliance requirements as part of the plan.

Conclusion

Especially with recent advancements, AI can unlock immense business opportunities, but there is often a tension with privacy considerations. Not only is the privacy regulation environment continuing to evolve, including with regional differences, but consumers are increasingly sensitive to the privacy postures of the products they use.

Sources

¹ McKinsey & Company. (2020). The consumer-data opportunity and the privacy imperative. https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/the-consumer-data-opportunity-and-the-privacy-imperative

² Salesforce. (2018). State of the Connected Customer. https://www.salesforce.com/content/dam/web/en_us/www/documents/e-books/state-of-the-connected-customer-report-second-edition2018.pdf

³ Cisco. (2023). 2023 Data Privacy Benchmark Study. https://www.cisco.com/c/dam/en_us/about/doing_business/trust-center/docs/cisco-privacy-benchmark-study-2023.pdf

⁴ Yahoo News. (2024). iPhone App Privacy Report: Which Apps Are Spying on You? https://news.yahoo.com/tech/iphone-app-spying-apple-app-140022007.html

⁵ Trūata. (2021). Global Consumer State of Mind Report. https://www.truata.com/news/data-privacy-consumer-report/

⁶ Deloitte. (2019). Open Banking: Switch or Stick? Insights into customer switching behaviour and trust. https://www.ausbanking.org.au/wp-content/uploads/2022/06/Open-Banking-Switch-or-Stick-Insights-Into-Customer-Switching-Behaviour-and-Trust-Deloitte-2019.pdf

⁷ Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., ... & Cardoso, M. J. (2020). The future of digital health with federated learning. npj Digital Medicine, 3(1), 119. https://www.nature.com/articles/s41746-020-00323-1

⁸ ITIF. (2023). Comments to Attorney-General's Department Regarding Australia's Privacy Act Review. https://itif.org/publications/2023/04/03/comments-to-attorney-generals-department-regarding-australias-privacy-act-review/

⁹ Gartner. (2022). Predicts 2022: Data and Analytics Strategies. https://www.gartner.com/en/documents/4002246

¹⁰ Hill, K. (2012). How Target figured out a teen girl was pregnant before her father did. Forbes. https://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/

¹¹ Pew Research Center. (2023). How Americans View Data Privacy. https://www.pewresearch.org/internet/2023/10/18/how-americans-view-data-privacy/