Where We Left Off

In Part 1 of this series, we examined the growing consumer expectation for deeply personalized GenAI experiences and the three structural data challenges that stand in the way: broken consent architecture, fragmented real-time integration, and a rapidly expanding cybersecurity attack surface. The problems are real, well-documented, and not going away on their own simply because the technology continues to advance.

This paper does not dwell further on the problems. Instead, it asks the more important and more actionable question: what can the organizations, platforms, and leaders in this space actually do about it? What follows is not a prescriptive roadmap, because no single paper can responsibly provide that. But there are several concrete directions worth pursuing seriously, along with specific next steps for the executives and operators who will need to make real decisions about this landscape sooner than most currently expect.

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E X E C U T I V E   S U M M A R Y

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→  The path to meaningful personalized AI requires collaboration across GenAI platforms, institutional data providers, and regulators, and no single organization will solve it acting alone.

→ Three directions deserve serious investment: GenAI platform data-sharing partnerships, open API standards for consumer-controlled data access, and emerging models that treat personal data as a value exchange.

→  CMOs should audit their own data architecture, build consent frameworks proactively, and reposition personalization as a continuous operating model rather than a campaign-level capability.

→  Content companies and agencies that build fluency in data portability and consent architecture will earn a seat at the table as the personalization data economy takes shape.

→  The organizations shaping the emerging standards are the ones already building toward them. Waiting for regulatory clarity or platform consensus is not a neutral position. It is a choice to cede influence.

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Section 3: Possible Steps Toward a Personalization-Ready Future

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No single organization and no single paper has the full answer to what personalized AI should look like or how to get there. The path from where we are to where consumers want us to be is not a straight line, and it will not be traveled by any one company or platform acting alone. But there are several directions that deserve serious attention from the organizations, platforms, and policymakers who will ultimately shape what personalized AI actually becomes.

3.1  Strategic Partnerships Between GenAI Platforms and Data Providers

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One of the most promising near-term directions is structured, consent-based collaboration between GenAI platform companies and the large institutional data holders that sit closest to actual consumer behavior, including airlines, financial institutions, and major retailers.

The value exchange is relatively straightforward in concept: data providers have the behavioral history that makes personalization meaningful, and GenAI platforms have the intelligence layer to act on it. What neither has yet built is the connective tissue between them, specifically the consent frameworks, data-sharing agreements, and revenue models that would make such collaboration viable, trustworthy, and sustainable over time.

Some early signals are encouraging. Financial data aggregators like Plaid have demonstrated that consumers will grant permissioned access to financial data when the value exchange is clear and the consent experience is well-designed. Open banking frameworks in the UK and the EU have shown that regulatory scaffolding can enable data portability without compromising consumer protection. These are not perfect models, but they point in a direction worth following seriously. The central question for the GenAI industry is whether platform companies and data holders are willing to do the hard collaborative work of defining what responsible data sharing looks like before regulators step in to define it for them.

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BY THE NUMBERS: THE COLLABORATION OPPORTUNITY

$454B projected size of the global personalization software market by 2031, representing the scale of the opportunity for organizations that move early. (Allied Market Research)

1.5x revenue growth rate for companies that lead in personalization compared to their peers, a gap that will widen as GenAI capabilities mature. (McKinsey)

Open Banking has enabled more than 7 million consumers in the UK to share financial data with third-party applications, demonstrating the viability of consent-based data portability frameworks at meaningful scale.

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3.2  Open API Standards and Real-Time Connectivity Frameworks

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Another direction worth serious exploration is the development of industry-wide open API standards that would allow GenAI tools to query consumer preference data in real time, with user-controlled permission toggles that are transparent, portable, and revocable by the consumer at any time.

The closest analogy is the OAuth authorization framework that governs how third-party applications access personal data today. OAuth allows a consumer to grant an application access to their email or calendar without handing over their password, giving the consumer control over what is shared, with whom, and for how long. A similar framework built for behavioral and preference data, designed to serve AI agents rather than web applications, could unlock the personalization potential consumers are beginning to anticipate while preserving the control and transparency they require as a condition of trust.

This is not a simple engineering problem, and it should not be treated as one. Defining which data types are shareable, how they are standardized across industries with different data structures, and how they remain current as consumer preferences evolve is a significant undertaking that requires industry coordination of the kind that rarely happens voluntarily. Even so, the organizations that begin building toward this model now, by opening APIs, standardizing preference schemas, and building consent management into their data architecture, will be structurally advantaged when an industry standard eventually arrives. The manufacturing world learned this lesson through the adoption of EDI and later through supply chain integration standards. The companies that built for interoperability early did not simply comply with the standard when it came. They helped define it, and that positioning gave them lasting influence over how the industry operated.

3.3  Personal Data as a Value Exchange and the Monetization Frontier

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Further out on the horizon, there is an emerging and genuinely consequential conversation about whether consumers might begin to think of their behavioral data not as a passive byproduct of using digital services, but as an asset they own and can choose to deploy intentionally in exchange for tangible value.

Under this model, the dynamic that has defined the data economy inverts. Instead of organizations extracting data passively as consumers move through their services, consumers would have the ability to supply high-quality, verified behavioral data to GenAI platforms in exchange for something meaningful, whether that is better personalization, reduced subscription costs, or in some models, direct financial compensation. The consumer moves from being the raw material of the data economy to being an active and informed participant in it.

This is not yet a mature market, and it would be premature to treat it as one. The infrastructure for consumer-controlled data assets, including personal data stores and verified preference exchanges, is early and fragmented. The regulatory environment for compensating consumers for data provision is largely undefined. Even so, the direction of travel is worth watching carefully, and the organizations that engage with it thoughtfully now will be meaningfully better positioned when consumer expectations around data ownership begin to crystallize into actual market demand.

Andrew's lens: Leading the go-to-market for Deloitte's first GenAI marketing platform taught me something important about the persistent gap between what is technically possible and what organizations are operationally ready to execute on. The technology roadmap almost always runs ahead of the organizational readiness to act on it, and the same is true in this space. The vision of consumer-controlled data monetization is technically feasible today. Whether the institutions involved can build the operating models, consent frameworks, and economic structures needed to support it at scale is a different and considerably harder question.

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The framework will not be built by any one company acting alone. It will be built by the organizations willing to sit at the table together and do the hard work of defining what responsible, reciprocal, and genuinely useful personalization looks like.

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Closing: Personalization Requires a New Kind of Collaboration

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The consumer expectation for deeply personalized AI is already here and already growing. The people using GenAI tools today are not imagining a future in which AI might someday know them. They are living in a present where that expectation feels reasonable and immediate, and they are experiencing the gap between what they expect and what currently exists with increasing awareness and decreasing patience.

Technology is advancing rapidly, and the model capabilities underlying personalization are genuinely impressive. What is not yet in place is the collaborative framework across industries, platforms, regulatory bodies, and consumers themselves that is needed to make genuine, trustworthy, and durable personalization possible at scale. This is a problem that no single company will solve by building a better model, and it is not a problem that regulation alone will resolve. It requires something harder and far less common: different kinds of organizations, with different interests and different incentives, sitting at the table together and doing the deliberate work of defining what responsible, reciprocal personalization looks like in practice.

My experience in the Army gave me a lasting appreciation for what becomes possible when disparate elements, each with different capabilities and different operational priorities, align around a shared mission with genuine commitment. The mission in this case is personalization that actually serves people, not as a data extraction model or a marketing tactic, but as a genuine service that makes the tools consumers interact with more useful, more relevant, and more worthy of the trust they are being asked to extend. That is a mission worth organizing around, and the conversation to begin that organization needs to start now.

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What Leaders Should Do Now

The personalization data challenge will not wait for the perfect framework to emerge from industry consensus or regulatory action. The organizations that will be best positioned when that framework arrives are the ones already building toward it. Here are concrete starting points for the executives who will need to navigate this landscape in the months and years immediately ahead.

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F O R   C M O S   A N D   B R A N D   L E A D E R S

1.  Audit your own data architecture before making further personalization investments. Before you can personalize effectively for customers, you need a clear and honest picture of what data you actually have, where it lives, and whether it is meaningfully connected across your systems. Most CMOs who undertake this audit are surprised by what they find, and the surprise is rarely a pleasant one.

2.  Ask your MarTech vendors the right questions about data readiness. Which of your current tools support real-time data access and open APIs? Which are actively building toward consent management capabilities? The answers will tell you which vendor relationships are worth deepening as the personalization landscape evolves.

3.  Build your consent framework before regulatory pressure forces the issue. The organizations ahead on this will help define the industry standard rather than scrambling to comply with one defined by others. Building a permissioned and auditable consent architecture now is not simply a compliance exercise. It is a competitive investment that will pay dividends as consumer expectations around data control continue to mature.

4.  Reposition personalization as an operating model rather than a campaign capability. The CMOs who will lead in the GenAI era are those who treat personalization as something that runs continuously, learns constantly, and informs every customer interaction throughout the relationship, not simply the next email or campaign send.

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F O R   C O N T E N T   C O M P A N I E S   A N D   A G E N C I E S

1.  Get ahead of the data portability conversation with your clients before they bring it to you. Your clients are beginning to ask how their content performs at the individual rather than segment level. If you cannot connect content performance to behavioral data in a meaningful way, you will find yourself losing that conversation to advisors who can.

2.  Build AI-ready data practices into your production model now rather than retrofitting them later. The agencies that will thrive in the GenAI era are those that can connect content creation to real-time preference data, using that connection to inform what gets made, for whom, and when. That capability requires data infrastructure and governance discipline, not just creative talent.

3.  Position yourself early in the partnership conversations that are beginning to form. The collaborations between GenAI platforms and institutional data providers will need intermediaries who understand both content and data architecture. Agencies that develop genuine fluency in consent frameworks and data governance will have a seat at that table. Those that do not will find themselves on the outside of it.

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F O R   E V E R Y   L E A D E R   I N   T H I S   S P A C E

1.  Stop waiting for regulatory clarity as a precondition for action. Regulation will come, but the organizations shaping that regulation are the ones already building responsible frameworks and bringing them into the policy conversation. Waiting is not a neutral or safe position. It is a choice to cede influence over how the industry develops.

2.  Invest seriously in your data governance foundation before scaling your AI ambitions. Data quality, taxonomy, and governance are not glamorous investments, and they are rarely the subject of board presentations. But they are the foundation on which every personalization strategy is ultimately built, and organizations that skip this step will find their AI investments consistently underperforming relative to expectations.

3.  Engage advisors who can work across the full stack of this challenge. The intersection of GenAI, personalization, data architecture, and consumer trust is genuinely complex and getting more complex as the technology and regulatory environment evolve simultaneously. The organizations navigating it well are not doing it alone, and they are not relying on any single type of expertise to guide them.

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Ready to Act? Let Us Build the Right Foundation Together.

If you are a CMO, content leader, or agency executive navigating the personalization data challenge, this is exactly the work CVA was built for. We help organizations assess where they stand, identify what needs to change, and build the operating models, data architecture, and governance frameworks to make GenAI investments deliver real results at scale.

Start with the free AI Depth Check™ at coastalviewadvisory.com

Or reach out directly: andrew.chan@coastalviewadvisory.com  •  +1.808.282.9098

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ALSO IN THIS SERIES  ←  PART 1 OF 2

The Personalization Gap: What Consumers Want and Why It Is So Hard to Deliver

Covers the consumer personalization imperative, the zero effort data expectation, the trust paradox, and the three structural data challenges blocking delivery at scale.

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About the Author

Andrew Chan is Co-Founder and Partner at Coastalview Advisory, Inc. He began his career as a U.S. Army Military Intelligence Officer before spending more than 11 years at Deloitte Digital building practices in data science, customer segmentation, MarTech architecture, and GenAI platforms, with advisory experience across TMT industries.

andrew.chan@coastalviewadvisory.com  •  linkedin.com/in/andrew-chan-808

© Coastalview Advisory, Inc. 2026  •  coastalviewadvisory.com  •  For Publication Use Only

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