JK's Diary

Prepare for the next wave: Consent Engineering

For years, Consent Management Platforms (CMPs) like OneTrust, TrustArc, and Osano served as the digital privacy gatekeepers of the web. They helped companies display those now-ubiquitous cookie popups and ensure that users gave (or didn’t give) permission for tracking. But while technically necessary for GDPR, CCPA, and similar regulations, CMPs have become more of a compliance checkbox than a meaningful privacy safeguard. We, as users, feel the frustrations of this broken process. Thanks to the evolution of AI and digital experiences, this model is changing.

The Problem: Consent Management Is Fragmented, Fatiguing, and Fading

With AI-first browsers like Comet (to be launched by Perplexity) explicitly designed to “track everything users do online” for hyper-personalized experiences, the locus of control is moving away from individual websites to the browser layer, where consent could be set once and respected everywhere.

In short, Browsers — not websites — are becoming the central actors in user data collection. This shift renders traditional CMPs increasingly irrelevant — unless they evolve.

AI Browsers Don’t Just Observe — They Act!

The implication: CMPs must become smarter or “agent-aware”. They’ll need to integrate directly with browsers and their APIs to:

• Interpret global consent settings issued by users.
• Detect when AI agents are scraping or collecting data.
• Ensure downstream systems (like adtech or analytics platforms) respect those browser-level preferences.

This isn’t hypothetical. OneTrust and BigID are already deploying AI-driven privacy agents and compliance automation tools, which could evolve to interface directly with browser AI.

Programmable & Portable Consent

Imagine a future where users set privacy preferences once — during browser setup — and those settings follow across every site, platform, and digital touchpoint. That’s programmable consent.

In this model:

• CMPs don’t just ask for consent; they interpret and enforce it.
• Consent signals become machine-readable, portable, and actionable across systems/devices.
• Privacy becomes not a moment in time, but a persistent layer of the digital experience.

This requires a fundamental re-architecture of CMPs — from UI overlays to backend orchestration engines.

The existing setup is not going to go away anytime soon. They will co-exist for a while, but the additional layer to address the emergence of AI browsers is inevitable in the near term.

The initial rollout of consent management at the browser level might be rigid or with limited options, but with subsequent rollouts, this could change. For example, browsers could provide options to set consent at website level, website category level, bookmarked/favorite sites level, or as simple as allowing websites to push their ubiquitous popups when a site is opened for the first time on the AI-browser and store the user preference for future visits on the browser.

Blueprint for CMP 2.0: Consent Engineering in Action

CMPs face an urgent need to redefine their value. Instead of focusing solely on front-end banners, they must shift toward being Consent Orchestration Engines or Consent Engineering Platforms —interpreting, enforcing, and governing consent across platforms, applications, and back-end data systems.

Few key opportunities and imperatives for CMPs:

§ Agent-aware and API-first with AI-Browsers

Consent signals will originate from browsers and autonomous agents. CMPs must build real-time API hooks to sync with browser preferences and ensure websites respect those choices.

§ Orchestration Across Platforms

CMPs must manage (and synchronize) machine-readable consent across all digital touchpoints (e.g., website, mobile app, SaaS tools), not just the web layer. Encoding consent in standardized formats (e.g., Global Privacy Control (GPC)) that downstream systems can interpret and enforce automatically is critical.

§ Consent-as-a-Service

Offer “consent-as-a-service” embedded at the edge (e.g., browser extensions, SDKs) to enforce rules downstream—in data warehouses, CDPs, marketing clouds.

§ Downstream Data Governance

It’s not just about capture—it’s about ensuring consent follows the data. I.e., data flow control, compliance logging, and privacy auditing for server-side and AI-powered data operations. CMPs must enforce usage restrictions in analytics, personalization, and advertising systems.

§ Consent Auditing & Logging (PrivacyOps)

Regulators want proof. CMPs can provide the audit layer for browser-generated preferences, creating reconciliations between user intent and system behavior. Deploy AI to detect tracking violations, scan for third-party risks, and auto-generate regulatory reports. Where applicable, collaborate with cloud providers or AI agents to enforce preferences.

Who’s Leading the Way?

Leading CMPs are taking steps to adapt to this new future. For example, there is a lot of investment in AI governance and automation by OneTrust. Use of AI/ML for consent management by BigID and so on.

These companies aren’t just reacting—they’re re-architecting.

What This Means for Privacy Leaders and Digital Teams

We’re at the beginning of a major shift. AI browsers will rewrite the rules of data privacy, and businesses that rely on outdated CMPs risk being caught flat-footed. Hence, the implications of this browser-centric future are profound:

• Chief Privacy Officers must start redefining what compliance looks like when consent is programmable and portable.
• Marketing and data teams need to reconfigure how they ingest and process user data—browser signals might override what your CRM thinks it knows.
• Engineering teams must build consent-aware architectures that support API-driven orchestration and server-side governance.

In short, the cookie banner era is ending. The age of dynamic, portable, agent-aware consent is here. It is time for you to:

• Audit your current CMP for readiness in an AI-agent web environment.
• Evaluate browser-level consent initiatives and their implications for your data strategy.
• Explore integration paths between your privacy stack and AI/automation tools.

Are these thoughts in your mind?

• How to evaluate your consent architecture for the AI browser era?
• Is your CMP strategy AI-agent ready?
• Should your next privacy investment be in compliance… or consent engineering?

Don’t get left behind. Reach out, and let’s collaborate on building a forward-thinking approach to consent that aligns with the browser-level revolution.

A Strategic Perspective on the Implications of AI-Powered Browsers

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“AI-Augmented”, “AI-Powered”, and “AI-First” browsers are more than just a technology trend—they represent a new digital operating system for businesses and consumers. Browsers like Arc, Zen, and others will fundamentally reshape how consumers discover, engage with, and stay loyal to brands.

This transformation will be most profoundly felt by Retail and Consumer Packaged Goods (CPG) firms, where the brand and product experience lies at the core of consumer engagement. The browser is no longer just a window to the internet—it’s the new gateway to experience. From my testing of the Arc browser, to Perplexity’s plans for a tracking-based AI browser for selling hyper-personalized ads, to OpenAI and Yahoo’s intent to acquire Chrome, it’s clear that the browser landscape is undergoing a tectonic shift.

At the heart of this shift lies the integration of AI, enabling personalization without compromising privacy, and dynamically capturing user context to drive tailored interactions. This demands a significant re-evaluation of existing digital strategies and may require redesigning brand/product portals. Early adopters will define the next wave of brand leadership, consumer trust, and loyalty.

The future of browsing is not “browsing” at all — it’s experiencing, understanding, and executing.

The real question for CxOs is no longer “Should we prepare?” — it is “How quickly can we adapt?”

Key Strategic Imperatives for Retail and CPG Firms

1. Rethinking Search, Discovery, and Product Data Design

• Move from keyword-based to intent-driven search. Implement semantic HTML tags and structured data (e.g., Schema.org) to ensure AI systems can understand context, content, and functionality such as checkout, FAQs, or form fills.
• For example: “Show me sustainably built, highly-rated daily trainer Nike shoes with a 4–8mm drop, no break-in time, and available for same-day delivery near me.”
• Only brands with well-structured, attribute-rich product data will win these high-intent micro-moments.

Strategic Action: Ensure product catalogs are richly detailed, semantically tagged, and discoverable via AI-native techniques (Schema.org, OpenGraph, etc.).

2. True Hyper-Personalized Shopping Experiences

• Retailers must shift from segments to individualized experiences based on real-time mood and intent.
• With neural processing units (NPUs) and on-device AI, personalization will increasingly occur locally—at the edge—offering users unique UI/UX for the same product.
• For example: While the John Doe is browsing for a shoe, the AI might understand the context (based on recent searches) and suggest items to pick for Jane Doe to make the occasion more memorable — leading to a personalized landing page, rendered by the browser.

Strategic Action: Embrace headless commerce and personalization-at-the-edge, powered by API-driven architectures.

3. Enhanced Customer Service and Interaction Models

• AI-powered browsers will interface directly with customer support bots, automating issue resolution and several other tasks across platforms.
• For example: If an order is delayed, the AI browser can check FedEx tracking, contact Etsy’s chatbot, confirm refund eligibility, and update the user—all autonomously.

Strategic Action: Invest in AI-interoperable customer service platforms with robust AI-friendly APIs to support cross-channel handoffs.

4. Cross-Brand Shopping & Product Comparisons

• AI browsers will allow multi-site, cross-brand comparisons without needing to visit multiple websites.
• For example: “Compare ingredients, pricing, and reviews of the top three night creams from ELC brands.” or “List the unique features of an OLED TV from Samsung and LG that users rave about on forums”.

Strategic Action: Provide trustworthy, transparent, and machine-readable product data to surface favorably in AI-driven evaluations.

5. New Monetization Models for the Browser Economy

• Expect pay-as-you-go or subscription models, where browsers monetize actions (e.g., $0.01 per curated recommendation).
• Brands may lose control if they do not actively participate in these ecosystems, have a strategy to lay the ground rules and negotiate for a win-win formula.

Strategic Action: Build digital strategies that make sure your brand’s ecosystem is AI-friendly or compatibility with AI commerce ecosystems.

The “AI-Aware” Digital Strategy Roadmap

A complete overhaul to become “AI-First” or “AI-Native” isn’t necessary overnight for Retail and CPG firms. Instead, a strategic pivot to “AI-aware” commerce systems will ensure readiness and steadiness in revenue:

Focus Area	Strategic Imperative
Semantic Web Architecture	Implement structured data and semantic HTML for AI readability.
High-Fidelity Product Data	Build machine-readable product catalogs (images, metadata, sustainability info).
API-Driven Infrastructure	Expose AI-friendly APIs for search, retrieval, and transactions.
Natural Language Optimization	Write content the way users speak their queries, not just keyword-stuffed pages.
Personalization at the Edge	Enable on-device, privacy-friendly personalization and UI adaptations.
Seamless AI Assistant Handoff	Design systems for fluid interaction between AI browsers and your brand’s AI agents.
Mobile & Accessibility Design	Future-proof digital assets for accessibility across devices.

What Leaders Must Do Next

Priority	Action	Timeline
Short-Term	Audit websites for AI readability and structured metadata.	Next 6 months
Mid-Term	Build API-first commerce experiences and semantic storefronts.	Next 12 months
Long-Term	Develop an AI-agent strategy and personalization-at-the-edge plan.	Within 18–24 mo.

Closing Thought

In the next 12–18 months, the most successful retail brands won’t be those with the most visually appealing websites. Instead, they’ll be the ones with the most AI-literate commerce systems.

The key to success lies in formulating a strategy around how to effectively train, provide data to, and collaborate with AI agents – essentially, “optimizing for AI shoppers” in the same way organizations currently optimize for traditional search engine optimization (SEO).

Those who build invisible, proactive, machine-friendly shopping experiences—designed for both humans and autonomous agents—will unlock outsized value.

To Retail and CPG CxOs:

Begin preparing now for your next significant customer—who may very well be an AI agent acting on behalf of your consumers. 🤖🛒