Not if, but how.

For conversational AI platforms, that question is especially delicate. These tools are built on an implicit promise to users: I am neutral. I am objective. You can trust my answers.

Even when you pay for subscriptions, you’re really paying for the feeling of objectivity.

So when ads inevitably arrive, these platforms must pay a Trust Tax, i.e., the cost of preserving user belief in the system’s integrity. And that pressure is doing something interesting: it’s pushing companies to finally solve one of digital advertising’s biggest sticking points: how to make monetization additive rather than disruptive.

If the major LLMs get this right, marketers stand to benefit from a more transparent, higher-performing, and far more accountable digital ecosystem.

Here’s the simple truth: Running millions of free-tier AI users on extremely expensive compute is not a sustainable business model. Subscriptions help, but they don’t close the gap.

Eventually, free users must become revenue-generating. In the digital economy, if you’re not paying for the product… You are the product.

And platforms aren’t waiting around. They’re already laying the groundwork for what an ad-supported AI ecosystem will need:

New Interfaces = New Real Estate
Chat-only layouts are giving way to sidebars, cards, maps, stock tickers, and structured result formats. These aren’t just UX improvements—they’re the containers into which future ads will naturally fit.

The Unseen Infrastructure Build-Out
Behind the scenes, platforms must assemble the same machinery that powers modern digital advertising, including real-time bidding, fraud detection, and attribution/measurement. 
The current “quiet period” in the market is a strategic reset. LLMs are prioritizing product quality while they build the massive backend needed to support responsible monetization.

The upside of all this is that paid placements may actually make AI product recommendations better.

LLM-generated suggestions today can be:

• Too obscure
• Inconsistent
• Outdated
• Or simply hallucinated

Paid placements introduce a sometimes-missing ingredient: real-world validation.

Quality signals
When a brand pays for placement, especially in a high-trust environment like conversational AI, it’s implicitly vouching for its product. That alone raises the bar.

Smarter ad models
Future formats won’t be cluttered or spammy. We predict something like:

• One or two sponsored options
• Filtered by performance signals, not just bid
• Ranked by satisfaction, relevance, and low return rates

That’s a world where advertising truly improves outcomes.

Conversational AI is becoming a true intermediary that can support, guide, and even close a sale. Whether the revenue model leans toward commissions or auctions, the economic truth remains: Attention is scarce. Demand is high. The highest-quality (or highest-paying) options rise first.

This is no different from search, marketplaces, and social feeds… except now the “surface area” for commerce is conversational, contextual, and deeply user-driven.

The platforms are racing to build responsible monetization models. While they do, brands have a rare runway to prepare. Here’s where to focus:

1. Invest in GEO (Generative Engine Optimization)
AI systems need structured, intelligible content. Clear product descriptions, documented specs, authoritative copy, and consistent metadata make it easier for LLMs to understand—and surface—your brand.

2. Elevate product feed quality
Your product feed is becoming the single most important input for AI-driven commerce. Accurate titles, clean attributes, strong imagery, correct pricing—that’s what determines whether your product is recommended or ignored.

3. Lock in distribution early
When AI platforms formally launch ad programs, early integrations will be the ones that scale first. Now is the time to strengthen your partnerships with key commerce and retail tech platforms.

4. Optimize for trust, not tricks
AI recommendations feel authoritative to users. That means:
Overpromising will backfire, poor reviews and high return rates will hurt ranking, and authenticity becomes a competitive advantage.

5. Build for adaptability
We are in a rapid-iteration era. This isn’t the moment for rigid strategies. Create systems that support continuous testing and fast pivots.

The next phase of competition in conversational AI isn’t about who monetizes first. It’s about who monetizes well.

Paid placements are not the enemy. Bad paid placements are.

Brands must treat ads as a feature to be thoughtfully designed, optimized, and held to the highest standard of user trust.

For trustworthy brands, this moment is a gift. If you focus on data quality, trust signals, and strategic readiness now, you’ll be positioned to lead in an AI-native commerce ecosystem that’s more accountable than anything we’ve seen before.

They’ve been digital.

Patient portals, virtual care, AI triage, remote monitoring, and wearable tech have become core to how people access and understand care. 

But this acceleration has exposed cracks:

• Digital products aren’t connected
• Clinicians are drowning in workflows that weren’t designed for them
• AI is everywhere, but trust is not
• The data that should power personalization is locked inside legacy systems

Growth in the healthcare industry is no longer about adding more, but about making what you already have work smarter. In 2026, the advantage will go to systems that can design trustworthy experiences, orchestrate intelligence across silos, and rebuild the workforce around human + AI collaboration.

Inspired by our recently published Now & Next 2026 Trends Report, these are the three trends that will matter most for health leaders working to repair the internal systems that shape external care.

Studies show that patients are surprisingly comfortable engaging with AI… when the interaction feels nonjudgmental.

For example, a 2023 JAMA Network Open study found patients disclosed sensitive information (such as alcohol use or medication non-adherence) more frequently to conversational AI than to clinicians, citing reduced fear of judgment.

But that trust is fragile. As soon as an AI moves from “listening” to advising, patients want to understand why the system is recommending a specific action.

That’s where explainable UX becomes essential. This UX preserves the sense of safety that made patients candid in the first place. Without explainability, the trust advantage quickly evaporates.

What trust-centered UX looks like

For patients:

• Care recommendations that explain why they’re being made
• Risk alerts that reference lab trends, symptoms, or wearable data
• Transparent reasoning embedded in patient-facing tools

For clinicians:

• Clinical decision support that highlights the exact data points behind a recommendation
• Audit trails that make AI outputs reviewable and defensible in documentation
• Tools that supplement—not replace—clinical judgment

Industries everywhere are moving away from monolithic technology toward composable architectures: modular layers where APIs, data services, and AI agents interoperate.

Healthcare’s interoperability challenges are well-documented and expensive. US administrative complexity across payers and providers is estimated to cost $600 billion to $1 trillion annually.

A composable intelligence stack can reduce this complexity by acting as the connective tissue between systems that weren’t designed to work together.

This enables:

• Unified patient views integrating labs, imaging, chart notes, wearables, and social determinants.
• Smarter AI models that can draw from broader, higher-quality data.
• Real-time operational optimization, from predicting throughput bottlenecks to forecasting staffing needs.

Most importantly, this isn’t a rip-and-replace strategy. It’s an orchestration layer that sits on top of the systems you already have, unlocking their potential without destabilizing operations.

The next decade belongs not to the organizations with the biggest teams, but the ones that reconfigure roles around human + AI collaboration.

Burnout is healthcare’s most pressing operational threat. Its leading source? Administrative overload. Physicians spend nearly two hours on EHR/desk work for every hour of direct patient care. Nurses devote more than a quarter of their shift to documentation. And administratively driven waste is estimated in the hundreds of billions of dollars each year in the United States.

AI can meaningfully reduce this burden.

Early pilots of AI-powered ambient clinical documentation tools have demonstrated a notable impact. For example, an American Academy of Family Physicians Innovation Lab study showed most clinicians experienced a 72% reduction in time spent on documentation when using AI assistants. While results vary by specialty and system, the time savings potential is clear.

In addition, automated administrative agents can now support tasks such as prescription refills, prior authorization preparation, referral routing, chart summarization, and inbox triage. 

In one notable pilot, The Permanente Medical Group’s AI scribes saved 1,794 working days in one year across their medical teams.  This trend is not about replacing clinicians. It’s about returning time to the humans who deliver care, enabling them to work at the top of their license.

The systems that will define healthcare in 2026 aren’t the ones adding more dashboards, tools, or workflows, but the ones making their existing ecosystems intelligent, interoperable, and humane.

Trust-centered UX will become essential for both patients and clinicians navigating AI-driven care decisions. Composable intelligence layers will determine whether organizations can finally break through decades of data fragmentation. And workforce transformation will hinge not on replacing clinicians, but on giving them back the time and clarity they’ve been losing to administrative drag for years.

Health systems that modernize their data infrastructure, apply AI responsibly, and redesign work around human–technology partnership will deliver safer, more connected, and more sustainable care. 

Our full 2026 Trends Report goes deeper into the forces accelerating this shift, from synthetic data for safe experimentation to the governance models required for clinician-built AI tools. For leaders shaping the next era of healthcare and wellness, it’s a roadmap for transforming the systems that define the experience.

The travel industry has spent the last decade optimizing the same touchpoints: shaving seconds off check-in flows, polishing booking funnels, and nudging loyalty members toward the next tier.

But in 2026, the battleground won’t be the booking path. It’s everything that happens before and around it: AI-driven discovery, glanceable UX, identity-rich experiences, and a level of operational intelligence that makes friction feel archaic.

Travelers don’t follow a funnel anymore. They bounce from a TikTok creator’s three-day Paris itinerary to an LLM-curated list of “hidden-gem” hotels to a last-minute live widget telling them their room is ready. And the brands succeeding in this landscape aren’t the ones with the prettiest websites. They’re the ones that are culturally tuned, technically legible, and quietly orchestrating the journey with almost magical anticipation.

Below are the three shifts reshaping travel and hospitality, and why they matter more than any conversion-rate optimization trick.

If there’s one disruptive force in travel right now, it’s that the traveler’s new “agent” isn’t a person, platform, or search engine. It’s an AI assistant that evaluates every flight, room type, amenity, and policy decision in milliseconds, and selects based on structured truth, not brand storytelling.

When a traveler asks their AI assistant, “Find me the best boutique hotel in Lisbon under $300 with late checkout,” or “Book the fastest flight to Chicago with a carry-on included,” the model evaluates structured data across:

• Airline NDC feeds
• Hotel PMS/CRS inventory
• OTA and metasearch APIs
• Amenity, accessibility, and sustainability attributes
• Cancellation and baggage policies
• Loyalty benefits and upgrade rules

The real competition happens long before a user ever lands on your domain. AI assistants are becoming the front door. And they don’t reward vibe; they reward verifiable, machine-readable clarity.

For airlines and hotels, this means the data layer is now your discoverability layer. It’s not enough to inspire travelers. You have to teach the machines why you’re the right choice. Every amenity, every fee, every policy must be structured and current. And the brands that lean into “Feed > Site” will own the next wave of demand.

Think about the convenience of seeing your approaching Uber’s location on your lock screen. Now stretch that feeling across the entire travel journey, from bag drop to check-out. That’s the new bar for travel UX: one where the most-loved brands are the ones you rarely have to open. 

Glanceable experiences are rewriting what “seamless” actually means. Gate changes no longer hide behind deep navigation. Boarding passes pin themselves automatically. A guest’s room key appears the moment housekeeping marks it ready. Baggage updates become simple, reassuring tiles. A driver approaching in two minutes becomes a calm, automated nudge rather than a mental to-do.

For hotels, this shift is quietly transformational. The “Your room is ready” notification is a convenience and it’s a signal of competence. It collapses wait times, lightens front-desk load, and creates a moment of generosity in a journey that’s often full of micro-stress. Airlines get the same tailwind when they surface real-time baggage or rebooking options proactively. Transit, attractions, and experiences are next.

This isn’t UX innovation for UX’s sake. It’s emotional design that reduces anxiety and increases trust. Travelers start to perceive your brand less as a “platform” and more as a companion quietly smoothing the edges of every transition.

Loyalty programs are about to run into a new challenger: travelers’ own AI assistants. When a guest says, “Find me the best hotel near the convention center,” their assistant doesn’t care about your tier status or perks. It cares about fit: price, flexibility, proximity, sentiment, amenities, and availability.
If a customer’s external AI is built to find the “cheapest” or “most efficient” option, your internal AI must be built to know that customer so well that “cheapest” becomes irrelevant. The future of loyalty is moving from points to prediction.

Your loyalty system should already know:

• This guest always requests a high-floor room away from the elevator.
• Their flight lands at 9 PM. Send the mobile check-in link and an in-room dining offer at 8 PM.
• This is their third stay with us this year. Proactively offer a complimentary late check-out.

This is how you meet customers in the moments that matter. You use your own data to orchestrate an experience so personal and preemptive that the guest never even asks their generic AI assistant to look for an alternative. You become their personal travel concierge, embedded in your system.

Travel is returning to what it has always been: a story of anticipation, movement, and memory. What’s changing is who helps travelers navigate that story. It’s no longer a glossy homepage or a perfectly timed email, but a web of intelligent systems interpreting signals, predicting needs, and smoothing every transition along the way.

Travel brands must embrace this shift. Feed the machines with structured truth. Design moments that matter to the traveler. And use loyalty not as a reward, but as a relationship–one that gets sharper, smarter, and more relevant with every stay, flight, and trip.

Our Now & Next: 2026 Trends Report explores how to get there, and what it takes to build journeys that feel almost impossibly well-orchestrated.

Developed by lead creative agency DEPT®, it forms part of the 155th anniversary celebration that launched earlier this year during the Fall/Winter season. The film brings the department store’s rich heritage to life and highlights the role de Bijenkorf has played in Dutch fashion and culture since 1870.

The film, directed by Johan Kramer and produced by Holy Fools, takes viewers on a journey through de Bijenkorf’s history, from 1870 to the present day. The common thread is the iconic brand ornament, which serves as the centrepiece of the story and symbolizes the connection between generations. The tree and ornament act as silent witnesses to evolving traditions, forming the heart of the holidays and inviting audiences to create new memories. A recurring family portrait provides transitions to each new generation.

Each time period has its own distinct character, enhanced by the choice of filming techniques and lenses. The film opens in black and white in 1870 and progresses through the warm Kodachrome tones of the 1950s to today’s digital present. This reflects the evolution of technology and style, while the essence – the ornament and togetherness – remains constant.

The film’s strength lies in its craftsmanship and attention to detail. Every costume, set piece, and prop was carefully chosen to capture the spirit of its era. Through editing and color grading, past and present flow seamlessly together, making the film feel like a living family album. The result is an atmospheric holiday film that brings together tradition, family, and de Bijenkorf’s signature elegance.

In the Sinterklaas campaign, we saw how a special friendship developed between Sinterklaas and Santa Claus. But when Sinterklaas returns to Spain, as tradition says, Anna notices that Santa misses his friend. So she comes up with a gift that perfectly captures what the holidays are all about: how a small gesture can mean so much.

For decades, the most valuable assets in banking, financial services, and insurance (BFSI) were kept in vaults. Today, the most valuable asset across these industries is, by far, customer data.

But that data is also a financial company’s biggest liability.
BFSI is in a high-pressure squeeze: Customers demand the hyper-personalized, “always-on” attention of a fintech startup, while regulators (and the public) demand iron-clad security, transparency, and fairness. How do institutions innovate at the speed of AI without breaking the trust they’ve spent decades building?

The financial organizations that make substantial progress in 2026 won’t just be incorporating one-off AI features into their apps. They’ll be re-creating their core systems around AI.

Inspired by DEPT®’s Now & Next: 2026 Trends Report, here are the three most critical shifts for BFSI leaders who need to turn disruption into a durable advantage in the next year.

No industry has a more valuable or extensive proprietary data set than the BFSI industry. You have decades of transaction histories, market analyses, and risk modeling data. This is your defensive “moat.”

Users might turn toward ChatGPT or use Google’s AI Mode to ask, “What is a 401(k)?” But what if, instead, they could go to their savings app and get a personally tailored response that answers, “What is the optimal 401(k) contribution for this specific customer based on their 10-year spending habits and stated retirement goals?”

Look at BloombergGPT, which was trained on decades of financial data and outperforms general-purpose models on complex finance tasks. This is the playbook. The goal is not to build a new public-facing chatbot. The goal is to build smart, secure, internal intelligence layers that fine-tune AI on your data, allowing your advisors, underwriters, and wealth managers to make smarter decisions, faster.

For BFSI, Explainable AI, or XAI, is a core compliance and business requirement.

When an AI agent denies a loan, flags a transaction for fraud, or suggests a specific portfolio, you must be able to explain why. A “black box” is a non-starter for both regulators and customers.
That means that the next generation of financial UX will be built on designed transparency. This means moving beyond “frictionless” and embracing “explainability.”

• “Why we suggest this” links under investment recommendations.
• “How this rate was calculated” tooltips on a mortgage offer.
• “Your score changed because…” alerts in a credit-monitoring app.

In a world where AI can generate anything, believability and understanding are the new simplicity and seamlessness. For BFSI, it’s the only way to operate.

This is the ultimate payoff for combining your proprietary data with a trustworthy interface.

For too long, “bank loyalty” has been a renewal-rate discount or a slightly higher interest rate. The future of loyalty is moving from generic perks to preemptive orchestration. Your systems should know what your customer needs before they do.

Instead of a generic email blast, this new system:

• Sees a customer’s large, stagnant checking balance and proactively suggests moving a portion to a high-yield savings account for the 3-month term they can afford.
• Identifies that a customer’s spending patterns now include “stroller” and “daycare” and automatically surfaces information on 529 college savings plans.
• Notices a customer’s business account is receiving international payments and introduces a low-friction FX service.

At DEPT®, we partner with Moody’s to navigate the new world of AI and find innovative ways to deliver customer value. 

The institutions that will pull ahead in 2026 aren’t the ones experimenting with isolated AI features. They’re the ones rebuilding their core around intelligence, trust, and orchestration. 

Proprietary data will become a competitive differentiator only if it’s secured, structured, and activated. Transparent UX will become a regulatory requirement and a customer expectation. And loyalty will be won not through perks, but through predictive, value-creating interventions delivered at exactly the right moment.

Our Now & Next: 2026 Trends Report unpacks the 15 shifts redefining digital finance, from synthetic data and new risk models to AI-governed operating frameworks and the next wave of personalization. It’s a helpful guide for those in BFSI ready to build durable, compliant, intelligence-powered growth.

Today, many brand interactions occur through non-human interfaces such as search engines, voice assistants, and generative AI. These systems don’t “feel” your brand the way a person does. Instead, they process it, interpreting meaning from structured data, consistent tone, and machine-readable relevance. Machines can now translate your brand before it ever reaches your audience.

Large Language Models (LLMs) prioritize coherence and consistency, parsing sentiment and analyzing metadata. While visual identity remains important, machines primarily consume the structure, message, and meaning inside your content. 

This is redefining discoverability, because as AI directly answers more queries, your brand’s visibility hinges on its interpretability. If your content lacks structure, consistency, and semantic richness, your brand won’t be referenced, regardless of how beautiful or polished it looks. This means you have to treat language as infrastructure, designed particularly for AI gatekeepers. 

Because user prompts often provide such rich context, AI can grasp specific situations and tailor answers individually. Meaning: LLMs and AI search platforms offer a massive opportunity for brands to connect with audiences at the precise right moment.

Building content for machines first and humans second requires an understanding of how AI processes information. This involves using Schema.org to structure web content and managing brand definitions in knowledge graphs. It also means moving beyond keywords to focus on “entities,” or consistent, clear references for your brand, products, and ideas. This prevents confusion and helps AI understand relationships. 

To associate your brand with a topic, your online content must frequently link them. Write directly and concisely, adding rich context. Strategic repetition of key terms aids AI comprehension. All content should be accurate, verifiable, and linked to trusted sources. 

Since LLM systems can store content in chunks, sections should be standalone, and semantic HTML is crucial for identifying purpose. FAQs and question-phrased headings also enhance LLM comprehension.

Your traditional tone-of-voice guide needs an upgrade for both human creators and AI models:

• Within your brand pillars, specify keywords, phrases, sentence structures, and acceptable sentiment ranges. 
• Create a brand terminology glossary, detailed grammar and punctuation rules, and preferred voice (like your English teacher, most LLMs prefer active versus passive). 
• Provide templates optimized for schema markup and entity extraction for various content types. 
• Establish guidelines for tagging existing content for AI training, defining metrics for evaluating AI-generated content against brand standards, and setting up feedback loops for continuous model refinement.

Your tone-of-voice guide no longer belongs solely to copywriters. Now, it’s also for LLMs. Generative AI will analyze your website’s language and synthesize your brand story for users who may never visit your site. So it’s up to you to engineer how your brand is perceived through every input. 

In short: Branding is now about instruction, not just expression.

Semantic branding is essential, but it doesn’t need to (and shouldn’t) sacrifice human connection. 

Maintaining creativity and brand personality amidst strict semantic rules can be challenging. You risk content becoming overly formulaic, lacking emotional nuance, or failing to truly resonate. Getting it right requires finding a delicate balance that ensures “algorithm-aware” writing enhances, rather than constrains, brand expression.

This dual-channel approach of semantic clarity for machines and emotional resonance for humans is about integration, not sacrifice. The goal isn’t to write for machines, but to ensure machines understand what we write for humans. 

Branding ultimately builds relationships with people. While machines filter, brand content aims to engage, inform, and persuade human audiences. This means continuing to craft compelling narratives and evoke genuine emotion. Semantic optimization helps AI find, understand, and expand content’s reach, but human validation remains the end goal.

Users who click through from chatbots to brand websites demonstrate high intent. This critical moment demands a seamless transition from machine-understood clarity to human-felt connection. 

The opportunity to convert is immense and relies on your ability to present precise, emotionally impactful, and consistent brand messaging. This calls for ongoing review by expert writers and designers, ensuring AI-optimized content is technically sound, deeply compelling, and aligned with human desires.

I’ve spent most of my career as a designer, where visual identity and human psychology were paramount. It feels slightly ironic how the very visuals I championed are becoming less central in an AI-first world. 

LLMs don’t “see” visual design. They extract rudimentary and often obvious sentiment from cues like typeface and color through associated metadata or descriptions. This presents an intriguing, though subtle, opportunity to strategically manipulate how LLMs portray your brand, subtly guiding their interpretation without direct visual understanding.

Jeff Bezos famously said, “Branding is what people say about you when you’re not in the room.”

That’s still true, but now it’s about engineering that conversation with a non-human intelligence, ensuring it understands and “sees” your brand well enough to speak for it.

In an era where AI curates what we see and recommends what we buy, successful brands will optimize for both human psychology and machine learning. And that means designing for the models that decide what people see, while ensuring our human message cuts through the digital noise.

Today, that formula is broken. The “shelf” is 10 different digital marketplaces, AI-generated search results, and subscription boxes. The “ad” is a 3-second TikTok glance-and-swipe. And soon, “loyalty” will compete with an AI assistant that can find a cheaper, better-rated alternative in a millisecond.

To grow in 2026, CPG leaders need a new operating model that treats structured product data, cultural visibility, and AI-driven loyalty as core commercial assets. Grounded in DEPT®’s 2026 Trends Report, the three biggest shifts that will rewrite CPG growth in the next year.

For decades, CPG branding has been about a feeling. You design packaging to catch someone’s eye in-store or to look great in a flat lay on Instagram. But what happens when your customer is an AI assistant? 

This shift is already underway. When a shopper asks, “best fragrance-free laundry detergent,” “nut-free protein bar,” or “sustainable dishwasher tabs,” the AI is scanning product data across:

• Amazon’s product graph
• Walmart’s and Target’s retail media algorithms
• Instacart’s structured attributes
• GS1/GDSN-fed catalogs
• Brand websites and “answer packets”

The assistant isn’t looking at your brand story or packaging. It’s evaluating: Can I parse this? Can I verify it? Can I trust it?

For CPG, this means the “Answer Layer” becomes as commercially important as the formula inside the bottle. CPG brands must optimize:

• Canonical product facts: ingredients, allergens, certifications, sustainability claims, and manufacturing origin.
• Attribute richness: variant-level details, usage instructions, functional benefits.
• Pricing, velocity, and availability data: synced accurately with retailer APIs.
• Schema and machine-readable structure: so assistants can rank you, recommend you, and cite you.

Brands that win the AI shelf will be poetic to people, but provable to machines. They pair strong cultural storytelling with airtight, structured data that allows retailer algorithms and AI assistants to confidently surface their products.

“TikTok Made Me Buy It” has evolved from a hashtag into a multi-billion-dollar commerce engine. For Gen Z and, increasingly, millennials, live shopping on platforms like TikTok Shop, Amazon Live, and WhatNot combines entertainment, community, and scarcity in a way a static product page never can.

And while live shopping isn’t new to beauty, apparel, or collectibles, CPG is now fully in the mix, and the mechanics uniquely suit the category.

Why live shopping works so well for CPG:

• High-frequency purchasing. A creator tasting, swatching, unboxing, or demoing products builds trust and drives fast conversion.
• Scarcity + flavor drops. Limited-time SKUs, seasonal releases, and small-batch items perform exceptionally well.
• Authenticity beats AI. In an era of deepfakes and synthetic imagery, live, imperfect video is a trust layer for categories like food, drink, and beauty.
• Bundles = margin. Live shopping supports AOV lift via multipacks, bundles, and “creator kits” tailored for the platform.

CPG brands have never owned loyalty. Retailers control most of the transactions, and retailer loyalty ecosystems (Target Circle, Walmart+, Amazon Subscribe & Save) dominate behavioral data.

In 2026, that tension gets sharper. Consumers start to experiment with personal AI agents to manage shopping lists, compare prices, switch between retailers, and automatically fulfill routine items. This creates both a threat and an opportunity:

The threat: Agentic commerce redirects loyalty

Personal AI may or may not care about your brand equity. But it will certainly care about price, availability, reviews, structured data quality, and whether your feed matches the user’s dietary or preference profile. If your product feed is incomplete or misaligned, you won’t even enter the conversation.

The opportunity: Orchestrated loyalty built on first-party signals

To counter the AI threat, CPGs must design loyalty systems that are:

• Data-light, not data-starved: Zero-party quizzes, packaging QR codes, content ecosystems, simple DTC experiences.
• Predictive: Triggered by consumption cycles (“you’re likely running low”), seasonality, and household behavior patterns.
• Retail media–integrated: Using 1P retailer signals for smarter targeting and more relevant replenishment offers.
• AI-fueled: Not just automating emails, but orchestrating offers, content, and timing across channels.

DEPT®’s work transforming NIVEA’s CRM into a predictive orchestration system, doubling engagement and reducing churn by 30%, shows how powerful this shift can be when content, data, and decisioning work as one.

In the age of personal AI, CPG loyalty becomes a race to stay relevant before the customer asks their assistant to look elsewhere.

Here’s the short list of the levers that will drive margin, visibility, and growth next year:

• Fix product data governance before touching anything “AI.” If attributes, claims, and pricing aren’t structured and synced across retailers, you can’t win AI-driven discovery.
• Build “answer packets” for your top 10 SKUs. Standardized, verifiable, machine-readable facts are the new foundation of CPG discoverability.
• Reallocate part of trade spend toward retail media + AI testing. Because retailer AIs now control more shelf visibility than endcaps.
• Treat live commerce as both demand gen and cultural storytelling. Not a gimmick, but a strategic high-velocity sales driver.
• Use AI for loyalty orchestration, not discount distribution. Replace blanket offers with predictive, margin-positive engagement.

The coming year will reward CPG brands that operationalize these shifts, not just acknowledge them. Fixing product data pipelines, showing up credibly in live commerce, and building AI-ready loyalty systems aren’t “innovation projects”—they’re core commercial infrastructure for 2026.

The companies that modernize fastest will seize margin, visibility, and retail leverage. Those that don’t will watch AI assistants and retailer algorithms quietly reallocate their market share.

Our full 2026 Trends Report outlines the 15 forces shaping that reality and the plays we’re already helping brands deploy to get ahead of it.

When the Belfast-born rap trio Kneecap took the stage at Glastonbury 2025, they made history for a jaw-dropping performance given in Irish Gaelic, a heritage language that has suffered a steady decline following centuries of British occupation. 

As a result of what’s been dubbed “The Kneecap Effect,” the unapologetically political band has accomplished something that decades of Irish school curricula have failed to do: They’ve made speaking a dying language aspirational for an entire generation of young Irish people.

On TikTok, #Gaeilge has accumulated over 120 million views and is filled with creators who are building followings by posting content trí Ghaeilge (through Irish) that spans everything from fashion hauls and makeup tutorials to cooking and farm life. Through the power of #Gaeilge, people learn the language through “incidental exposure,” by scrolling, not studying.

This is subtly radical. Traditional language revival efforts often focus on restoring languages to their true historical form. The trouble with this, however, is that most heritage languages don’t translate to the modern world. 

By contrast, the Kneecap Effect recontextualizes Gaelige in the lifestyles and experiences of today’s youth. The band and the community of young Irish speakers they’ve fostered online understand that languages are living, evolving entities. For them, new words, slang, and syntax are inevitable—innovation that is itself a form of resistance.

“This creative process is a form of self-expression and community-building, defying traditional, purist notions of language,” as one linguist explained. “It’s also a declaration: we refuse to assimilate into linguistic monoculture, but we also refuse to be trapped in the past.”

Gaelige isn’t the only heritage community thriving on TikTok: #IndigenousTikTok and #NativeTikTok have amassed 605.5 million and 3.4 billion views, respectively. 

During the short-lived US TikTok ban, Indigenous creators on the platform raised the question of whether or not the US’s tribal sovereignty policy for Indigenous nations applied to digital sovereignty as well. 

While the TikTok ban proved to be brief, the debate around Indigenous digital sovereignty has since extended to data and AI. As global corporations rush to feed AI systems with diverse language data, Indigenous voices risk becoming the next form of raw material. 

In 2021, Ray Taken Alive, a member of the Lakota nation, found out that recordings of his grandmother, a renowned Lakota teacher and speaker, had been copyrighted by the Lakota Language Consortium (LLC), a nonprofit founded by two European linguists that has allowed the recordings to be used to train LLMs. 

Efforts like the LLC often claim an altruistic mission, citing inclusivity, accessibility, and preservation. But Indigenous technologists see their actions as an example of “algorithmic colonialism,” the process by which Indigenous data, art, and language are mined to train technologies that primarily benefit outsiders.

“We [as Indigenous people] can’t let our language become a dataset someone else uses to make a profit,” Ray Taken Alive said during an interview on the podcast Code Switch. “We have to build the servers ourselves.”

These words capture the essence of the movement for Indigenous data sovereignty, which has spread throughout the world:

• In Canada, Inuit developers are designing AI speech models that remain fully under Inuit control. 
• In Finland, Sámi technologists are using blockchain to manage consent for cultural recordings. 
• In New Zealand, Te Hiku Media, a Māori-run organization, is pioneering an Indigenous-led AI initiative that trains language models using data owned entirely by the Māori community.

At DEPT®, we believe that authentic cultural representation demands deep collaboration, not external interpretation. To that end, we embraced a community-led co-creation process when we helped the Nyiyaparli Living Language Project (NLLP) build a mobile-first language learning game designed to preserve Nyiyaparli and keep it alive forever 

Despite being part of a culture over 41,000 years old, only 11 fluent Nyiyaparli language speakers remained when we began our work together. By the time it was completed, that number had dwindled to eight—a sobering reminder of the importance of the NLLP’s mission.

Unfortunately, the Nyiyaparli people aren’t alone. Many heritage languages risk extinction due to the loss of aging community leaders, who are often the last strongholds of vast cultural wisdom. A crucial part of language preservation, therefore, is to pass this knowledge on to younger generations. 

When native Hawaiians faced the loss of their language in the 1980s, for example, a grassroots movement to revitalize ‘Ōlelo Hawaiʻi found monumental success by focusing on childhood education and the creation of independent schools that fully immersed students in the language. 

Fast forward to today, the NLLP recognized the incredible educational opportunity that gaming presents. Gen Alpha has been referred to as the Roblox Generation for good reason: 94% of children aged 0-14 play video games.

With Nyiyaparli Widi (“widi” = “game”), players explore real locations across Nyiyaparli land as they collect cultural items, complete educational quests, and learn everyday words along the way. 

For Nyiyaparli children, the game offers a pathway to connect with their heritage in a format that speaks to their digital-native upbringing. For the broader community, it ensures that precious cultural knowledge doesn’t disappear with the passing of elders.

At a time when young people feel fatigued by global crises and wary of institutional power, these linguistic revivals represent a declaration of identity in a world that often feels unmoored. 

It’s a trend that reveals how digital natives don’t want to feel like they fit in; they want to feel like they belong.

For brands, this shift signals a profound cultural realignment. Younger audiences are dispersing into networks of niche spaces, each with its own norms, aesthetics, and desires. 

To reach them, brands can no longer rely on strategies that treat a youth audience as a monolith. They must understand the dynamics of niche communities, learn the languages (literal and symbolic) they use, and be prepared to prioritize collaboration over performance metrics.

Hoenderboom joins from DDB Amsterdam, bringing extensive international experience from BBDO New York, where he created some of the industry’s most awarded work.