At LP Solutions, we’re gathering industry perspectives through a short Travel & Transportation Loyalty Pulse Check. We want to understand how the progress of the past decade compares with expectations, and what has held the industry back where those expectations have not been met.

The survey asks participants to reflect on the trends they believed had the greatest potential, those that delivered tangible results, and the barriers that prevented others from reaching their potential.

We invite professionals working across travel loyalty programs, technology, consulting, financial services, partnerships, marketing, and customer experience to share their perspectives.

Take the Two-Minute Industry Pulse Check

That sounds obvious, yet most programs do the opposite. They start with a points mechanic copied from a competitor, bolt on some tiers, and hope it changes how people see value in interacting with the program .

In this blog post, we'll break down how the best brands design reward programs that change customer behavior, including:

• The design principles that separate the programs customers use from the ones they ignore
• How leading brands across retail, quick-service restaurants (QSR), grocery, travel, and B2B put those principles to work
• Why most programs stall at execution even when the loyalty strategy is sound

Gamified participation, app-based earning, and member-gated pricing have pushed loyalty into the center of how people shop. Getting the design right is what turns that shift into repeat behavior instead of giveaway cost.

What does good reward program design actually mean?

Good reward program design starts with thinking how you can create value for your customers while driving profit for your business.

That framing flips the usual sequence. Most teams start with the reward, such as 2% back or a free coffee after 10, then work backward from there. High performers do the opposite, starting with the behavior they want to change and engineering the reward to make that behavior happen.

At enterprise scale, the same logic means rewarding profitable activity across the customer relationship. The legacy core transaction becomes one loyalty behavior among several, alongside partner spending and connected services, and each one needs to reinforce the economics of the business.

The 6 principles behind high-performing programs

A few design principles show up again and again across the brands that consistently outperform. Following them takes discipline.

• Behavior design: Rewards should reinforce profitable behavior and protect margin. A good strategy balances customer delight with financial discipline. The discipline is deciding what behavior the program pays for before deciding how much to pay.
• Emotional loyalty: Customers who feel appreciated are more likely to stay and spend. Yet many programs still center on rational mechanics instead of emotional value. That gap is what high performers are closing.
• Engagement: Programs built entirely around purchases tend to compete on discount depth. Stronger programs also reward behaviors such as registering products or joining a community. These behaviors build stickier relationships and generate first-party data that a discount never will.
• Simplicity: The plainest earn mechanics, a single point per dollar or a clear visit-to-reward path, keep participation high because members can see the value at a glance. Complexity increases churn rate.
• Paid tiers: Paid loyalty models have moved into categories like hotels, and retailers are layering free and paid benefits. Paid models generally shift rewards away from hard currency toward emotionally resonant benefits like exclusive access and free shipping.
• Customization: Tailored offers are a leading reason US online adults join loyalty programs, and the economics work when the design is right. Brands often overestimate how valued customers feel, and that perception gap is the most important design problem to solve.

Why the importance-effectiveness gap is the real problem

Most reward programs fail because the organization can't execute on what it already believes matters.

According to Harvard Business Review and Talon.One, loyalty programs are strategically important to most leadership teams, but execution lags behind ambition. 77% call loyalty programs strategically important, while only 50% rate execution as effective. The same pattern shows up in promotion software and loyalty operations alike.

With loyalty programs now everywhere, member attention is the scarce resource, and that crowding raises the bar on execution.

Silos between loyalty and CRM teams create conflicting member experiences, inconsistent channel journeys, and measurement nobody trusts. These teams are all spending the same customer's attention, and they often work against each other without realizing it.

For enterprise and executive teams, reward program design has to include the execution layer underneath it. Disconnected systems are what create the importance-effectiveness gap. When loyalty logic lives in one tool and reward execution lives in another, every change needs engineering work, and that friction keeps good ideas stuck in the backlog.

Harvard Business Review and Talon.One also found that organizations integrating promotions and loyalty report benefits across customer loyalty and sales. Reward strategy gets stronger when the systems behind it connect enough to support one customer view.

How retail brands design programs that protect margin

Putting value behind a membership firewall keeps retail brands from discounting their way to volume.

In beauty, Sephora shows how reward program design can move beyond transactional points and coupons. Beauty Insider Challenges mix online and in-store actions, transactional and non-transactional engagement, and rewards such as brand experiences and deluxe samples. The challenges drove over 2 million new member signups and tripled participation versus original forecasts. At scale, gamified participation turns a promotion into an acquisition and engagement tool while protecting margin.
 

Sephora encourages customer engagement through its renowned Beauty Insider program.

Image source

That logic extends well beyond beauty. Across apparel and fast fashion, the strongest programs blend tiers, status, and values-based earning rather than leaning on price alone.

Most retail teams hit a wall on the operational side of running campaigns like these. The practical mechanism is a shift from blanket value to personalized, rule-driven rewards. It lets brands sell more without training customers to wait for the next sale.

H&M follows the pattern too. It has a base Member tier and a Plus tier earned through cumulative spend, plus Conscious points for buying sustainable items. The sustainability-linked earning mechanic now shows up across sportswear, apparel, and coffee programs. That suggests ESG-linked loyalty is shifting from a differentiator to an expectation.

What QSR brands get right about frequency

Quick service programs move occasional guests toward routine through the app, which is both the delivery mechanism and the data layer.

Starbucks Rewards is the model. The 2026 program launch replaced a single flat membership with three tiers, Green, Gold, and Reserve, that reward members with faster earning as they engage more. Bonus Stars for digitally reloading a Starbucks Card keep payment and activity inside the app, where Starbucks can see and act on it. Rewards members now visit two to three times more often than non-members.

Scooter's Coffee shows how automation turns the same app-based model into steady frequency. Automated visit challenges run in the background, and dayparting promotions time offers to specific parts of the day. The incentive lands when a repeat visit is most likely, rather than as a blanket discount. Real-time fraud checks protect welcome offers without adding friction.

"Talon.One’s API-first Rule Engine has given us the incredible flexibility to automate gamified challenges and detect fraud in real time."

Anne Schultheis

Director of Loyalty and CRM at Scooter's Coffee

McDonald's frames its program explicitly as a frequency play for existing guests. With nearly 90% of the U.S. population visiting each year, the goal is more visits per existing guest. By the end of 2025, the program had reached about 210 million active members across more than 70 markets, and loyalty members visit far more often than non-members.

A word of caution sits underneath all of this. QSR loyalty fatigue is rising. Members enrolled in many programs are less likely to actively earn and redeem. Dissatisfaction with programs is also increasing. More programs rarely create more loyalty.

How grocery turns loyalty data into a profit center

Grocery programs face razor-thin margins and enormous transaction frequency. Consumer packaged goods (CPG) trade funding can turn the data itself into a revenue stream.

Kroger Plus captures over 95% of customer transactions across roughly 63 million households. Its data subsidiary, 84.51°, generates over 150 million customer touchpoints and 1.9 billion unique coupons annually, with each household receiving a personalized mix based on past purchases. The economics shape the design. The CPG brand pays for access to targeted households, the retailer earns margin plus media revenue, and the customer sees relevant offers. That three-way payoff holds only because the personalization is precise enough to justify the funding.

This is exactly the kind of complexity that breaks general-purpose retail tools. Grocery rewards have to account for basket composition and manufacturer funding allocation. They cannot rely on a blanket basket discount. A strategy that works for center-store packaged goods can wreck margin on fresh produce. The infrastructure has to handle that nuance natively. Grocers tend to need a flexible data model that maps their own business logic rather than forcing their data into a vendor's rigid schema.

Tesco Clubcard runs a member-gated pricing model in the UK, with 77% of regular grocery shoppers signed up and average loyalty savings ranging from 17% to 25%. One warning belongs in any member-versus-non-member pricing design. The same review found that a limited number of loyalty offers showed pricing patterns suggesting regular prices may have been inflated during the promotion period. That puts regulatory and reputational risk on the table.

Where financial services, travel, and B2B push the design forward

Some of the most interesting program design is happening in categories where the old playbook fits poorly.

In financial services, Bilt Rewards created a category by turning rent into rewards. The program supports points earning and redemption across rent, travel, shopping, and fitness, which makes the reward experience broader than a single card transaction. Its ecosystem includes 5M+ members, 40,000+ merchant partners, and one in four U.S. apartment buildings in its network. Campaigns can launch in hours rather than months. Differentiation increasingly comes from what the reward system enables, alongside the payment product attached to it.

"I don’t have a technical background, but Talon.One makes me a highly effective contributor. I can build and launch complex campaigns without needing to code."

Sydney Segal

Director of Reward Strategy at Bilt

Travel is rethinking loyalty for brands outside the airline frequent flyer model. TUI launched its first global loyalty program in 2026, a free three-tier structure spanning flights, hotels, packages, and cruises. Its broader model combines progression-based rewards, member-exclusive offers, and gamified elements. Wyndham went further with a $95-a-year paid hotel program, the first major paid loyalty experiment by a U.S. hotel group. Whether subscription economics work in travel at scale is still unproven, and Wyndham's program trades hard currency for access and status.

B2B shows loyalty working as a retention tool in the most concrete terms. SiteOne's field reps used to rely on cash discounts to win back lapsed customers. A points multiplier now produces the same retention at a lower incentive cost, as long as the platform handles non-transactional engagement mechanics natively. A discount becomes the fallback only when the platform cannot support those mechanics. The broader shift in B2B promotions and loyalty moves away from passive volume rebates toward active incentives that reward partners for selling on a brand's behalf, alongside buying from it.

What separates high performers from everyone else

A 2020 analysis of 50 loyalty programs from top brands found only 18% were experience-led, 6% had connected offerings, and 2% had reached ecosystem-level design. The vast majority were still running phase-one earn-and-burn mechanics that ring hollow with consumers, who increasingly prefer emotional and experiential benefits.

Across categories, access beats discounts, and exclusive events and front-of-line benefits are becoming central to loyalty. Time-to-value is another discipline most programs underinvest in, since rewards that take too long to earn weaken perceived value, as do points that go unused. The personalization customers trust tends to be grounded in purchase history rather than broader tracking.

Custom-built loyalty systems are expensive to build and maintain, and legacy systems tie up resources that could go toward better rewards. High performers tend to run on integrated platforms instead, where loyalty, promotions, and customer data work from a single source rather than separate tools stitched together after the fact.

Reward program design comes down to fit between a clear behavioral goal, the economics that make it sustainable, and infrastructure flexible enough to keep adjusting. The real shift is treating loyalty strategy and reward execution as one connected system, so every reward does measurable work as incentive marketing rather than quietly draining margin.

For teams rethinking how their program is built, Talon.One is an enterprise incentives engine that runs loyalty and promotions on one platform with real-time decisioning. It personalizes incentives at scale and gives marketing teams the autonomy to launch and adjust campaigns as the data changes.

1. Skewed scores create a false sense of customer experience.

2. Overrated successes lead to problematic marketing efforts.

3. Innovation becomes non-existent.

4. Inflated ratings encourage anti-competition

While trust is the biggest roadblock to generative AI adoption, according to Pew research, other concerns include AI explainability, ethics, and bias.

We also know that just five companies paid €4.21 billion ($4.56 billion) in GDPR fines alone for customer data breaches between 2021 and 2025, according to GDPR  Tracker.

The stakes for AI governance are immense. And marketers are feeling these roadblocks acutely. Every AI tool that touches your campaigns carries the same risks that keep your IT team up at night: brand violations, data misuse, and regulatory exposure.

Meanwhile, the customer data that feeds those campaigns and the brand assets that shape AI-generated output flow ungoverned through dozens of SaaS tools.

AI Governance has become a board-level operational priority. Unfortunately, governance has earned a bad rep because most programs are built to block rather than let marketing move faster with confidence.

The programs that succeed do the opposite. They first make the data foundation and brand knowledge governable. They then ensure the rest of marketing can run at the speed the business demands.

Accelerated by the EU AI Act and the rise of autonomous agentic AI, enterprise teams must adapt rapidly.

Highlights

• Governance is a cross-functional mandate: Effective oversight requires active collaboration between the marketing teams, Chief Data Officer, security, legal, and business stakeholders.
• AI governance extends beyond the model: True risk management requires governing the underlying data foundation and operational brand knowledge, not just the algorithms.
• The Composable CDP is the structural anchor: A warehouse-resident architecture applies access controls and data lineage rules before data ever reaches an AI system or marketing campaign.
• Brand assets are governable data: Agentic AI reasoning against undocumented brand rules produces autonomous brand-policy violations at scale.
• **Marketing must own the brand governance layer: **IT can govern the model and legal can govern compliance, but only marketing can govern whether AI output represents the brand correctly.

More than a model audit: what is AI governance?

AI governance is the comprehensive set of processes, standards, policies, roles, and tools that direct how an organization develops, deploys, monitors, and retires AI systems. The ultimate goal is to ensure that, throughout their existence, those systems remain legally compliant, transparent, safe, and fair.

This discipline covers both internal models built by your data science teams and third-party AI tools procured from external vendors. Many organizations focus entirely on internal algorithms, overlooking the fact that vendor AI carries the same regulatory and reputational risks.

Modern governance must also account for brand knowledge, a category most traditional definitions completely miss. Generative and agentic AI systems reason against brand assets to produce output. Without strict governance at this brand layer, AI can produce rapid, autonomous brand violations that** erode customer trust faster than a data breach.**.

AI governance Vs. data governance Vs. AI ethics

To understand the scope and function of AI governance, differentiate it from two closely related disciplines:

• AI ethics establishes the foundational values and principles of governance. It asks what an AI system should fundamentally do to avoid harm.
• Data governance manages data quality, access controls, and data lineage. It focuses entirely on the information that feeds corporate systems.

AI governance acts as the operational bridge connecting these two disciplines. It combines ethical guidelines and data protection rules with structural risk management, accountability mechanisms, and model oversight.

All three components are necessary for the modern enterprise. But none of them alone can protect a business from algorithmic discrimination, regulatory penalties, or large-scale brand damage.

Why this is now a marketing leadership problem

Three distinct forces converged to make AI governance a marketing priority, not just an IT one:

• **Every marketing team is now deploying AI. **Marketing teams are increasingly adopting AI tools. That means marketing now owns significant AI risk, whether or not a governance framework acknowledges it.
• **Brand violations scale faster than data breaches. An ungoverned AI system sending off-brand creative to millions of customers or including unapproved claims in content causes brand damage that compounds with every impression. ** In fact, 65% of executives state that agentic AI will require significantly stricter ethical guidelines than current AI systems, according to IBM IBV.
• It's now written law: The EU AI Act transformed theoretical guidelines into strict, enforceable legal obligations that apply to the AI systems marketing teams use every day — not just internal models built by data science.

Combined, these factors make oversight a non-negotiable requirement for modern marketing strategy.

Risks of improper governance

Governance frequently earns a reputation as an organizational roadblock. Most programs are designed strictly to prevent action rather than making the underlying architecture governable. For marketing teams, the consequences mean more bottlenecks and slower speed to launch campaigns because the formal process is too cumbersome.

Governance programs that fail tend to fall into one of three categories:

• Compliance-only governance: This is when the organization delegates AI oversight entirely to legal teams without giving marketing and other business stakeholders a formal role. Without the people who actually deploy AI in campaigns at the table, governance becomes a gate that slows work without improving output quality.
• Model-only governance: This type of governance focuses entirely on auditing algorithms. It fails because a model that possesses a perfect audit trail but relies on fragmented customer data and undocumented brand rules constitutes pure governance theater.
• Static auditing: Treating oversight as a static audit rather than a dynamic operational practice creates additional governance bottlenecks. Programs built exclusively to pass an initial review will inevitably fail as models drift, regulations evolve, and agentic systems execute more tasks autonomously.

The programs that actually work invert this usual order:

• They first make the data foundation and brand knowledge governable
• They then layer on the necessary model audits and policy documents
• They ensure ongoing audits throughout the AI lifecycle

What ungoverned AI actually costs marketing teams

These are the specific risks marketing teams face when AI systems operate without governance — and the reason this isn’t a problem you can delegate entirely to IT.

False or misleading product claims

AI sometimes fabricates capabilities, performance metrics, or customer results. A generated ad claims a feature that doesn’t exist. A lifecycle email cites ROI benchmarks the company never published. A landing page states “best in class” with no supporting evidence. The content looks polished and authoritative, which makes it harder to catch — and more likely that sales teams will repeat the claims because they saw them in “official” marketing content.

Compliance and regulatory violations

AI can lack awareness of industry-specific regulations. Financial services content implies guaranteed returns. Healthcare campaigns include unapproved medical claims. Personalized content suggests data practices that violate privacy regulations. For public companies, AI-generated content may include language that could be interpreted as forward-looking statements.

Brand drift and positioning erosion

Without a governed brand context layer, AI may gradually shifts messaging away from approved positioning. Different value propositions appear across channels. Product descriptions contradict each other. Messaging becomes generic rather than differentiated. This happens slowly enough that no single piece triggers a review, but over months, the cumulative drift weakens market perception and confuses prospects.

Biased or exclusionary content

AI is known to reproduce biases present in its training data, which can include stereotypical imagery, exclusionary language assumptions, culturally insensitive copy in global campaigns. These outputs can reach millions of impressions before anyone flags them, especially in automated channels where human review is minimal.

Copyright and intellectual property exposure

AI-generated content can unintentionally mirror competitor copy, include copyrighted visual elements, or reuse proprietary content from training data.

Privacy and confidential information leaks

Teams can unknowingly expose sensitive information to AI systems, or AI-generated content includes confidential details such as unreleased product plans, customer names or data, or internal metrics not intended for public release.

Hallucinated sources and fabricated research

AI can generate authoritative-sounding citations that don’t exist such as fake analyst quotes, invented customer testimonials, nonexistent research findings, fabricated market statistics. These are especially dangerous in thought leadership content, case studies, and sales enablement materials where credibility depends on verifiable sources.

Competitive misinformation

AI can incorrectly characterize competitors with incorrect feature comparisons, inaccurate pricing, or misrepresented capabilities. This may show up in competitive battlecards, comparison pages, and sales content that reps use in live conversations.

Crisis amplification

A small AI error has the potential to propagate across channels before anyone catches it such as an incorrect promotional offer, a misleading product announcement, a tone-deaf automated response during a sensitive news moment. The speed and scale of AI-generated content means errors reach more people, faster, with less human review.

SEO and AI search visibility damage

Ungoverned AI-generated content can harm discoverability due to low-quality pages that dilute domain authority, factual inaccuracies that get indexed by search engines and cited by AI answer engines, or content that contradicts existing website messaging and creates conflicting signals. Once information is indexed, it either negatively impacts brand visibility in search or persists incorrectly in AI-generated answers long after the source content is corrected.

Four principles every AI governance framework needs

Marketing leaders don’t need to become governance experts, but understanding these four pillars helps you advocate for the right controls when your team is deploying AI at scale.

Transparency and explainability

Transparency requires that stakeholders thoroughly understand how an AI system makes decisions. This means documenting not just the final output but also the underlying reasoning and data inputs that drive the conclusion.

Fairness and bias control

Bias almost always enters an AI system at the data layer. Skewed training data produces skewed models, which produce algorithmic discrimination

Brand and inclusivity guidelines also belong in this category. A model trained on corporate language that excludes specific audience segments will produce output that perpetually excludes those users in every personalized experience it generates.

Accountability and ownership

Accountability in AI governance requires that an organization explicitly identify who is responsible when an AI system causes harm or violates policy. Without distinct ownership, accountability diffuses across various departments. This often results in scenarios where no one takes corrective action.

Privacy and security

AI systems ingest and process vast amounts of personal and sensitive data. Regulations such as GDPR compliance and the EU AI Act impose rigorous data privacy requirements on any system that handles this information.

AI also introduces unique security risks: model-inversion attacks designed to reconstruct training data, adversarial inputs and prompt injection, unintended data leakage from generative outputs, and agent hijacking.

Marketing teams using AI for personalization and audience targeting need to understand that the customer data flowing through these systems is subject to the same security requirements as any other enterprise AI deployment.

The three AI Governance frameworks worth knowing

Marketing leaders don't need to invent governance structures from scratch. Several global organizations have developed comprehensive frameworks to guide enterprise compliance and risk management — and even if your IT or legal team owns the implementation, understanding them helps you advocate for marketing’s seat at the table.

• The NIST AI Risk Management Framework (Govern, Map, Measure, Manage) is the most widely adopted in US-regulated industries and most likely to shape your organization’s internal structure.
• The EU AI Act is the world’s first enforceable AI regulation, classifying systems into risk tiers from minimal to unacceptable. If your marketing campaigns target EU customers or your AI tools process EU customer data, this applies to you directly.
• The OECD AI Principles, adopted by 40+ countries, focus on human rights, transparency, robustness, and accountability. They signal where regulation is heading even in markets that haven’t passed formal AI legislation yet.

Why marketing must be at the governance table

AI governance is never successfully owned by a single business function. But in most organizations, marketing isn’t even in the room when governance decisions are made, despite being deployers of AI tooling.

Leadership sets the tone

Ultimate operational and financial accountability rests with the CEO and the corporate board. Leadership sends a clear signal to the enterprise by investing visibly in responsible AI through governance training, dedicated policies, and internal standards.

The governance cap marketing must close

Effective AI governance committees typically include internal audit, data engineering, the CDO, CISO, and legal counsel, which are necessary but insufficient.

The team most governance models leave out is marketing operations and brand. Generative AI consumes brand assets to produce output across ad creative, email copy, landing pages, personalized content. If no one on the governance committee understands brand guidelines, approved claims, and voice rules, the AI can produce autonomous brand-policy violations at scale.

IT can govern the model and legal can govern compliance, but only marketing can govern whether AI output represents the brand correctly.

If you’re a marketing leader and your organization has an AI governance committee, your team needs a seat.

Governance applies across the entire AI lifecycle

Effective governance isn’t a one-time deployment review. It applies at every stage: before training (auditing data sources, verifying consent, establishing quality baselines), at deployment (documenting decision logic, model assumptions, and known limitations), and continuously after launch (monitoring for drift, maintaining audit trails, revalidating performance).

For marketing leaders, the critical insight is that the AI systems generating your campaigns went through each of these stages, and governance gaps at any stage affect the quality and safety of what reaches your customers.

Technical flaws compound: a data quality defect in a traditional database affects a fraction of reports; that same defect embedded in training data contaminates every prediction the model makes and every campaign decision that follows.

Agentic AI makes continuous monitoring especially critical. An agent managing your lifecycle program or generating ad variants is making autonomous decisions at a speed and scale that manual review can’t match. Real-time oversight is critical to prioritize over periodic audits in order to catch drift before it reaches customers.

The three governance program layers (and the one many skip)

Most players in the AI industry treat governance strictly as a model-layer or policy-layer problem. We argue that governing AI also means governing the data, brand assets, and workflows that feed those systems.

If your customer data is fragmented across SaaS tools and your brand assets live in unstructured documents, only governing the models consuming them is pure governance theater.

The structural answer is to anchor oversight at the foundation.

A model is only as governed as its inputs

Every AI model is only as governed as the training data and the information it consumes at inference time. A well-documented algorithm running on unverified, fragmented customer data provides a dangerous illusion of security.

The EU AI Act mandates documented data governance systems for high-risk AI. This regulation explicitly calls for strict oversight at the operational data layer, proving that model governance alone is legally insufficient.

For marketing specifically, this means the customer data powering your personalization, audience targeting, and AI-driven campaign decisions must be governed at the source, not just at the model layer.

Brand knowledge is the governance gap marketers must close

Generative and agentic AI tools heavily consume brand assets to generate autonomous output, including editorial standards, approved claims, visual standards, and voice rules

When these assets are ungoverned or only exist as static files, the AI might execute fast, autonomous brand-policy violations.

Consider this scenario: your brand team spent months developing updated messaging guidelines, but they live in a PDF on a shared drive. Your performance marketing team is using an AI tool to generate ad variants at scale. The AI has no access to the current guidelines — it’s working from training data that may include outdated positioning, discontinued products, or unapproved claims. An automated campaign sends thousands of off-brand ads before anyone notices.

The model was recently revalidated. The decision logic is sound. There’s no detectable bias. All audit trails are clean. There's nothing to tell you that the system did anything wrong. The problem was ungoverned brand assets.

Governing operational brand knowledge means providing a structured brand context that the AI can reason against in real time with a live, queryable representation of your brand guidelines, approved assets, voice rules, and product claims that updates as your brand evolves.

This is the difference between AI that produces more content faster and AI that produces more on-brand content faster. For heads of brand and creative who worry that “AI for creative” means “AI replaces creatives,” a governed brand context layer is the mechanism that keeps humans in creative control while letting agents handle production volume.

Anchoring governance at the data warehouse

A modern cloud data warehouse serves as the natural governance anchor for enterprise customer data. It already provides sophisticated role-based access controls, detailed data lineage tracking, and automated quality monitoring.

A Composable CDP runs on this warehouse rather than copying customer data into a completely separate vendor system. This architecture preserves all existing governance protocols, eliminating the need to re-govern data scattered across dozens of operational SaaS tools.

For customer data specifically, strict rules regarding consent, privacy, and PII must apply before the information ever reaches an AI model. You can't reliably enforce these rules post-hoc through model explainability alone.

Hightouch enables marketing teams to govern the customer data and brand context flowing into AI systems through a Composable CDP foundation.

With Customer Studio, AI Decisioning, and the brand context layer, teams can securely route warehouse-resident customer data to downstream applications while maintaining full data lineage and ensuring that every AI-generated campaign asset is built on governed brand knowledge.

For example, a marketing team deploying an AI-generated campaign needs absolute certainty regarding which customer data and brand assets are feeding the AI. The Composable CDP and brand context layer give admins granular control over access, ensuring only approved, governed information informs the AI's creative output.

Building an AI Governance program that works

Transforming ethical principles into operational action requires a phased approach. For marketing leaders, the goal is to ensure marketing has a seat at the table and that the program addresses the brand and campaign risks that only marketing can see.

Three maturity levels

• The informal stage: You rely entirely on values-based guidelines and internal ethics committees. You lack any formal technical structure. Marketing teams deploy AI tools with no governance review.
• The ad hoc stage: You implement specific risk policies in response to isolated incidents. This is common for companies with limited AI production experience. Marketing governance is reactive. Someone catches an off-brand AI output and a new review step gets added.
• The formal stage: Features a comprehensive, fully documented framework. AI governance is closely aligned with standards like the NIST AI Risk Management protocol. Marketing operations has a formal governance role, brand assets are treated as governable data, and AI-generated campaign output is monitored continuously.

Your six-step AI governance roadmap

So how do you go from the informal to the formal stage? You can follow this roadmap:

• Establish the governance committee by assembling a cross-functional team including the CDO, CISO, legal, data engineering, marketing operations, brand leadership, and business unit leads. Define a strict charter and a clear escalation path for potential violations.
• Inventory your AI systems to build a comprehensive list of every model in development or production. Explicitly include third-party vendor AI. Classify each system by its inherent risk level using the EU AI Act framework or NIST tiers.
• Define your accountability structure by specifically naming a responsible owner for every AI system in your inventory. For marketing AI systems, ensure the brand team has explicit sign-off authority on outputs that represent the brand.
• Govern the data foundation first by auditing the customer data and brand assets that feed your models. Establish data lineage, access controls, quality baselines, and consent documentation for everything flowing into AI systems. A Composable CDP architecture like Hightouch makes this highly implementable. Fragmented SaaS architectures render it nearly impossible.
• Implement continuous monitoring by deploying automated bias detection, drift monitoring, and performance alert systems. Establish immutable audit trails for AI decisions and enforce scheduled periodic revalidation. For marketing, add brand-compliance monitoring: are AI-generated outputs staying within approved guidelines, claims, and voice rules?
• Train the organization thoroughly. Governance fails when it resides solely with a compliance team. All AI system owners, data engineers, brand owners, marketing leaders, and business stakeholders require comprehensive baseline training on ethical guidelines.

Governance is the moat, not the bottleneck

Most marketing leaders hear “AI governance” and think: slower approvals, more bureaucracy, another blocker to shipping campaigns.

But the marketing organizations that have the greatest success with AI are the ones that figure out governance first. Not just because regulation requires it, but because governed AI is better quality. And better quality outputs mean your team spends less time reviewing and more time on strategy.

If you're ready to govern the customer data and brand context that feed your AI systems, Hightouch's Composable CDP-aligned foundation gives marketing teams the access controls, lineage, and operational brand knowledge required to scale AI safely now and in the years to come. Talk to our team to modernize your data strategy today.

Frequently asked questions

Q1: What is AI governance and why does it matter now?

AI governance is the system of processes, standards, and roles that direct how an organization develops, deploys, and monitors AI. It matters now because it's no longer an optional future-state endeavor. It's an immediate operational necessity after the EU AI Act entered force in 2024.

Q2: How is AI governance different from data governance?

Data governance specifically manages data quality, access controls, and data lineage. Its goal is to control what information AI runs on. AI governance is far broader, encompassing data, AI models themselves, the brand context consumed, accountability structures, and regulatory compliance.

Q3: Why is the data foundation critical to AI governance?

The data foundation is critical because any AI model is only as governed as the training data and information it consumes. A well-documented algorithm running on unverified or fragmented customer data creates a dangerous illusion of security, often referred to as governance theater.

For the first time, AI is supporting authors, marketers, content teams, and operators, the people doing the work inside the platform every day. That makes this a broader workflow story.

The Shift: From Developer Productivity to Workflow Acceleration

What stands out about AEM’s AI capabilities is who they help across the full range of daily work. AI is becoming embedded across the work of content creation and updates, asset discovery and reuse, experience optimization, governance and compliance, and development and troubleshooting. The story here is about workflow transformation.  

The “Simple” Page Update That Never Is  

Every content team knows this pattern. The request sounds simple, whether it is to update a headline, remove a component, swap an image, or adjust a style. Then the “simple change” turns into opening the page, finding the right component, previewing the update, and making sure nothing else moved. AI Assistant lets users begin with intent, asking it to update this text, remove that section, or change this style. It removes the friction that comes from needing to know every path in advance.

Relevant AEM AI Capability: 
Brand Experience Agent via the Experience Production Agent, especially the Content Update Job 

The Form Request That Is Never Really “Quick”

“Can we just spin up a form?” has probably delayed more afternoons than anyone wants to admit. Even a basic form requires fields, structure, validation, layout, and the inevitable clarification that arrives after the first version is already done. The same applies to communications like letters or PDF-based outputs.

AI Assistant lets users describe the form or communication they need and get to a workable starting point faster. Review still happens. The difference is that the set up effort reduces significantly.

Relevant AEM AI Capability: 
Brand Experience Agent via the Experience Production Agent, including the Form Creation Job and Communication Creation Job 

Finding Content Should Not Feel Like a Side Quest

Most organizations have plenty of content. Finding it is the harder problem. The asset, the content fragment, and the form created months ago, may all exist. But locating any of it can depend on folder memory, naming conventions, metadata quality, and one teammate who somehow knows where everything lives.

The Content Advisor Agent changes how users search. Instead of searching the way the repository wants, users can search the way people think by describing what they need. That makes discovery more natural, makes reuse more likely, and in most organizations, reuse is one of the easiest ways to move faster without creating even more content chaos.

Relevant AEM AI Capability: 
Content Advisor Agent, especially the Content Discovery Job 

When One Asset Needs Multiple Versions

Finding the asset is only the beginning. Then comes the real ask, finding a web version, a mobile version, a social version, a better cropped variation, a different format, and maybe a slight adjustment ideally all before someone says, “This should be easy.”

AI-powered optimization allows users to request variations directly, generating outputs based on need rather than building each version manually. The use case is straightforward, and that is usually where the most useful AI applications sit.

Relevant AEM AI Capability: 
Content Advisor Agent, especially the Content Optimization Job 

Modernization Needs More Than Good Intentions

Every organization has legacy experiences to be modernized. Modernization often competes with whatever feels more urgent, which means it gets postponed until it is both urgent and painful. The Experience Modernization Agent helps teams incrementally evolve legacy experiences toward more modern, cloud-ready patterns, reducing the effort required to start and sustaining progress incrementally. It can make modernization easier to start with a clearer path to it.

Relevant AEM AI Capability: 
Brand Experience Agent via the Experience Modernization Agent and the Experience Modernization Job 

Every Platform has a Pipeline Story

Developers are part of this picture. No matter how smooth content workflows become, pipeline failure still happens. And when builds fail or deployments stall, teams need to get to the root cause quickly. That is where the Development Agent supports the troubleshooting process, helping narrow the problem faster without replacing engineering judgment.

This is the most familiar AI story in the mix. The difference is that in AEM, developer troubleshooting is only one part of the larger workflow story.

Relevant AEM AI Capability: 
Brand Experience Agent via the Development Agent, especially the Pipeline Troubleshooting Job 

Governance Works Better When It Shows Up Earlier

This may be the least flashy part of the story, and among the most important. Speed without control is just a faster way to make mistakes, and governance is what keeps that from happening. As content updates, asset discovery, and output creation become faster and more natural, keeping governance close to the work becomes even more important. Brand rules, permissions, rights, and expiry checks are not things anyone wants to discover late. The Governance Agent helps bring those checks into the flow, keeping them from becoming a final hurdle.

Relevant AEM AI Capability: 
Governance Agent, including Brand Governance, Permissions and Rights Management, and  Digital Rights Management 

Why AEMʼs AI Story Feels Different

AEMʼs AI approach delivers value across the actual work of running digital experiences, from content updates, search, asset reuse, optimization, modernization, troubleshooting, and governance. That impact reaches the people doing the work every day, from the author making content changes, the marketer trying to find reusable assets, the team creating forms and communications, the governance lead keeping things aligned, and the developer solving a pipeline issue before it becomes everyone else's issue. The goal is to reduce the small, persistent frictions that slow digital work down, with fewer unnecessary steps between the ask and the outcome. The most useful AI applications do exactly that, making everyday work simpler and less cumbersome. AI Agents in AEM bring that into the workflows that power enterprise content operations, and that is what makes them worth paying attention to.

The 2026 Incentive Strategy Roadmap is designed to help organizations navigate the evolving role of incentives in driving engagement, loyalty, and measurable business outcomes. Across this series, we explore how incentives are shifting from simple reward mechanisms to fully integrated experience frameworks that influence behavior at every stage of the customer journey.

Earlier articles have examined the evolving market landscape, the importance of structured program design through KPI frameworks, and the role of speed, personalization, and digital payments in modern incentive programs. This month’s focus builds on those foundations by exploring one of the most critical and often under evaluated components of success, experience and fulfillment.

As participant expectations continue to rise, organizations must ensure that every interaction, from enrollment to reward delivery, is seamless, responsive, and designed to build trust.

End-to-End Participant Experience

A high performing incentive program begins with a clear understanding of the participant journey and how that journey aligns with the brand’s objectives. Before mapping touchpoints, organizations must define what success looks like for the program and ensure the experience supports both customer engagement and financial outcomes.

Each program should be evaluated individually to establish a baseline of success. This includes collecting key metrics, participant feedback, and observations that reflect how the experience is performing. From there, patterns can be identified across programs, allowing organizations to replicate successful approaches and build consistent standards over time.

Mapping the participant journey is not just about process visibility, it is about understanding how service delivery impacts both customer sentiment and program performance. Indicators such as repeat calls, escalations, and transfer rates help reveal where friction exists and where workflows are effective. When these metrics are minimized, they often signal a well designed experience with strong communication and intuitive touchpoints.

Personalization plays a critical role in shaping this journey. Customers respond to experiences that recognize them as individuals. Something as simple as using a participant’s name consistently across interactions, emails, and reward communications can strengthen emotional connection and build trust. Personalized interactions signal that the program is designed for the individual, not the masses, which increases engagement and satisfaction.

When organizations align journey design with program objectives, they create experiences that are not only seamless but also strategically effective.

Customer Care, Communication, and Support Models

Customer care is a core component of the incentive experience, not a secondary function. It represents a direct extension of the brand and plays a key role in maintaining participant trust and satisfaction.

Effective support models combine skilled agents, advanced technology, and structured processes. This includes omni channel communication across phone, email, and chat, supported by automation tools that improve efficiency without sacrificing quality.

Measured outcomes demonstrate the value of this approach. Programs that integrate automation and artificial intelligence have achieved reductions in handle time of up to 20 to 30 percent, along with improvements in quality scores above 96 percent and first call resolution rates in the 93 to 94 percent range. These improvements reflect both operational efficiency and enhanced participant outcomes.

Many programs aim to balance self service with live support. Self service tools such as portals and interactive call flows can handle approximately 25 to 30 percent of routine inquiries, including reward status checks and eligibility questions. This allows agents to focus on complex interactions where problem solving, empathy, and accountability are critical.

A defining characteristic of high performing teams is ownership. Rather than transferring issues or providing partial answers, agents take responsibility for resolving each interaction completely. This concierge level approach helps ensure that participants feel supported and valued throughout the process.

Transparency further strengthens this model. Calibration sessions with clients provide a structured process for reviewing interactions, aligning on quality standards, and identifying trends. These sessions reinforce accountability and create opportunities for continuous improvement.

Reducing Friction from Claim to Fulfillment

Download our Friction Reduction Checklist by clicking on the image of it in the article. 

Reducing friction is essential, but it must be approached with balance. Not every program requires the same level of speed and simplicity. The degree to which an experience should be frictionless depends on the financial objectives of the sponsoring brand and the structure of the program itself.

Organizations must maintain a clear understanding of both financial and customer experience goals, ensuring that all business rules are intentionally integrated into the program design. This alignment allows programs to support growth and engagement while also maintaining control over costs and operational impact.

Friction often appears during claim submission, especially when documentation or eligibility validation is required. Participants may struggle with uploads, unclear instructions, or incomplete understanding of requirements. To address this, programs should provide guided workflows, clear instructions, and accessible support resources that reduce confusion without removing necessary controls.

Speed of fulfillment continues to be a major driver of satisfaction, but it must be supported by operational readiness. Organizations must anticipate demand through forecasting, understand the potential impact of promotional offers, and prepare for volume fluctuations.

Scaling fulfillment requires flexibility. This includes having cross trained teams, expanded operational capacity when needed, and access to automation tools that can handle increased workloads. Strong partnerships across the fulfillment supply chain ensure that delivery capabilities align with program demand, especially during surges.

Accuracy is equally critical. Every participant interaction presents an opportunity to confirm key details such as name, address, email, and reward selection. Errors in this stage can lead to delays, dissatisfaction, and increased support volume. Preventing these issues requires coordination across all touchpoints, including client sales channels and service teams.

Ultimately, reducing friction is not about eliminating every barrier. It is about designing an experience that is intuitive, efficient, and aligned with program objectives, ensuring participants can complete actions while maintaining program integrity.

Bringing Experience and Fulfillment Together

Experience and fulfillment must be designed with both the participant and the business in mind. Programs that balance customer expectations with financial goals create stronger outcomes for both engagement and performance.

When organizations align experience design, operational execution, and business rules, they create systems that are scalable, efficient, and capable of delivering consistent results across programs.

 Key Terms in the Article

Participant experience, the full journey a participant takes from program entry through reward fulfillment

Omni channel support, a service model that provides consistent communication across multiple channels such as phone, email, and chat

First call resolution, the ability to resolve a participant issue during the initial interaction

Self service, tools that allow participants to complete actions independently, such as checking status or submitting claims

Fulfillment, the process of delivering rewards to participants after validation

Calibration sessions, structured reviews of customer interactions used to align quality standards and identify improvement opportunities

Friction, any barrier or complexity that prevents participants from completing actions easily

Data Points and Market Insights

Customer expectations are shifting toward more personalized engagement across incentive and loyalty programs. Research highlighted in a Forbes article shows that loyalty programs are moving away from one size fits all structures and toward hyper personalization, where experiences are tailored to individual preferences and behaviors. This shift reflects a growing demand for relevance, as consumers increasingly expect programs to adapt to their needs and deliver more meaningful interactions.

Participation in loyalty and incentive programs remains extremely high, with most consumers belonging to at least one program and many actively engaging across multiple programs. 

Customer experience plays a critical role in retention. Studies show that consumers value good customer service nearly as much as product quality when deciding whether to stay loyal to a brand. 

Personalization continues to influence engagement, with a significant portion of consumers more likely to repurchase from brands that tailor experiences to their preferences. 

From a performance standpoint, customer retention remains a key driver of profitability. Research indicates that even small improvements in retention can lead to substantial increases in profit, reinforcing the importance of experience design. 

Fulfillment speed also directly impacts satisfaction and retention. Industry data shows that fast delivery is a major factor in repeat purchases, and delays can quickly lead customers to disengage or choose alternative brands. 

Finally, incentive marketing continues to evolve as a strategic discipline. The Incentive Marketing Association global research initiative brings together insights from hundreds of industry leaders, reinforcing the importance of aligning experience, technology, and fulfillment to drive results.