The Rise of Agentic AI & the Future of Retail Loyalty
In the next phase of AI evolution, what I call Agentic AI — autonomous agents that reason, plan and act on behalf of users — will reshape not just how consumers shop, but how brands and retailers earn their trust and loyalty. In retail, this shift presents both enormous opportunity and real risk. Below, I walk through how Agentic AI may alter the brand-consumer relationship across the customer journey, why trust is the linchpin, where things can go off the rails, and how brands and retailers must respond if they are to stay relevant in this new paradigm.
What is Agentic AI — and why it matters for loyalty
Before delving into customer journeys, it’s useful to define what we mean by Agentic AI and what makes it qualitatively different from today’s “AI assistants” or recommendation systems.
Agentic AI is not just responsive — it can reason over goals, orchestrate multiple sub-tasks, invoke tools or APIs, maintain context/memory, and decide when to escalate or defer.
It can act on behalf of the user, executing workflows (e.g. placing orders, applying discounts, managing returns) rather than simply offering suggestions.
Because of that capacity to act, it can become a longer-term proxy or “digital shopping assistant” with evolving preferences, reputational memory, and even autonomy thresholds (i.e. how much it can act on its own vs. asking for confirmation).
In retail, that means the AI agent may become the interface between consumer and brand — one that interfaces with multiple brands, marketplaces, and platforms. The brand is no longer just interacting with the consumer — it must also win over, influence, or cooperate with the consumer’s agent.
That shift has consequences for loyalty: the agent will optimize for the consumer’s objectives (price, convenience, fulfilment, trust, privacy, brand affinity), meaning that brands may need to become “agent-friendly” and earn inclusion or preference by those agents, or risk being bypassed entirely.
A journey view: how Agentic AI changes the five stages
Let me run through some concrete, hypothetical but grounded in current capabilities, examples across the classic journey stages: Awareness → Onboarding → Transaction → Service → Loyalty.
My running hypothesis is the following: Consumers get more personalized, more frictionless, more context-aware experiences — but brands must compete for the agent’s “mindshare,” not just the consumer’s.
1. Awareness / Discovery
Today: Consumers see ads, content, influencer posts, search engine results, SEO, social media. With Agentic AI: The agent will proactively scan for opportunities, promotions, or new products that align with the consumer’s long-term profiles and goals, rather than waiting for the consumer to search.
Example: My shopping agent notices that my running shoes are aging, my historical stride data suggests I'm due for a new pair, and sees a limited-time drop from one brand. It surfaces that as an “alert” in my morning briefing (vs me fishing around).
Example: A brand that has built an API or “agent integration” might get preferential promotion from the user’s agent — for instance, getting to “bid” to be included in the agent’s shortlist based on past performance, loyalty history, or service levels.
Example: Because agents can filter based on non-price criteria (delivery speed, returns flexibility, sustainability, loyalty perks), the discovery landscape becomes less about who paid for the biggest ad, and more about who earns the agent’s trust. (E.g. from a recent eMarketer article: “Even if one retailer is a little more expensive, some consumers may choose to buy from them because they want a specific loyalty perk.”) (EMARKETER)
Here, brands that invest in rich, structured product metadata, open APIs, and agent-friendly discovery (including “pay for inclusion” or “agent bidding”) will be advantaged.
2. Onboarding / consideration
Today: The consumer visits your site/app, signs up (with KYC or registration), browses, maybe enters preferences, sees recommendations. With Agentic AI: The consumer’s agent (or sub-agent) may do the heavy lifting — filling in preferences, mapping brand catalogs, negotiating with the brand directly, or even onboarding the consumer automatically (subject to permissions).
Example: I tell my agent, “If there is a style upgrade that matches my fit and color profile, notify me.” The agent pre-filters and presents an outfit set. When I click “yes,” the agent completes my profile with the brand, fills my size, uploads my preferences, accepts default terms, and even initiates a trial or first sample shipment.
Example: The brand’s onboarding flow includes an “agent integration step” — the consumer can link their shopping agent, giving scoped API access (e.g. “agent may browse and place orders within my credit cap”). The agent then can help narrow selections, negotiate bundles or discounts, and customize offers.
Example: The agent might run A/B tests across brands: it can try two brand onboarding flows (Brand A vs Brand B) with simulated “phantom” preferences, see which one gives better margins, usability or trust, then push me toward the preferred one.
In short: onboarding becomes invisible, contextual, and partly “behind the scenes,” with agent-mediated optimization.
3. Transaction / Purchase
Today: Consumer adds to cart, enters payment, applies coupon codes, chooses shipping, finalizes checkout. With Agentic AI: The agent can autonomously pick the optimal moment, seller, discount, payment scheme, shipping option, and execute the transaction on behalf of the user (with guardrails), perhaps even delaying or splitting shipments, applying discounts or loyalty credits dynamically, or negotiating upgrades in real time.
Example: My agent spots that adding a small curvature cable to my cart might trigger a free shipping threshold; it automatically includes it. Or it checks that a competitor is offering a flash coupon if you checkout in 3 minutes, so it switches the merchant mid-flow.
Example: The agent might preemptively negotiate a price match or auto-apply loyalty credits (or even switch to “agent bundle” offers) to reduce total cost.
Example: The agent might dynamically choose shipping modes (same-day, store pickup, locker) based on predicted convenience, likely delay risks or weather, and prioritize across multiple ongoing demands (e.g. balancing my need to get new shoes by weekend with cheaper shipping).
Example: The agent may do multi-brand bundling: it sees I need shoes, socks, and insoles. It may procure some from brand A, some from brand B, to optimize overall cost and convenience.
Because the agent can optimize across brands and sellers, the competitive margin in that transactional moment becomes razor thin unless the brand has structural advantage (loyalty, convenience, returns, unique SKUs).
4. Service / support
Today: Consumer reaches out via call, chat, email, returns, complaints; they repeat context, escalate, wait, switch channels. With Agentic AI: The agent acts as a proxy in service interactions, handling escalations, providing self-service, negotiating return exceptions, tracking delivery issues, and intelligently deflecting or escalating to human agents only when needed.
Example: My agent handles the return of faulty shoes — it checks warranty, suggests alternate sizes, logs the case, requests pick-up, and dynamically reroutes funds. I barely intervene.
Example: The agent proactively monitors my order; if there’s a delay, it already negotiates a refund or expedited shipping with the brand’s service API — I just see “Your agent fixed your shipment; enjoy a credit of €5.”
Example: The agent monitors my product usage (say via IoT or usage triggers) and initiates preventative support calls: “Your filter is reaching end of life — would you like a replacement?”
Example: When agentic AI is deeply integrated, the brand’s service system may treat the user’s agent as “the customer” — routing priority, offering agent-level SLAs, or even giving “agent status tiers” (silver, gold, platinum) to encourage agent loyalty.
This kind of frictionless, continuous support strengthens the link between consumer and the brand via the agent, but to succeed, the agent must trust the brand’s service experience.
5. Loyalty & retention
Today: Brands use points, tiers, perks, email campaigns, re-engagement. With Agentic AI: Loyalty becomes baked into the agent’s heuristics — the agent will “prefer” brands that have historically delivered well under the consumer’s value function (delivery, returns, service, pricing, exclusivity). In effect, the agent will “stick” to brands that maximize utility, unless another brand violates trust or becomes strictly better.
Example: My agent gives a “loyalty premium” weight to brands that consistently delivered with fewer issues, or that offered exclusive agent APIs, better warranty or bundled services. The agent might even discount loyalty credits implicitly in its decision weight.
Example: Brands may offer “agent incentives” (e.g. priority in the agent’s algorithm, agent rebates, co-marketing) to stay favored.
Example: The agent could perform “loyalty churn monitoring” itself: it sees, for instance, when a competitor’s offer temporarily beats the current brand, or when the brand has had a service lapse, and alert me or switch partially.
Example: Over time, the agent’s memory of brand interactions becomes richer (product reliability, support quality, returns history), and the brand becomes part of the agent’s “trusted set.” Losing that trust costs far more than losing a last-mile sale.
This dynamic induces a feedback loop: brands that perform well become more trusted, so their inclusion probability rises; that in turn drives more volume, better data, and improved agent performance — compounding advantage. (Cognizant even argues this may lead to winner-take-all dynamics within AI ecosystems). (cognizant.com)
Trust as the foundational currency
All of the above depends on trust — the consumer must trust the agent, and the agent must trust the brand (or at least have confidence in it). There are multiple dimensions of trust in this world:
Explainability / transparency — The agent should be able to explain why it chose a brand, made a tradeoff, or switched mid-flow.
Reliability and consistency — A single misstep (bad fulfillment, broken promises, surprise fees) can downgrade the brand’s “agent score” permanently.
Privacy, consent, and control — The consumer and their agent must know the boundary of access (what the agent can vs cannot do), data sharing, revocation, fallback to human oversight.
Accountability — If something goes wrong (e.g. returns failure, fraud, wrong order), who is accountable—the agent, the brand, the platform?
Security and fraud defense — Agents must guard against malicious actors, spoofing, supply chain compromise.
Interoperability and standards — Brands may need industry standards or protocols (APIs, agent certification, audit trails) to assure agents of safety and conformance.
Without trust, consumers will be reluctant to fully hand over autonomy to agents. Survey data suggests (Phaedon) that while many users are optimistic about AI in retail, trust remains a limiter: e.g. Mintel finds that younger cohorts are more likely to adopt, but even older cohorts express skepticism, especially when AI feels forced or opaque.
In a related vein, organizations must evolve measurement beyond NPS — instruments that capture “algorithmic trust,” consistency, correction over time, transparency, and agent satisfaction. NTT Data (NTT DATA Study) argues that traditional NPS misses the nuances of AI-driven experiences (explainability, consistency, control).
Additionally, studies in AI adoption show (ResearchGate) an age gradient in both willingness and trust: older individuals often have more reservations about data sharing and AI, and lower interest in granting autonomy. For example, a survey published in 2025 found that older age groups show lower willingness to provide personal data and less enthusiasm for AI use, owing to perceived risks of misuse and weaker trust.
Other work in LLM adoption (though not fully agentic yet) suggests (arXiv) a composite effect of gender, age, and education: adoption is higher among younger and more technically educated groups.
We don’t yet have robust public data on “agentic AI adoption rates by age group” in retail specifically, but these general adoption trends suggest that younger, digitally native cohorts may lead, especially if trust, explanation, and control mechanisms are baked in.
One must also watch for adversary effects: when agentic platforms become transactional themselves (e.g. Perplexity starting to let users order via the agent). For example, if an AI agent (e.g. on Perplexity or “Agent GPT marketplace”) spontaneously pivots from recommendation to direct transaction, it could disintermediate or bypass brands. The agent becomes the “merchant of record” — whether intentionally or inadvertently. In that scenario:
The agent (or agent platform) may extract margin or take commissions, becoming a gatekeeper rather than a facilitator.
The consumer’s loyalty shifts from the brand to the agent ecosystem.
Brands with weaker integration or lower APIs may become “invisible” to agents, losing share.
Agents may favor brands that participate in their fee or inclusion mechanisms, biasing choice.
The agent’s transactional bias may push toward simpler commodity purchases (e.g. staples, frequent consumables) at scale, leaving brands competing to be in agent presets or priority lists.
In that world, brands could become “white-labeled” commodities in the agent’s funnel unless they actively engage.
Thus, the adversary risk is that agents evolve from assistants to brokers — and the brand must avoid being relegated to a line item in the agent’s arbitrage.
How brands and retailers should respond
Given this emerging shift, here is a strategic playbook for brands and retailers:
1. Become agent-friendly: open APIs, data contracts, and “agent incentives”
Don’t treat agents as adversaries. Instead, build standardized APIs and protocols so agents can query catalog, pricing, availability, loyalty status, return policies, and even submit orders. Offer preferential access, or “agent rebate” schemes or tiered “agent program” benefits to incentivize agents to include you in their shortlist.
2. Prioritize frictionless, consistent execution
Because the agent’s future trust depends heavily on past brand performance, brands must excel in fulfilment, returns, clarity, accuracy, and service. Any failure is memorized. Brands that deliver flawless experiences will be “locked in” by agents.
3. Use omnichannel presence and physical assets as differentiators
Here my hypothesis is especially powerful: retail brands that maintain physical stores and trained staff have a structural advantage that agents will value. Why?
The agent can recommend “store pickup + in-store personalization” or “agent booking appointment at local store” as part of the value function.
Physical presence enables services that purely digital actors cannot replicate (try-ons, human advice, experiential activations).
The agent may route consumers to nearest store inventory or optimized services, offering better UX and reducing shipping friction.
Trained staff can partner with the agent via in-store devices or dashboards (e.g. a store associate gets alerted by the agent: “customer X is walking in; here is their preference profile”).
Thus, brands should integrate agent awareness into their omnichannel stack (POS, store apps, staff dashboards) so that the online agent and offline staff act as a unified service fabric.
4. Invest in agent-trust infrastructure (transparency, auditability, fallback)
You must build controls, audit logs, guardrails, escalation paths, and explanation layers so that agents (and by extension consumers) trust you. Offer “agent SLAs,” clear policies, recourse paths, and alignment with privacy regimes.
5. Monitor and optimize agent metrics, not just human metrics
Track metrics like “agent inclusion rate,” “agent reversal rate,” “agent complaints,” “agent loyalty score,” and design feedback loops for calibration. Treat agents as key customers.
6. Start small, pilot, and iterate
Agentic AI is still nascent; pilot use cases in high-value, low-risk scenarios (e.g. subscription replenishment, returns, loyalty reactivation). Learn from feedback, measure, and evolve. Implement human-in-the-loop fallback where appropriate.
7. Educate consumers and build awareness
Many consumers don’t yet fully grasp what an “agent” is or what tradeoffs it implies. Use marketing to explain the benefits, transparency, and controls. Help build trust. Show use cases.
8. Position your brand’s identity, values, and emotional promises
Because agents optimize rational tradeoffs, emotional brand identity, differentiated narrative, and human values become even more important to tip decisions. Brands that evoke meaning, alignment, or community may sway agent decision weights.
Closing reflections
We stand at the cusp of a shift where agents become the intermediaries, not just “digital advisors.” In this world, the consumer still matters, but so does the agent. Brands that win will be those that earn agent trust — not just consumer loyalty — by being frictionless, reliable, transparent, and deeply integrated.
My central hypothesis — that Agentic AI will lead to much more personalized and connected consumer experiences — is, I believe, correct. But it also means that loyalty becomes more “earned” and continuously evaluated, rather than static. The brand that once won a consumer’s loyalty may lose it if it fails to satisfy the agent’s heuristics.
For retailers and brands, the path forward is clear: lean into omnichannel strengths, ensure your physical and human touchpoints become part of agent workflows, invest in deep operational excellence, create agent-friendly infrastructure, and above all, design for trust at every level.
