The customer journey, from discovery to conversion, is now idiosyncratic and eccentric, shaped by personal idiosyncrasies such as channel preferences, mood and ever-changing levels of interest. AI agents now make it possible to meet the customer wherever and whenever he wants.
New data from BrightEdge shows that AI agents are already driving a third of brand-related search traffic. What customers see about your business depends on what the agent finds and surfaces, making consistency of information critical.
You deeper: From search to response engines: how to optimize for the next age of discovery
The challenge is that the large language models (LLMs) that power enterprise AI struggle with complex workflows, unstructured proprietary data, siled systems, and strict compliance requirements. They are not built to deliver accurate, context-rich answers at enterprise scale.
To bridge that gap, websites must evolve into data hubs that feed consistent, structured content across channels and touchpoints.
Websites must evolve for agentic experiences
Tools like Google’s AI overview make search an intelligent, personalized experience. Soon, users will expect websites to do the same: anticipate their needs and reduce the effort required to research, plan and make decisions.
You deeper: From search to AI agents: the future of digital experiences
Doing this consistently and on a large scale is not easy. People-led processes fall under volume and silos fragment the experience. The hidden costs are high: lost knowledge, missed opportunities and weakened loyalty.
While AI agents are supposed to reduce friction and deliver unified experiences at scale, not all agents are created equal. Enterprises have two options when deploying AI agents: horizontal agents and vertical agents.
Horizontal agents: the generalists
Tools like Gemini or ChatGPT work across all industries and can surface information, but they have limitations.
- Lack of depth: They struggle with nuances in the sector.
- Lack of context: Every brand is unique, and generalists often miss that detail.
- Technical limits: Even with Retrieval-Augmented Generation (RAG), they merge answers from predefined knowledge bases, often resulting in disjointed results.
Generalists handle simple questions and answers, but rarely anticipate needs, personalize effectively, or generate results like leads and conversions.
Vertical agents: the specialists
These are built for specific industries and business use cases. They are trained in your products, policies and brand voice and evolve as the company grows.
Unlike generalists, vertical agents deliver context-rich, branded and results-driven experiences – from discovery and qualification to conversions, upsells and loyalty. They act as true digital representatives of the brand, maintaining consistency across every touchpoint.
With advances in multi-agent frameworks, orchestration layers, vector databases, and cloud-native infrastructure, the brittle, rules-based bots of the past – which broke easily when conversations strayed from scripts – have evolved into enterprise-level agents that can meet these expectations at scale.
Payoffs from a well-designed AI agent
When designed well, AI agents reduce friction and increase loyalty. The results are visible in how well they handle omnichannel interactions and personalization.
Omnichannel personality
A single agent can reach customers across your website, app, social platforms, and messaging channels. Voice and style remain consistent, while information shared on one channel carries over to the next.
The payout: Less friction, greater efficiency, and a seamless, connected experience.
You deeper: Integrate SEO into omnichannel marketing for seamless engagement
Hyper-personalization
Well-designed agents integrate deeply with systems such as CDPs, CRMs and booking engines. They track customer history, loyalty status and preferences and adapt in real time. Each interaction sharpens their knowledge, evolving experiences from personalized to hyper-personalized.
The payout: Stronger commitment, customer satisfaction and lasting loyalty.
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Orchestrate the customer journey with AI agents
AI agents deliver the most value when they are aligned with the entire customer journey and not just used for one-off tasks. In hospitality, this means supporting every phase: from discovery and booking to the stay itself and the follow-up after checkout.
- Discovery: Agents help travelers plan trips, explore destinations and create itineraries by offering relevant events, activities and offers.
- Conversion: Agents integrate with booking systems to answer availability questions, apply promotions and simplify reservations.
- Experience: Agents personalize on-site interactions and recommend restaurants, activities or upgrades based on guests’ history and preferences.
- After purchase: Agents maintain post-stay engagement by offering loyalty benefits, sending benefit reminders, and suggesting repeat visits.
When AI agents are connected throughout the customer journey, they can deliver innovative, seamless experiences. That means fewer irrelevant messages, less friction and more timely, personalized interactions, leading to smoother journeys, lower churn and higher conversions.
You deeper: How AI agents are revolutionizing digital marketing
Industry use cases for vertical AI agents
Hospitality
When a guest arrives at a hotel location, the agent connects to the CDP to gather information, identify persona, and anticipate intent. It shows relevant offers in real time.
- Anonymous visitors: Highlight pet-friendly stays, family packages, dining options and activities you can plan during your stay.
- Logged in or returning guests: Offer reservation changes, stay extensions and updated recommendations, remembering past visits, preferences and loyalty rewards.
The effect is memory on a grand scale, creating a ‘personal concierge’ experience.
Financial services
When a potential customer asks about savings accounts, the AI agent uses personal data and previous interactions to anticipate what he or she wants to know. It then responds with clear, structured information such as APY, cost and eligibility, making it easier for the customer to take the next step.
- Loan Explorers: Accompanied by workflows that capture intent, instantly prequalify and set up meetings.
- Returning customers: Receive customized follow-ups, updated rates, pre-approved offers and next steps.
The agent acts like an advisor, not a chatbot, strengthening engagement, trust and loyalty.
Case study: outdoor catering brand
At an outdoor hospitality brand, we implemented a multi-agent framework for supervisors to streamline guest interactions. When a guest asked, “What family activities are available this weekend and can I book a cabin at the lake?” the system coordinated multiple agents:
- Supervisor: Interpreted the intent and forwarded the request.
- Q&A Agent: General real estate questions answered.
- Event agent: Popped up weekend activities such as barbecue evenings and guided walks.
- Trip Planner: Suggested routes using mapping and planning tools.
- Booking agent: Checked cabin availability and completed reservation.
- Lead collector: References passed to the CRM for follow-up.
The result: guests seamlessly moved from discovery to booking in one conversation. The brand reduced churn, captured more leads and achieved a measurable impact:
- 99.35% customer satisfaction.
- A 68% improvement in query resolution.
- Expected annual cost savings of more than $500,000.
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