Blog iAdvize 2026

How To Prepare for Agent-to-Agent Commerce

iAdvize — Fri, 24 Apr 2026 02:01:33 GMT

Agent-to-agent commerce is coming. As artificial intelligence continues to advance, we're moving toward a future where AI agents—not humans—will be the primary drivers of e-commerce transactions.

For retailers, this represents both an enormous opportunity and a significant challenge. If you're not preparing now, you risk being left behind.

What Is Agent-to-Agent Commerce?

Agent-to-agent commerce refers to transactions conducted between artificial intelligence agents.

In this model, AI shopping agents act as buyers, interacting with AI seller agents (or storefronts) to discover products, negotiate terms, and complete purchases—all without direct human intervention.

This is fundamentally different from how e-commerce works today.

Currently, humans browse storefronts, compare products, read reviews, and make purchasing decisions. In agent-to-agent commerce, AI agents do all of this automatically.

These agents can:

Learn individual preferences and adapt purchasing behavior over time
Compare products across multiple retailers instantly
Negotiate prices and terms
Process transactions autonomously
Provide feedback to improve future interactions

Example: A Wedding Shopping Scenario

To understand how agent-to-agent commerce works, consider this example:

Sarah is planning her wedding. Instead of spending weeks researching venues, caterers, florists, photographers, and decorators, she sets up an AI shopping agent with her preferences, budget, guest count, and wedding date.

Her agent then:

Contacts multiple venue agents simultaneously
Negotiates availability and pricing
Requests quotes from catering agents based on her menu preferences
Compares florist options and pricing
Books a photographer based on style preferences and availability
Coordinates all bookings to ensure alignment

Meanwhile, each vendor’s AI agent is:

Responding to multiple buyer agents
Providing real-time availability and pricing
Negotiating terms
Processing payments
Updating inventory and schedules in real-time

The entire process —which would normally take weeks of human research and negotiation— is completed in minutes or hours.

Sarah’s agent coordinates across all vendors, ensures everything is scheduled correctly, and provides her with a comprehensive summary of all bookings and pricing.

This is agent-to-agent commerce in action.

How AI Shopping Agents Interact With Each Other and Shoppers

Agent-to-agent commerce works by connecting richly structured data, secure transaction APIs, and orchestration layers that allow AI agents to “talk” directly to storefronts based on what a customer has sent them to shop for.

With every product attribute (size, color, price, availability, compatibility, materials, reviews, etc.) served in a standardized format (like JSON, XML, etc.), AI agents can query, compare, and qualify products without human intervention.

AI shopping assistants or “agents” could come in various forms:

Generative AI Platform Shopping Companions

Perplexity, Gemini, and ChatGPT all have shopping capabilities built in. Users can ask these agents to find products, compare prices, and make recommendations. As these agents become more sophisticated, they’ll be able to complete purchases autonomously.

Voice Assistants

Alexa, Google Assistant, and Siri are already being used for some shopping tasks. As these assistants become smarter and more capable, they’ll handle more complex purchasing workflows.

Multi-Agent Systems Within Single Storefronts

Some forward-thinking retailers are already experimenting with multi-agent systems. These systems use multiple AI agents working together to personalize the shopping experience, answer customer questions, manage inventory, and process orders.

The transition from today’s human-driven e-commerce to agent-to-agent commerce won’t happen overnight.

But it’s already beginning. Retailers that understand these trends and prepare accordingly will be best positioned to thrive in the future.

How Retailers Need to Prepare for Agent-to-Agent Commerce

If you’re a retailer, the question isn’t whether agent-to-agent commerce will happen—it’s whether you’ll be ready when it does. Here’s how to prepare:

1. Structure Your Product Data

AI agents need clean, consistent, structured product data to make intelligent purchasing decisions. This means: - Complete product information (features, specifications, images, pricing) - Consistent data formats across all products - Accurate inventory tracking - Regular data quality checks and updates - Integration with your product information management (PIM) system

Your current storefront might be designed for human browsing, but AI agents need machine-readable data. If your product data isn’t properly structured, AI agents won’t be able to effectively interact with your storefront.

2. Enable Secure Transactions

Agent-to-agent commerce requires automated transactions. This means your payment processing needs to be: - API-enabled for machine-to-machine transactions - Secure and encrypted - Capable of handling micro-transactions - Integrated with fraud detection systems - Compliant with relevant regulations
You’ll also need to consider new security challenges. How do you verify that the agent making a purchase is authorized to do so? How do you prevent fraudulent transactions by compromised agents?

3. Move to an AI Storefront

Your current storefront is designed for human visitors. An AI storefront is designed for machine interaction. This means: - APIs that AI agents can interact with directly - Machine-readable product catalogs - Automated response to agent queries - Real-time inventory management - Flexible pricing and negotiation capabilities

This doesn’t mean your human-facing storefront goes away. Instead, you’ll have both: a human-friendly storefront for browsing, and an AI-friendly API for agent interaction.

4. Start Training Your AI Shopping Assistant

If you’re going to compete in the agent-to-agent commerce space, you need your own AI assistant.

This assistant should:

Learn your products inside and out
Understand your shoppers’ needs and preferences
Respond intelligently to every query
Handle negotiations and pricing discussions
Process transactions securely

The earlier you start training your AI assistant, the more sophisticated it will be when agent-to-agent commerce becomes mainstream.

5. Track New Traffic Types

As agent-to-agent commerce grows, you’ll see new types of traffic:

Machine-to-machine API calls from buyer agents
Automated product queries and comparisons
Bot-driven negotiations and transactions
Bulk purchases from aggregator agents

Your analytics need to evolve to track these new traffic types.

You need to understand:

How many transactions are being driven by agents vs. humans
Which products are most popular with agents
What pricing and terms agents are negotiating for
How agent-driven traffic converts differently than human traffic

6. Incorporate Agentic SEO Practices

SEO has always been about helping humans find your storefront. Agentic SEO is about helping AI agents discover and interact with your products.

This includes:

Structured data markup (Schema.org, JSON-LD)
API documentation and accessibility
Clear product information and specifications
Transparent pricing and availability
Response times and reliability metrics

The Next Era Belongs to Agent-to-Agent Commerce

Agent-to-agent commerce is not a distant future concept—it’s already beginning.

The AI shopping assistants, voice commerce, and multi-agent systems we see today are the early stages of a fundamental shift in how e-commerce works.

Retailers that begin preparing now—by structuring their data, enabling secure APIs, training AI assistants, and adapting their SEO strategies—will be best positioned to thrive in the agent-to-agent commerce era.

The question isn’t whether agent-to-agent commerce will happen. The question is: will you be ready?

Agent-to-Agent Commerce Starts with You

Learn how to lanch you AI storefront with a one-on-one consultation with our team to craft a tailor-made launch strategy.

Book Consultation

The 6-step checklist to launch your AI shopping assistant

Guerric SANT — Fri, 24 Apr 2026 02:01:32 GMT

Inside this checklist

This checklist walks you through implementation, from choosing the right solution to measuring results.

Each step includes practical actions, technical requirements, and the metrics that matter, based on what's actually working for 350+ brands today.

[Replay] Agentic Commerce : The New Era of AI-Powered Shopping Experiences

iAdvize — Wed, 18 Feb 2026 09:45:00 GMT

Fifty-three percent of U.S. shoppers plan to use generative AI to assist their online purchases this year. Generative AI traffic to retail sites has doubled every two months since late 2024. Agentic commerce is not just coming—it’s happening.

eCommerce is entering a new phase—and it won’t be led by static websites or chatbot popups. It’ll be powered by AI agents, guiding shoppers with intelligence, context, and intent. AI is no longer a feature, but the foundation of the shopping experience.

From ChatGPT to Gemini, consumers are already discovering and comparing products with the help of AI. And now, they’re arriving on retail sites expecting the conversation to continue—not restart.

In this forward-looking session, we’ll unpack the rise of Agentic Commerce—an AI-native shopping model where the buyer journey is fluid, personalized, and increasingly co-piloted by intelligent agents on both sides. Learn how early-adopting brands are transforming their eCommerce experience with AI shopping assistants today—and laying the groundwork for tomorrow’s AI storefronts.

Speakers:

• Mike de la Cruz, President, iAdvize
• Ed Billmaier, Vice President of Customer Experience, Viome
• Aaron Magness, CMO, Full Glass Wine Co
• Eric Fong, Lead Product Manager, Daydream
• Moderated by: Veronika Sonsev, Co-Founder, CommerceNext

What the AI Storefront Looks Like and Why It Converts Better

iAdvize — Wed, 04 Feb 2026 10:00:00 GMT

The traditional e-commerce flow hasn’t fundamentally changed in nearly two decades. Brands are stuck accepting a measly 2-3% conversion rate. Agentic commerce stands to replace this broken e-commerce model to drive better results for shoppers and retailers. Here we’ll break down why traditional e-commerce storefronts are failing and why agentic commerce is rising up to take its place.

Let’s say a shopper goes to a skincare retailer hoping to build a routine. In today’s world, they take a generic quiz that emails the results, get shown products that are sold out, or browse around aimlessly on PDPs.

But soon, they’ll be pulled into a new type of storefront that can instantly build them a curated regimen based on their budget, skin concerns, and favorite ingredients. Their most specific questions get answered not with canned responses but real expertise, reviews, visuals and key details surfaced at the right time. They check out seamlessly, set up a subscription, and come back for more. The storefront builds on their history instead of starting from scratch—all from the same place.

This is the promise of the AI storefront, where shoppers can depend on AI to suggest and make key buying decisions with minimal input and effort—and it’s the next evolution in agentic commerce.

The Traditional E-Commerce Experience: Where Friction Begins

Before we dive into AI storefronts, let’s understand the limitations of today’s standard online shopping experience.

When a shopper arrives at a traditional e-commerce storefront, they’re greeted with:

Homepage Design: A static layout featuring promotional banners, category navigation, and featured products
Search & Browse: Limited navigation options—either using search (which requires knowing what they want) or browsing categories (which requires time and effort)
Product Pages: Static information with images, descriptions, specs, and customer reviews
Recommendations: Basic algorithmic suggestions (“Customers also bought…”) that lack context
Checkout: A multi-step process that asks for information the shopper has already provided

This approach works reasonably well for shoppers who know exactly what they’re looking for. But for the vast majority of shoppers—those exploring options, comparing alternatives, or seeking personalized guidance—the experience is friction-filled and often leads to abandonment.

Consider a common scenario: A shopper visits a skincare storefront looking to address fine lines around their eyes. With a traditional e-commerce approach, they might:

Land on the homepage (generic welcome experience)
Search for “eye cream” (requires them to know the category)
See 50+ product options (overwhelming choice)
Read multiple product descriptions (time-consuming)
Check reviews and compare (more time investment)
Finally select a product (after significant friction)

The process is exhausting, and many shoppers abandon before making a purchase.

The AI Storefront Experience: Personalized Guidance at Scale

Now imagine the same scenario with an AI Storefront:

A shopper arrives and immediately sees a conversational interface (AI Shopping Assistant) that greets them warmly: “Hi! I’m here to help you find exactly what you need. Tell me a bit about your skincare goals—what brings you here today?”

The shopper responds: “I’m looking for something for fine lines around my eyes. I have sensitive skin and prefer natural ingredients.”

The AI, powered by advanced natural language understanding, immediately:

Understands their specific concern (fine lines around eyes)
Captures their skin type (sensitive)
Notes their preference (natural ingredients)
Filters the product catalog in real-time
Presents 2-3 personalized product recommendations with specific reasoning: “Based on what you’ve shared, I think [Product X] would be perfect for you because it’s specifically formulated for sensitive skin and contains [natural ingredient Y] which is known for reducing fine lines.”

The shopper can ask follow-up questions: “Does it have any fragrance?” or “How long does a jar last?” The AI answers immediately with accurate product information, addressing specific concerns before they become objections.

When the shopper is ready to purchase, the AI facilitates a smooth transition to checkout—often with one-click purchasing, since the shopper has already shared the information needed to complete the transaction.

The entire experience takes minutes instead of the 15-30 minutes a traditional e-commerce shopper journey might require. More importantly, the shopper feels understood, guided, and confident in their purchase decision.

Key Differences: Chatbots vs. AI Shopping Assistants vs. AI Storefronts

Before we go deeper, it’s important to clarify three distinct concepts that are often confused:

Chatbots: Customer service tools designed to answer questions, resolve issues, and handle support inquiries. They’re reactive—shoppers must initiate contact and ask for help.
AI Shopping Assistants: Conversational interfaces designed to help shoppers find products and make recommendations. They proactively guide shoppers through the discovery process but operate within a traditional e-commerce framework.
AI Storefronts: A complete reimagining of the online shopping experience where the conversational interface IS the primary way shoppers discover, explore, and purchase products. The entire storefront is organized around conversational commerce rather than traditional category/search navigation.

The distinction matters because it fundamentally changes the shopper experience and the business outcomes.

What Makes an AI Storefront Different?

Why E-Commerce is Broken, and How Agentic Commerce Fixes It

iAdvize — Thu, 15 Jan 2026 03:15:00 GMT

The traditional e-commerce flow hasn’t fundamentally changed in nearly two decades. Brands are stuck accepting a measly2-3%conversion rate. Agentic commerce stands to replace this broken e-commerce model to drive better results for shoppers and retailers. Here we’ll break down why traditional e-commerce storefronts are failing and why agentic commerce is rising up to take its place.

Most E-Commerce Brands Still Struggle to Drive Conversions and Deliver Great UX

Even top brands suffer from high bounce rates, low conversions, and an underwhelming user experience. Why? They tend to rely on:

Ads optimized for clicks, not experience
Static PDPs overloaded with information
FAQ sections that go unread
Search bars and filters that miss context
Passive chatbots that wait for engagement
Analytics and A/B testing tools only give incremental improvements

Retailers overwhelm shoppers with choices, but fail to guide them to the right decision and drive the sale—despite having access to more customer insights than ever. The burden ultimately rests on every shopper to research and find what they want. When they can’t, they bounce… or increasingly, they use AI.

Agentic Commerce: New E-commerce Shopping Behaviors and Expectations

Fed up with poor online shopping experiences, consumers are turning to genAI assistants like ChatGPT, Gemini, and Perplexity to help them shop, bypassing traditional search and endless browsing across PDPs altogether. Adobe reported a 1200% increase in generative AI-driven traffic to retail sites just since July of 2024.

Traditional Search vs. Agentic Commerce Search

The shift makes sense, consumers want instant, curated results with less effort.

Traditional storefronts can’t deliver that. But ahead-of-the-curve retailers implementing agentic commerce tactics can, and they’re already starting to.

Agentic Commerce Drives Bigger, Faster Wins for Retailers

AI shopping agents anticipate intent, personalize results, and guide the shopper like a trusted advisor. Agentic commerce succeeds where traditional e-commerce fails: turning browsing into buying.

Children’s furniture brand Mon Lit Cabane rolled out an
AI shopping assistant on product pages to help proactively
guide parents to the right piece of furniture. The impact?

Engagement on PDPs skyrocketed
Post-conversation conversions hit 8%
Average order value rose 30%
More than €1M in incremental revenue was generated
in just three months

…and this is just one of many examples.

When retailers stop making shoppers do all the work, and instead guide them with AI-backed confidence, the shopping experience becomes a win-win.

Three Takeaways for E-Commerce Leaders

1. Stop relying on the browse-and-hope model.

PDPs need to be proactive decision engines, not static brochures. Reframe your highest-intent pages as guided experiences that anticipate and curate to go from doubt to purchase more seamlessly.

2. Get your data ready for agentic commerce.

To be discoverable by AI agents, retailers must structure product data, enable secure APIs, and implement “agentic SEO” so their catalog can be indexed and surfaced in conversational queries. Forward-looking brands are already making LLM optimizations today to prepare for agent-driven transactions.

3. Launch fast, learn faster.

Retailers don’t need to rebuild from scratch: they can launch an AI storefront with iAdvize Copilot, run an agentic readiness assessment, quickly launch an AI shopping assistant, and learn from early results to scale fast.

AI Storefronts as the Next Frontier of E-Commerce

The next era of commerce won’t start with a search bar but with an AI agent. And if your brand isn’t visible to agents, it won’t be visible to customers.

Agentic commerce isn’t a trend. It’s a structural shift redefining how products are discovered, evaluated, and purchased. As PayPal’s CEO predicted, 25% of e-commerce spend could be agent-driven by 2030, and the retailers that move first won’t just capture higher conversions and new revenue streams, they’ll set the standards everyone else will follow.

The good news is you don’t need to rebuild from scratch. Getting started is fast. An agentic commerce readiness assessment can help you benchmark where you stand today and map the fastest path to launch your AI storefront.

Are You Ready for Agentic Commerce?

Get your readiness score and a tailored AI storefront strategy in a free 30-minute consultation.

Book Now

AI Shopping Assistants: A Complete Guide

iAdvize — Fri, 03 Oct 2025 21:00:00 GMT

Online shopping has come a long way, but the traditional ecommerce experience still leaves much to be desired.

Customers get stuck scrolling through hundreds of product pages, typing in vague search terms, and wading through irrelevant results.

Filters help, but they’re rarely precise enough to understand the nuance of what someone actually wants. Even with flashy web design and recommendations, many shoppers still leave their carts behind — frustrated, overwhelmed, or just unconvinced.

That’s where AI shopping assistants come in.

These intelligent tools are changing the game by making online shopping feel more like an in-store experience, only faster, more personalized, and always available.

With the ability to interpret natural language, intelligently anticipate needs, and offer tailored suggestions in real time, AI assistants remove friction from the buying process and help customers feel understood.

The result? Higher engagement, fewer abandoned carts, and better conversion rates that drive real growth for ecommerce brands.

What is Conversational Commerce? Definition and How to Get Started

iAdvize — Thu, 25 Sep 2025 13:30:00 GMT

Reaching ecommerce customers requires more than offering quality products.

Shoppers expect your business to understand their needs, answer questions instantly, and provide personalized recommendations, all while delivering a frictionless buying journey. Meeting these expectations at scale is a major challenge, especially with the impersonal and standardized nature of traditional ecommerce.

This is where conversational commerce comes into play.

By incorporating real-time, two-way communication into the ecommerce experience, businesses can introduce the personal touch that online shopping often lacks.

Customers benefit from tailored assistance at each stage of their journey, while businesses unlock new ways to boost engagement and drive conversions without sacrificing efficiency.

In this piece, you’ll learn what conversational commerce is all about, the benefits it provides to both businesses and customers, and how to successfully leverage it at your company.

What is conversational commerce, and how does it work?

Conversational commerce is how companies use AI and related technology to better connect with customers and facilitate an engaging digital shopping experience. Instead of chatbots that provide canned responses, conversational commerce provides customers with guided support that mimics the experience of speaking with a knowledgeable in-store associate. When done right, conversational commerce helps businesses reduce friction and guide customers toward faster, more confident purchase decisions.

Every stage of the customer journey can benefit from this approach. During the discovery phase, an AI-powered shopping assistant can recommend products based on preferences or past purchases. While comparing options, the assistant can provide side-by-side feature breakdowns. And at checkout, it can address concerns about payment or shipping. Post-purchase, conversational channels can be used for order tracking, returns, and personalized upsell opportunities.

AI plays a critical role in making conversational commerce scalable and effective. Natural language processing (NLP) allows systems to understand and respond to customer questions effectively, while machine learning enables continuous improvement based on past interactions. With AI, businesses can provide support that feels personal and intuitive, even when serving thousands of customers simultaneously.

The benefits of conversational commerce

Conversational commerce transforms the way customers interact with your brand by making every exchange more timely and personal. Businesses that adopt this approach can see stronger customer relationships, reduced friction during the buying process, and higher customer lifetime value (CLV) from each shopper. The following benefits highlight how conversational commerce delivers measurable improvements at every stage of the customer lifecycle.

A better customer experience

Conversational commerce provides customers with consistent, high-quality interactions across every touchpoint. Unlike traditional customer service models, where experiences may vary depending on the representative or the channel, conversational commerce leverages AI-powered tools and intelligent workflows to ensure every shopper receives accurate information quickly. This leads to a smoother, more reliable shopping experience that eliminates barriers to engagement while making your brand feel more approachable and dependable.

A higher number of repeat customers

Repeat customers are one of the most valuable assets for any ecommerce business, and conversational commerce helps nurture those long-term relationships. Personalized conversations create a sense of familiarity and loyalty, showing customers that your business understands their needs and preferences. For example, today’s AI shopping assistants can remember past purchases and suggest complementary products during the next interaction, making the customer feel recognized and valued.

Over time, this level of personalization encourages customers to return instead of turning to competitors. Instead of being just another transaction, your business becomes a trusted partner in their shopping journey. That stronger connection pays dividends in higher CLV, helping your business grow without needing to spend as heavily on new customer acquisition.

Fewer abandoned carts

Cart abandonment is a significant ecommerce challenge that conversational commerce directly addresses. Many shoppers abandon carts because they have unanswered questions, concerns about shipping costs, or doubts about the product. Conversational tools can step in at these critical moments to provide instant reassurance, clarify details, or even offer personalized discounts, turning hesitation into action and ensuring fewer sales slip through the cracks.

More upselling and cross-selling

Conversational commerce also creates opportunities to maximize revenue through upselling and cross-selling. Intelligent systems can recommend higher-value alternatives or related products during the buying process, tailoring suggestions based on customer behavior and purchase history. For instance, a shopper buying a laptop might be prompted to consider a more powerful model or add accessories like a carrying case or extended warranty.

What makes these suggestions effective is the conversational context. Instead of feeling like generic promotions, recommendations are presented as helpful advice within the natural flow of an interaction. This not only boosts average order value (AOV) but also strengthens the perception that your brand is focused on delivering value rather than pushing products.

AI and conversational commerce

AI makes conversational commerce possible, enabling the speed, personalization, and accuracy that customers expect. The specific technologies driving the shift from clunky, robotic responses to human-like conversations include NLP, machine learning, and generative AI.

NLP allows AI shopping agents and voice assistants to understand customer intent and respond in ways that feel natural. For example, if a shopper types, “I need shoes for hiking in Hawaii,” NLP processes the entire phrase to identify context, like the activity (hiking) and the environment (Hawaii). The system can then recommend lightweight, breathable hiking shoes instead of generic footwear, making the conversation both relevant and helpful.

Machine learning, as you might expect, helps conversational commerce tools become more intelligent over time. Machine learning systems analyze thousands of interactions to identify patterns and improve the performance of AI shopping assistants. If customers frequently ask about shipping costs after adding items to their cart, machine learning can automatically train AI shopping assistants to proactively surface shipping details before checkout. This continuous improvement helps businesses fine-tune the customer experience without needing constant manual updates.

Generative AI takes conversational commerce a step further by producing dynamic, context-aware responses. Instead of relying solely on pre-written scripts, generative AI can adapt its messaging to match tone, style, or even brand voice in real time. A customer asking for gift recommendations, for instance, might receive a personalized suggestion like: “Based on your past purchases and budget, here are three thoughtful options your recipient might love.” This capability makes conversations feel more human, even when no live agent is involved.

When combined, these AI technologies enable businesses to scale personalized customer engagement in ways that were previously impossible. A small ecommerce store can now offer the same level of attentiveness as a large retailer, while big brands can maintain one-on-one interactions with millions of customers simultaneously. From answering basic FAQs to guiding complex purchases, AI ensures conversational commerce provides real value to customers.

How to start using conversational commerce

Before you can reap the benefits of conversational commerce, you need a structured plan for rolling it out. The right strategy married to the right platform, connected to accurate company data and personalized for your shoppers, can transform your company’s ecommerce performance.

1. Find the right conversational commerce platform

The first step is choosing the right platform for conversational commerce. You should look beyond simple chatbots and instead opt for a modern AI shopping assistant. These intelligent tools do more than providing rote answers to basic questions, proactively engaging customers and predicting their needs.

The best platforms can recommend products based on individual taste, answer policy and inventory questions accurately, and hand conversations off to human agents without friction. From highlighting complementary items to steering customers toward top-rated products, this level of personalization fosters customer confidence and satisfaction with every purchase.

In short, look for a solution that is capable of making proactive decisions to keep customers moving down the sales funnel.

2. Integrate your conversational commerce tool with company data

Customers won’t make a purchase in the absence of trustworthy information. That’s why your AI shopping assistant needs real time access to accurate data on products, pricing, promotions, shipping windows, and more. Ensure your platform can connect your product information management (PIM) software, customer relationship management (CRM) system, and other relevant tools. As you integrate data, prioritize protecting sensitive data to maintain customers’ trust in your brand. The best AI shopping assistants are GDPR and CCPA compliant, as well as ISO 27001 certified, providing trustworthy results out of the box.

3. Personalize and train your platform

Personalization is what turns conversational commerce into a revenue driver. When your AI shopping software adapts to customer intent and preferences while taking into account important context, shoppers feel understood and complete purchases with confidence. Look for an AI assistant that lets you carefully tailor the tone of responses by defining business rules, constraints, and escalation processes. You should also develop a human feedback loop by asking team members to review transcripts, tag great answers, and flag misses, with the goal of steadily improving performance over time.

4. Monitor and improve the conversational commerce experience

Monitoring the impact of conversational commerce on your business helps sustain a great customer experience and keep your ROI high. Adopt a solution that provides real time reporting on key performance indicators (KPIs) like conversion rate, revenue per conversation, and AOV. Use dashboards and alerts to surface anomalies and dive deeper as needed, such as by replaying specific sessions. Then leverage the insights you gain to build a more natural and effective conversational commerce experience.

Examples of how to use conversational commerce

There are countless ways that conversational commerce can change your ecommerce shopping experience for the better.

Imagine a shopper browsing your site for running shoes. Instead of scrolling through dozens of options, they can ask the AI assistant for “shoes under $100 designed for long-distance running.”

The assistant filters the catalog instantly, provides comparisons, and suggests complementary products like moisture-wicking socks or insoles. This shortens the path to purchase and makes the shopper more confident that they are choosing the right product.

Conversational commerce can shine during checkout as well.

Many customers abandon carts due to uncertainty about things like shipping times, return policies, or promo codes.

An AI shopping assistant can address these questions in real time, answering queries like “Will this arrive before Friday?” or “Can I return this if it doesn’t fit?” This, along with the ability to add products to the cart directly from the conversation, removes barriers that would otherwise cause hesitation.

Other powerful applications involve post-purchase support.

Instead of calling customer service, a buyer can message the assistant to track their order, request an exchange, or learn how to use their new purchase. By automating these interactions, you lighten the load for your support team while still providing the fast, reliable service today’s customers expect.

Seasonal campaigns and promotions also benefit from conversational commerce.

During Black Friday or back-to-school shopping, your AI shopping assistant can proactively surface limited-time offers, bundle deals, or trending products tailored to the shopper’s preferences. Rather than relying on generic email blasts or banners, it delivers personalized suggestions exactly when customers are ready to buy, creating urgency and excitement.

Finally, conversational commerce plays a key role in building long-term customer relationships.

By learning from each interaction, the assistant refines its recommendations and tone over time, greeting returning shoppers with contextually relevant insights.

It remembers past purchases, anticipates needs, and turns one-time buyers into loyal advocates. This blend of personalization and convenience gives forward-thinking retailers an edge in the crowded ecommerce marketplace.

Deliver an exceptional customer experience with AI shopping assistants

Conversational commerce makes it possible to deliver the kind of shopping experience customers crave: fast, personalized, responsive, and seamless at every step. With an intelligent, customizable AI shopping assistant, you can remove friction from the customer journey, reduce cart abandonment, and create the kind of genuine engagement that builds loyalty and drives revenue growth.

iAdvize’s AI Shopping Assistant takes this vision further by predicting shoppers’ needs, offering tailored product recommendations, and engaging shoppers from the very first page view.

Contact iAdvize today

See how Copilot for Shoppers can boost conversion rates at your company by up to 10x.

AI in Customer Service: How to Get Started

iAdvize — Wed, 24 Sep 2025 19:45:00 GMT

The customer service landscape is changing. Companies are increasingly turning to artificial intelligence to improve their operations, reduce costs, and deliver better customer experiences. If you’re considering implementing AI in customer service but aren’t sure where to start, this guide is for you.

What is AI in Customer Service?

AI in customer service refers to the use of artificial intelligence technologies to automate, streamline, and enhance customer support operations. This can include AI shopping assistants that answer common questions, machine learning models that route tickets to the right agents, or AI-powered systems that provide real-time suggestions to human agents.

The key difference between AI in customer service and traditional automation is that AI can learn from data, adapt to new situations, and even understand the nuances of human language and intent. This makes it far more powerful and flexible than rigid rule-based systems.

Why Implement AI in Customer Service?

There are several compelling reasons to add AI to your customer service operations:

24/7 Availability

One of the most immediate benefits of AI in customer service is that it can provide support around the clock. While your human agents sleep, your AI systems can be resolving customer issues, answering questions, and collecting information. This dramatically improves customer satisfaction and reduces the time customers spend waiting for help.

Faster Response Times

AI can respond to customer inquiries instantly. Rather than waiting in a queue for an available agent, customers can get immediate answers to their questions. For many routine inquiries, this instant response is all that’s needed.

Cost Savings

Implementing AI can significantly reduce customer service costs. By automating routine tasks, you can handle more customer inquiries with fewer human agents. You can redirect your most experienced team members to more complex, high-value interactions.

Better Customer Data

AI systems collect vast amounts of data about customer interactions. This data can be analyzed to understand customer needs, preferences, and pain points. These insights can then be used to improve your products, services, and overall customer experience.

Consistent Quality

Human agents have bad days. They get tired, distracted, or frustrated. AI systems, on the other hand, provide consistent, reliable service every single time. This consistency can significantly improve customer satisfaction.

Examples of AI in Customer Service

There are many different ways AI can be applied to customer service. Here are some of the most common applications:

AI Chat Assistants

AI-powered systems can handle a wide range of customer inquiries. They can answer frequently asked questions, help customers troubleshoot problems, and even process simple transactions like returns or refunds. Unlike traditional systems that rely on pre-programmed responses, modern AI assistants can understand complex customer requests and respond in a natural, conversational way.

Intelligent Ticket Routing

When a customer submits a support ticket, an AI system can analyze the content and automatically route it to the agent best suited to handle it. This might be the agent with the most expertise in that area, or the agent with the lightest current workload. This ensures faster resolution and better customer satisfaction.

AI-Powered Knowledge Base Suggestions

Your knowledge base is a goldmine of information, but customers often don’t know where to find the answer to their question. AI can analyze customer inquiries and suggest relevant knowledge base articles, helping customers find answers faster and reducing the load on your support team.

Personalized Recommendations

AI can analyze customer data and behavior to make personalized product or service recommendations. This not only improves the customer experience but also drives additional sales and revenue.

Conversation Summarization

Long customer conversations can be time-consuming to review. AI can automatically summarize conversations, extracting the key information and action items. This saves time and ensures nothing important falls through the cracks.

Predictive Support

Rather than waiting for customers to contact you with a problem, AI can predict when a customer is likely to have an issue and proactively reach out with a solution. This dramatically improves customer satisfaction and reduces support costs.

How to Get Started with AI in Customer Service

Ready to implement AI in your customer service operations? Here’s how to get started:

1. Define Your Goals

Before implementing any AI solution, clearly define what you want to achieve. Do you want to reduce response times? Lower costs? Improve customer satisfaction? Different goals will lead to different AI solutions.

2. Audit Your Current Process

Take a detailed look at your current customer service operations. Where are the bottlenecks? What types of inquiries are most common? Which tasks consume the most time? This audit will help you identify where AI can provide the most value.

3. Start Small

Don’t try to overhaul your entire customer service operation overnight. Instead, start with a small pilot project. This might be implementing an AI system for frequently asked questions, or using AI to help with ticket routing. Starting small allows you to learn, iterate, and prove the value of AI before scaling up.

4. Choose the Right Partner

There are many AI solution providers out there. Choose one that understands your industry, has experience with companies of your size, and offers the specific features you need. The right partner can make the difference between a successful implementation and a failed one.

5. Train Your Team

Your human agents will be working alongside AI. Make sure they understand how the AI works, how to use it, and how to handle situations where the AI needs assistance. Proper training ensures smooth integration and maximum value from your AI investment.

6. Monitor and Optimize

After implementation, continuously monitor how your AI is performing. Are your goals being met? Is customer satisfaction improving? Are costs decreasing? Use this data to optimize your AI system and make adjustments as needed.

7. Iterate and Improve

AI systems improve over time. Continuously look for ways to expand AI’s role in your customer service operations. As your system learns and improves, you’ll find new opportunities to apply it.

AI in Customer Service with iAdvize

iAdvize offers two AI-powered solutions designed to enhance customer service:

Support Desk

This solution provides real-time assistance to your human agents. As agents interact with customers, the AI analyzes the conversation and offers relevant suggestions, recommended actions, and information that can help the agent resolve the issue faster and more effectively.

iAdvize’s AI Shopping Assistant

This solution enhances the shopping experience by providing AI-powered shopping assistance. It can answer product questions, provide personalized recommendations, and help shoppers find what they’re looking for.

Both solutions leverage advanced AI technology to improve efficiency, reduce costs, and enhance customer satisfaction.

Conclusion

AI in customer service is no longer a futuristic concept—it’s a present-day reality that’s transforming how companies support their customers. By understanding the benefits of AI, exploring different applications, and following a strategic approach to implementation, you can harness the power of AI to improve your customer service operations.

The key is to start small, learn from your implementation, and continuously iterate. With the right approach and the right partner, AI can dramatically improve your customer service operations while reducing costs and improving customer satisfaction.

Black Friday & Cyber Monday 2025: Why AI Is the New Playbook

iAdvize — Wed, 03 Sep 2025 08:15:00 GMT

2024’s e-commerce holiday numbers broke records to the tune of billions of dollars, with $41.1 billion spent between Thanksgiving and Cyber Monday alone. The Q4 stretch from November to year-end is still the highest stakes shopping period of the year, but using last year’s playbook won’t drive the same results in 2025.

Shopping behaviors and expectations are shifting, and top retailers are already responding with AI-enhanced assistants and shopping experiences, but you don’t have to be Amazon or Walmart to take a page from their playbook.

The Problem with Traditional Black Friday/Cyber Monday E-commerce Tactics

Most retailers are facing the most expensive and competitive landscapes to date. They depend on strong discounts and heavy ad spend to drive Black Friday and Cyber Monday (BFCM) traffic and sales. Yet these one-time discounts won’t bring a shopper back.

But before they even get to a return visit, most shoppers bounce in just a few seconds, causing retailers to lose out on nearly $14 billion on BFCM revenue alone. Shoppers can’t find what they’re looking for fast enough: products get trapped behind basic search and filtering, pages load slowly, irrelevant campaign videos and pop ups get in the way.

Customer service teams get overwhelmed by repetitive questions and struggle under the pressure of 24/7 holiday coverage across high site traffic. Chatbots can help alleviate this, but they aren’t set up to truly guide customers down a path that converts.

It’s a chaotic experience for everyone. But it doesn’t have to be.

Why Retailers Are Turning to AI shopping assistants for BFCM and Beyond

Forward-thinking retailers are already turning to agentic commerce and AI shopping assistants to transform their sales and strategy—and not just during BFCM, but their year-round operations.

Agentic commerce simplifies the “shop-for-me” journey. Consumers arrive with intent, and AI doesn’t just speed up the process, it guides them toward the right purchase. This relevance and support are key, especially during BFCM, when decisions are made in seconds.

It also ensures your storefront is connected to AI search assistants like ChatGPT, Perplexity, and Gemini, where more shopping journeys now begin. By making your catalog and brand discoverable and shoppable through these AI interfaces, you capture intent earlier, guide shoppers toward purchase, and stay visible in a landscape shifting towards AI.

How Retailers Are Getting Ready for BFCM

AI shopping assistants are experts in the entire product catalog but go beyond basic filtering, search, and FAQs to bring up the exact results a customer is looking for and answer their specific questions—all in one place—things which dramatically improve engagement and conversion.

Lifestyle brand Kendra Scott entered the 2024 holiday season knowing that traditional e-commerce tactics wouldn’t be enough. SVP of Digital and E-commerce, Kamanasish Kundu saw huge potential in rethinking their AI strategy by transforming their support chatbot add-on into a true sales-enabled AI shopping assistant. In little time, the brand trained the assistant “in a way that mirrors how we actually sell: emotionally, intentionally, and with brand fluency.” This clearly resonated with customers during that critical holiday sales window, contributing to impressive results across their business :

160% YoY growth in AI-influenced revenue
6× lift in conversion rates
9× increase in shopper engagement
AI accounted for 6% of total sales during the season

Pop-culture collectible manufacturer Sideshow’s customers aren’t your average shopper: they’re a passionate fan base who knows every detail of their favorite characters, series, and franchises. Chatbots couldn’t go into enough detail and static PDPs were burying them. The AI shopping assistant, however, could hold its own with even the most knowledgeable superfans, surface the right key details for each individual, and make it easy to compare collectibles, driving an 8x higher conversion rate and 94% CSAT just two months after implementing it.

Get Your Storefront Ready for BFCM 2025: A Quick Checklist

Preparing For the Sale

Implement an AI shopping assistant now.
You can trial and set it up in days to start testing and teaching your system. When peak traffic hits, your AI shopping assistant will already be optimized to convert high-intent shoppers, protect your margins, and capture revenue.
Prep your AI layer with BFCM-specific knowledge.
Update the assistant’s knowledge base and macros with promo rules, exclusions, shipping SLAs, and returns. Make sure you have well-structured attributes, rich metadata with natural language for your product details, descriptions, and FAQs. This offloads repetitive questions from CX, keeps answers consistent, builds shopper confidence, and preserves momentum at checkout.
Make the site instant and mobile-first.
48% of BFCM sales come from mobile and drive 78% of traffic. Cut heavy apps/widgets, trim pop-ups, and test on phones where the majority of BFCM traffic lands. Every second saved reduces bounce and lets your AI shopping assistant engage sooner.

The Shopping Experience

Don’t rely on search bars and filtering to help customers find your products.
AI shopping assistants provide immersive experiences on the UI and UX front that showcase dynamic content as a customer browses. A PLP with an AI shopping assistant transforms from a static brochure into a tailor-made guide for each customer.
Show fresh social proof where shoppers decide.
Surface recent reviews on PDPs and let the assistant summarize them (“Most buyers loved the fit and gift packaging”). In BFCM, quick confidence signals close the gap between browse and buy.
Merchandise urgency and clarity directly on PDPs.
Add countdown timers, low-stock flags, strike-through pricing, and “Holiday Best Seller” tags. These on-page cues pair perfectly with AI prompts (“Only 14 left—want to grab your size now?”) to turn intent into action.
Get your storefront ready for agentic commerce.
More and more shoppers are starting their search with AI instead of traditional search. To avoid missing out on qualified traffic, start preparing to integrate with leading platforms like ChatGPT or Perplexity.
Think beyond the sale.
Acquisition is one thing. But how will you keep the relationship alive afterwards? An AI shopping assistant can welcome a customer back without starting from scratch to continue building on their purchase history and preferences.

The Fastest Way to Get Ready Before The Holiday Shopping Rush

iAdvize helps brands tackle the holiday shopping season with more peace of mind thanks to our AI shopping assistant. By assisting retailers in:

Guiding visitors to the right offer
Answering key questions instantly
Handling traffic peaks without hurting the experience
Keeping the connection alive after purchase

It’s an easy lever to pull that strengthens your efforts and strategies. Deploying it in days, not months, you can start getting signals and driving results before the holiday shopping season is in full swing. By refining your BFCM strategy now, you’ll drive more Q4 revenue, and be set up for a successful 2026 when agentic commerce really hits its stride.

Be Ready for BFCM. Starting Now.

There's still time to strengthen your BFCM strategy — kick it off today with a free trial.

What Is Agentic Commerce? Why Every E-Commerce Leader Must Act Now

iAdvize — Mon, 25 Aug 2025 19:15:00 GMT

The search bar is dead.

Consumers no longer want to type keywords and scroll. They now start with AI assistants. Unlike search bars, AI assistants don’t stop at discovery: they compare, decide, and even checkout. The funnel collapses into a single interaction.

In two years, will consumers still visit your site, or will they simply ask ChatGPT or Perplexity to buy for them?

The End of Browse-and-Hope Commerce

For decades, online shopping meant browse-and-hope: search, menus, filters, and endless scrolling. The outcome? 97% of journeys fail. Conversions are stuck at 2–3%. Billions in ad spend and optimization haven’t changed the math.

The problem is structural. Browse-and-hope commerce puts the work on the shopper. And shoppers are done. They’re abandoning keyword search and option overload. Retailers, frustrated by declining ROI, are ready for something better or risk being left behind.

What Is Agentic Commerce? It’s Shop-for-Me Commerce

Agentic commerce is where AI does the shopping. It anticipates intent, guides decisions, and even completes purchases for the shopper. The funnel doesn’t shrink. It collapses into a single step.

This is the Shop-for-Me journey: consumers offload discovery, comparison, and purchase to AI assistants. They set the intent, and AI does the work. The path from “I want it” to “I have it” collapses into a single interaction.

The adoption curve has been steep:

Adobe tracked one trillion U.S. retail visits and found traffic from generative AI surged 1,200% in just seven months, doubling every two months.
Vertical agents are reshaping categories. AI fashion assistants Alta and Daydream prove Shop-for-Me will be specialized, not generic.
AI assistants won’t stop at discovery. Capgemini found 71% of shoppers want generative AI woven into the entire shopping journey: consideration, decision-making, and purchase.

This is a structural reset, not hype. That’s why Perplexity is partnering with PayPal. Why Visa and Mastercard are embedding AI in checkout. Why every vendor is racing to be AI compatible.

The reset is now. Brands that adapt first will win. Those that don’t will be ignored.

Why Brands Still Matter in an AI-First World

If consumers start with ChatGPT or Perplexity, do brand websites disappear? Not if they deliver the three Cs:

Confidence: authenticity, reputation, trust, and support.
Curation: exclusive drops, loyalty perks, and special collections.
Connection: immersive experiences that tell your story and build preference.

The storefront will not vanish, but there are now two audiences:

Consumers who want brand connection but expect frictionless agentic journeys
AI assistants that are becoming decision-makers themselves

Your storefront must serve both or become invisible.

Your AI Shopping Assistant Is Now Mission-Critical

Every major retailer is embracing Shop-for-Me: from Amazon to Walmart, Zara to Zalando.

Amazon CEO Andy Jassy put it bluntly: “every part of our retail customer experience will be reinvented with generative AI.” Launched in 2024 and promoted for Prime Day 2025, Amazon Rufus is already projected to drive $700M in sales.

You don’t need to be at Amazon’s scale. Kendra Scott re-framed its AI assistant as a sales enabler fueling a 160% YoY growth and a 6× conversion lift. Similarly, beauty brand Mademoiselle bio recreated the high-touch in-store experience online achieving a 6× conversion rate.

The playbook is clear: AI assistants aren’t optional. They are the new growth engine for retail.

Your AI Shopping Assistant is Your Next Storefront

AI shopping assistants are rapidly evolving to become the brand’s storefront, serving both consumers but their personal AI assistants. They are progressing along a clear maturity curve:

Yesterday (2024): AI Chatbots
Built for support, answered reactive questions, clunky interfaces.
Today: AI Sales Associates
Built for conversion, proactive recommenders, blends into the shopping experience.
Tomorrow (2026+): AI Storefronts
Autonomous, anticipatory, serving both humans and their AI agents.

What began as support is becoming the storefront. The first to reach AI Storefront status will own the customer relationship.

Three Moves to Win in Agentic Commerce

Agentic commerce will reshape the economics of online retail. Expect:

Less direct traffic. Journeys begin with AI, diverting direct traffic. SEO shifts to GEO (Generative Engine Optimization).
Higher conversions. Visitors will be more intentional and ready to buy. Leaders should target 20% conversion, not 2%.

E-commerce leaders should act now with three moves:

Make your AI assistant the centerpiece. It’s no longer optional. Align your CEO, business, and tech leaders to make it your differentiator.
Launch and learn. Pilot an AI assistant to build data moats, test conversion impact, and accelerate readiness. Launch fast, even if limited. Learn faster.
Build your playbook. Measure which AI interactions lift conversions, reduce returns, or increase cross-sell.

The Next Two Years Belong to Agentic Commerce

Agentic commerce isn’t coming. It’s here. Browse-and-Hope is collapsing. Shop-for-Me is rising.

By 2027, AI will assist all shopping journeys. The question isn’t whether consumers will delegate shopping. The question is: will they delegate to you or to your competitor?

Leaders who act now will secure the customer relationship. The rest will be reduced to suppliers for someone else’s AI.