Virtual Assistants for Machine Customers: Where AI-Powered Shopping Stands Now

Welcome to the era of machine customers, when AI is not just supporting but also deciding, acting, and transacting on users' behalf.

This transition is part and parcel of a larger change – agentic commerce. In this model, AI systems act on a user’s behalf, making decisions and carrying out tasks with a degree of independence.

We will discuss how virtual assistants are becoming powerful purchasing agents, the technologies that are making this transformation possible, and what companies need to do to get ahead in a world where the customer may be a machine.

AI Virtual Assistants for Purchasing

An assistant becomes useful in commerce when it can do four things well

• Understand natural language with context
• Access live product and pricing data
• Authenticate users and process payments securely
• Learn from past behavior to improve recommendations

That combination changes the role of the assistant. It is no longer just a search tool. It becomes a shopping agent with real transactional power.

The market context makes the strategic case difficult to ignore. The global virtual shopping assistant market was valued at approximately $3.8 billion and is projected to reach between $8.5 billion by 2032. Let’s review some real-world examples of top virtual assistants as of today.

1. Consumer Shopping Assistants

Consumer-facing AI shopping assistants are the most visible expression of conversational commerce today. Their primary function is to shorten the path from intent to purchase, replacing keyword search with conversation, and static product listings with dynamic, context-aware recommendations.

Amazon Rufus / Alexa for Shopping

In May 2026, Amazon retired the Rufus name and absorbed its capabilities into a more powerful product: Alexa for Shopping. The move unified Rufus's product expertise with Alexa+'s personalized context across hundreds of millions of devices. The result is an assistant that carries memory across a customer's phone, laptop, and Echo devices. It is tracking shopping history, past conversations, and browsing behavior to inform every interaction.

The architectural decision was deliberate. Alexa already existed in over 600 million active endpoints worldwide. Rufus had 300 million app users. Merging them under a single AI identity didn't just combine the features—it multiplied the surface area. Every Echo device became a shopping interface. The main Amazon search bar became a conversational entry point. Customers no longer needed to navigate to a separate chat window.

Kate Spade AI Gift Concierge

Amazon has now taken the next step: packaging the architecture behind Alexa for Shopping as a product other retailers can license through AWS. The service, called Agentic Shopping Assistant on AWS, allows retailers to deploy their own branded AI shopping tools—typically in around 60 days.

The first production deployment of that AWS platform was Kate Spade's AI Gift Concierge, launched in April 2026. Developed by Tapestry on Amazon Bedrock using AgentCore (running on Anthropic's Claude Haiku 4.5), the tool chats with shoppers about occasion, recipient, and style preferences and then surfaces product recommendations. It also utilizes behavioral data from Kate Spade customers via Alexa for Shopping interactions to adjust responses, while still maintaining the Kate Spade brand voice.

Preliminary data indicate the chat is really working: the solution is expected to drive conversions on shopping sessions at a rate 3.5 times higher than the traditional keyword search. The implication is not just a better chatbot—it's evidence that structured intent-gathering outperforms the browse-and-filter model for gift purchasing decisions.

Kmart Joy

Kmart Australia's Joy assistant, launching in June 2026, takes a different approach to the same problem. Built on Google Cloud's Gemini Enterprise for Customer Experience platform, Joy integrates natural language product search with augmented reality features—virtual try-on for clothing. Its "see it in my space" function previews furniture in the shopper's own environment. The assistant is embedded in the Kmart app, combining conversational product discovery with visual decision-support in a single interface.

Note: Joy represents a broader trend: AI assistants that don't just answer questions but reduce the perceptual risk of purchase decisions. Virtual try-on and in-space preview address the primary source of hesitation for clothing and home goods.

2. B2B Procurement Platforms

Procurement transactions are subject to contractual restrictions, approval hierarchies, supplier relationship politics, compliance regulations, and occasionally monetary risks. So the AI assistant must enable a transaction under a set of rules that varies by organization, category, and supplier.

Ivalua IVA (Intelligent Virtual Assistant)

Ivalua's IVA is embedded across the full source-to-pay lifecycle: from supplier research and RFP drafting to contract analysis and invoice processing. What distinguishes IVA from simpler automation is its policy-driven behavior. Instead of adhering to strict if-then rules, IVA interprets the thresholds and constraints that an organization has defined and takes the next best action based on context. It escalates when it meets a situation that needs human decision-making, instead of continuing. Such a behavior pattern keeps autonomous procurement systems from becoming liabilities.

CloudEagle.ai

CloudEagle.ai is purpose-built for SaaS procurement — a category that has grown complex enough to warrant dedicated tooling. Enterprise software portfolios typically involve hundreds of vendors, overlapping contracts, renewal cycles distributed across a calendar year, and usage data that is rarely fully consolidated. CloudEagle addresses this by providing AI-powered vendor management, pricing benchmarking, and purchase workflow automation on a single platform.

For procurement teams managing significant SaaS spend, the assistant's ability to surface renewal risks, identify underutilized licenses, and generate vendor benchmarks before contract negotiations represents a structural efficiency gain. The value is not in replacing procurement decisions — it is in making sure those decisions are informed by data that currently exists but is rarely used.

SAP Ariba with Joule

Joule, SAP's AI copilot, is now embedded directly into procurement workflows across sourcing, supplier management, contract management, and buying.

What that means in practice:

1. a Bid Analysis Agent that automatically evaluates complex bids, including total cost of ownership;
2. AI supplier response summaries that consolidate questionnaire data for faster contracting decisions;
3. Joule-assisted invoice creation that guides employees outside of accounts payable through the submission process;
4. and AI contract support that handles routine inquiries, generates summaries, and flags discrepancies against historical contracts.

The architecture behind this is SAP's proprietary RPT-1 language model, built specifically to understand relational enterprise data within SAP systems. Unlike general-purpose LLMs, it's designed to operate with the structured data patterns that exist inside SAP's application ecosystem. This provides contextual precision that horizontal models typically lack in enterprise procurement settings.

Early adoption of Joule for Ariba was completed in September 2025. More than 30 sourcing and procurement use cases were released before the end of the year.

Digital Assistant Commerce Solutions: Key Features and Business Value

Across all of these products, certain capabilities consistently define what makes an AI purchasing assistant commercially useful rather than merely convenient.

1. Contextual memory is the foundation. An assistant that forgets previous interactions forces the user to repeat themselves and cannot build preferences over time. The most effective tools (whether Alexa for Shopping, Cleo, or Zip) maintain a running model of the user's history, preferences, and constraints that informs every subsequent interaction.

2. Constraint handling differentiates useful agents from sophisticated search bars. A procurement assistant must consider options as per the boundaries of the budget, vendor whitelists, contract terms, and approval logic all at once. A consumer shopping assistant has to take into account price, delivery time, return policy, and personal preference. The capacity to maintain various constraints in tension and yet provide a meaningful recommendation is what makes these tools truly intelligent rather than just speedy.

3. Action execution closes the loop. The distinction between a recommendation engine and a purchasing assistant is whether it can act, whether it can place the order, trigger the approval, initiate the renewal, or complete the payment. Many of the most significant developments in this space over the past two years have been about closing this gap between suggestion and execution.

4. Integration depth determines the practical ceiling of what any of these tools can do. An assistant that isn’t able to access the relevant systems—ERP, CRM, bank portals, vendor catalogs, procurement platforms—is blind to the information it can get. The broader an assistant’s integration, the more confidence it has to make a decision.

5. Governance and auditability matter most in enterprise settings, where accountability is not optional. Zip's Superagents, SAP Ariba's Joule, and TIS's assistant are all designed with audit trails, access controls, and human approval gates that make autonomous action compatible with enterprise compliance requirements. This is what separates a deployable enterprise solution from a capable prototype.

The Protocol Layer: MCIP and the Standardization of Machine Customer Commerce

Integration depth determines what any AI purchasing assistant can do in practice. However, integration alone does not solve a more fundamental problem: the e-commerce landscape has no common language. Shopify speaks REST. WooCommerce speaks a different dialect. Enterprise systems speak SOAP. Every platform structures product data, search responses, and order workflows differently. For an AI agent trying to operate across multiple stores or suppliers, that fragmentation means writing and maintaining custom integration code for every platform it needs to reach. This represents a significant and ongoing engineering burden that limits the practical scope of autonomous commerce.

Machine Customer Interaction Protocol (MCIP) is a purpose-built response to that problem. Rather than requiring AI agents to learn each platform's API individually, MCIP provides a single standardized interface: a protocol that works with any store and for any commerce operation.

MCIP's current implementation covers intelligent product discovery—the first and arguably most critical module of the full machine customer lifecycle. Cart management, checkout orchestration, and order tracking are on the roadmap. For organizations evaluating AI commerce infrastructure today, the protocol's modular architecture means that integration built now extends naturally to transactional capabilities as they come online. The more significant immediate implication is discoverability: stores that are not structured for machine-readable, semantically searchable product data will become progressively harder for AI purchasing agents to find and evaluate, regardless of how strong their human-facing experience is.

Why WebRTC Matters in AI Commerce

Among the very critical technologies that drive real-time AI commerce is one that is underappreciated: WebRTC, a.k.a. Web Real-Time Communication. In AI-driven commerce, WebRTC enables experiences like:

• Live conversations with AI agents through voice or video
• Real-time product consultations using a device camera
• Low-latency interactions between assistants and e-commerce systems

That matters because agentic commerce is moving toward faster, more conversational interactions. When an assistant will be helping someone select a product, answer additional questions, or confirm an order live, the infrastructure needs to be speedy and dependable.

WebRTC helps make that possible.

The Infrastructure Layer: LiveKit and Real-Time AI Agent Operation

WebRTC provides the communication standard, but it does not solve every production challenge. That is where LiveKit comes in.

LiveKit offers the infrastructure layer management developers require to build production-quality voice and video experiences, including scalability, media routing, and reliability.

Most of the products described above rely on an underlying infrastructure that handles the hard engineering problems of real-time AI interaction. LiveKit is one of the most significant players in this layer. OpenAI uses LiveKit for ChatGPT Voice. Meta runs on it. Character.ai runs on it. Thousands of developers building voice-based AI agents—for commerce, support, booking, and sales—use it as the foundation for their real-time communication layer.

LiveKit’s capabilities relevant to agentic commerce include:

• AI agent orchestration: Several agents can collaborate or delegate work
• Voice activity detection: Conversations are more fluid and less scripted
• Scalable architecture: Handles thousands of concurrent sessions
• Multimodal Input: Voice, video, and data converge to an integrated experience

LiveKit Cloud handles global distribution, autoscaling, and infrastructure management, removing the operational burden from teams focused on agent behavior and user experience. For high-volume deployments (call centers, large-scale customer service operations, real-time commerce platforms), a self-hosted deployment becomes economically attractive above roughly 100,000 minutes per month.

The practical relevance of this layer to commerce is significant. As AI purchasing assistants move into voice channels, real-time interaction quality becomes a product quality issue.

What This Means in Practice

The AI purchasing assistant market is not converging on a single model. It is fragmenting into specialized tools adapted to different contexts, constraints, and user needs.

In consumer retail, the contest is between platforms with large user bases building native assistants—Amazon, OpenAI, Google—and brands deploying their own conversational tools to maintain customer relationships and conversion rates on their own channels. The risk for brands that don't act is not just missing a feature. It’s losing visibility in the environments where their customers increasingly begin their purchasing decisions.

In enterprise procurement, the shift is from workflow automation to agentic execution. The tools that matter are not those that surface information faster, but those that can take action across systems with appropriate governance controls, reducing cycle times, eliminating manual handoffs, and operating within policy constraints without requiring human supervision of each step.

Mobile AI Virtual Assistants for Purchasing

For most people, the most visible entry point into agentic commerce is the smartphone. Several mainstream assistants already boast sizable user populations, and each one offers a slightly different approach to the business.

Siri

Apple’s Siri has enriched its commerce functionality via Shortcuts, SiriKit, and the ability to work with apps and services within the Apple ecosystem.

Siri can assist with shopping via:

• Apple Pay
• Wallet
• Maps
• iMessage
• Third-party app integrations

With the introduction of Apple Intelligence, Siri is getting better at reasoning and contextual understanding. That could make it much more capable as a transaction assistant.

Siri’s main advantage is ecosystem depth. Apple users already move between devices, payment tools, and apps within a tightly connected environment. That creates a smoother path from intent to purchase.

Bixby

Samsung’s Bixby is also unique, as it is closely tied to Samsung hardware and software. As it’s part of the Samsung world, Bixby has access to:

• Device cameras
• Sensors
• Native Samsung apps
• Samsung Pay

Its visual search functionality is especially relevant to commerce. Users can point their phone at a product and pull up similar items, price comparisons, and purchase suggestions.

So, Bixby is more than just a voice assistant. It’s a live product finding engine that blends offline and online shopping behaviors to feel practical rather than experimental.

Google Assistant and Gemini on Android

Google’s assistant, now increasingly shaped by Gemini, benefits from Google’s strength in search, shopping data, and merchant relationships.

Its commerce capabilities include integration with:

• Google Shopping
• Google Pay
• Merchant networks
• Browsing and search history
• Shipment tracking and reminders

Google’s biggest advantage is context. It can connect shopping behavior with search behavior, price trends, purchase timing, and user preferences. That makes it particularly strong at proactive commerce.

For example, it can:

• surface relevant deals
• remind users to reorder products
• suggest alternatives when prices change
• track deliveries automatically

Cortana

Microsoft’s Cortana has moved away from consumer shopping to enterprise productivity.

Although it no longer plays a similar role in consumer commerce to that of Siri, Bixby, or Google Assistant, it still holds some value in enterprise, especially in Microsoft 365.

In an enterprise, Cortana can help with:

• Procurement workflows
• Supplier coordination
• Business purchasing
• Internal productivity tasks tied to transactions.

What Businesses Should Consider Before Adopting Agentic Commerce

Adopting AI-driven commerce is not just a technology decision. It also requires thoughtful planning around trust, control, and operational fit.

Here are four areas businesses should evaluate carefully.

1. Data Governance

AI agents making purchase decisions need access to data, but that access must be clearly governed.

Organizations should define:

• what data an agent can use
• how consent is handled
• where audit trails are stored
• how data is minimized and retained

Good governance is not optional. It is the baseline for responsible deployment.

2. Authentication and Fraud Prevention

If AI agents can initiate or complete transactions, identity verification becomes critical.

Businesses should consider:

• multi-factor authentication
• anomaly detection
• transaction monitoring
• behavioral biometrics

The more authority an AI agent has, the stronger the controls need to be.

3. Human Oversight

Not every purchase should be fully automated.

Low-risk, high-frequency purchases are a good fit for autonomy. High-value or sensitive decisions often still need a human review layer. The best systems preserve efficiency without removing accountability.

4. Integration Depth

A virtual shopping assistant is only as useful as the systems it can connect to.

Before deployment, businesses should assess compatibility with:

• retailers
• payment providers
• logistics systems
• procurement software
• product databases

Weak integrations can turn a promising solution into a frustrating one very quickly.

Build Autonomous Commerce Systems with Clover Dynamics

AI assistants are moving toward greater autonomy, and the line between browsing, advising, and buying will keep getting thinner. That means organizations need to think about questions like:

• Can our products be discovered and evaluated by AI agents?
• Are our commerce systems structured for machine-assisted purchasing?
• Do our payment, fulfillment, and product data systems support this shift?

Clover Dynamics designs and deploys voice-enabled machine customers, conversational commerce agents, and autonomous procurement systems with the integration depth and governance infrastructure that enterprise-grade autonomy requires.

Clover Dynamics services include the development of AI agents, voice-enabled machine customers, conversational commerce, and much more.

Ready to explore what machine customer infrastructure looks like for your organization? Talk to Clover Dynamics