Editorial
Research Report
The 2026 State of CX Outsourcing
The Gist
- The customer is no longer always human. Machine customers—AI assistants, procurement bots and connected devices—are increasingly initiating, filtering and completing transactions on behalf of people and organizations.
- Experience design must expand beyond screens. Structured data, stable APIs and machine-verifiable trust signals now determine whether automated agents can discover, evaluate and transact with your business.
- Governance becomes competitive advantage. As AI agents commoditize, differentiation shifts to execution quality, observability and consent-driven infrastructure that machines can reliably integrate with and trust.
Customer experience has historically assumed a human on the other side of the interaction: a person browsing, comparing, asking questions and making decisions with a mix of logic and emotion.
That assumption is breaking.
A growing share of interactions are being initiated, filtered and sometimes completed by software acting on behalf of people or organizations—AI assistants, procurement bots and connected products that trigger service and purchasing actions automatically and uses a crisp definition for this shift: machine customers are nonhuman economic actors that obtain goods or services in exchange for payment.
In practice, the "customer" may look like a voice assistant raising a support request, a procurement system negotiating terms, or a device reordering consumables based on telemetry.
Several independent signals show why CX leaders should treat this as a structural change—not a novelty:
- Gartner forecasts that by 2026, 20% of inbound customer service contact volume will come from machine customers.
- Gartner also positioned machine customers among its top strategic technology trends, noting that by 2028, connected products will exist with the potential to behave as customers, with economic impact measured in trillions of dollars by 2030.
- Behavioral evidence from digital commerce is accelerating. Reported that traffic from generative‑AI sources to U.S. retail sites rose sharply (including 1,200% growth in February 2025 vs July 2024), based on analysis at very large scale. Later, Adobe published that generative‑AI traffic to retail sites was up 4,700% year‑over‑year in July 2025.
The implication for CX is decisive: experience design must widen from human interfaces to machine interfaces—data, APIs, protocols and governance that allow automated clients to discover, evaluate, transact and resolve issues safely.
Table of Contents
- FAQ: Machine Customers and the Agentic Economy
- What Makes a Machine Customer Different From a Human Customer
- The Machine-Customer Journey: From 'Sense, Decide, Transact' to 'Browse and Buy'
- Telemetry-Driven Replenishment: The Device Becomes the Initiator
- Subscription Logistics: Monitoring Becomes the 'Co-Customer'
- Enterprise Procurement: Negotiation and Contracting at Machine Speed
- Principles for Machine-First CX: Making Your Business Legible to Agents
- Data Is the Storefront
- Stability Beats Cleverness in APIs
- Observability Is Experience
- The Near Future: Commoditized Agents, Differentiated Governance
FAQ: Machine Customers and the Agentic Economy
Editor’s note: Five essential questions CX leaders should be asking as AI agents, connected devices and procurement bots begin acting as economic customers.
What Makes a Machine Customer Different From a Human Customer
The temptation is to treat machine customers as "just another digital channel." That understates the change. A channel still assumes the decision-maker is human. Machine customers change the decision logic itself.
Gartner's public research emphasizes three behavioral differences that matter for customer experience strategy:
- Machines buy based on data and logic; rather than emotion or relationship cues.
- Machines don't need to be delighted; they prioritize successful task completion and reliability.
- Machine journeys are more deterministic; they tend to be repeatable, measurable and "linear" relative to how humans browse and reconsider.
This reframes classic CX work. The strongest brand story and the most polished UI may be invisible if an agent's selection logic cannot parse your offering, verify constraints or trust the transaction path.
A second nuance matters for credibility: machine customers will not dominate every category equally. Gartner notes that where emotion is core (for example, indulgence and identity-driven purchases), machine customers may assist rather than fully decide. The near-term reality is hybrid: humans remain, but more decisions are delegated or pre-filtered by software.
Finally, machine customers are not only about buying. They are also increasingly about service. Gartner anticipated a wave of automated service requests raised by smart products and stated that organizations without a machine customer strategy may struggle to distinguish machine contact from human contact, degrading performance in unexpected ways.
The Machine-Customer Journey: From 'Sense, Decide, Transact' to 'Browse and Buy'
A useful way to design for non-human customers is to stop thinking in screens and start thinking in loops. Machine customers typically follow a cyclical pattern:
detect → evaluate → execute → verify → update.
Three real-world patterns show how this already works across consumer and enterprise contexts.
Telemetry-Driven Replenishment: The Device Becomes the Initiator
Dash Replenishment Service (DRS) documentation describes a model where connected devices reorder supplies when needed. The published flow includes a customer selecting what to replenish, authorization to access replenishment APIs, transmission of consumption data and an automated order when supplies are low—followed by order status notifications back to the device ecosystem.
Two CX-relevant lessons live inside the technical documentation:
- DRS explicitly addresses API versioning and backwards compatibility, highlighting that machine customers depend on stable contracts.
- The program includes testing/certification concepts, signaling that for machine customers, "experience quality" includes predictable behavior under automation, not just UI polish.
These programs also evolve, which is itself an experience factor. A support FAQ notes that the original Amazon Dash replenishment program was discontinued on in March of 2023, pointing users to an updated auto-reordering approach. For machine CX, the takeaway is not branding—it is that automated clients must survive program transitions without breaking.
Related Article: OpenAI vs. Google: Two Visions for the Future of Agentic Commerce
Subscription Logistics: Monitoring Becomes the 'Co-Customer'
Instant Ink is a subscription where an internet-connected printer monitors ink levels and triggers proactive shipment when ink runs low. Even though a human pays the bill, the behavioral "customer" in the moment of action is the monitoring loop plus its rules.
This pattern matters beyond printing. Any data-connected product (industrial or consumer) can become a service-raising, part-ordering actor. That is why Gartner framed machine customers as smart devices or assistants performing service activities on behalf of human customers.
Enterprise Procurement: Negotiation and Contracting at Machine Speed
In B2B settings, machine customers increasingly appear as procurement agents automated supplier negotiation program is a widely cited example of a buyer-side agent interacting with human suppliers at scale.
A release about retail deployments reported that the negotiation chatbot had closed deals with 68% of suppliers approached, generated average savings of 3%, and that 83% of suppliers described the chatbot as easy to use; it also claimed the system can negotiate with 2,000 suppliers simultaneously.
A separate report referencing the same underlying deployment described suppliers preferring chatbot negotiation and reiterated the 2,000-supplier scale claim, indicating the data circulated beyond a single announcement. (Where metrics originate from vendor reporting, the responsible CX stance is to treat them as directionally informative, validate in your own context and design governance for measurement.)
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For customer innovation, the procurement example is a preview: the "relationship layer" does not disappear, but routine negotiation and contract optimization becomes machine-mediated—driven by structured terms, constraints, and rapid iteration.
Principles for Machine-First CX: Making Your Business Legible to Agents
If human CX is about perception and emotion under constraints, machine CX is about legibility, determinism and verifiability under automation. The table below summarizes the shift in practical design targets.
| Design dimension | Human-centric CX target | Machine-centric CX target |
|---|---|---|
| Discovery | Persuasive content and navigation | Structured, machine-readable offer data and consistent identifiers |
| Evaluation | Comparison, reassurance, social proof | Verifiable attributes, eligibility logic, certification flags, explicit SLAs |
| Transaction | Frictionless checkout and trust cues | Idempotent actions, predictable responses, tokenised credentials, traceability |
| Service | Empathy, escalation, recovery | Machine-readable status, deterministic error semantics, bot-to-bot resolution paths |
| Reliability | "Feels fast" and "looks trustworthy" | Measurable uptime/latency, stable API contracts, consistent data formats |
Three principles consistently surface across the most credible sources.
Data Is the Storefront
Agents cannot choose what they cannot parse. Gartner's "buy based on data and logic" point becomes a data governance requirement: attribute completeness, schema consistency and unambiguous definitions determine whether an agent can even evaluate your offer.
BCG similarly recommends making data and platforms "agent-ready," explicitly calling for structured, machine-readable content and rich product schema to remain visible as discovery shifts to AI interfaces.
Stability Beats Cleverness in APIs
Humans tolerate quirks; automated clients scale them into outages. Amazon's DRS documentation highlights versioning and backwards compatibility as first-class concerns, signaling that machine CX is built on predictable contracts rather than interface novelty.
Observability Is Experience
When machines are the user, monitoring is not merely IT hygiene; it is an experience guarantee. If an agent cannot reliably interpret error responses, latency changes, or partial failures, the "experience" degrades instantly and silently.
This is visible in how leaders discuss the new funnel. BCG notes that more browsing and product selection may occur inside LLM interfaces, with only final purchase steps happening on a brand's site—meaning you may not "see" experience failures in your traditional customer journey analytics unless you instrument agent pathways explicitly. Trust becomes protocol: governance, identity, and safe autonomy at scale
Machine customers intensify an old CX truth: convenience without trust does not scale. What changes is the mechanism of trust. For machine customers, trust is increasingly expressed through identity, consent, cryptography, and auditable intent.
This is clearest in payments, where the cost of ungoverned automation is immediate fraud and disputes.
Visa and Mastercard Codify Trust for Machine-Led Transactions
Visa has positioned "agentic commerce" as a strategic direction and describes Visa Intelligent Commerce as an API suite to enable consent-driven payments by AI agents, including tokenisation, authentication, transaction signals, and other controls. In November 2025, Visa also described a Trusted Agent Protocol designed to help merchants recognise and verify trusted AI agents, stating it uses agent-specific cryptographic signatures to distinguish legitimate agents from malicious bots while maintaining visibility of the consumer behind the agent.
Multiple sources corroborate the broader direction. A report on Visa's rollout described consumer-set spending limits with agents executing tasks like search, travel booking, and grocery ordering, framing the goal as reducing checkout friction. Visa is partnering with AI developers to connect agents to the payments network and stressed the need for user controls and legitimacy checks for transactions.
Building parallel infrastructure. Mastercard's published Agent Pay Acceptance Framework describes registering and verifying agents before permitting transactions, enabling agents to transact with "agentic tokens" (cryptographically secure credentials), and adopting verification approaches designed to minimize merchant integration lift.
For CX leaders, the governance lesson is bigger than payments: machine customers require machine-verifiable trust signals. Governance moves from policy documents to living technical controls: identity verification, consent capture, rate limits, anomaly detection, audit trails and dispute-handling processes that can reconstruct what an agent was authorised to do at the moment of action.
This is also where your organization's "customer experience" and "developer experience" converge. If you cannot identify and govern automated customers, you may either (a) block legitimate machine customers and lose revenue invisibly, or (b) accept untrusted automation and pay in fraud, downtime, and reputational damage.
The Near Future: Commoditized Agents, Differentiated Governance
The most important strategic change may not be that AI agents exist—it is that they are becoming common. As agents commoditize, differentiation shifts to execution quality, data readiness, and governance maturity.
BCG frames the competitive risk clearly: as the "front door" of ecommerce moves toward agentic interfaces, laggards may only see the impact once traditional KPIs (traffic, conversion) have already shifted. Adobe's data provides a behavioral underpinning: AI-referred traffic is not theoretical; it is rising sharply and measurable in mainstream retail browsing patterns.
Meanwhile, Gartner's service forecasts suggest a second front: inbound service interactions increasingly initiated and resolved by automated systems. This creates a dual design requirement: your organization must serve humans well and provide deterministic, governed pathways for machines to transact and resolve issues at machine speed.
My conclusion emerges from the combined evidence: machine customers will reward organizations that are easiest to integrate with, safest to transact with and most reliable to verify. The CX discipline does not become less important; it becomes broader. It must now include:
- Structured offer truth (data)
- Stable interaction contracts (APIs)
- And enforceable trust mechanisms (governance)
In the agentic economy, the brands that win will not be those with the most impressive demos. They will be the ones whose systems are chosen repeatedly by machines because the rules are clear, the signals are verifiable, and the experience never breaks when nobody is watching.
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