More than 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, or insufficient risk controls. Often, the difference between the projects that survive and the ones that don't comes down to the conversation with the vendor before you sign the contract.

Most organizations have moved past AI experimentation and into production. But production doesn't mean the hard problems are solved. Your team spent six months deploying a tool that handles password resets and checks order status. The complex calls that actually drive churn still land on a human agent's desk.

These 30 questions expose whether an AI technology was built as a modern platform for enterprise production or simply for shiny demos.

1. Does the Vendor Measure Resolution or Deflection?

Deflection measures whether an interaction avoided a human agent. Resolution measures whether the customer's problem was solved—the first time. Most vendor dashboards report the former and call it the latter.

Avoid this by working with a vendor that offers a DWR (did we resolve) survey, which tracks whether customers confirmed their problem was resolved or not. A modern AI platform and agents should achieve DWR rates of more than 90%.

To find out whether a vendor measures resolution or hides behind deflection metrics, ask these four questions:

1. "Can you show us resolution rates, not deflection rates, from a comparable production deployment, with the specific measurement methodology and time period?"

2. "What happens to interactions your AI cannot complete? Walk us through the escalation flow, including how context transfers to the human agent."

3. "What is your repeat contact rate after AI interactions? How do you track whether customers call back for the same issue?"

4. "How do you verify that a customer's problem was actually solved, not just that the interaction ended?"

2. Was the Platform Built for Voice or Retrofitted from Chat?

Most AI support platforms started with chat and added voice later. The architecture was built on text-based reasoning with a speech-to-text layer on top. This causes issues when customers need to solve multiple things at once.

For example, a caller says: "I need to check my balance, update my address, and I'm also calling about a charge I don't recognize." Modern AI platforms with voice-native systems should handle all three within a single conversation. But retrofitted systems force them into sequential processing, forcing the customer to repeat themselves at each step.

There are three capabilities that separate voice-native from voice-retrofitted architecture:

• Multi-intent handling that processes several requests within a single conversation.

• Latency low enough that the caller never notices a delay, measured against a P95 latency performance metric.

• Mid-call language switching and interruption handling that prove the system was built for live conversation

The platform should be designed for voice from the ground up, with support for emotion-aware processing and dozens of languages, including mid-sentence language switching.

To find out whether voice was baked in or bolted on, ask these questions:

1. "Was your platform designed for voice from the beginning, or was it originally a chat product adapted for voice? When did voice capabilities first ship?"

2. "Can your platform handle multi-intent utterances within a single conversation? Show us production call recordings demonstrating this at live call volume, not scripted demos."

3. "How does your platform handle a caller who starts in English, switches to Spanish mid-sentence, then returns to English? What is the measured latency impact? Can you provide P95 metrics?"

4. "If a caller interrupts your AI mid-response, what exactly happens? Does it stop immediately, finish its current sentence, or continue talking over the customer?"

3. What Does Deployment Require?

Enterprise AI rollouts routinely stretch well beyond what a vendor’s sales team told the customer. The demo scoped twelve use cases. Six months in, three are live. The gap usually comes down to integration complexity, internal change management, and the engineering hours nobody budgeted for.

Before signing, get clarity on three costs that sales decks minimize: the FTEs required during implementation versus steady state, the professional services bill, and who owns what if you terminate the contract.

To find out what a deployment will cost your organization, ask these four questions:

1. "How many internal FTEs are required during implementation versus ongoing operations? Document this from comparable enterprise deployments, not your fastest or smallest."

2. "What professional services are required versus optional, and what is the range of professional services costs for enterprises of our size and complexity?"

3. "Who owns agent configuration, prompts, and outputs? If we terminate the contract, what happens to our workflows, training data, and model configurations?"

4. "Can our operations team modify agent behavior post-launch without filing engineering tickets or engaging vendor professional services? Show us how."

4. How Do You Handle Foundation Model Risk?

Enterprise AI dependency extends beyond model quality to single-provider risk, proprietary tooling lock-in, and ecosystems you cannot easily leave.

Multi-model orchestration reduces dependence on any single provider and creates flexibility around cost, latency, and failover.

To find out how exposed you are to a single provider, ask these four questions:

1. "Which foundation models does your platform use for different tasks? Can your platform orchestrate across multiple LLM providers within a single customer interaction?"

2. "What happens to live customer interactions if your primary LLM provider experiences an outage? Do you have automatic failover? And what is the measured impact on latency and accuracy during a switch?"

3. "How do you handle forced model deprecations or API migrations from providers? What was your response to the last major provider change?"

4. "Can we bring our own models or use alternative providers without re-architecting? Where does our enterprise data get stored, in your proprietary platform or in portable, standards-based storage?"

5. How Does the Platform Improve After Launch?

Most vendor conversations focus on launch-day accuracy. But total cost of ownership depends on how the platform continues to improve after go-live.

Look for modern AI platforms that surface specific improvement opportunities tied to conversation data, not dashboards your team has to interpret from scratch. The strongest systems recommend policy or routing changes, link those recommendations to real tickets, and route updates through a human review gate before anything touches production.

To find out whether the platform improves under governance or just promises autonomous learning, ask these four questions:

1. "How does your platform support post-launch improvement: through governed recommendations, scheduled retraining, or both? Show us an improvement trajectory from an existing deployment, month one through month 12."

2. "Does your platform automatically identify recurring issues across conversation data and recommend specific actions, or does it only surface reports that our team needs to interpret manually?"

3. "Does your platform generate suggested policy or routing changes based on production data? What approval mechanisms exist before those changes reach production?"

4. "What is your rollback capability if a generated or human-approved change degrades performance? How quickly does the rollback take effect?"

6. What Does Integration Look Like with Legacy Systems?

Voice AI platforms typically offer direct API integration and browser-based execution. Most enterprises need both because their stack includes systems with modern APIs alongside legacy systems that have none.

Browser-based agents operate inside existing interfaces, logging in like a human agent and completing tasks without API integrations. For systems with open APIs, vendors connect through direct calls that inject determinism into the AI conversation. The combination matters because most enterprise stacks are mixed. Some systems were built last year. Some were built in 2004.

To find out how the vendor handles systems your team runs on, ask these questions:

1. "Does your platform require direct API access to every backend system, or can it operate through existing browser-based interfaces without integration work?"

2. "For our specific legacy infrastructure, the systems built in-house with limited or no API access, what is your concrete integration approach? Walk us through a comparable deployment."

3. "What is the realistic engineering FTE commitment during implementation versus ongoing maintenance? Not your fastest deployment, your median timeline for enterprises with our infrastructure complexity."

4. "When our underlying systems undergo UI changes, version updates, or patches, what components of your solution break, and which resources restore functionality?"

Find the Gap Between Vendor Claims and Deployment

These questions are designed to help you identify the gap between vendor claims and deployment reality across metrics and ownership of data, configurations, prompts, and outputs.

Ask for production metrics from comparable deployments at a similar scale. The vendors who answer these questions with production data and transparent methodology are worth evaluating further.

Modern AI agents should reach production in about two weeks through a consultative deployment model and should achieve DWR rates of at least 90% in production. The platform you choose should maintain enterprise-grade compliance across PCI DSS 4.0.1, SOC 2, ISO 42001, ISO 27001, GDPR, HIPAA, and CPRA.