AI in CX Benchmark Report 2025 vs. Customer Service Benchmarks

Forethought’s report page describes insights from 500+ companies, while Lucidworks’ 2025 survey page describes research involving more than 1,100 enterprises. Those numbers may look comparable in a search result, but they represent different research questions and should not be used as interchangeable proof that an AI agent will improve a particular support team’s results.

The AI in CX Benchmark Report 2025 usually means Forethought’s report. This guide compares it with Freshworks, Lucidworks, Smart Communications, Verint, and Zendesk so a small SaaS team can identify useful evidence, avoid over-reading headline statistics, and design a measurable embedded-support pilot. The practical payoff is a clearer basis for deciding where an agent should answer from documentation, retrieve verified customer information, take a guarded action, or escalate to a person.

What the AI in CX Benchmark Report 2025 measures#

Forethought’s AI in CX Benchmark Report 2025 is the most direct match for the query. Its public report page positions the research around AI’s effects on customer experience, efficiency, and support operations, and it references insights from more than 500 companies. A separate Forethought article is explicitly framed around input from 600+ CX leaders.

That scope makes Forethought particularly relevant to leaders deciding how AI should participate in support. It is not the same thing as a controlled experiment that proves an AI tool caused a particular CSAT increase, cost reduction, or resolution-time improvement. Public summaries can identify reported patterns and implementation concerns, but a small team should review the report’s own methodology, question wording, respondent mix, and definitions before turning any headline into a KPI target.

The useful question is therefore not, “What percentage did another company report?” It is:

• Which support jobs are sufficiently repeatable to automate?
• Which answers can be grounded in approved documentation?
• Which requests require a verified customer-data lookup?
• Which actions are reversible, policy-bound, and safe to perform automatically?
• Which outcomes require a human owner from the start?

Forethought’s emphasis on AI in CX can help structure those questions. It cannot, on its own, validate a specific subscription-change workflow or establish that a small SaaS team will reproduce another organization’s outcomes.

Forethought vs. Freshworks: AI strategy versus service operations#

Freshworks’ Customer Service Benchmark Report 2025 is best read alongside Forethought rather than as a substitute. The Freshworks resource is framed as a customer-service benchmark report and focuses on operational trends teams adopt to improve efficiency across industries. Forethought is more directly concerned with AI in customer experience and support operations.

What Forethought can guide

Forethought is useful when the team is deciding whether it needs an answer-only assistant or an AI customer-support agent that can help resolve defined requests. For example, an embedded agent may answer “Where is the SAML setup guide?” from current documentation, then hand off a request to change an organization’s billing owner because that request involves authorization and customer data.

This distinction aligns with the approach described in how to build a grounded AI support agent for small SaaS teams. Documentation is a suitable grounding source for product questions. Customer-specific information should be retrieved only when necessary and only after the relevant identity and authorization checks.

What Freshworks can guide

Freshworks is more useful when a support lead needs a disciplined operational baseline. Its report can inform questions such as:

• Which measures does the team already track: first response, time to resolution, backlog, reopen rate, or CSAT?
• Where is workload concentrated: onboarding, billing, access, integrations, or incident-related requests?
• Is the intended automation solving a volume problem, a resolution-quality problem, or an after-hours coverage problem?

The supplied public description does not establish that Freshworks provides directly comparable benchmarks for every SaaS size, geography, or support model. Teams should not assume a metric is comparable until they confirm its definition and cohort in the report. A five-person B2B SaaS support function with complex account permissions should not equate its workload with a broad cross-industry service average.

Lucidworks vs. Forethought: adoption claims versus capability maturity#

The Lucidworks 2025 AI Benchmark Survey addresses a broader question than customer support: enterprise AI adoption and capability maturity. Its public description says the study covers more than 1,100 enterprises. That enterprise orientation makes it useful as a reality check when vendors or internal stakeholders use terms such as “autonomous AI agents” without defining the work an agent can safely perform.

Forethought and Lucidworks can be complementary:

• Forethought helps a CX leader think about AI’s role in customer support and reported support outcomes.
• Lucidworks helps a buyer assess the wider gap between AI ambition and deployed capability across enterprises.

Neither source should be treated as direct evidence that a public chat widget can safely access account records or execute changes. Publicly visible AI capability, enterprise survey responses, and production-grade support controls are different categories of evidence.

For a small SaaS team, the meaningful maturity test is concrete. Suppose a customer asks to downgrade a subscription. A capable workflow needs defined permissions, a way to establish the requesting user’s authority, policy checks for eligibility, a clear description of the change, an approved integration, a record of what happened, and a route to a human when the case is ambiguous. “Agentic” is not a safety or quality guarantee by itself.

The implementation pattern is covered in how to add AI support to a SaaS website without unsafe automation: constrain actions to known policies rather than granting broad access because a request appears routine.

Smart Communications and Verint: consumer trust is a separate evidence type#

Smart Communications’ 2025 research announcement addresses consumer willingness to use AI for critical life advice. Its headline states that half of consumers embrace AI for that type of advice. This is valuable evidence about trust and acceptance, especially because the subject matter includes higher-stakes decisions than ordinary product troubleshooting.

Verint’s State of Customer Experience 2025 is also consumer-oriented. The supplied report description emphasizes perceived CX benefits of AI, including time savings and broader acceptance. Neither source should be used as an operational benchmark for a SaaS support team’s ticket resolution time or as proof that customers approve every automated workflow.

Consumer research can nevertheless guide interface and policy design. It supports testing whether the experience is clear, quick, and easy to exit—not simply whether the bot prevented a ticket. For example:

• A customer asking how to export data may prefer a fast, cited documentation answer.
• A customer asking why an invoice changed may need a verified account lookup plus a clear explanation.
• A customer disputing a charge, reporting possible account compromise, or asking for an exception should be able to reach a human without negotiating with an agent.

The high-stakes context in Smart Communications’ research also limits transferability. A financial, healthcare, or insurance decision is not identical to a SaaS configuration question. Small teams should use it to set conservative trust boundaries, not to infer precise SaaS conversion, retention, or CSAT outcomes.

Zendesk CX trends: contextual design research, not a 2025 scorecard#

Zendesk’s CX Trends hub is relevant because it discusses AI agents, richer customer context, and personalized journeys. But readers using this comparison on September 2, 2026 should distinguish report editions carefully. The current public hub may present a newer edition than the 2025 material being compared here.

That date distinction matters. A 2025 report can explain the themes shaping that year’s AI in customer experience trends; it should not be described as current 2026 evidence without reviewing the relevant edition and methodology. Likewise, claims about Zendesk samples, percentage findings, or 2026 research should be cited to the specific edition rather than carried into a 2025 comparison by implication.

For small SaaS teams, Zendesk’s broad design theme is still practical: useful support needs enough context to avoid making customers repeat themselves. Yet context should not become indiscriminate retention or retrieval of customer data. A support agent needs only the information required for the case at hand—for example, plan status to explain a feature entitlement—not a complete customer profile for every documentation question.

A single evidence-to-implementation framework for AI agents#

The six sources are most useful when translated into one operating framework. Forethought and Freshworks can inform support strategy and operational questions; Lucidworks can temper claims of maturity; Smart Communications and Verint can inform customer trust expectations; Zendesk can inform context and journey design. None replaces internal evidence from a controlled rollout.

A small SaaS team can use four stages:

1. Grounded answers. Start with documentation-backed questions such as setup, permissions, integrations, and product behavior. Record the source article used for each response.
2. Verified lookups. Add narrow access to customer records only for scenarios that require it, such as confirming plan entitlement or locating an order. Verify identity and authorization before disclosing protected information.
3. Guarded actions. Permit only well-defined actions, such as a policy-approved subscription update, with conditions, confirmation where appropriate, and an audit trail.
4. Human escalation. Route low-confidence, sensitive, irreversible, disputed, or exceptional cases to a person with the conversation context and relevant facts attached.

Zealoop describes its product as an embedded AI support agent that learns from company documentation, securely looks up customer data, and performs guarded support actions through a chat widget. Those are product capabilities described by Zealoop, not conclusions established by any external benchmark report. A buyer should still validate the precise integrations, access controls, action policies, logging, and escalation workflow needed for its own environment.

What to measure: resolution rather than deflection alone#

Every report category points toward a common caution: an unanswered or abandoned conversation is not necessarily a successful resolution. “Deflection” may be useful as a workload measure, but it should not be the sole success metric for an AI customer-support agent.

A practical scorecard contains at least six measures:

• Grounded-answer rate: share of answers supported by an approved documentation source.
• Contained-resolution rate: share of cases completed without human support and without a near-term reopen, using a team-defined observation window.
• Median time to resolution: tracked separately for AI-contained, AI-assisted, and human-only cases.
• Escalation quality: share of handoffs containing the customer’s question, attempted steps, and the relevant verified facts.
• Action success rate: share of guarded actions completed correctly, including reversals or corrections where applicable.
• CSAT by case type: separate documentation answers, lookups, actions, and escalations instead of relying on one blended score.

Consider two cases. An agent can answer “How do I invite a teammate?” from an approved guide. The key measures are grounding, clarity, containment, and any reopening. A request to cancel an annual subscription has a different risk profile: the team should also measure authorization accuracy, policy compliance, confirmation quality, execution accuracy, and the ability to recover from an error.

This is why benchmark findings should become test hypotheses. If a team expects AI to reduce time to resolution, it should predefine the issue types, baseline period, safety checks, and success threshold before rollout.

Which should you choose?#

A small SaaS support team does not need to select one report as universal truth. The better choice depends on the decision.

Choose Forethought when the immediate question is how CX leaders are approaching AI support and action-capable agents. It is the closest match to the AI in CX Benchmark Report 2025 search intent.

Choose Freshworks when the team first needs to organize its service-operation baseline. Use it to sharpen questions about workload, efficiency, and service performance, while verifying that the report’s definitions fit the team’s own environment.

Choose Lucidworks when evaluating broad autonomous-AI claims. Its enterprise benchmark framing is useful for resisting vague maturity claims, although it is not a dedicated SaaS support-operations study.

Choose Smart Communications when trust, consequential communications, or regulated-style expectations shape the workflow. Choose Verint when service principles around time savings, self-service, and access to people are the priority. Use Zendesk for directional thinking about context and customer journeys, with the report year checked before citing any finding.

For an embedded SaaS agent, the recommended rollout order is documentation answers first, verified lookups second, tightly guarded actions third, and reliable human escalation throughout. That sequence creates evidence from the team’s actual customers rather than asking a cross-market benchmark to settle a product-policy decision.

Verdict#

Forethought is the direct reference for the AI in CX Benchmark Report 2025. Freshworks adds an operational-service perspective, Lucidworks adds an enterprise AI-maturity perspective, Smart Communications and Verint add consumer-trust perspectives, and Zendesk adds AI-agent and journey-design context.

The reports are informative precisely because they are not identical. A small SaaS team should preserve those differences, cite each source directly, and use the research to design internal tests. The most defensible AI-support program measures grounded resolution, secure data handling, correct actions, useful handoffs, time to resolution, and CSAT—not a single deflection number.

FAQ#

What does the 2025 AI in CX Benchmark Report measure?

The term usually refers to Forethought’s report on AI in customer experience, efficiency, and support operations. Forethought’s public report page references insights from 500+ companies, while its related summary is framed around 600+ CX leaders. It is best used to understand reported adoption patterns and support strategy questions, not as controlled causal proof of a specific performance gain.

What are the most important AI customer-experience trends for 2025?

Across Forethought, Freshworks, Lucidworks, Smart Communications, Verint, and Zendesk, recurring themes include AI agents, support efficiency, customer trust, contextual experiences, and continued human access. The evidence types differ: operational benchmarking, enterprise adoption research, consumer research, and CX-leader perspectives should not be collapsed into one market-wide statistic.

How are AI agents affecting resolution times, efficiency, and CSAT?

The cited reports provide directional context about AI in support and customer experience, but they do not establish one universal causal effect on resolution time, efficiency, or CSAT for small SaaS teams. A team should test its own agent against a baseline, separating documentation answers, customer-data lookups, guarded actions, and human escalations when measuring time to resolution and CSAT.

Which 2025 CX benchmark report is most useful for a small SaaS support team?

Forethought is most useful for an AI-support strategy discussion, while Freshworks is useful for customer-service operational questions. Lucidworks helps assess broad claims about autonomous AI maturity. Smart Communications and Verint are better used for customer trust and acceptance principles. No report replaces a scoped pilot with defined safety controls and outcome measures.

What evidence supports AI customer-service actions and customer-data lookups?

Benchmark research can support the idea that customers value timely, successful resolution, but it does not prove that unrestricted data access or autonomous account changes are safe. Product-level evidence is needed: documented authorization checks, minimum-necessary data retrieval, policy constraints, confirmation rules, action logs, error handling, and a human escalation route. These controls should be tested in the team’s actual support workflows.