The Tool Desk
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What chatbots can do well
A customer-support chatbot is most useful when the task is common, clearly defined, and supported by dependable information. Examples include checking an order’s status, finding a shipping estimate, locating a return policy, or getting a straightforward answer about a product. If the bot can retrieve the relevant current information, it may give customers a quick first response without requiring them to wait for an agent.
That can improve access to basic help outside an agent’s working hours and reserve human attention for questions that need investigation, judgment, or empathy. But faster access is not the same as successful resolution: a quick answer that is wrong, incomplete, or difficult to escalate may create another contact rather than solve the first one.
Potential benefits—and what the evidence actually shows
Quicker responses to recurring questions
For well-documented, repeat questions, a chatbot can provide an immediate response instead of placing every customer in a human-agent queue. A retailer case study summarized by Gartner illustrates one possible result: Gartner’s 4 April 2024 abstract says that Solo Brands’ generative AI chatbot resolved 75% of customer interactions, up from a 40% resolution rate. This is a result reported for one retailer’s deployment, not a controlled estimate of what other businesses should expect.
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More support capacity, without assuming every conversation can be automated
Automating suitable routine tasks may let agents spend more time on exceptions and complex cases. The operational benefit depends on what the bot actually resolves, whether customers have to repeat themselves after escalation, and how often an automated interaction leads to a repeat contact. A deflection figure alone cannot show whether customers got a satisfactory answer.
Freshworks reported that, in its 2024 benchmark study, resolution times were up to 38% shorter and customer satisfaction scores improved by up to 6%. The vendor said the study used anonymized data from 19 million Freshdesk tickets and 37 million Freshchat conversations across more than 25 industries, covering January 2023 through April 2024. These are vendor-published findings from platform usage data; they should not be read as a guarantee that adding a chatbot will produce those results.
Product and policy help during shopping
In ecommerce, conversational support can help customers find product information, understand delivery or return terms, and get assistance while deciding what to buy. The practical benefit is reducing the effort required to find relevant answers. It depends on the quality and currency of the product, inventory, shipping, and policy information available to the system.
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Sales claims require more caution. Liquid Web reports that, among ecommerce businesses using AI chatbots, 75% reported an average sales or lead increase of 20%. Liquid Web describes this as proprietary survey research. It is a business-reported result, not evidence that a chatbot caused a 20% increase or that an individual store should expect one. A retailer-specific sales outcome likewise cannot establish a general conversion lift.
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Not every customer-facing benefit requires a bot to handle a conversation alone. A 2025 randomized field experiment at a meal-delivery company studied AI-generated suggestions for human agents. The researchers found that AI assistance helped agents respond faster, engage customers more deeply, and improve customer sentiment. Effects varied by conversation type and were weakest for repeat complaints tied to systemic issues. This is evidence about AI supporting human agents—not evidence that autonomous chatbots produce the same outcomes.
Chatbots, agent assistance, and human-only service compared
| Approach | Where it may fit | What the evidence supports | Main caution |
|---|---|---|---|
| Customer-facing chatbot | Recurring questions with clear answers and access to relevant, current information. | Gartner’s 2024 abstract reports a 75% resolution rate for Solo Brands, up from 40%; this is one retailer’s case-study result. | A case result is not a universal benchmark. Resolution figures need to be considered alongside satisfaction, repeat contact, and escalation. |
| AI suggestions for human agents | Helping an agent respond in a live conversation, particularly when a person still needs to interpret the situation. | A 2025 randomized field experiment at a meal-delivery company found faster responses and improved customer sentiment, with effects that varied by conversation type. | The experiment evaluated agent assistance, not an autonomous chatbot. Repeat complaints tied to systemic issues saw the weakest effects. |
| Human-only service | Questions that call for personal judgment, a sensitive response, or investigation of an unresolved problem. | Three online-panel studies published in 2024 found higher satisfaction, repatronage intentions, and recommendation acceptance with human service than with chatbot service in the study setting. | Those study findings are context-dependent and do not establish that human-only service is best for every routine task. |
| Chatbot service with light-touch human intervention | Automating or assisting with an interaction while retaining a human role when appropriate. | In the same 2024 trust research, chatbot service combined with light-touch human intervention matched the more costly human-only interaction in the study setting. | The finding does not mean every implementation or handoff will produce the same customer outcome. |
Where chatbots can disappoint customers
Questions outside the bot’s knowledge
A bot may sound confident without having the information needed to answer accurately. For ecommerce, stale product details, order information, or policies can turn a quick reply into a misleading one. Automation is a poor fit when an answer depends on a customer’s specific history, a judgment call, or facts the system cannot access.
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Complex, sensitive, or unresolved problems
A menu or generated answer may not be enough for a billing dispute, a damaged order, a repeated failure, or a complaint that needs someone to investigate. A bot that keeps customers in an unproductive loop can undermine trust. Human service performed better than chatbot service on satisfaction, repatronage intentions, and recommendation acceptance in the setting studied in three 2024 online-panel studies. That finding is a reason to preserve meaningful human access, not proof that every chatbot interaction will dissatisfy customers.
Repeat complaints caused by a wider problem
AI assistance cannot substitute for fixing the underlying cause of a recurring complaint. In the 2025 meal-delivery field experiment, assistance had its weakest effects on repeat complaints tied to systemic issues. When a customer reports the same unresolved problem again, a faster suggested response may help less than identifying and correcting the operational failure.
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A 2025 article on ecommerce service and conversion identifies limited customer understanding and data-privacy concerns as chatbot challenges. Those concerns make it important to consider what information a customer is being asked to share and whether the interaction is clear. The available article summary does not establish a detailed risk assessment or specific legal or security requirements.
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How to measure whether a chatbot helps
Set a baseline before deployment and separate the results for the tasks the bot is meant to handle. A single automation or deflection percentage can hide unresolved questions, repeat contacts, and poor customer experiences. Track service outcomes and commercial outcomes separately; an increase in sales occurring alongside chatbot use does not, by itself, show the chatbot caused it.
- Task-specific resolution: Did the customer get the needed answer or complete the intended task?
- Customer satisfaction and trust: How did customers rate the interaction, and do results differ for bot-only, agent-assisted, and human-handled conversations?
- Escalation and handoff: How often did the bot hand a conversation to a person, and did the person receive enough context to continue without making the customer start over?
- Repeat contact: Did customers return about the same issue after the bot interaction?
- Time to resolution: Measure time to an actual outcome, not just the speed of the first response.
- Commercial outcomes: For shopping assistance, examine sales or leads alongside customer and service outcomes, and avoid treating correlation as proof of a chatbot-driven lift.
Compare results by inquiry type and complexity. A bot might perform well on order-status questions yet struggle with repeated complaints; combining all interactions can conceal that difference. This distinction matters because observed chatbot, human-service, and agent-assistance outcomes vary with the task and study setting.
How to decide what to automate
- Choose a bounded set of recurring questions. Start with tasks that have clear, documented answers rather than attempting to automate every conversation.
- Check the information the bot can use. Confirm that it can access the current facts needed for the selected tasks, such as the relevant order, product, or policy details. If those facts are unavailable or unreliable, a fast answer is not a dependable answer.
- Define when a person should take over. Provide a workable route to human help for complex, sensitive, unresolved, or repeated issues. The handoff should preserve useful conversation context so the customer does not needlessly start again.
- Establish task-level baselines and success measures. Record current resolution, satisfaction, time to resolution, repeat contact, and escalation for the tasks in scope. For shopping use, measure commercial outcomes separately from service quality.
- Review outcomes by question type. Look for cases where the bot gives incomplete answers, customers return about the same problem, or escalations are difficult. Adjust which tasks it handles rather than treating a high overall resolution percentage as sufficient.
Frequently Asked Questions
Can chatbot customer service match human service agents on customer satisfaction?
It can depend on the task and the role of the human. In three online-panel studies published in 2024, human service led to higher satisfaction than chatbot service in the study setting, while chatbot service with light-touch human intervention matched the more costly human-only interaction. These findings do not establish one outcome for every support operation.
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Do chatbots increase ecommerce conversion rates?
The evidence described here does not establish a general causal conversion increase. Liquid Web’s reported sales and lead figures are proprietary survey responses, while an individual retailer’s reported outcome is specific to that business. Neither is a guarantee of a comparable result for another store.
Should a business automate every support conversation?
No. The evidence supports using automation selectively and keeping human help available for cases where an answer needs judgment, investigation, or a response to an unresolved problem. A chatbot’s suitability depends on the question, the information it can access, and the quality of its handoff.
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