What to look for in AI-powered customer enquiry software for small businesses

AI-powered customer enquiry software can help small businesses streamline their operations and improve customer satisfaction. But with so many options available, it can be difficult to know what to look for.

Firstly, consider the functionality of the software. Does it allow you to track customer interactions, manage leads, and automate tasks? Is it integrated with other tools and systems you use?

Next, think about the user interface. Is it easy to navigate and use? Are the reports and analytics useful for making informed decisions?

Data ownership is also a critical consideration. Who owns the data you enter into the system, and how will you access it when you need to?

When evaluating AI-powered customer enquiry software, look for tools that help small businesses manage customer follow-up, automate structured customer contact and reduce missed appointments.

It's also essential to consider the cost of the software. While some options may be cheaper upfront, they can often cost more in the long run due to their limited functionality and lack of support.

By considering these factors, you can find an AI-powered customer enquiry software that meets your business needs and helps you achieve your goals.

To ensure the success of your CRM, it's also vital to set basic CRM for a five-person team. This includes setting up customer data, setting customer behaviour, licence to use the system, practising good customer service, recognising customer feedback and managing customer interactions efficiently.

Additionally, small businesses should back up their CRM regularly to prevent data loss in case of technical issues.

Furthermore, it's essential for small business owners to know about software data ownership and ensure that they have offline access options for their software.

Finally, when switching software, avoid doing so too often. This can lead to wasted time and resources on retraining staff.

How to Put This Into Practice

When comparing tools, test context retention directly: send a short back-and-forth of three or four messages and see whether the fourth reply still remembers what was said in the first. A surprising number of tools treat each message as a fresh conversation, which means customers end up repeating themselves — one of the most common complaints about automated enquiry handling. Next, test the handoff to a human. Ask a question that should trigger escalation (a complaint, a pricing negotiation) and check what the human actually receives: do they get the full conversation history, or just the last message with no context, forcing them to ask the customer to start again? Check whether every AI-generated reply is logged with a timestamp and is searchable later — this matters both for resolving disputes and for spotting patterns in what customers actually ask. Finally, ask directly where customer data is stored, whether it's used to train external models, and how deletion requests are handled.

A Worked Example

A five-person letting agency piloted two AI enquiry tools side by side for a month. Tool A answered questions accurately in isolation but lost context after each message, so a prospective tenant who asked "does it have parking?" after already naming a specific property got a generic answer instead of one specific to that flat. Tool B retained the full thread and answered correctly. On escalation, Tool A simply sent an email saying "customer needs a call back" with no detail, while Tool B forwarded the entire conversation transcript, so the agent could pick up mid-conversation without asking the tenant to repeat themselves. The agency chose Tool B despite it costing £15 more per month, because the escalation handoff alone was saving roughly ten minutes of back-and-forth per enquiry.

Common Mistakes

• Not testing multi-message context before buying, only checking single-question accuracy
• Assuming "human handoff" means the same thing across tools without checking what data actually transfers
• Ignoring whether AI responses are logged and searchable until a dispute makes it urgent
• Not asking where customer conversation data is stored or whether it trains third-party models
• Choosing the cheapest tool without weighing the time cost of poor handoffs on staff

A Simple Checklist

• Test a multi-message conversation and confirm context carries through to later replies
• Trigger an escalation and check exactly what information the human receives
• Confirm every AI reply is logged with a timestamp and is searchable
• Ask directly about data storage location, retention period, and deletion process
• Check whether the tool distinguishes between factual answers and answers requiring judgement
• Get the answer to escalation and data-handling questions in writing, not verbally from a sales rep

By implementing efficient digital workflows, small businesses can significantly boost productivity and make informed decisions with data-driven insights at their fingertips. — Editor, AppSoluteTec