Robert Gerstmann, co-founder of Sinch, asks the question: how willing are customers to let AI spend their money – really?
With Fashion Week not long behind us, customers are writing up their wish lists ahead of Black Friday with renewed inspiration for their wardrobes. Meta’s new agent, Muse, offers a glimpse into how those purchases could look in the future, with AI helping customers find products and pay on their behalf.
For some, the idea of a digital personal shopper that can keep them right on trend may sound like a promising proposition, but research shows that when it comes to handling money, customers are more reluctant to hand control over to AI.
Sinch’s BFCM 2026 consumer survey found that most (81%) of global consumers are confident that an AI assistant can handle order tracking and shipping updates. However, that number falls to 62% when it comes to payment or billing changes.
Whether or not consumers trust AI agents throughout their retail journey depends on the task that the agent is carrying out. So, whilst overall consumer enthusiasm for AI may appear high, only 43% say they think AI will make shopping easier. As such, there is work to be done for retailers to build consumer trust with AI-powered agents carrying out more complex tasks on their behalf.
Start with the questions that decide the sale
Delivery times, product details and returns policies can be the final thing that determines whether a shopper completes the checkout stage, or not. These are opportunities for retailers to demonstrate the value of AI through accurate, useful answers that help customers make confident purchasing decisions.
By starting with straightforward enquiries, retailers can build familiarity with AI before asking shoppers to trust it with more complex tasks. For example, an AI agent that quickly explains whether an item can arrive before a certain date, or if a certain size is in stock, gives the customer a reason to complete the purchase. Rather than navigating multiple pages, they can receive answers tailored to their specific needs. After purchasing, the same chatbot could answer questions about shipping and delivery,checking the latest order status to explain where a parcel is and when it is expected to arrive.
However, these benefits depend on connecting customer communications with the systems holding product, order and delivery information. A chatbot that provides order updates, needs to reflect the latest order status, ultimately giving shoppers timely information and a straightforward way to get further help.
Keep checking agents once they’re live
Consumer confidence in AI falls as the task becomes more consequential. For retailers, building that confidence means ensuring AI agents consistently provide accurate, reliable information, particularly when customers are relying on answers to make purchasing decisions. An agent might confidently provide incorrect information about a returns policy or delivery deadline, leaving a customer satisfied in the moment but potentially damaging their trust in the brand when the mistake comes to light.
According to Sinch’s AI Paradox Report, 69% of retail organisations have rolled back AI agents due to governance failures, highlighting the challenges of ensuring agents perform reliably once deployed. Setting rules before deployment is important, but retailers also need ongoing monitoring to understand how agents are performing in real customer conversations. This means identifying inaccurate responses, spotting when agents operate outside their intended boundaries and intervening before problems undermine customer confidence.
Put what shoppers share to work
Sinch research shows 77% of consumers value personalised recommendations during big sales moments like the holiday season – but for 42%, only if it’s relevant.
To deliver a truly tailored experience, retailers can use smart conversations across channels like WhatsApp or RCS to provide more valuable recommendations. By asking questions that help them understand what shoppers want, agents can make their next recommendations more relevant, showing the customers where AI actually makes shopping easier. Asking about preferences, budget or the occasion can help customers find something they want to buy more efficiently.
French fashion retailer, Maje, put this into practice with a holiday campaign where they asked customers to chat with an RCS messaging bot about their style preferences, budgets and occasions, then presented personalised gift ideas to customers. They found that 40% of customers who started a conversation continued to Maje’s website, demonstrating the pull of a personalised experience.
The lesson for retailers is to build on interactions using information the customer has shared in these conversations. During Black Friday, that could mean helping a shopper narrow down gift options within their budget, while leaving the ultimate purchase decision in their hands. This gives shoppers a practical benefit from the conversation and a reason to engage again.
Measure resolution, not just automation
Despite the hype, research shows consumers are becoming more selective when it comes to using AI across their shopping experience, forcing retailers to reflect on their strategy.
The number of conversations handled by AI tells retailers little about whether the conversations
contributed to a better customer experience. Retailers should instead track resolution, repeat contact, satisfaction and, for pre-purchase enquiries, conversion. These measures help identify where AI removes friction and when it simply moves it elsewhere.
Earning the right to do more
Shoppers’ caution does not necessarily mean they reject AI altogether, but retailers need to be specific about its purpose and careful about its authority. In practice, that means proving AI on everyday questions first, monitoring agents once they’re live, using what shoppers share to make conversations more relevant and measuring resolution rather than automation. Accurate answers, clear permissions and accessible human help are how brands earn the confidence to let AI do more.
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You are in: Home » AI » GUEST COMMENT Retailers have work to do earning customer trust in AI
GUEST COMMENT Retailers have work to do earning customer trust in AI
Robert Gerstmann
Robert Gerstmann, co-founder of Sinch, asks the question: how willing are customers to let AI spend their money – really?
With Fashion Week not long behind us, customers are writing up their wish lists ahead of Black Friday with renewed inspiration for their wardrobes. Meta’s new agent, Muse, offers a glimpse into how those purchases could look in the future, with AI helping customers find products and pay on their behalf.
For some, the idea of a digital personal shopper that can keep them right on trend may sound like a promising proposition, but research shows that when it comes to handling money, customers are more reluctant to hand control over to AI.
Sinch’s BFCM 2026 consumer survey found that most (81%) of global consumers are confident that an AI assistant can handle order tracking and shipping updates. However, that number falls to 62% when it comes to payment or billing changes.
Whether or not consumers trust AI agents throughout their retail journey depends on the task that the agent is carrying out. So, whilst overall consumer enthusiasm for AI may appear high, only 43% say they think AI will make shopping easier. As such, there is work to be done for retailers to build consumer trust with AI-powered agents carrying out more complex tasks on their behalf.
Start with the questions that decide the sale
Delivery times, product details and returns policies can be the final thing that determines whether a shopper completes the checkout stage, or not. These are opportunities for retailers to demonstrate the value of AI through accurate, useful answers that help customers make confident purchasing decisions.
By starting with straightforward enquiries, retailers can build familiarity with AI before asking shoppers to trust it with more complex tasks. For example, an AI agent that quickly explains whether an item can arrive before a certain date, or if a certain size is in stock, gives the customer a reason to complete the purchase. Rather than navigating multiple pages, they can receive answers tailored to their specific needs. After purchasing, the same chatbot could answer questions about shipping and delivery,checking the latest order status to explain where a parcel is and when it is expected to arrive.
However, these benefits depend on connecting customer communications with the systems holding product, order and delivery information. A chatbot that provides order updates, needs to reflect the latest order status, ultimately giving shoppers timely information and a straightforward way to get further help.
Keep checking agents once they’re live
Consumer confidence in AI falls as the task becomes more consequential. For retailers, building that confidence means ensuring AI agents consistently provide accurate, reliable information, particularly when customers are relying on answers to make purchasing decisions. An agent might confidently provide incorrect information about a returns policy or delivery deadline, leaving a customer satisfied in the moment but potentially damaging their trust in the brand when the mistake comes to light.
According to Sinch’s AI Paradox Report, 69% of retail organisations have rolled back AI agents due to governance failures, highlighting the challenges of ensuring agents perform reliably once deployed. Setting rules before deployment is important, but retailers also need ongoing monitoring to understand how agents are performing in real customer conversations. This means identifying inaccurate responses, spotting when agents operate outside their intended boundaries and intervening before problems undermine customer confidence.
Put what shoppers share to work
Sinch research shows 77% of consumers value personalised recommendations during big sales moments like the holiday season – but for 42%, only if it’s relevant.
To deliver a truly tailored experience, retailers can use smart conversations across channels like WhatsApp or RCS to provide more valuable recommendations. By asking questions that help them understand what shoppers want, agents can make their next recommendations more relevant, showing the customers where AI actually makes shopping easier. Asking about preferences, budget or the occasion can help customers find something they want to buy more efficiently.
French fashion retailer, Maje, put this into practice with a holiday campaign where they asked customers to chat with an RCS messaging bot about their style preferences, budgets and occasions, then presented personalised gift ideas to customers. They found that 40% of customers who started a conversation continued to Maje’s website, demonstrating the pull of a personalised experience.
The lesson for retailers is to build on interactions using information the customer has shared in these conversations. During Black Friday, that could mean helping a shopper narrow down gift options within their budget, while leaving the ultimate purchase decision in their hands. This gives shoppers a practical benefit from the conversation and a reason to engage again.
Measure resolution, not just automation
Despite the hype, research shows consumers are becoming more selective when it comes to using AI across their shopping experience, forcing retailers to reflect on their strategy.
The number of conversations handled by AI tells retailers little about whether the conversations
contributed to a better customer experience. Retailers should instead track resolution, repeat contact, satisfaction and, for pre-purchase enquiries, conversion. These measures help identify where AI removes friction and when it simply moves it elsewhere.
Earning the right to do more
Shoppers’ caution does not necessarily mean they reject AI altogether, but retailers need to be specific about its purpose and careful about its authority. In practice, that means proving AI on everyday questions first, monitoring agents once they’re live, using what shoppers share to make conversations more relevant and measuring resolution rather than automation. Accurate answers, clear permissions and accessible human help are how brands earn the confidence to let AI do more.
Image © Sinch
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