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How to Establish Trust and Unlock Customer Data to Turn the AI Promise into Profit

Kathryn Murphy
Twilio

Businesses are dazzled by the promise of generative AI, as it touts the capability to increase productivity and efficiency, cut costs, and provide competitive advantages. With more and more generative AI options available today, businesses are now investigating how to convert the AI promise into profit.

One way businesses are looking to do this is by using AI to improve personalized customer engagement. In fact, seven out of 10 companies say they are already using AI to personalize content and marketing.

AI makes it possible to deliver truly unique and individualized experiences for every single customer, building loyalty and increasing efficiency along the way. Using AI, businesses can move away from the one-to-many marketing strategy and provide truly individualized, one-to-one experiences.

With any emerging technology, there are also major risks — including lack of access to the right data, transparency, and trust. In order to gain a return on AI customer experience investments, businesses must maximize their data and prioritize trust.


The Customer Data Disconnect

While businesses have put a greater focus on collecting data, many are struggling to put this data to work, especially when it comes to AI.

According to Twilio's 2024 State of Customer Engagement Report, which surveyed over 4,700 executives and 6,000 consumers, only 16% of businesses strongly agree that they have the data they need to understand their customers. This lack of data is a huge and potentially costly setback for businesses. Without the right customer data, businesses risk building experiences and communications that are at best generic or at worst wrong. With more and more businesses actively considering how to incorporate AI into their experiences, having the right customer data is essential to success.

In order to best understand where customer experience data gaps exist, businesses should start with these tactics:

Conduct a customer journey audit to find out what's missing and identify the pain points preventing conversion. This audit is the foundation for a holistic customer journey map that will help businesses activate their data to provide better customer experiences.

Unlock warehouse data. While most businesses have a wealth of information in their data warehouses, few can actually activate it. To solve this problem, businesses can connect their customer data platform (CDP) data with the data in their warehouses, promoting a deeper understanding of each customer and their interactions with the brand.

Integrate AI directly into the contact center to collect and share data in real time, including events across platforms leading up to the moments prior to a customer calling a contact center. This helps customer service agents provide personalized experiences without getting overwhelmed by the deluge of information, and ultimately resolve customer issues faster and more effectively.

AI opens the opportunity to analyze the large volumes of collected customer data to gain actionable insights and deliver personalized experiences to customers. Taking stock of what data and potential barriers exists will help businesses capitalize on the AI opportunity in a way that meets customer expectations.

Navigating Today's AI Trust Landscape

However, with emerging malicious use cases like AI-generated images diminishing consumer trust in AI, it's essential that companies improve data transparency and security when delivering customer experiences powered by AI. Failure to do so puts customer trust and loyalty at risk.

Consumers want to know exactly how businesses leverage their data. Almost half (49%) of consumers say they would trust brands more if they openly disclose the use of customer data in AI-powered interactions. The problem, however, is that businesses believe they are already doing this well. According to this same report, while 91% of brands say they're transparent with customers around how AI uses their data, only 48% of customers agree. The bottom line for businesses is consumers need to experience personalization in a trusted way. It has to be clear to the consumer that the brand knows certain information and is leveraging it to personalize for the end users benefit. There is a thin line between too much and awesome — brands need to be wary to not cross it.

AI's value is unlocked when businesses can securely harness customer data to develop a deep understanding of each customer and improve each interaction over time. But to build customer trust, businesses need to be open and clear with their customers on how they are using customer data in AI-powered experiences. Twilio introduced a clear and concise way for businesses to provide greater transparency in how they use this data, called AI nutrition labels. These AI nutrition labels operate like the labels on everyday grocery items and give an inside view into exactly what data went into creating each AI-powered experience, how exactly it is being used and more. This transparency helps to instill trust and shows a commitment to the privacy of customers.

Unlocking the AI ROI Promise

When businesses are able to build trust through transparency and activate their data, using AI in customer engagement stands to impact the bottom line. According to the report, consumers will spend 54% more with a brand that personalizes to them. AI can help power the personalization that secures this loyalty — 48% of consumers say they've made a repeat purchase from a company based on the level of personalization they received.

Businesses who use AI to power personalized customer experiences are reaping the rewards. Seven in 10 companies already leverage AI to personalize content and marketing and are realizing a number of benefits, including higher customer satisfaction scores (45%), better data-driven decision-making (41%) and improved market segmentation and targeting (41%).

AI and generative AI have the potential to transform how businesses connect with and retain their customers. The businesses that invest in trust and in building a strong data foundation stand to not only maximize their AI investments, but also maximize their potential profits.

Kathryn Murphy is SVP of Product and Design at Twilio

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How to Establish Trust and Unlock Customer Data to Turn the AI Promise into Profit

Kathryn Murphy
Twilio

Businesses are dazzled by the promise of generative AI, as it touts the capability to increase productivity and efficiency, cut costs, and provide competitive advantages. With more and more generative AI options available today, businesses are now investigating how to convert the AI promise into profit.

One way businesses are looking to do this is by using AI to improve personalized customer engagement. In fact, seven out of 10 companies say they are already using AI to personalize content and marketing.

AI makes it possible to deliver truly unique and individualized experiences for every single customer, building loyalty and increasing efficiency along the way. Using AI, businesses can move away from the one-to-many marketing strategy and provide truly individualized, one-to-one experiences.

With any emerging technology, there are also major risks — including lack of access to the right data, transparency, and trust. In order to gain a return on AI customer experience investments, businesses must maximize their data and prioritize trust.


The Customer Data Disconnect

While businesses have put a greater focus on collecting data, many are struggling to put this data to work, especially when it comes to AI.

According to Twilio's 2024 State of Customer Engagement Report, which surveyed over 4,700 executives and 6,000 consumers, only 16% of businesses strongly agree that they have the data they need to understand their customers. This lack of data is a huge and potentially costly setback for businesses. Without the right customer data, businesses risk building experiences and communications that are at best generic or at worst wrong. With more and more businesses actively considering how to incorporate AI into their experiences, having the right customer data is essential to success.

In order to best understand where customer experience data gaps exist, businesses should start with these tactics:

Conduct a customer journey audit to find out what's missing and identify the pain points preventing conversion. This audit is the foundation for a holistic customer journey map that will help businesses activate their data to provide better customer experiences.

Unlock warehouse data. While most businesses have a wealth of information in their data warehouses, few can actually activate it. To solve this problem, businesses can connect their customer data platform (CDP) data with the data in their warehouses, promoting a deeper understanding of each customer and their interactions with the brand.

Integrate AI directly into the contact center to collect and share data in real time, including events across platforms leading up to the moments prior to a customer calling a contact center. This helps customer service agents provide personalized experiences without getting overwhelmed by the deluge of information, and ultimately resolve customer issues faster and more effectively.

AI opens the opportunity to analyze the large volumes of collected customer data to gain actionable insights and deliver personalized experiences to customers. Taking stock of what data and potential barriers exists will help businesses capitalize on the AI opportunity in a way that meets customer expectations.

Navigating Today's AI Trust Landscape

However, with emerging malicious use cases like AI-generated images diminishing consumer trust in AI, it's essential that companies improve data transparency and security when delivering customer experiences powered by AI. Failure to do so puts customer trust and loyalty at risk.

Consumers want to know exactly how businesses leverage their data. Almost half (49%) of consumers say they would trust brands more if they openly disclose the use of customer data in AI-powered interactions. The problem, however, is that businesses believe they are already doing this well. According to this same report, while 91% of brands say they're transparent with customers around how AI uses their data, only 48% of customers agree. The bottom line for businesses is consumers need to experience personalization in a trusted way. It has to be clear to the consumer that the brand knows certain information and is leveraging it to personalize for the end users benefit. There is a thin line between too much and awesome — brands need to be wary to not cross it.

AI's value is unlocked when businesses can securely harness customer data to develop a deep understanding of each customer and improve each interaction over time. But to build customer trust, businesses need to be open and clear with their customers on how they are using customer data in AI-powered experiences. Twilio introduced a clear and concise way for businesses to provide greater transparency in how they use this data, called AI nutrition labels. These AI nutrition labels operate like the labels on everyday grocery items and give an inside view into exactly what data went into creating each AI-powered experience, how exactly it is being used and more. This transparency helps to instill trust and shows a commitment to the privacy of customers.

Unlocking the AI ROI Promise

When businesses are able to build trust through transparency and activate their data, using AI in customer engagement stands to impact the bottom line. According to the report, consumers will spend 54% more with a brand that personalizes to them. AI can help power the personalization that secures this loyalty — 48% of consumers say they've made a repeat purchase from a company based on the level of personalization they received.

Businesses who use AI to power personalized customer experiences are reaping the rewards. Seven in 10 companies already leverage AI to personalize content and marketing and are realizing a number of benefits, including higher customer satisfaction scores (45%), better data-driven decision-making (41%) and improved market segmentation and targeting (41%).

AI and generative AI have the potential to transform how businesses connect with and retain their customers. The businesses that invest in trust and in building a strong data foundation stand to not only maximize their AI investments, but also maximize their potential profits.

Kathryn Murphy is SVP of Product and Design at Twilio

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...