The triple convergence
We’re in the eye of a storm where three powerful forces are colliding: the explosion of customer data, the rapid advancement of artificial intelligence (AI), and intensifying economic and geopolitical uncertainty.
Economic uncertainty breeds changing consumer behaviors and budget constraints
The explosion of customer data is making it hard to find the right signal
Rapid advancement of AI is allowing brands to more efficiently reach customers
This convergence is reshaping the way businesses must operate — and creating a new imperative: a strong customer data foundation built to help brands target and retain customers through economic headwinds and ready to take advantage of AI opportunities.
Economic pressure demands operational agility
Economic pressure is forcing brands across industries to re-evaluate how they operate, prioritize efficiency, and maximize the value of every customer interaction. The changes businesses are making during these uncertain times are not temporary — it’s a permanent shift.
Retail: Margins are being pinched with rising costs, and customers are getting more budget-conscious. According to Reuters, consumer sentiment is at a three-year low due to recent uncertainty.
Travel and Hospitality: With price sensitivity and unpredictable booking behavior, personalized offers that work are more critical than ever. A Skift survey finds international travelers are less likely to visit the US.
Automotive: Supply chain unpredictability and evolving buyer preferences mean the margin for error in customer targeting is razor thin. According to S&P Global, "auto tariffs could impact as much as 45% of light-duty vehicles sold in the US."
In each case, companies must do more with less. AI offers a path forward — but only if their data foundation is in order.
The customer data crisis
Brands are collecting five times more customer data than they did just four years ago, according to IDC. Every click, purchase, and customer service interaction creates a digital breadcrumb — yet most organizations still lack a coherent customer view. Leveraging an inaccurate or incomplete customer data foundation means:
Customer insights that are used to drive overall strategy are misleading your business.
Customer experiences that are meant to engage and grow the value of your customers are not relevant, often having the opposite effect.
AI investments meant to automate and accelerate key initiatives are making decisions on misleading data.
Why do brands struggle to create a unified view of their customers? Because the majority of innovation has been happening in analytics and personalization, while the hardest problem of data unification has barely evolved. Legacy systems, built around siloed channels and static rules, simply can’t keep up with today’s data complexity and scale. And as the adage goes, garbage in, garbage out. This results in suboptimal revenue growth and the inability to take advantage of the next generation of AI.
AI has arrived. Are you ready?
AI capabilities are advancing at breakneck speed. AI agents have the potential to deliver one-to-one personalization at scale that has eluded most brands. Machine learning models can spot patterns across billions of data points. Large language models understand context and intent to serve customers the best possible experience.
In the face of tight budgets and rising pressure, AI isn’t a luxury — it’s a necessity. C-suite executives have drawn the line that the time for AI adoption is now. However, according to MIT Technology Review Insights, 78% of global companies are not “very ready” to deploy natural language AI tools like LLMs and agents. The biggest barrier? Data readiness.
Why customer data is the bottleneck
While many teams rush to apply AI to personalization or insights, the real bottleneck to seeing the expected returns from these investments is further upstream: messy, mismatched customer data.
The explosion of customer data comes from a proliferation of channels and applications, each defining customer identity and capturing data differently, which will only get more complex with AI. And customer data has always been complex — people (and their data) change — they move, get married, sign up for multiple accounts with slightly different names and new emails. Add to that the massive volume, human error typos, and siloed systems, and you get the reality most teams face: disjointed, unreliable data that undermines even the most advanced AI initiatives.
Build an AI-ready data foundation
Enterprises serious about AI transformation must begin with an AI-ready data foundation:
Accurate, unified customer profiles with transparent matching methodologies
Flexible data models that can both incorporate new data sources quickly and shape data as the foundation for new AI initiatives and activation channels
Governance and access controls that ensure responsible AI use
Without this foundation, AI becomes another disappointing investment. Because even the smartest model is only as good as the data it's trained on.
AI’s most urgent job: identity and data management
What organizations need first isn’t customer-facing AI Agents — it’s Agentic AI for the tedious, complex work of customer data stitching. There is an opportunity for AI agents to lighten the burden of complex code for Identity Resolution on data engineering teams, allowing them to focus on strategic decisions around model tuning and data QA.
Jobs to be done with manual code or processes before AI | With an AI agent |
Manually identify all your tables with customer PII | AI analyzes available tables and identifies tables with relevant customer data |
Convert source data with different schemas and structures into a single format matching and merging | AI analyzes and annotates data with semantic tags, with the option to validate |
Iterate to remove bad PII data by writing code with complex criteria, manually checking results, then updating removal conditions | AI and OOTB rules identify common patterns with the option to customize |
Iterate over data standardization and cleaning logic by creating cleaning functions, manually reviewing samples, and adjusting logic | AI converts data into a standardized format for ID resolution, recognizing common aliases and matching them across various formats |
Write imprecise fuzzy matching code, configure matching rules, and constantly update matching logic to resolve identity and link data between source systems onto a golden record. | AI and ML models run standardized data through models that review and compare billions of data points Models trained for ID Resolution accurately identify transitive connections, with the option to layer in rules as needed |
Without clean, connected customer data, every downstream AI use case — no matter how sophisticated — will be built on shaky ground. Identity resolution isn’t just an operational step, it’s the linchpin of AI-driven transformation.
The path forward
The future of customer data is not just about AI — it’s about how prepared your organization is to use AI where it matters most. It starts with a strategy to maximize the value of customer data, a modern AI-powered platform to accelerate data readiness, and strong data governance.
The question is no longer whether AI will transform customer data foundations — it's whether your organization will lead or follow in this transformation. The best customer data wins.
To learn more about how Amperity’s CDC helps you build unified customer profiles with AI-powered architecture, check out our 2-minute demo.