AI is only as smart as the data it sees. AI needs clean customer identity and a single, trusted record of who the customer truly is to personalize, predict, and automate effectively.
Most companies still treat a single person as five or more separate profiles, scattered across customer relationship management (CRM), e-commerce, paid media, and service systems. This fragmentation results in off-target outputs, wasted ad spend, and unnecessary privacy risks.
Co-founder and CTO of Amperity.
How can businesses fix this? By building a solid, unified data foundation centered around identity resolution. With customer identities resolved and profiles consolidated, businesses can feed AI the clean, single source of truth it needs to deliver accurate personalization and actionable insights at scale.
The old “build vs. buy” debate in customer data platforms no longer fits today’s AI-driven world. The real question is how to combine tools and practices to move faster, maintain accuracy, and scale effectively, starting with a reliable identity resolution layer.
No single customer data tool meets every need, so whether using a homegrown, commercial, or hybrid approach, the key principles are the same: prioritize data quality, embed a robust identity resolution core, select the right mix of tools to move quickly, and maintain a foundation that can grow and adapt as AI and business requirements evolve.
Here are five strategies for building a stronger data foundation that fuels AI results:
1. Unify identities so AI stops guessing
If your systems disagree about who a customer is, every downstream decision degrades. Start by connecting your primary sources – email, web, point-of-sale, and service – and agreeing on what constitutes the go-to record for a customer.
Treating identity as a living, evolving product allows for the application of machine learning models that continuously refine matches, ensuring the single-customer view remains accurate over time.
Use strong matches where you have them, like loyalty ID, and clear rules for tricky cases (nicknames or recycled emails). Treat identity as a living product, not a one-off clean-up.
Unifying identity is the prerequisite for meaningful AI that drives business results.
2. Feed AI clean data, not clutter
Most AI failures trace back to bad inputs, not bad algorithms. Don’t let broken data in. Incomplete histories, duplicate fields, and outdated inputs all mislead AI outputs before they even start.
A duplicate profile can lead to a loyal customer being treated like a first-time buyer while outdated contact details can send costly campaigns to dead inboxes.
Gartner discovered that organizations that fail to enable and support their AI cases through a ready data practice will see over 60% of AI projects fail to deliver. Clean, reliable, and current customer data is the difference between an AI agent guessing at the next best step and an agent that can drive measurable revenue and loyalty.
3. Buy for speed, then build for differentiation
Building a customer data platform from scratch might initially seem appealing, but identity resolution is not a simple query problem. It requires machine learning, experimentation, and continuous tuning to scale. When it comes to identity resolution — the bedrock of every downstream capability — speed and precision are critical.
This is where a hybrid approach can become powerful. Buying proven tools can accelerate time-to-value ratios by building the foundation, while building custom applications on top lets you differentiate where it matters most.
This could mean layering custom business rules for merging profiles or extending software onto loyalty apps.
“Building with” allows businesses to determine what tools to invest in that will enable them to build the capabilities for future innovation.
An overall shift to a hybrid approach will reduce the time spent developing a platform, freeing engineering teams to focus on more strategic tasks.
4. Adopt a composable stack
No single platform excels at every core capability – identity, personalization, activation, analytics, and governance – and forcing an all-in-one solution usually leads to compromise.
Stitching together a composable approach and selecting best-in-class tools for each function gives companies the flexibility to think about their own needs and prioritize accordingly.
A composable setup, built and connected by different modules, allows brands to swap out individual tools when regulations change or new AI opportunities emerge without disrupting their entire system.
Combining specialized tools with broad-based data warehouses achieves the precision required for personalization, consent, and AI governance.
5. Build governance into the foundation
As AI raises the stakes for customer trust, inaccurate or misused data poses a risk for compliance violations and reputational damage. End consumers increasingly expect brands to take a privacy-first approach, which is an expectation that’s easiest to meet when every customer profile is a single, auditable record.
A single, resolved customer record simplifies consent management, audit logs, and data-quality checks. It also reduces the risk of “consent drift” (when a customer’s preferences change but the system fails to reflect that change).
By resolving identities first, you build a governance framework that can evolve with GDPR and CCPA without rearchitecting the entire stack.
Governance ensures that a customer’s data is handled responsibly, including data privacy, security, consent management, and compliance with global regulations. Platforms that leverage first-party data over third-party cookies are leading the charge and keeping pace with changing customer needs.
Debate to direction
The age-old “build vs. buy” debate oversimplifies what it actually takes to work with customer data in the era of AI. Instead of “to build or not to build,” brands should consider what a tailored combination of tools (anchored by a reliable identity resolution foundation) will allow them to move faster, more accurately, and scale with confidence.
AI is quickly becoming the enterprise’s core operating system. Still, even the most advanced models require a solid data foundation centered around identity resolution to unlock its full potential, driving smarter decisions and more meaningful customer experiences.
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