Build vs. Buy: Optimize AI by Fixing the Data Foundation

Artificial intelligence is advancing rapidly and transforming the way entire industries operate. Companies are trying to keep up – whether it’s GenAI or intelligent agents – and discover how AI tools can help them stand apart from the competition, power more personalized customer experiences, and ultimately grow their business.

As companies rush to implement these tools, many realize the real challenge is the quality of the data feeding the AI models, not the AI models themselves.

Problems such as bad data, fragmented systems, and incomplete customer information can make AI less accurate and useful. Instead of helping them meet their objectives, these issues can skew results and, worse, produce limited or inaccurate insights.

This is why the “build vs. buy” question for customer data challenges has become ever more critical.

Should enterprises allocate precious resources to developing a customer data tool internally, tailored to their specific needs? Or should they invest in a ready-made solution that can deliver speed, reliability, and faster time to value?

The real answer? It’s not a binary choice – it lies in stepping away from a yes-or-no decision and into a nuanced exploration of where to innovate, where to partner, and how to future-proof your AI investments. That starts with understanding the organization’s foundational data.

The Data Quality Imperative 

The truth of AI is that it only works only as well as the data behind it.

Even the best algorithms can’t yield useful results if the data is incomplete or unorganized, and businesses in industries such as retail and travel & airlines are realizing that their data foundation is not yet ready to support AI at scale. Brands are swiftly discovering that unified, high-quality data is the key to making AI successful.

A significant part of this challenge is identity resolution, which consolidates disparate customer records into a single source of truth: real-time customer profiles. This profile helps resolve issues such as misspelled names, duplicate emails, and inconsistent addresses across various systems. If identity is wrong, it can cause problems in other systems and processes, such as unreliable and inaccurate segmentation and targeting, inaccurate predictions and insights, and “personalization” that misses the mark entirely.

Decision-makers must focus on data quality before investing in AI. When every customer interaction, such as a purchase or website visit, connects to a single trusted profile, AI can provide accurate recommendations that lead to enhanced cross-departmental collaboration and stronger customer trust.

Build vs. Buy in the AI Era 

Many leaders’ first instinct is to build a custom data platform internally, as this solution offers control, alignment with unique business processes, and the sense of creating a proprietary asset. But the cost can be steep, involving time-consuming development, continuous maintenance, and the need to stay current with evolving standards for privacy, compliance, and security.

On the other hand, buying an existing tool offers tried-and-true platforms that can accelerate time-to-value and reduce risk. Enterprise-ready solutions come with pre-built connectors, governance frameworks, and identity resolution capabilities that have been tested across industries. However, inflexible, pre-packaged solutions may not be able to support the ebb and flow of modern business needs or fit the bespoke use cases businesses are trying to solve for.

In reality, the choice isn’t just whether to build or buy.

It’s about identifying a strategy that focuses on using proven tools as a base and allowing teams to make it their own by layering in custom features.

The Hybrid, Composable Approach

Business objectives vary, and so should the tech used to achieve them. The most effective data strategy is one that is composable, which involves identifying the specific functions needed and assembling a stack that drives progress toward its goals.

This means breaking down the data problem into distinct purposes, such as identity resolution, unification, governance, and activation, and then selecting the best available tools to handle them.

Instead of trying to force a rigid, single platform to accommodate every need, composability enables enterprises to integrate solutions into a flexible, interconnected ecosystem. The result is a data stack that mirrors the complexity of the business while avoiding the challenges of one-size-fits-all systems.

• Scale and precision: This hybrid model combines horizontal breadth with vertical depth, resulting in an architecture that provides both scale and precision.
• Built for adaptability: A combined approach is also inherently more resilient. Since each function is modular, businesses can swap in new tools, upgrade specific capabilities, or expand into additional data domains without disrupting the system.

And flexibility and scalability are the names of the game when it comes to responding more effectively to market shifts, regulatory changes, and evolving customer expectations.

Best Practices for Making Data Work 

The build vs. buy decision is only one part of the equation.

The primary goal is to ensure that the business is generating a strong return on customer data, which requires a deliberate approach to building a foundation that AI can rely on.

1. Prioritize identity first: This is the first and most fundamental step in bringing all customer data together into accurate, 360-degree profiles. Skipping this step causes features such as personalization, analytics, and AI algorithms to function suboptimally or ineffectively. Investing early in identity resolution ensures that every interaction connects to a single customer record.
2. Buy for speed, build for differentiation: Use proven solutions for basics like data integration, privacy, and quality control. These tools provide stability and enable you to move more efficiently. Internal teams can then focus on building the features and experiences that make a business stand out.
3. Embrace composability: Treat data infrastructure as modular, not monolithic. A composable stack enables the addition of new tools, incremental modernization, and the fulfillment of regulatory requirements without complete disruption. This flexibility allows leading organizations to adapt to market and consumer expectations with less risk and more potential upside.
4. Treat data as a key business asset: Data isn’t just an IT issue. It’s the foundation for personalization, customer trust, and maximizing the benefits of AI throughout the organization. By making data quality a clear business goal, every investment, from marketing to product development, is based on solid information.

By following these steps, companies can turn raw data into valuable insights that drive AI projects and support long-term growth.

Unlocking AI’s Full Value 

“Build vs. buy” oversimplifies what it takes to prepare customer data for AI. The real question is: what combination of tools and practices will enable organizations to move faster, with greater accuracy, and on a foundation that scales?

Looking ahead, successful organizations depend on finding the right balance of buying what helps them move faster, building what makes them unique, and always focusing on data quality.

Written by Tony Owens. Have you read?
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