Our competitors are using AI for personalization; why aren’t we there yet, and what’s it going to take?

That’s a fair question, and it’s one I hear from a lot of executive teams—especially when competitors are marketing flashy AI-powered personalization.

Let’s start by being clear: AI personalization is not a feature you simply buy and turn on. It’s an outcome of foundational capabilities you build deliberately over time. If we’re not “there yet,” it’s likely because a few essential pieces aren’t in place or are only partially mature.

Why aren’t we there yet?

1. Data Fragmentation and Quality:
   AI personalization depends on clean, consolidated data—think a unified customer record that tracks behaviors, transactions, preferences, and context in near real time. If your data is siloed across systems (CRM, ERP, web analytics, call centers), inconsistent, or incomplete, AI can’t produce meaningful recommendations. In fact, training models on poor-quality data is worse than doing nothing because it erodes trust in your outputs.

2. Data Governance and Access:
   Even if you technically have the data, governance issues—like unclear ownership, lack of metadata, and inconsistent standards—make it hard to access, interpret, and trust. That’s why many companies who try to leap directly into AI end up stalling mid-project. They realize too late that governance isn’t overhead—it’s what makes AI usable at scale.

3. Infrastructure Readiness:
   AI models require robust infrastructure: scalable cloud data platforms, modern pipelines for ingestion and transformation, and APIs to feed recommendations into customer-facing channels. If your architecture is still oriented around batch processing or legacy on-prem systems, you’ll struggle to achieve real-time personalization.

4. Organizational Readiness and Talent:
   Even with the right data and technology, personalization doesn’t work without people who understand how to operationalize it—data scientists to build and tune models, data engineers to manage pipelines, and business stakeholders who can interpret and act on the results. If these roles are underdeveloped or operating in isolation, progress will be slow.

What will it take to get there?

I recommend you think in terms of a phased, deliberate approach:

• 1. Assess Current State and Build a Roadmap:
  Start with an honest inventory of your data assets, governance maturity, technical architecture, and skill sets. This isn’t an academic exercise—it will identify exactly where the friction is and what needs fixing.

• 2. Fix the Fundamentals First:
  Before chasing AI, establish clean, governed, accessible data. That often means consolidating data sources, applying consistent definitions, and modernizing storage and processing platforms. You don’t need perfection, but you need a reliable foundation.

• 3. Design for Measurable Business Outcomes:
  Personalization isn’t a goal in itself. Frame use cases—like improving cross-sell conversion or reducing churn—and define success metrics upfront. This ensures you build the right models and measure impact credibly.

• 4. Pilot in Controlled Environments:
  Start small. Run a pilot on a narrow use case with a limited audience. Use it to validate the model’s performance, fine-tune data pipelines, and confirm that the output integrates cleanly into customer touchpoints.

• 5. Scale and Industrialize:
  Once pilots succeed, scale incrementally. Build repeatable processes, embed governance, and invest in training so business and technical teams can continuously improve models.

In short: It’s not that we’re behind—it’s that we’re building deliberately. Anyone can bolt an AI toolkit onto their stack and claim they have personalization. But to make it reliable, trustworthy, and profitable, you need the scaffolding in place. And when we do it right, we’ll not only catch up—we’ll be able to move faster, because our foundation will be solid.