Personalized engagement can deliver rich, relevant experiences that make customers feel understood and drive revenue growth. Companies excelling at personalization generate 40% more revenue than average players, with typical revenue lifts of 10-15%.1 And with the proliferation of AI agents at our disposal today, personalization is getting easier.
The catch, of course, is that you need good data — and that is where most businesses fall down.
The Data Quality Crisis
AI has captured everyone’s interest as the standout technology for delivering compelling personalized customer engagement. But results depend entirely on the quality of the data consumed. This remains a challenge for nearly every business — certainly for those I work with on a regular basis. This shouldn’t be a surprise to anyone. One recent report shows that organizations use an average of 275 cloud applications, and for large enterprises with more than 10,000 employees, that number rises to 660.2 This ever-increasing volume and complexity of data creates operational friction and risk.
Where is poor quality data holding your organization back?
One of the most common problems I see is data that is fragmented and siloed across multiple systems and teams. This is typical in large organizations, especially those spread across geographies or who have grown through acquisition. But smaller businesses can also experience these challenges. While you may be able to manage your data initially, it will become unwieldy as you grow. Eventually you will hit a tipping point and value leakage will begin — caused by poor data quality.
Inevitably, data silos and data fragmentation lead to data that is inconsistent and unreliable data. I consistently see poor data quality undermine customer engagement efforts, resulting in missed revenue opportunities, damaged brand value, and lost market share. For example, poor marketing data can lead to ineffective campaigns, wasting budget and failing to convert customers.
Better Data Quality Enables AI-Driven Personalization
The foundation for successful personalization is reliable, high-quality data. AI agents just accelerate the outcomes. And the old adage of “garbage in = garbage out” has never been more real — today it’s on steroids. Organizations that define data standards and stewardship; integrate systems; and automate data validation rules; protect data quality and create a reliable single source of truth.
With clean, unified data, businesses can build rich, AI-driven, hyperpersonalized customer experiences in real time. Imagine identifying a customer the moment they visit your website and tailoring product recommendations, offers, and content based on their history, preferences, and behavior. This is no longer hypothetical. Businesses that are leveraging AI agents are already transforming customer engagement and seeing the results.
AI-Driven Personalization Increases Revenue
- H&M deployed an AI-powered virtual shopping assistant that resolved 70% of customer queries without human help, increased conversion by 25%, and sped up response times threefold. This resulted in higher customer satisfaction and a noticeable sales revenue bump within six months.3
- Bank of America’s Erica AI assistant has handled over 1 billion customer interactions, reducing call center traffic by 17% and increasing mobile engagement by 30%, saving millions in operational costs while improving service quality.3
- Lufthansa Group implemented AI chatbots that resolved 80% of customer queries without human agents, shortened response times by 60%, and lowered live agent dependency by 40%, enhancing customer experience during peak demand periods.3
- Sephora used unified customer data to power its AI-based recommendation engine, delivering relevant, real-time product suggestions. The result? A 15% increase in average order value and 20% boost in conversion rates.4
Many businesses are also seeing a correlation between AI personalization and revenue growth — and are investing accordingly. The customer experience (CX) personalization industry is estimated to grow to $11.6 billion in 2026, an increase of 65% over the $7.6 billion spend in 2020.5
Manage Your Shadow AI Agents
Whether you like it or not, you already have “shadow” AI agents embedded in your systems, through platforms like Microsoft, Google, and Salesforce. While these tools can add value, unmanaged AI agents can create complexity and risk, much like shadow IT.
To maximize AI’s value and control the risks, companies should adopt solutions such Boomi Agentstudio, which includes Agent Control Tower, a vendor-agnostic dashboard that monitors and manages all of your AI agents in one place, ensuring you stay in control of data quality, customer experience, and business outcomes.
Value Comes From the Right Data, Not More Data
Your future AI success doesn’t depend on having more data. It depends on having the right data, at the right time, in the right hands.
That’s why investing in data quality is not just a technical necessity — it is a strategic imperative. Clean, connected, and governed data empowers AI agents to act with intelligence, to offer the right product, at the right moment, in the right context. And when you can do that at scale, you don’t just improve NPS — you grow revenue.
To learn more about the correlation between trusted AI and trusted data, watch our webinar, “The Path to High Data ROI: From Pipeline Automation to Managed AI Agents” — with live Q&A on July 10 at 11am SGT / 1pm AEST.
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Sources
- McKinsey, The value of getting personalization right-or wrong-is multiplying
- 2025 SaaS Management Index, Zylo
- How AI Agents Are Driving ROI: 10 Useful Case Studies from the Real World
- Maximizing Revenue: Product Recommendations and Personalization in Luxury Online Retail
- The personalisation economy: how is AI affecting businesses and markets?