<p>In 2026, Dell continues to position itself as the backbone infrastructure provider for enterprise AI and hybrid cloud.</p> <p>The central theme at Dell Technologies World 2026 was clear: enterprise AI is moving from experimentation to production, with organizations increasingly seeking to run AI closer to where enterprise data already lives through hybrid, governed infrastructure.</p> <p>&#34;CIOs are aggressively pivoting to hybrid AI,&#34; said Dell founder Michael Dell during Monday's keynote. &#34;The risk is not the cloud -- the risk is losing control of your data, your cost, your security, your intellectual property and your speed.&#34;</p> <p>Dell also announced an expansion of its AI Factory strategy, positioning it as a framework for helping enterprises deploy and run frontier models in on-premises and hybrid environments. The company emphasized this approach as a way to improve control over data governance, model choice and long-term cost management tied to large-scale AI deployments.</p> <p>Across sessions, speakers framed those infrastructure investments as foundational to delivering more proactive, contextual and automated customer experiences. However, as discussions shifted from infrastructure to customer-facing outcomes, such as personalization and service automation, a recurring theme emerged across multiple sessions and interviews: data readiness remains a bigger barrier to AI personalization than the technology itself.</p> <blockquote> <div>  <figure>   Everything you need to make a good decision is scattered across different systems in completely different formats.  </figure>  <figcaption>   <strong>Faizel Khan</strong>Founding AI engineer, Landing Point  </figcaption>  <i></i> </div></blockquote> <p>&#34;The biggest challenge is the data underneath it,&#34; said Faizel Khan, founding AI engineer at Landing Point recruiting firm, in an interview with TechTarget. &#34;And it's not even a quality problem. It's that everything you need to make a good decision is scattered across different systems in completely different formats.&#34;</p> <section> <h2><i></i>Why AI personalization breaks down in practice</h2> <p>In a session titled &#34;AI-powered personalization: The customization of the customer experience,&#34; panelists brought the infrastructure conversation down to the practical reality of how enterprises are trying to operationalize personalization at scale.</p> <p>Across the discussion, speakers described a clear shift in customer expectations: users increasingly expect brands to recognize them as individuals, with context carried across every interaction, rather than treating each engagement as a standalone request. That expectation is colliding with how most enterprises are still structured -- in fragmented systems, siloed teams and sequential workflows that slow down decision-making.</p> <p>&#34;We're still structured very much in silos, and if individual business units are solving just a portion of the customer journey, you end up with a fragmented experience,&#34; said Marybeth Pearce, vice president of global enterprise sales at Comcast Business, in the session.</p> <p>Panelists gave several examples of how personalization breaks down in practice. For instance, companies may have the data to understand customer behavior, but that information is often distributed across CRM systems, ticketing tools and internal knowledge bases that don't easily connect in real time. The result is delayed or inconsistent experiences that undermine the very personalization efforts enterprises are trying to offer.</p> <p>The panel also repeatedly returned to a key tension: personalization is no longer just about content or targeting, but about operational speed and coordination across the organization. When insights take weeks to move from data to execution, the opportunity for real-time personalization disappears.</p> <p>&#34;The organizations achieving the strongest outcomes are treating AI personalization as an operational transformation challenge involving governance, workflows, customer strategy, organizational alignment and human oversight,&#34; said Matt Hasan, PhD and CEO of aiResults technology consulting firm, in an interview with TechTarget.</p></section>       <section> <h2><i></i>4 best practices for AI personalization</h2> <p>Speakers on the CX panel shared various tips for AI personalization, many of which aligned with insights from other enterprise leaders TechTarget interviewed.</p> <h3>1. Build a unified data foundation</h3> <p>Enterprises often push toward real-time personalization before the underlying data foundation is ready -- and that's where execution starts to break down. Most organizations already have plenty of customer data, but it sits fragmented across tools and platforms.</p> <p>When systems aren't aligned, AI can surface insights but can't reliably act on them across the customer journey. The prerequisite for real-time personalization is not more data, but more usable data -- connected, normalized and accessible in a way that supports decisions.</p> <p>&#34;The moment you try to scale before the foundation is ready, it falls apart,&#34; Khan said.</p> <h3>2. Start with one use case at a time</h3> <p>Successful AI personalization isn't about scaling everything at once. It's about narrowing scope, aligning around a specific outcome and sequencing execution in a disciplined way.</p> <p>Rather than broad transformation programs, organizations can start with well-defined use cases where the data and systems can actually support delivery, then expand through structured iteration.</p> <p>&#34;Companies actually succeeding at real-time personalization aren't doing it everywhere -- they're doing it really well in one place,&#34; Khan said.</p> <h3>3. Make data usable, not locked away</h3> <p>Organizations that succeed with AI personalization stop treating data as something fragile or siloed. Instead, they connect it across teams, systems and workflows so it can flow into decisions and customer experiences.</p> <blockquote>  <div>   <figure>    Stop treating your data like this precious thing in a box -- use it.   </figure>   <figcaption>    <strong>Jocelyn Chen</strong>AI and data leader, EY   </figcaption>   <i></i>  </div> </blockquote> <p>The shift is from ownership to enablement. Data moves from a protected asset controlled by one function to something shared, operationalized and used in real time across the business.</p> <p>&#34;Stop treating your data like this precious thing in a box -- use it. Harness it. Just start connecting, share it, democratize it,&#34; said Jocelyn Chen, AI and data leader at EY, in the session.</p> <h3>4. Build accountability and transparency into AI from day one</h3> <p>Trust in AI systems depends on whether organizations can explain how decisions are made -- and whether accountability is built in from the start. Transparency is not an afterthought. It is a design principle.</p> <p>&#34;If you can show people why an agent made a decision and how it got there, trust follows naturally. Transparency isn't a feature you add later. It's the foundation. Build it in from the start or you're just hoping nothing goes wrong,&#34; Khan said.</p> <p>In production, that principle also shows up in how organizations handle failure. The emphasis shifts from avoiding mistakes entirely to owning them clearly when they happen.</p> <p>&#34;If there's a bump, own it. Tell the customer, 'That algorithm wasn't right. We apologize for the mistake,'&#34; Pearce said.</p> <p>Trust and transparency remain critical, but they do not require perfection. As organizations move AI systems into production, the expectation is not error-free performance, but visible accountability when issues arise. The emphasis is shifting toward building systems that can be explained, corrected and improved in real time -- without slowing adoption. Organizations must move forward with intent, stay transparent with customers and acknowledge and fix mistakes when they happen.</p> <p><em>Tim Murphy is a site editor and writer for the IT Strategy team at TechTarget.</em></p></section>

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<p>At Dell Technologies World 2026, Dell emphasized its role in enterprise AI and hybrid cloud, highlighting the shift from experimentation to production. Founder Michael Dell noted the importance of data control amidst the rise of hybrid AI. However, challenges in data readiness hinder AI personalization, as data is often fragmented across systems. Experts suggest building a unified data foundation and focusing on specific use cases to improve personalization efforts.</p>

Dell pushes AI personalization, but data hurdles remain