AI is transforming experience creation beyond basic content generation
The way digital experiences are built has changed. We’re past the days when AI was used to tweak headlines or adjust writing tone. Now, it’s generating entire user interfaces and writing production-grade code. This shift is happening across digital experience platforms (DXPs) today. Marketers are becoming direct contributors to product interfaces. Developers are bypassing boilerplate tasks. Speed goes up. Costs go down.
Platforms like Builder are doing this right now. Their visual AI assistant turns a Figma design or even a basic visual prompt into clean, usable code. Pick your framework, React, Vue, whatever, you’ve got a launch-ready UI. Kajoo offers a similar approach by connecting Figma designs to their AI engine to power migrations to platforms like Sitecore and Optimizely. That’s hours of dev work eliminated. And Sitecore’s Stream is lining up next with AI that generates interfaces or component libraries aligned with your brand’s rules. Prompts in, working code out. And if you want to hand it off to a developer, no problem, Sitecore’s early demos already include React code generation.
This is redefining the developer sprint. Marketers can now create components without coding. Developers can focus on high-leverage tasks. It’s a smarter distribution of effort, and it’s accelerating every team working inside a DXP.
AI models are improving proactive performance optimization
Data is everywhere. But insights? Those are harder to find, unless you bring in the right tools. Today’s AI models deployed inside DXPs are no longer waiting for you to define the problem. They’re analyzing before you even ask, then offering guidance. This shift matters. It turns reactive businesses into proactive ones. It also reduces the time wasted wading through analytics dashboards.
Take Adobe’s AEM Site Optimizer. It suggests changes, new content combinations, layout tweaks, even code-level improvements. Optimizely’s AI, known as Opal, generates new audience segments, then feeds targeted recommendations back into your campaign workflows while experiments are live. That’s a closed loop system. And Contentful, after acquiring Ninetailed, is feeding behavior data directly into personalization engines. Segments are formed, digital experiences recomposed, it’s data-driven iteration with real-time velocity.
None of this requires a data science team to sit behind it. These systems surface insights, test hypotheses, and deploy changes with minimal overhead. Whether it’s personalization, A/B testing, or SEO improvements, you’re seeing smarter execution with fewer bottlenecks.
For C-suite leaders focused on marketing ROI and operational efficiency, the capability to automate optimization is critical. It removes the latency between insight and action. It also democratizes decision-making, junior marketers get guidance, while senior teams allocate resources more effectively. Performance bottlenecks, especially in digital, are often hidden in the details. AI brings those details forward, fast.
AI-driven localization is revolutionizing global content translation
Language barriers slow momentum. They create operational drag in international markets. That’s changing because AI now enables fast, accurate translations that follow brand rules and maintain tone. It’s precise, customized, and scalable.
Uniform, listed as a Visionary on Gartner’s DXP Magic Quadrant, integrates directly with OpenAI to handle real-time content translations. It also allows content teams to input prompts that guide the translation output, ensuring consistency in language style and terminology. What used to take teams days working through external language service providers can now be completed in minutes, without leaving the platform. Builder does this too, simple prompt in, localized content out. Sitecore plans to include the same functionality within its broader Stream roadmap.
Once you train the system to understand your tone, region-specific differences, and preferred vocabulary, it keeps improving. This shifts localization from outsourced expense to in-platform capability. That matters at scale.
For companies with a global strategy, localized content is performance-critical. AI translation built into the DXP stack minimizes friction. It reduces the feedback loop between global content creation and deployment. More importantly, it does this while maintaining the integrity of your messaging across geographies. Quality and context matter just as much as speed.
This also limits third-party software dependency, streamlining both risk management and data governance. By keeping translations in-platform, your teams maintain greater control, and your time-to-market in new regions shortens.
AI agents are unifying disparate marketing tools across product suites
Today’s digital teams often use multiple tools, CMSs, testing software, CRM platforms, content planning tools, asset management systems. That fragmentation creates disconnects. But with AI orchestration inside modern DXPs, each of those tools can now operate as part of a larger, coordinated system.
Adobe’s Agent Orchestrator shows what’s possible. It activates purpose-specific agents for tasks like targeting campaigns or resolving content issues. These agents share context, update each other, and progress workflows without micromanagement. It’s the orchestration of intent across systems, not just tasks.
Optimizely’s Opal takes a unified view of campaigns from ideation all the way through to personalization. It doesn’t lose context between systems. Decisions made during planning don’t disappear when it’s time to publish. Sitecore’s Stream, part of its Intelligent DXP vision, adds operational intelligence to content planning, asset management, web experience delivery, customer data integration, and testing, all connected through agentic workflows.
From an executive operations standpoint, orchestration matters because it unlocks scale. It allows product, marketing, and engineering teams to align without heavy coordination costs. The fewer handoffs, the fewer dropped details. AI acts as a dynamic coordinator, keeping tools in sync, maintaining context, and ensuring progress.
It also minimizes the risk of redundant workflows across silos. If your personalization system doesn’t talk to your content system, performance stalls. Agentic coordination solves that by linking them into unified experiences, without centralizing everything in one rigid platform.
AI is being embedded as a core component
The current shift in digital experience platforms is about rebuilding platforms with AI at their foundation. This changes how decisions are made, how experiences are created, and how performance is managed.
Leading vendors aren’t treating AI as a plugin, they’re redesigning their architectures to make AI the decision layer across product lines. From experience creation to optimization recommendations, AI sits at the core, not the edges. These platforms are structured to let AI see the full picture, customer behavior, content engagement, performance data, and act on it without being directed step-by-step.
Sitecore’s Stream initiative reflects this clearly. Stream isn’t AI bolted on, it’s a system designed with AI as a core operating logic. Every workflow, content planning, personalization, testing, is connected by intelligent agents capable of making context-aware decisions. Adobe and Optimizely are heading the same direction: aligning their product suites around orchestrated AI decision layers that interact across teams, not just inside single tools.
For executives, the move from additive to embedded AI has long-term operational and strategic consequences. Platforms built around AI increase adaptability. They are naturally more capable of scaling, learning, and adjusting to customer needs without constant reconfiguration.
With AI embedded deeply, systems become more autonomous and predictive. They know what’s underperforming and adjust accordingly. They see inefficiencies and surface corrections before the team identifies the problem. This prevents stagnation, reduces the cost of inaction, and keeps experiences aligned with evolving market expectations.
The operational lift is clear, faster iterations, better visibility, less need for heavy internal alignment across divisions. That kind of structure helps maintain momentum without overcomplicating team workflows.
AI helps Agile marketing teams achieve more with limited resources
In practice, many teams don’t have large headcounts, deep developer support, or the time to manage every workflow manually. AI makes that level of support possible, without needing to increase the size of the team.
Inside DXPs, AI now automates recurring tasks, segment creation, customer journey mapping, A/B test design, even localization. This matters most for lean marketing teams trying to stay competitive with fewer resources. AI fills capacity gaps, day in and day out, across campaign planning, personalization, and content production.
For instance, platforms like Contentful rapidly suggest audience segments based on live behavioral data, while Optimizely’s Opal uses AI for campaign ideation, development, and optimization. These aren’t small efficiency wins. They fundamentally shift what a small team can deliver. AI also reduces risk, by guiding decisions that would otherwise rely entirely on experience or trial-and-error.
For executives under pressure to expand reach without expanding overhead, this changes the equation. AI turns limits into leverage. Teams get more output, more confidence, and faster learning cycles, without needing a larger payroll or increased tech complexity.
It also decentralizes capability. Junior marketers or regional teams enter the optimization loop without waiting for centralized support. This accelerates how quickly the entire organization can act on insights, launch updates, or test new ideas. Fewer silos. Fewer delays. Smarter execution.
At the strategic level, AI becomes a multiplier of both efficiency and control. The result: increased velocity, higher quality output, and better use of human capital.
Key executive takeaways
- AI redesigns creation workflows: Leaders should prioritize platforms where AI goes beyond text generation and enables UI and code development, accelerating delivery cycles across design and engineering.
- AI drives strategic performance gains: Execs should implement AI-powered segmentation, testing, and SEO tools to move from reactive analysis to proactive campaign optimization.
- Localization gets faster and smarter: Companies expanding into global markets should leverage built-in AI translation to streamline localization while preserving message consistency and brand tone.
- Agentic AI enables tool orchestration: Decision-makers should invest in DXPs that use coordinated AI agents to unify disconnected tools, reducing manual handoffs and improving cross-team effectiveness.
- AI is the new architecture: Leaders evaluating digital stacks should consider platforms built with embedded AI, as these systems deliver repeatable performance improvements and faster adaptation across workflows.
- Lean teams gain scale with AI: Organizations with limited headcount should adopt AI-driven DXPs to multiply output, accelerate testing, and maintain quality without growing operational complexity.
