How Forward-Thinking Teams Extend the Adobe Experience Ecosystem with Real-World AI

AI is moving fast. Adobe offers a powerful foundation, and many clients are now looking beyond native tools to keep pace with innovation. At Rightpoint, we work with organizations across industries, and we consistently see the same trend: companies want more from AI. Adobe continues to invest in GenAI tools across its ecosystem, from GenStudio and Firefly to Brand Concierge and Agent Orchestrator. These tools offer valuable capabilities, but enterprise-scale use cases often demand more flexibility, deeper integrations, and broader orchestration. That’s where layering in purpose-built AI around Adobe really shines.

Where we see the greatest success is when clients adopt a hybrid approach. They are integrating AI around Adobe rather than relying solely on Adobe-native capabilities. In this post, we walk through practical, client-tested AI applications that complement Adobe Experience Cloud tools like AEM Sites, Assets, Target, and Workfront.

GenAI Search That Understands Content

Search is at the core of many Adobe implementations, whether in AEM Sites, Assets, headless CMS builds, or Commerce. But Adobe does not offer a generative or semantic search product out of the box.

We help clients implement semantic and generative search solutions, often using Retrieval-Augmented Generation (RAG) pipelines, with tools like Azure AI Search and Coveo AI. These platforms bring natural language understanding to content stored across AEM, DAM, SharePoint, and other systems.

These solutions support both internal teams and customer-facing experiences:

• For employees, semantic search accelerates knowledge discovery across fragmented content stores, improving productivity and decision-making.

• For customers, generative search enables more relevant, conversational access to product details, support documentation, and service content.

Why This Works:

• RAG enables semantic understanding of user queries, improving content relevance even when keywords do not match exactly.

• Clients report faster discovery, improved support deflection, and better search usability.

According to Adobe and Yext, in state government sites, answers to 99% of user questions already exist, but are hidden in silos or buried in legacy systems. The same is true for most enterprises: content is there, but customers and employees can’t find it. AI-powered search helps surface that knowledge and unlock its value.

Our clients using AI-powered search with Adobe content are seeing measurable gains in both findability and user satisfaction, especially when layered on top of AEM-managed content and DAM assets. For more on how we’ve productized this approach, explore OmniOne, Rightpoint’s composable AI platform that combines generative search, intelligent orchestration, and enterprise integration to unlock siloed content across AEM, SharePoint, and beyond.

Conversational Interfaces That Go Beyond Chatbots

Many enterprise teams have tried out-of-the-box chatbots, only to find them rigid, hard to scale, or off-brand. Today’s users expect AI assistants that understand their intent, reflect the brand’s voice, and provide real answers.

We help clients extend or customize chatbots powered by OpenAI, Claude, and open-source or self-hosted LLMs, often integrating directly with AEM Sites and Adobe Commerce front ends. These assistants are trained on product documentation, support content, and internal knowledge bases to ensure accurate, on-brand responses.

Why This Works:

• Chatbots improve product discovery, customer service, and onboarding with 24/7 availability.

• They reduce friction, increase self-service success, and offer measurable reductions in support center volume.

At organizations using self-service tools like AI-powered chatbots, an estimated 54% of customer issues are resolved without requiring human support, according to Salesforce. This not only deflects calls but improves overall service efficiency and response time.

These conversational experiences also support internal use cases. For example, we can build chat-based assistants for AEM authors and developers to help streamline common tasks, such as updating metadata, checking page history, or locating component code, without switching contexts or navigating multiple tools.

Agentic Workflows That Automate Around Adobe

Adobe Experience Platform Agent Orchestrator, announced at Adobe Summit 2025, points to a future where multiple AI agents collaborate across digital experience platforms. However, the full set of features is still evolving. In the meantime, we are helping clients build their own agentic workflows that deliver value today.

Examples of agentic use cases in the Adobe ecosystem include:

• Agents that identify stale or underperforming AEM content and trigger review workflows

• Metadata generators for DAM assets using AI vision and language models

• Workfront automations that pre-fill briefs or route tasks based on campaign history

These automations are typically powered by GPT or Claude models, orchestrated through Azure AI Foundry or LangChain, and integrated with Adobe’s REST APIs for seamless execution and governance.

Adobe’s work with Pfizer shows that AI-powered content workflows can reduce marketing production time by more than 50%— a benchmark that aligns with what many of our clients are looking to achieve as they explore agentic AI integrations with AEM and Workfront.

AI Tools That Accelerate Development

We are also helping development and QA teams speed up delivery using tools like GitHub Copilot, Cursor, and Windsurf. These AI tools assist with boilerplate AEM code, Sling Models, dialog stubs, and test generation.

For project and delivery teams, Microsoft Copilot, ChatGPT, and Spinach provide valuable support. They can summarize meetings, generate architecture diagrams, and draft planning documentation without slowing down the process.

Why This Matters:

• Teams see faster iteration cycles and fewer context switches between tools.

• Engineers can focus more on architecture and innovation, and less on setup and boilerplate.

Our teams have been actively guiding developers through change adoption efforts to embed AI-powered tooling into daily workflows. As this behavior change takes hold, the gains are significant. In some cases, developers have reported completing two months’ worth of work in just two weeks using tools like Windsurf. Once fully embraced, we’ve seen teams achieve up to a 40% improvement in development efficiency.

In GitHub’s research study, developers using Copilot completed tasks 55% faster than those without it. At Rightpoint, we’re seeing similar acceleration in development workflows as teams adopt Copilot-style tools for AEM code scaffolding, template setup, and test generation.

Smart Segmentation and Personalization

Adobe Target and Real-Time CDP provide a strong foundation for rules-based personalization. Many clients, however, want to go beyond that with predictive segmentation and AI-driven customer insights.

We can help organizations integrate external ML models for:

• Behavioral clustering

• Propensity scoring

• Lookalike audience generation

These models inform campaign content and delivery timing, improving engagement and conversion rates. We can integrate predictive models into Adobe workflows using Adobe I/O Runtime, custom APIs, or middleware, enabling dynamic audience activation in Journey Optimizer and Target. In some cases, we also use GenAI tools to generate high-value audience groupings based on behavior, feeding them directly into Adobe campaigns.

Prudential Financial achieved a 135% increase in campaign engagement within 30 days by utilizing Adobe Journey Optimizer in conjunction with generative AI tools.

Content Decisioning That Adapts in Real Time

AI-driven content decisioning takes personalization a step further. Rather than predefining which message should go to which audience, these systems make real-time decisions based on customer behavior and predicted outcomes.

We help clients:

• Define scoring rules for content blocks

• Train models that optimize engagement

• Integrate offer decisioning across pages, apps, and emails

Adobe’s Offer Decisioning and Journey Optimizer support this functionality natively. When external models offer more flexibility, we assist with lightweight integrations that keep business users in control. This approach allows content to adapt dynamically without requiring authors to build every variation manually.

Smarter Experiences, Faster

Adobe Experience Cloud is an incredibly capable platform. But when it comes to AI, the most effective clients we work with are the ones who bring in purpose-built tools to extend Adobe’s capabilities.

By layering in solutions like OpenAI, Claude, Azure AI Studio, LangChain, Coveo AI, and Adobe I/O Runtime, organizations can deliver faster, more intelligent customer experiences without waiting for native features to catch up.

At Rightpoint, we help clients:

• Evaluate where AI fits within the Adobe stack

• Build real-world use cases that drive customer and business value

• Integrate GenAI tools safely, responsibly, and at enterprise scale

We bring ideas, real-world examples, and technical depth to every conversation. If your team is exploring how to make Adobe smarter with AI, we’d love to connect.