How Databricks AI Tools Revolutionize Customer Experience with Smarter AI Agents
Artificial intelligence (AI) is reshaping customer experience (CX) across industries. However, many enterprises still struggle to deploy AI agents at scale. Most AI models lack awareness of enterprise data, leading to inaccurate, inconsistent, or poorly governed outputs. Databricks has introduced a suite of new tools to address these challenges. These innovations like Databricks AI tools will help businesses integrate, monitor, and scale AI agents confidently.
With 85% of enterprises using Generative AI (GenAI), organizations need solutions that ensure accuracy, governance, and scalability. Databricks AI tools focus on centralized governance, seamless integration, and human-in-the-loop workflows. These enhancements will empower businesses to use AI agents effectively while maintaining trust and compliance.
Why AI Struggles to Deliver Great CX
AI holds immense potential for improving customer interactions, automating support, and personalizing experiences. However, many AI-powered solutions fail in real-world scenarios. Several factors contribute to this:
- Lack of enterprise data awareness – AI models often work in isolation and fail to leverage company-specific data.
- Inconsistent governance – Organizations struggle to enforce compliance and security across multiple AI models.
- Poor integration – AI systems must work within existing workflows to provide a seamless experience.
- Difficulty in measuring accuracy – Without effective evaluation methods, AI agents may deliver unreliable responses.
Databricks AI tools directly address these challenges. By improving governance, monitoring, and integration, businesses can confidently scale AI agents beyond experimental pilots.
Key AI Tools That Enhance CX
1. Centralized Governance with Mosaic AI Gateway
Managing AI models across an enterprise can be complex. Different teams may use multiple AI models, making it difficult to enforce governance and security. The Mosaic AI Gateway provides a unified platform for managing AI models.
- Seamless integration – Businesses can manage both open-source and commercial AI models in one place.
- Improved monitoring – Teams gain real-time insights into AI performance, accuracy, and compliance.
- Stronger security – IT teams can enforce policies and protect sensitive data across all AI models.
With a single governance framework, organizations can ensure AI-driven experiences remain reliable, consistent, and secure.
2. AI-Powered Conversations with Genie Conversational API
Customers expect instant, natural interactions with AI-powered chatbots. However, most chatbots fail to deliver personalized responses because they lack enterprise data context. The Genie Conversational API solves this by enabling seamless chatbot integration into popular business apps.
- Supports natural conversations – AI chatbots can now understand and respond to users more effectively.
- Works across multiple platforms – The API integrates into Microsoft Teams, Slack, and SharePoint, ensuring easy adoption.
- Retains context – AI agents can remember past interactions, improving continuity in conversations.
By embedding AI-driven chatbots directly into everyday applications, businesses can enhance CX and boost employee productivity.
3. Enhanced AI Accuracy with Agent Evaluation Review App
One of the biggest challenges in AI deployment is measuring and improving model accuracy. Without structured feedback, AI agents may continue making the same mistakes. The Agent Evaluation Review App provides a streamlined solution.
- Simplifies human feedback – Experts can review AI outputs and provide targeted corrections.
- Reduces manual effort – No need for spreadsheets or custom-built applications to track performance.
- Improves AI decision-making – Continuous feedback helps AI agents learn and adapt over time.
This tool allows businesses to fine-tune AI accuracy systematically, leading to better customer interactions and reduced errors.
4. Scaling AI with Provision-Less Batch Inference
Deploying AI models at scale often requires complex infrastructure setup. This slows down adoption and increases operational costs. The new Provision-Less Batch Inference tool eliminates this barrier.
- Runs batch AI workloads with a single SQL query – No need for manual provisioning or resource allocation.
- Reduces operational costs – Businesses can process large-scale AI workloads efficiently.
- Enables unstructured data integration – AI agents can analyze diverse data formats seamlessly.
By simplifying AI deployment, enterprises can extend AI-driven customer experiences across multiple touchpoints.
What This Means for Customer Experience
Databricks AI tools enable organizations to build smarter, more reliable AI agents. This has a direct impact on customer experience in several ways:
More accurate AI-driven interactions – AI agents can access enterprise-specific data, leading to more relevant responses.
Improved AI governance and compliance – Businesses can monitor AI models centrally, ensuring compliance with industry regulations.
Faster AI adoption and scalability – The elimination of infrastructure complexity helps enterprises deploy AI faster.
Seamless chatbot integration – Customers benefit from smarter AI assistants embedded within their daily workflows.
Craig Wiley, Senior Director of Product for AI/ML at Databricks, highlights the importance of trust and confidence in AI adoption. He states:
“Many enterprises still struggle to deploy AI agents in high-value use cases due to concerns around accuracy, governance, and security. For these organizations, it’s confidence, not just technology, that presents the biggest hurdle to extracting the full data intelligence benefits of Generative AI. The new tools address these challenges head-on, enabling businesses to move beyond pilots and into full-scale production with AI agents they can trust.”
Real-World Impact: Altana’s AI Transformation
Companies like Altana are already benefiting from these innovations. Ian Cadieu, CTO of Altana, shares how Batch AI with AI Functions has streamlined their workflows:
“Batch AI with AI Functions is streamlining our AI workflows. It’s allowing us to integrate large-scale AI inference with a simple SQL query—no infrastructure management needed. This will directly integrate into our pipelines, cutting costs and reducing configuration burden. Since adopting it, we’ve seen dramatic acceleration in our developer velocity when combining traditional ETL and data pipelining with AI inference workloads.”
This success story demonstrates the power of simplified AI deployment. By reducing operational complexity, enterprises can unlock new AI-driven opportunities while maintaining efficiency.
Final Thoughts: The Future of AI in CX
As businesses continue to embrace AI, trust, governance, and ease of use will define success. Databricks AI tools provide the foundation for scalable, enterprise-ready AI agents. By eliminating integration barriers, improving accuracy, and simplifying governance, organizations can deliver better customer experiences at scale.
With these innovations, AI is no longer just an experimental technology—it’s a customer-centric solution. As enterprises move beyond AI pilots, those that prioritize governance and usability will lead the way in CX transformation.
