Sprinklr introduces AI agents and copilot capabilities for CX platforms

Customer experience platform provider Sprinklr announced a suite of AI-powered capabilities on September 29, 2025, introducing tools designed to automate and enhance customer interactions across multiple channels. The announcement, made at the company's CXUnifiers 2025 conference in Nashville, includes Sprinklr Copilot, Sprinklr AI Agents, and enhanced Customer Feedback Management capabilities.

The new capabilities span Sprinklr's existing product suites: Sprinklr Social, Sprinklr Marketing, Sprinklr Insights, and Sprinklr Service. According to the company's press release, these additions represent an expansion of its AI-native platform for unified customer experience management.

Technical architecture and deployment

Sprinklr Copilot functions as a conversational interface across the company's platform. The tool enables users to interact with dashboards through natural language queries rather than manual navigation of charts and key performance indicators. The system provides real-time alerts for anomalies and workflow issues, with all responses backed by citations to ensure transparency.

The copilot can be configured for customer service agents to improve first contact resolution rates and customer satisfaction scores. Rather than requiring users to apply manual filters to datasets, the interface accepts questions such as "tell me what failed last week" to surface relevant information.

Sprinklr AI Agents represent autonomous tools built directly into the platform architecture. According to Amitabh Misra, chief technology officer at Sprinklr, "With AI Copilots and Agents that deploy in days, organizations can augment human teams — unlocking new levels of productivity and delivering real results rapidly, consistently, and with enterprise-grade safety and governance."

The agents are designed to understand Sprinklr's data models and journey vocabulary without external integration requirements. They maintain context when customers switch between voice, chat, email, and social channels, eliminating the need for customers to repeat information. The system includes self-training capabilities that learn from human interactions and escalate issues when necessary.

Customer feedback management enhancements

The updated Customer Feedback Management system incorporates AI throughout the survey collection and analysis process. Adaptive surveys adjust questions in real time based on responses, reducing survey length while attempting to increase response rates. The system validates automated insights against social media data to provide additional context beyond traditional survey responses.

The platform connects solicited feedback from surveys with unsolicited feedback from social channels and other sources. This unified approach creates what Sprinklr describes as a single source of truth for customer sentiment data. The closed-loop feedback capabilities include AI-generated recommended actions rather than only identifying issues.

Platform integration and governance

The company emphasizes that its AI agents are grounded in enterprise data and business rules to minimize hallucinations. Security and governance controls are embedded in the system architecture rather than added as separate layers. Sprinklr reports that deployment timelines measure in days rather than months, though specific implementation timeframes were not disclosed.

Market positioning and customer base

Sprinklr currently serves approximately 1,900 enterprise customers, including Microsoft, Procter & Gamble, and Samsung. The company states that 60 percent of Fortune 100 companies use its platform. Rory Read, president and CEO at Sprinklr, stated: "Today's customers are in the driver's seat — they expect brands to meet them where they are, with context, speed, and insight. Our platform is built to help brands lead in this new era of engagement, connecting with their audiences in a bold, differentiated way."

The announcement positions Sprinklr against both legacy customer experience platforms and newer AI-first competitors. The company's argument centers on the limitations of retrofitting AI capabilities onto existing systems versus building AI into the platform architecture from the ground up. Sprinklr claims more than 10 years of domain-specific AI development experience.

Implications for marketing operations

Adobe launched AI agents for business customer experience automation on September 10, 2025, introducing a similar platform for managing customer experiences through specialized agents. Marketing teams now manage customer interactions across numerous channels simultaneously, each with distinct AI tools and interfaces.

Sprinklr's unified platform approach addresses fragmentation issues that arise when marketing, service, and insights teams operate separate systems. The company's focus on connecting data across channels reflects operational challenges that emerged as brands expanded their digital presence. Teams historically struggled with customers who began interactions on social media, continued via email, and concluded through phone support—with each channel maintaining separate records.

The Customer Feedback Management updates specifically target the disconnect between structured survey data and unstructured social media commentary. Traditional market research methods captured periodic snapshots of customer sentiment through surveys, while social listening tools provided continuous but disconnected streams of feedback. Sprinklr's integration of these data sources enables teams to identify whether survey responses align with or diverge from social media discussions.

Deployment considerations

The AI agents operate within Sprinklr's existing platform infrastructure, accessing customer data, conversation histories, and business rules that organizations have already configured. This design reduces the technical implementation work required compared to integrating external AI systems. Organizations do not need to export data or establish new API connections for the agents to function.

However, the effectiveness of the AI agents depends on the quality and completeness of data within the Sprinklr platform. Organizations with fragmented data across multiple systems would need to consolidate that information before the AI agents could deliver comprehensive results. The company has not disclosed pricing structures for the new capabilities or whether they require separate licensing from existing Sprinklr subscriptions.

Training requirements for teams adopting the new tools remain unclear. While the copilot interface uses natural language, effective use may require understanding which queries produce actionable results. Similarly, configuring AI agents to align with specific business processes likely demands expertise in both the Sprinklr platform and the organization's operational requirements.

Performance metrics and validation

Sprinklr has not released performance benchmarks for the AI agents or copilot capabilities. Metrics such as accuracy rates for the copilot's responses, containment rates for AI agents handling customer inquiries, or improvements in first contact resolution were not included in the announcement. The company emphasized deployment speed and enterprise-grade governance but did not provide specific data points.

The claim that AI agents can deploy "in days" contrasts with typical enterprise software implementation timelines. Traditional customer experience platforms often require months of configuration, integration, and testing before full deployment. The accelerated timeline Sprinklr describes would represent a substantial reduction in implementation overhead, though actual deployment duration likely varies based on organizational complexity and use case specifics.

Competitive landscape dynamics

Salesforce CEO Marc Benioff disclosed on July 30, 2025, that AI agents currently perform 30 percent to 50 percent of all work within Salesforce itself, with these systems resolving 85 percent of the company's customer service inquiries. The customer experience management market includes specialized vendors focused on specific channels alongside comprehensive platforms attempting to unify disparate touchpoints.

However, research from Salesforce AI Research published June 10, 2025, found that leading AI agents achieve only 58 percent success rates in single-turn business scenarios, with performance dropping to 35 percent in multi-turn interactions. The study examined 19 distinct business tasks across customer relationship management systems.

Sprinklr competes with both point solutions for individual channels and integrated platforms from larger software vendors. The company's emphasis on AI-native architecture rather than retrofitted capabilities represents an attempt to differentiate from competitors who added AI features to existing products. Whether this architectural distinction translates to measurable performance advantages remains to be demonstrated through customer implementations.

The announcement arrives as enterprises evaluate how to deploy generative AI capabilities within customer-facing operations. Early implementations of AI chatbots and automated response systems produced mixed results, with some organizations reporting improved efficiency while others experienced customer frustration with AI interactions. Sprinklr's focus on enterprise-grade governance and explainable AI responses addresses concerns that emerged from initial deployments.

Google Cloud released its second annual AI study on April 18, 2025, revealing that 88 percent of early adopter organizations implementing AI agents report positive return on investment across multiple business applications. The comprehensive survey examined 3,466 senior business leaders from global enterprises generating more than $10 million annual revenue.

Adverity announced AI-powered intelligence capabilities on September 12, 2025, introducing conversational AI and intelligent agents for marketing analytics automation. The London-based company built its intelligence layer on AI and Model Context Protocol technology, enabling different tools, agents, and teams to work together.

Timeline

• September 29, 2025: Sprinklr announced AI-powered capabilities including Sprinklr Copilot, Sprinklr AI Agents, and enhanced Customer Feedback Management at CXUnifiers 2025 conference in Nashville
• September 12, 2025: Adverity debuts AI-powered intelligence layer for marketing analytics automation
• September 10, 2025: Adobe launches AI agents for business customer experience automation
• July 30, 2025: Salesforce CEO details AI agents handling 50% of company work
• July 27, 2025: McKinsey analysis reveals AI agents reshape advertising landscape
• June 29, 2025: Salesforce study reveals enterprise AI agents fail 65% of multiturn tasks
• April 18, 2025: Google Cloud survey reveals 88% ROI spike among AI agent early adopters

Summary

Who: Sprinklr, a customer experience management platform provider serving approximately 1,900 enterprise customers including Microsoft, Procter & Gamble, and Samsung, announced the new capabilities. Rory Read serves as president and CEO, while Amitabh Misra is chief technology officer.

What: The company introduced Sprinklr Copilot (a conversational AI interface), Sprinklr AI Agents (autonomous tools for customer interactions), and enhanced Customer Feedback Management capabilities that integrate survey data with social media feedback.

When: The announcement was made on September 29, 2025, at the CXUnifiers 2025 conference in Nashville.

Where: The tools operate within Sprinklr's unified customer experience management platform, which spans social media management, marketing, advertising, customer feedback, and contact center operations across multiple channels including voice, chat, email, and social media.

Why: The announcement addresses enterprise challenges with fragmented customer data, disconnected tools across marketing and service teams, and the need for AI capabilities that integrate with existing workflows rather than requiring separate systems. Marketing teams managing customer interactions across multiple channels face operational complexity that unified platforms attempt to resolve.