Tag: AI Agents

For marketers, the “golden goal” has always been to deeply understand customers, enabling more effective cross-selling, upselling, and targeted campaigns. The promise of maximising wallet share hinges on this fundamental principle. Imagine having technologies that can analyse customer journeys deeply, uncovering meaningful, real-time insights into customer behaviour and sentiment. This rich, dynamic data could empower marketing teams to move beyond static profiles, gaining immediate visibility into how customers react to campaigns, messages, and interactions across all channels. 

Data Fragmentation: The CMO’s Blind Spot 

However, achieving this goal has become increasingly difficult. The modern marketing stack, built upon CRM, content marketing platforms, retargeting ad solutions, social listening tools, and countless other applications, often operates in silos. This wide, disconnected array of tools creates a significant challenge: making sense of the fragmented data. Efforts to truly understand customers and identify valuable prospects frequently fall short of desired outcomes. The lack of integration and the sheer volume of disparate data leave marketers struggling to connect the dots and extract actionable insights. 

Unified Customer Vision: AI Agents for Intelligent Marketing 

The solution lies in leveraging AI agents that operate seamlessly in the background. By implementing AI agents, CMOs can gain a comprehensive understanding of their customers, enabling them to run more effective campaigns, drive greater wallet share, and build stronger, more meaningful customer relationships. 

These intelligent agents can bridge the gaps in customer data, sentiment, and campaign perception by accessing and processing information across the entire marketing stack. By learning from metadata, successful and failed campaigns, and a broad range of customer insights – including conversational and digital contact centre data – these agents can provide a unified view of the customer. 

What They Bring to the Table 

• Unified Data Access. AI agents can traverse siloed marketing applications, extracting and correlating data from various sources. 

• Real-Time Insight Generation. They can analyse customer interactions, including social media sentiment, conversational AI data, and voice bot interactions, to provide dynamic, real-time insights. 

• Autonomous Action & Adaptation. Agentic workflows can adapt to campaigns, email blasts, and lead generation activities autonomously, refining strategies and messaging on the fly. 

• Content Curation & Optimisation. Content curation agents can tailor content based on real-time customer feedback and preferences. 

• Proactive Opportunity Identification. By identifying gaps in customer understanding and campaign performance, AI agents can empower marketers to uncover new opportunities for engagement and growth. 

Extending AI Agent Value: Practical Applications for the Modern CMO 

Beyond unified data access and autonomous action, AI agents offer a wealth of practical applications that can revolutionise marketing operations. Consider the following scenarios: 

• Automating Time-Consuming Tasks. Identify and offload repetitive, manual tasks associated with campaign execution and lead generation to a team of AI agents, freeing up valuable human resources for strategic initiatives. 

• Enhancing Sales Pipeline Intelligence. Leverage AI agents to extract insights from sales pipelines and customer feedback, enabling data-driven campaign adjustments and improved sales alignment. 

• Real-Time Sentiment Analysis. Deploy multiple AI agents to monitor customer sentiment across conversations and social media platforms, providing immediate feedback on campaign effectiveness and brand perception. 

• Strategic Scenario Planning. Use AI agents to formulate and evaluate various marketing spend scenarios across different channels and agencies, optimising resource allocation and maximising ROI. 

• Dynamic Campaign Monitoring. Implement AI agents to track campaign performance in real-time, allowing for immediate adjustments and optimisation. 

• Event Sentiment Analysis. Employ AI to monitor customer sentiment during live events, providing immediate insights into audience reactions and engagement. 

• Unlocking Conversational Intelligence. Extract valuable insights from sales conversations and contact centre interactions, feeding them into future sales strategies and upselling opportunities. This extends beyond relying solely on CRM data, providing a richer, more nuanced understanding of customer interactions. 

By implementing these capabilities, CMOs can transform their marketing operations, moving from reactive to proactive, and ultimately driving greater customer engagement and business success. 

The “Wow” Factor: Agentic AI and Unified Data 

Ultimately, the pursuit of a seamless customer journey and deeper conversational engagement hinges on bridging the persistent departmental disconnect. Despite each team interacting with the same customer, data remains siloed, hindering a holistic understanding and unified approach. 

The missing link lies in fostering a dynamic, interconnected data ecosystem where insights from campaigns, social listening, contact centre conversations, chatbot interactions, VoC programs, marketing applications, and CRM flow freely and mutually reinforce each other.  

This is where Agentic AI steps in. By empowering AI agents to adapt and act autonomously across these diverse data sources, we create a symphony of customer intelligence. These agents, working in harmony, unlock the potential for real-time, actionable insights, enabling marketers to craft truly exceptional, “wow” moments that resonate deeply with customers. In essence, Agentic AI transforms fragmented data into a unified, powerful force, driving unparalleled customer experiences and forging lasting brand loyalty. 

The promise of AI agents – intelligent programs or systems that autonomously perform tasks on behalf of people or systems – is enormous. These systems will augment and replace human workers, offering intelligence far beyond the simple RPA (Robotic Process Automation) bots that have become commonplace in recent years.

RPA and AI Agents both automate tasks but differ in scope, flexibility, and intelligence:

7 Lessons for AI Agents: Insights from RPA Deployments

However, in many ways, RPA and AI agents are similar – they both address similar challenges, albeit with different levels of automation and complexity. RPA adoption has shown that uncontrolled deployment leads to chaos, requiring a balance of governance, standardisation, and ongoing monitoring. The same principles apply to AI agent management, but with greater complexity due to AI’s dynamic and learning-based nature.

By learning from RPA’s mistakes, organisations can ensure AI agents deliver sustainable value, remain secure, and operate efficiently within a governed and well-managed environment.

#1 Controlling Sprawl with Centralised Governance

A key lesson from RPA adoption is that many organisations deployed RPA bots without a clear strategy, resulting in uncontrolled sprawl, duplicate bots, and fragmented automation efforts. This lack of oversight led to the rise of shadow IT practices, where business units created their own bots without proper IT involvement, further complicating the automation landscape and reducing overall effectiveness.

Application to AI Agents:

• Establish centralised governance early, ensuring alignment between IT and business units.
• Implement AI agent registries to track deployments, functions, and ownership.
• Enforce consistent policies for AI deployment, access, and version control.

#2 Standardising Development and Deployment

Bot development varied across teams, with different toolsets being used by different departments. This often led to poorly documented scripts, inconsistent programming standards, and difficulties in maintaining bots. Additionally, rework and inefficiencies arose as teams developed redundant bots, further complicating the automation process and reducing overall effectiveness.

Application to AI Agents:

• Standardise frameworks for AI agent development (e.g., predefined APIs, templates, and design patterns).
• Use shared models and foundational capabilities instead of building AI agents from scratch for each use case.
• Implement code repositories and CI/CD pipelines for AI agents to ensure consistency and controlled updates.

#3 Balancing Citizen Development with IT Control

Business users, or citizen developers, created RPA bots without adhering to IT best practices, resulting in security risks, inefficiencies, and technical debt. As a result, IT teams faced challenges in tracking and supporting business-driven automation efforts, leading to a lack of oversight and increased complexity in maintaining these bots.

Application to AI Agents:

• Empower business users to build and customise AI agents but within controlled environments (e.g., low-code/no-code platforms with governance layers).
• Implement AI sandboxes where experimentation is allowed but requires approval before production deployment.
• Establish clear roles and responsibilities between IT, AI governance teams, and business users.

#4 Proactive Monitoring and Maintenance

Organisations often underestimated the effort required to maintain RPA bots, resulting in failures when process changes, system updates, or API modifications occurred. As a result, bots frequently stopped working without warning, disrupting business processes and leading to unanticipated downtime and inefficiencies. This lack of ongoing maintenance and adaptation to evolving systems contributed to significant operational disruptions.

Application to AI Agents:

• Implement continuous monitoring and logging for AI agent activities and outputs.
• Develop automated retraining and feedback loops for AI models to prevent performance degradation.
• Create AI observability dashboards to track usage, drift, errors, and security incidents.

#5 Security, Compliance, and Ethical Considerations

Insufficient security measures led to data leaks and access control issues, with bots operating under overly permissive settings. Also, a lack of proactive compliance planning resulted in serious regulatory concerns, particularly within industries subject to stringent oversight, highlighting the critical need for integrating security and compliance considerations from the outset of automation deployments.

Application to AI Agents:

• Enforce role-based access control (RBAC) and least privilege access to ensure secure and controlled usage.
• Integrate explainability and auditability features to comply with regulations like GDPR and emerging AI legislation.
• Develop an AI ethics framework to address bias, ensure decision-making transparency, and uphold accountability.

#6 Cost Management and ROI Measurement

Initial excitement led to unchecked RPA investments, but many organisations struggled to measure the ROI of bots. As a result, some RPA bots became cost centres, with high maintenance costs outweighing the benefits they initially provided. This lack of clear ROI often hindered organisations from realising the full potential of their automation efforts.

Application to AI Agents:

• Define success metrics for AI agents upfront, tracking impact on productivity, cost savings, and user experience.
• Use AI workload optimisation tools to manage computing costs and avoid overconsumption of resources.
• Regularly review AI agents’ utility and retire underperforming ones to avoid AI bloat.

#7 Human Oversight and Hybrid Workflows

The assumption that bots could fully replace humans led to failures in situations where exceptions, judgment, or complex decision-making were necessary. Bots struggled to handle scenarios that required nuanced thinking or flexibility, often leading to errors or inefficiencies. The most successful implementations, however, blended human and bot collaboration, leveraging the strengths of both to optimise processes and ensure that tasks were handled effectively and accurately.

Application to AI Agents:

• Integrate AI agents into human-in-the-loop (HITL) systems, allowing humans to provide oversight and validate critical decisions.
• Establish AI escalation paths for situations where agents encounter ambiguity or ethical concerns.
• Design AI agents to augment human capabilities, rather than fully replace roles.

The lessons learned from RPA’s journey provide valuable insights for navigating the complexities of AI agent deployment. By addressing governance, standardisation, and ethical considerations, organisations

can shift from reactive problem-solving to a more strategic approach, ensuring AI tools deliver value while operating within a responsible, secure, and efficient framework.

The Customer Experience (CX) space is set to witness significant advancements in 2025, particularly with the rise of Agentic AI.

Unlike GenAI, which despite enormous promise, has struggled to deliver scalable solutions, Agentic AI offers dynamic, scalable improvements for brands.

With AI agents and an expanding digital AI workforce, front and back-office automation is becoming more independent.

These AI-driven systems will enable precise information retrieval, intelligent, human-like conversations, autonomous decision-making, and seamless customer interactions without constant intervention from CX teams.

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In 2024, technology vendors have heavily invested in AI Agents, recognising their potential to drive significant value. These tools leverage well-governed, small datasets to integrate seamlessly with applications like Workday, Salesforce, ServiceNow, and Dayforce, enhancing processes and outcomes.

2025 is poised to be the year of AI Agent adoption. Designed to automate specific tasks within existing workflows, AI Agents will transform customer experiences, streamline operations, and boost efficiency. Unlike traditional AI deployments, they offer a gradual, non-disruptive approach, augmenting human capabilities without overhauling processes. As organisations adopt new software versions with embedded AI capabilities, 2025 will mark a pivotal shift in customer experience delivery.