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LARGE ACTION MODELS: REVOLUTIONIZING CUSTOMER EXPERIENCE IN CALL CENTERS AND BPOS

LAM

Welcome to the Large Action Models (LAMs) Era

The rapid evolution of artificial intelligence has heralded a new milestone: the era of Large Action Models (LAMs). While earlier advancements in AI focused on data processing and understanding, LAMs signify a shift towards autonomous decision-making and task execution. With their ability to plan, reason, and act, LAMs are transforming industries at an extraordinary pace, particularly in customer service and business process outsourcing (BPO). 

A Timeline of AI Evolution Toward LAMs 

  • 1960s-1980s: Early AI and Rule-Based Systems – Basic AI models like Eliza could simulate human conversation using predefined scripts, but they lacked reasoning abilities. 
  • 1990s-2000s: The Emergence of Machine Learning – AI models like Deep Blue, which defeated Garry Kasparov, demonstrated the potential for AI-driven decision-making. 
  • 2010s: The Rise of Large Language Models (LLMs) – NLP-based models such as GPT and BERT improved text comprehension, establishing a foundation for automation. 
  • 2020s: The Birth of LAMs—AI innovations like DeepSeek R1 and Alibaba’s advancements have introduced models that can perform complex actions beyond text comprehension. 
  • Today marks the dawn of the LAM Era – AI-driven automation now includes comprehensive process management, seamlessly integrating AI with business workflows to ensure flawless execution. 

From Language to Action: What Distinguishes LAMs

Traditional AI tools, such as large language models (LLMs), have demonstrated remarkable proficiency in understanding and generating text. These tools form the foundation for chatbots, virtual assistants, and content generation systems. However, their capabilities are limited to passive interactions; they can suggest or offer guidance but cannot perform actions. LLMs go beyond comprehension by integrating advanced reasoning, planning, and action-execution capabilities. 

For example, while a chatbot based on an LLM may inform a customer about the available phone plans, a system powered by an LAM could enhance this by identifying the best plan according to the customer’s usage patterns, initiating the upgrade, updating the billing system, and confirming the change—all without human intervention. This ability to perform end-to-end tasks positions LAMs as transformative in customer-facing industries.

Strategic Business Considerations for LAM Adoption

Although LAMs offer significant potential, their implementation necessitates strategic planning to ensure sustained business value. Key factors to consider include: 

  • Competitive Differentiation: BPOs and call centers must integrate LAMs to enhance efficiency and create distinctive customer experiences that set them apart from their competitors. 
  • Challenges of LAM Adoption: The strategy must address data integrity, workforce displacement, regulatory complexity, and AI governance from the very beginning. 

Assessing the success of LAM requires organisations to use performance metrics, including cost savings, improvements in customer satisfaction, and increases in operational efficiency.

Improving Call Center Operations with LAMs

  • Reducing Average Handling Time (AHT)
    • LAMs can significantly reduce AHT by automating repetitive tasks such as identity verification, account lookups, and form submissions. For instance, when a customer calls to dispute a transaction, a LAM can swiftly authenticate the caller, retrieve account details, and provide the relevant information. Simple disputes can even be resolved independently.
  • Empowering Agents with Real-Time Insights:
    • LAMs integrate seamlessly with existing CRM systems, providing agents with real-time insights during customer interactions. By analysing historical data and contextual cues, LAMs can recommend the most effective course of action or suggest upselling opportunities, thereby enabling agents to deliver personalised and proactive service.
  • Scaling Support Without Scaling Costs:
    • Unlike traditional systems that require substantial resources to scale, LAMs can manage high volumes of queries across various channels—voice, chat, email, and social media. They guarantee consistent service quality, even amid fluctuations in demand. 

A Structured Execution Framework for LAM Integration.

For successful LAM deployment, businesses should embrace a systematic approach: 

  1. Pilot Phase: Test LAM automation in limited customer service workflows before scaling up. 
  1. Early Adoption: Broaden LAM usage to include data-driven decision-making and sentiment analysis. 
  1. Enterprise-Scale Integration: Achieve comprehensive integration of LAMs across various customer touchpoints. 
  1. Continuous Optimisation: Use feedback loops and AI governance frameworks to improve LAM efficiency. 

Managed Services Interventions: The Role of People, Processes, and Technology

A successful LAM deployment necessitates alignment among people, processes, and technology. 

  • People
    • Reskilling Employees: Training agents to work alongside AI for valuable interactions.
    • Workforce Transition Strategies: Preparing for AI enhancement in comparison to workforce displacement.
  • Processes 
    • Operational Workflow Redesign: Ensuring that LAM-driven automation supports human agents. 
    • Service Level Agreements (SLAs) for AI-Driven Customer Experience: Aligning LAM performance with customer service expectations. 
  • Technology
    • Integration with AI Ecosystems: LAMs should collaborate with RPA, analytics, and cloud contact centers. 
    • AI Governance and Compliance: Ensuring LAM complies with GDPR, CCPA, and CX security standards. 

Critical Risks, Challenges, and the Competitive Landscape

  • Regulatory & Compliance Risks 
    • Considerations of GDPR and CCPA regarding AI-driven customer data processing. 
    • Bias and Fairness in AI: Ensuring transparent and non-discriminatory decision-making. 
  • AI Security and Fraud Prevention
    • LAMs must protect against adversarial AI attacks and address the deficiencies in customer fraud detection.
  • Industry Trends & Competitive Risks
    • DeepSeek and Alibaba’s advancement in AI is transforming the BPO and call center AI market. 
    • Scaling AI-driven customer service while reducing costs is increasingly becoming a competitive advantage. 

The Future of CX with LAMs

Integrating LAMs into call centers and BPOs signifies a new era for customer experience. By automating routine tasks, enhancing personalisation, and ensuring consistent service quality, LAMs empower businesses to exceed customer expectations while optimising operational efficiency. 

As LAM adoption accelerates, businesses that embrace this technology will be well-positioned to lead in an increasingly competitive customer experience landscape. Imagine a call center where routine tasks are resolved in seconds and personalized support is available around the clock—a reality made possible by LAMs. 

A Call to Action for Call Centers and BPO’s

The LAM era has arrived, and BPOs and contact centers must act swiftly to align their digital transformation strategies. 

Businesses should:

✅ Evaluate AI Preparedness and Strategic Alignment.  

✅ Utilize advancements in open-source artificial intelligence. 

✅ Revise workflows to improve collaboration between AI and humans.  

✅ Create a framework for governance and compliance in AI deployment. 

The future of customer experience is intelligent, proactive, and underpinned by artificial intelligence—LAMs will be vital in facilitating this transformation.