How Agentic AI and GenAI Are Revolutionising Next Best Action: Unlocking New Capabilities and Value

The concept of Next Best Action (NBA) has long been a cornerstone of customer-centric strategies, enabling businesses to deliver personalised recommendations and optimize customer interactions. However, with the advent of Agentic AI and advancements in Generative AI (GenAI), NBA is undergoing a transformative evolution. These technologies are not only enhancing the precision and scalability of NBA but also unlocking entirely new capabilities that were previously unimaginable.

What is Agentic AI and How Does It Enhance NBA?

Agentic AI refers to AI systems that operate with a high degree of autonomy, making decisions and taking actions without constant human intervention. Unlike traditional AI, which follows predefined rules, Agentic AI can interact with its environment, adapt to new situations, and pursue specific goals. This makes it particularly well-suited for NBA, where dynamic decision-making and real-time adaptability are critical.

Key Capabilities of Agentic AI in NBA:

Autonomous Decision-Making: Agentic AI can analyse vast amounts of data, predict outcomes, and determine the best course of action without human input. For example, in retail, it can autonomously recommend products based on real-time customer behavior and preferences.
Multi-Step Reasoning: Unlike traditional NBA systems, which often focus on single-step optimisations, Agentic AI can break down complex tasks into sequential steps. For instance, in healthcare, it can assist doctors by recommending diagnostic tests, interpreting results, and suggesting treatment plans in a coordinated manner.
Continuous Learning: Agentic AI systems improve over time by learning from feedback and new data. This ensures that NBA recommendations become increasingly accurate and relevant.

Incremental Capabilities Over Previous NBA Generations

The integration of Agentic AI and GenAI into NBA systems has introduced several incremental capabilities that significantly enhance their effectiveness:

Hyper-Personalisation:

Previous NBA: Relied on static customer profiles and historical data.
Agentic AI: Leverages real-time data and contextual understanding to deliver hyper-personalised recommendations. For example, in e-commerce, Agentic AI can analyze browsing patterns, purchase history, and even social media activity to suggest products that align with a customer’s current interests.

Proactive Decision-Making:

Previous NBA: Reacted to customer actions (e.g., recommending a product after a purchase).
Agentic AI: Anticipates customer needs and takes proactive actions. For instance, in banking, it can detect signs of financial stress and offer tailored solutions before the customer even realises they need help.

Cross-Channel Integration:

Previous NBA: Operated in silos, with limited integration across channels.
Agentic AI: Seamlessly integrates data from multiple touchpoints (e.g., online, in-store, mobile) to provide a unified human (customer + employee) experience. For example, a retail customer might receive a personalised offer via email after browsing products in-store.

Complex Workflow Automation:

Previous NBA: Focused on simple tasks like recommending products or sending reminders.
Agentic AI: Automates complex workflows, such as managing multi-step marketing campaigns or optimising supply chain operations. In logistics, it can predict demand fluctuations and adjust inventory levels in real-time.

Case Studies: Agentic AI and GenAI in Action

1. Retail: Fashion Retailer’s AI-Driven Inventory Management

A global leading fashion retailer has leveraged Agentic AI to revolutionise its supply chain and inventory management. By analysing real-time sales data and market trends, the AI system predicts demand fluctuations and adjusts inventory levels accordingly. This has not only reduced stockouts and overstocking but also improved operational efficiency and customer satisfaction.

2. Healthcare: Multi-Agent Diagnostic Systems

In healthcare, Agentic AI is being used to create multi-agent diagnostic systems that collaborate like a team of medical specialists. For example, an AI agent might analyse medical images, while another interprets lab results. Together, they provide a holistic diagnosis and treatment plan, significantly improving patient outcomes.

3. Insurance: Automated Claims Processing

A Dutch insurer has implemented Agentic AI to automate 90% of its claims processing. The AI system reviews claims, assesses their validity, and processes payments without human intervention. This has freed up claims adjusters to focus on complex cases, improving both efficiency and customer satisfaction.

4. Financial Services: Fraud Detection and Prevention

One of the worlds largest payment processors uses Agentic AI to monitor transactions in real-time and detect fraudulent activities. The system analyses transaction patterns, identifies anomalies, and flags suspicious behavior for further investigation. This has significantly reduced fraud losses and enhanced human (customer + employee) trust.

Research Insights from PwC

PwC’s research highlights the transformative potential of Agentic AI and GenAI in NBA. According to their analysis, these technologies can drive significant value across industries by:

Enhancing Efficiency: Automating routine tasks and optimizing workflows, leading to cost savings and productivity gains.
Improving Human Experience: Delivering personalised and proactive recommendations that increase customer satisfaction and loyalty.
Enabling Innovation: Unlocking new business models and revenue streams through advanced data analytics and decision-making capabilities.

PwC also emphasises the importance of responsible AI practices, such as ensuring transparency, fairness, and accountability in AI systems. This is particularly critical in high-stakes applications like healthcare and finance.

The Future of NBA with Agentic AI and GenAI

As Agentic AI and GenAI continue to evolve, their impact on NBA will only grow. Future advancements may include:

Emotionally Intelligent AI: Systems that can understand and respond to customer emotions, further enhancing personalisation.
Collaborative AI Networks: Multi-agent systems that work together to solve complex problems, such as managing global supply chains or coordinating large-scale marketing campaigns.
Ethical AI Frameworks: Robust governance frameworks to ensure that AI systems align with human values and ethical principles.

Conclusion

Agentic AI and GenAI are revolutionising Next Best Action by introducing unprecedented levels of autonomy, personalisation, and efficiency. From retail and healthcare to finance and insurance, these technologies are unlocking new capabilities and driving significant value across industries. As businesses continue to adopt and refine these systems, the future of NBA promises to be more dynamic, intelligent, and customer-centric than ever before.

By embracing these advancements, organisations can not only stay ahead of the competition but also create meaningful and lasting value for their customers.

For further reading, explore PwC’s insights on GenAI and Agentic AI: PwC GenAI Growth Opportunities and Agentic AI Use Cases.