Agentic AI: Disrupting Retail and SME Banking

AgenticAI in Retail and SME Banking

For decades, banks have served as centralised hubs for financial services. Still, a tectonic shift is unfolding, propelled by a new class of intelligent technology: Agentic AI, which refers to autonomous systems capable of sensing, reasoning, and acting, leading to a profound change.

Unlike traditional automation or even generative AI, these intelligent agents do not merely follow commands but are designed to learn continuously, adapt, and collaborate to achieve desired results. Retail and SME banking are two sectors at the forefront of this transformation, both experiencing substantial shifts as agent-driven systems redefine customer interaction, decision-making processes, and operational efficiency.

AgileIntel forecasts that agentic AI is set to generate multi-billion-dollar opportunities in both sectors by 2030, with growth fueled by goal-oriented agents capable of making context-aware decisions at scale.

What is Agentic AI?

Agentic AI builds on generative AI by enabling autonomous, goal-oriented behaviour. Unlike systems that only respond to prompts, agentic AI can plan workflows, adapt based on context, and continuously improve its performance.

Key capabilities include the ability to:

• Break down complex goals into subtasks

• Utilize memory to enhance learning and improvement over time

• Engage with enterprise systems (CRM, ERP, LOS)

• Collaborate with humans or other agents

  
  

In essence, agentic AI functions like a digital colleague, capable of autonomously executing business actions such as loan approvals, dynamic pricing adjustments, or customer issue resolutions.

Comparison of AI Approaches

• Traditional AI → rule-based, focused on narrowly defined tasks.

• Generative AI → creates content like text or images, operating primarily in a reactive mode.

• Agentic AI → Combines reasoning, memory, and action to plan workflows, adapt from prior interactions, and collaborate effectively with humans or other agents.

By integrating reasoning, adaptability, and autonomous execution, agentic AI enables organizations to enhance operational efficiency, decision-making, and scalability.

Transforming Retail with Agentic AI

The retail sector has consistently prioritized speed, personalization, and efficiency. Traditionally, retailers depended on rules-based automation, such as weekly price adjustments, batch inventory updates, and fixed customer service scripts. However, with the advent of agentic AI, this model is evolving towards continuous and adaptive decision-making. The impact can be seen across four key areas:

1. Customer Service at Scale

Retail agents are increasingly handling routine service interactions from diagnosing order issues to processing returns and scheduling repairs. Unlike scripted chatbots, these agents can manage cases end-to-end, involving human staff only for exceptional situations.

Salesforce’s Agentforce, widely adopted by retailers, autonomously guides customers through troubleshooting processes, achieving a 65% self-service rate.

2. Dynamic Merchandising & Pricing

Agents are in constant observation of real-time demand, competitor pricing, and inventory levels. Rather than waiting for weekly assessments, they can modify promotions and prices instantaneously, resulting in measurable revenue increases.

3. Omnichannel Orchestration

Customers transition effortlessly between applications, websites, and social media platforms. Agentic systems guarantee that shopping carts, offers, and customer support remain aligned. In contrast to isolated automation, agents manage the entire customer journey, improving conversion rates and minimizing drop-offs.

4. Store Operations & Associate Support

Retail associates gain from agents that recommend the “next best action,” whether it involves restocking shelves, preparing pickup orders, or dynamically modifying staff schedules based on store traffic. For example, Walmart has implemented centralized AI agents utilized by nearly 900,000 employees, streamlining operations across customer, supplier, and developer interactions.

AgileIntel predicts that the market for agentic AI in retail is projected to be valued at US$2.6 billion in 2025, and forecasted to grow to US$17.4 billion by 2030, reflecting a compound annual growth rate (CAGR) of 46.3%.

Reshaping SME Banking through Agentic AI

For small and medium-sized enterprises (SMEs), banking services frequently face challenges due to outdated systems, intricate compliance requirements, and slow decision-making processes. Agentic AI is assisting banks in streamlining, expediting, and securing these services. Key areas of impact include:

1. Enhanced Underwriting

Conventional underwriting relies significantly on fixed credit scores. Agentic AI analyses real-time borrower behavior, supply chain indicators, and ecosystem data, facilitating contextual lending decisions that cater to the specific needs of SMEs.

2. Streamlined Processes

Historically, loan applications and compliance verifications have been prone to errors and are time intensive. With agents managing screening, document verification, and fraud detection, banks have reported a decrease in processing times and an increase in first-pass approval accuracy.

3. Innovative Lending Opportunities

SMEs now have access to pre-approved offers, dynamic pricing, and integrated credit options, which help minimize borrower drop-offs. Agents also support the cross-selling of contextual products an area where SMEs previously had limited access.

4. Legacy System Integration

Rather than undertaking expensive system overhauls, banks can implement agentic AI as a cognitive layer over existing loan origination systems (LOS). This approach enables quicker deployment and mitigates the risks associated with core system replacement.

5. Futuristic Fraud Detection

In contrast to static rule-based systems, agentic AI identifies subtle behavioral anomalies and reacts promptly to potential fraud. This capability enhances trust in digital banking for SMEs.

With these capabilities, the market for agentic AI in financial services is projected to grow from US$2.6 billion in 2025 to US$31.2 billion by 2030, reflecting an impressive CAGR of 64.5%. Leading banks such as Citi, Wells Fargo, HSBC, and Bank of America are already increasing their investments to capitalize on this opportunity.

Why This Matters for Retailers and Banks Alike

1. Competitive Edge

For retailers, agentic AI facilitates real-time optimization of revenue. For SME banking, it enables quicker loan disbursement and enhances customer trust. Both industries benefit from cost reductions and increased agility.

2. Collaboration Between Humans and AI

Agents are designed to complement human efforts rather than replace them, enhancing decision-making processes. Retail staff utilize them for prioritizing tasks, while bankers employ them for compliance and fraud detection. This hybrid approach ensures that human oversight is maintained where necessary.

3. Expansion of the Market

The dual-sector adoption indicates significant growth, with retail and SME banking collectively expected to surpass US$48 billion by 2030. In the retail sector, the market is anticipated to grow from US$2.6 billion in 2025 to US$17.4 billion, propelled by dynamic pricing strategies, inventory optimization, and AI-enhanced customer service.

In the realm of SME banking, the potential is even greater, rising from US$2.6 billion to US$31.2 billion, driven by the need for expedited credit, improved fraud detection, and seamless integration with existing legacy systems.

Challenges to Overcome

Despite the numerous opportunities, there are challenges to consider:

• Regulatory scrutiny in the banking sector necessitates that agents not only clarify the decisions made but also provide justifications, detailing data sources and reasoning.

• Effective change management is essential as associates, loan officers, and SMEs must have confidence in the outputs generated by agents.

• Security and governance frameworks are required to ensure that agents operate within established parameters, including rollback mechanisms and audit trails.

Conclusion

Agentic AI has transitioned from a futuristic idea to a current reality, actively transforming the landscape of retail and SME banking. In the retail sector, it enhances personalized shopping experiences, enables real-time pricing adjustments, and streamlines store operations. With SME banking, it is revolutionizing underwriting processes, bolstering fraud prevention measures, and improving loan accessibility.

With impressive double-digit growth rates and significant adoption by industry leaders, agentic AI is poised to become the foundational element of digital commerce and finance. For businesses that choose to adopt this technology, the benefits will extend beyond mere efficiency; it will signify a move towards an era characterized by adaptive, intelligent, and customer-focused operations.