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Explore the future of agentic AI with specialized networks, self-organizing systems, and dynamic knowledge layers transforming industries over the next 18 months.
Why is AI important in the banking sector? | The shift from traditional in-person banking to online and mobile platforms has increased customer demand for instant, personalized service. |
AI Virtual Assistants in Focus: | Banks are investing in AI-driven virtual assistants to create hyper-personalised, real-time solutions that improve customer experiences. |
What is the top challenge of using AI in banking? | Inefficiencies like higher Average Handling Time (AHT), lack of real-time data, and limited personalization hinder existing customer service strategies. |
Limits of Traditional Automation: | Automated systems need more nuanced queries, making them less effective for high-value customers with complex needs. |
What are the benefits of AI chatbots in Banking? | AI virtual assistants enhance efficiency, reduce operational costs, and empower CSRs by handling repetitive tasks and offering personalized interactions. |
Future Outlook of AI-enabled Virtual Assistants: | AI will transform the role of CSRs into more strategic, relationship-focused positions while continuing to elevate the customer experience in banking. |
As someone building an AI startup, I'm convinced we're entering a new phase of artificial intelligence—one defined not by singular, powerful AI models, but by the convergence of specialized AI agents. Let me share what I believe the next 18-24 months hold for agentic AI, based on emerging patterns we're seeing across industries.
Traditional approaches to enterprise AI have focused on building monolithic systems – single, powerful models designed to handle a wide range of tasks. But this approach is reaching its limits. Just as human organizations benefit from specialized expertise, AI systems are evolving toward networks of specialized agents, each focused on specific aspects of complex problems.
In banking, there is an expectation of monolithic AI customer service system being replaced with network of specialized agents :
This shift from a single system to a network of specialists yielded remarkable results. Beyond the immediate improvements in resolution rates and cost reduction, they discovered something more valuable: the system could evolve and adapt to new challenges without requiring complete retraining or restructuring.
By Q3 2025, we'll likely see the emergence of "agent marketplaces" – ecosystems where organizations can discover, test, and deploy specialized AI agents. Think of it as an App Store for AI capabilities.
Early indicators suggest three types of agents will dominate:
These agents excel in domain-specific tasks.
In healthcare, for instance, we're seeing the emergence of diagnostic agents trained in specific medical specialties, each bringing deep expertise to their particular domain. Similarly, legal compliance agents are being developed with jurisdiction-specific knowledge, ensuring organizations can navigate complex regulatory landscapes effectively.
The second category focuses on business function expertise. These agents specialize in universal business challenges like supply chain optimization, customer sentiment analysis, and predictive maintenance. Their power lies in their ability to apply deep functional knowledge across different industry contexts.
Perhaps most crucial are the integration agents that tie everything together. These specialists handle the complex task of ensuring smooth communication and data flow between different systems and agents. They manage data transformation, cross-platform communication, and security compliance, acting as the connective tissue of the intelligent enterprise.
Here's what's fascinating:
While individual specialized agents might cost more initially, networks of them are proving more cost-effective than general-purpose AI systems.
The most exciting development I'm seeing is the emergence of self-organizing agent networks. Instead of humans defining how agents should interact, these networks are beginning to:
For example in manufacturing - a network for quality control can be deployed that can automatically:
Contrary to common fears about AI replacing humans, we're seeing a fascinating trend: organizations with advanced agent networks are hiring more high-level strategists and "AI orchestrators." These roles focus on:
The key is shifting from operational to strategic roles:
The implications are clear: organizations need to start thinking about AI not as a single solution but as an ecosystem of specialized capabilities.
Success in the next 18-24 months will depend on:
OpenAI's recent release of Swarm has validated what many of us in the field have long suspected: the future belongs to lightweight, adaptable frameworks. But what's coming next goes far beyond current capabilities.
We're seeing the emergence of what I call the "Three-Tier Intelligence Stack":
The first tier represents how AI systems manage and utilize knowledge. Traditional Retrieval-Augmented Generation (RAG) approaches, while powerful, are no longer sufficient for the demands of modern enterprises. We're now seeing the emergence of Dynamic Knowledge Networks that transform how AI systems learn and adapt.
Consider this example: A financial services firm can implement this new approach to fraud detection. Instead of relying on static rules and historical patterns, their system now maintains a living knowledge network that:
The result is not just better fraud detection—it’s adaptive fraud prevention that evolved alongside new threats.
The second tier focuses on how AI systems process information and make decisions. This layer represents perhaps the most significant departure from traditional AI architectures. Rather than applying fixed decision models, systems at this level can:
For example, in healthcare, implementing this architecture can showcase remarkable improvements in diagnostic accuracy. Their system could combine:
All while adjusting its diagnostic approach based on the specific medical context and urgency of each case.
The final tier manages how AI systems turn decisions into actions. This layer handles:
One of the most exciting developments I'm seeing is in how agent networks handle memory. Traditional approaches to AI memory are being completely changed.
Instead of centralized knowledge bases, we're moving toward distributed memory networks, where:
As agent networks become more complex, new security paradigms are emerging:
Perhaps the most significant technical challenge ahead is integration. We're seeing innovative solutions emerge:
From our experience, here are the most critical mistakes organizations make:
Many organizations fall into what I call the "more is better" fallacy when it comes to data. Accumulating petabytes of historical data, believing it would give their AI systems an edge, will inevitably give rise to challenges with
The solution isn't more data—it was smarter data management. Implementing dynamic knowledge management principles can lead to:
Another common mistake is trying to build a single, all-powerful AI system. A retail client initially invested millions in developing a "master" AI system to handle everything from inventory management to customer service. The results were disappointing:
After shifting to a network of specialized agents, they can see:
Perhaps the most insidious error is building systems that can't evolve. Organizations often optimize for current needs without considering future adaptability. This leads to:
For organizations looking to prepare for these changes, here's what you need to focus on:
Based on our research and development work, here are the technical innovations I expect to see in the next 12-18 months:
As you begin your journey with agentic AI, remember:
The future of enterprise AI isn't about replacing human intelligence – it's about creating systems that can work alongside us, augmenting our capabilities and helping us tackle increasingly complex challenges. The organizations that understand and embrace this vision will be the ones that thrive in the age of intelligent networks.
Fluid AI is an AI company based in Mumbai. We help organizations kickstart their AI journey. If you’re seeking a solution for your organization to enhance customer support, boost employee productivity and make the most of your organization’s data, look no further.
Take the first step on this exciting journey by booking a Free Discovery Call with us today and let us help you make your organization future-ready and unlock the full potential of AI for your organization.
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