Embedded AI To Become the New Baseline in Banking
AI in banking is moving past the experimental phase with large language models and stepping into a more continuous, embedded role. As generative AI evolves from simple copilots and conversational pilots to specialized, domain-focused solutions, banks are tackling big questions around efficiency ratio, governance, and regulatory alignment. The next wave of maturity will center on smaller, purpose-built models, explainability baked in from the start, and proactive agents that quietly work behind the scenes. In 2026, expect AI to power embedded intelligence in core areas such as onboarding, payment orchestration, and real-time fraud detection. Banks will increasingly measure AI value not by its novelty but by its ability to act predictably, comply seamlessly, and deliver intelligence as an integral part of operations rather than as a separate layer.
Authors:
Ramprasath Ganesaraja, Puneet Chhahira, Arjun Singh, Sudhindra Kadur Keshava Murthy
Hybrid Intelligence - Banks to Shift from LLMs to SLMs for Better GenAI Enablement
Most GenAI initiatives in banking today revolve around large language models (LLMs), deployed through copilots, chat interfaces, or controlled pilots. While LLMs have proven useful for experimentation, concerns around cost, data exposure, and regulatory fit limit their role in core workflows. In 2026, banks are expected to move toward a hybrid intelligence model, where small language models (SLMs) play a far more prominent role. These SLMs will be domain-trained, purpose-built, and easier to govern, making them better aligned with the operational and regulatory realities of banking.
SLMs reinforce data sovereignty by enabling deployment entirely within on-premises environments and private clouds. Rather than replacing LLMs, this shift will position them as supervisors, using knowledge distillation techniques to transfer insights from massive LLMs into smaller, specialized SLMs for specific functions. Context engineering techniques will help ensure SLMs operate with domain-specific knowledge, reducing hallucinations and improving predictability. Institutions that succeed will embed GenAI deeper into operations, balancing innovation with predictability and control.
Explainability and Compliance Move Closer to the Core of AI Design
Limited explainability of LLMs and SLMs remains a critical barrier to AI adoption in banking. While models generate outputs, tracing decisions, understanding data influence, and validating regulatory alignment often remain opaque, especially in highly regulated markets. In 2026, banks will prioritize explainability-by-design, leveraging emerging techniques in model observability, traceability, and governance. Regulatory frameworks such as the EU AI Act will accelerate this shift, mandating transparency, risk controls, and operational resilience for AI systems.
Techniques like RAG will help banks maintain transparent decision trails, linking outputs to source data for easier auditing and compliance. These capabilities will make AI systems easier to justify and regulate, enabling broader use in risk-sensitive domains such as lending, compliance, and financial crime management. Institutions that lead will treat transparency not as an add-on but as a foundational design principle.
From Assisted Intelligence to Agentic Banking at Scale
In 2026, banks will progress along the AI continuum, reaching different maturity levels across lending, payments, servicing, risk, and operations. While some will still be consolidating value from AI-assisted, human-led models, others will already be scaling AI-powered workflows or experimenting with AI-native reimagination. In 2026, the intent and direction will change as banks will stop treating AI as a set of isolated tools and deliberately start advancing each journey to its next logical state. The focus will be less on “where we are” and more on how fast and safely we can move to our target state.
Leading banks will use AI copilots to drive productivity, redesign high-impact processes around AI agents, and embed services into partner platforms through agentic ecosystems. For instance, agents predict customer requirements and execute actions without explicit input like managing credit limits or handling recurring payments. This shift will also bring invisible AI in motion as intelligence is embedded into processes rather than exposed through interfaces, delivering seamless transitions, automated orchestration, and smarter outcomes. While no bank will move all journeys at the same speed, momentum across the continuum will become non-negotiable. By 2026, competitive advantage in banking will be defined not by AI experimentation, but by the ability to consistently move each journey forward along the continuum–turning AI into a scalable, governed, and ecosystem-ready operating capability.
Road Ahead: AI as an Embedded Discipline
As AI moves into this next phase, the challenge shifts from deploying models to embedding intelligence into the fabric of banking systems. Progress will depend on how deliberately banks design for explainability, governability, and contextual adaptability - how models are trained, supervised, and orchestrated under constant regulatory and operational guidelines. Institutions that stay ahead will treat AI not as a standalone capability but as an embedded discipline, one that anticipates actions, optimizes processes, and evolves continuously without disrupting the core.
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