The AI inflection point
Executives across banking and capital markets (BCM) have moved beyond experimenting with AI to embed it at the core of how risk is managed, how customer experience is delivered, how revenue is generated, and how talent evolves. Firms are no longer implementing AI in silos; instead, they are embedding it into their operating fabric across risk, compliance, customer experience, and revenue‑driving functions to decide, act, and learn alongside humans, unlocking the autonomous enterprise.
This shift requires more than advanced AI models. To unlock enterprise-wide value, financial institutions must strengthen process intelligence, modernize data architecture, elevate workforce AI fluency, and evolve governance to keep pace with innovation and regulatory expectations.
Genpact's Autonomy by Design report included responses from 60 senior BCM leaders. This report explores their views and how firms can move from siloed AI use cases to connected intelligence across risk, compliance, and customer support to create safer, faster, and more resilient financial institutions.
These perspectives are designed to help BCM leaders translate ambition into action and shape what their own autonomous enterprise can become.
Four levels of AI maturity
By assessing respondents across six dimensions – adoption effectiveness, decision autonomy, governance rigor, operational enablement, regulatory compliance, and technical capability – we break down AI maturity across four levels.
Only 15% of BCM organizations are considered leaders
Leaders have strategic foresight and executional rigor as they enhance processes, build architecture with cohesive implementation strategies, and systematically resolve legacy technology constraints.
BCM is more mature than most industries, ranking joint second with insurance for the percentage of leaders and with a smaller share of emerging organizations. But the size of the scaling cohort shows that many institutions still need to overcome integration and governance barriers to translate progress into enterprise-wide leadership.
Where BCM sets the pace in enterprise AI
BCM has laid some strong technical foundations to help scale AI initiatives across the enterprise.
Strong technology and data leadership
BCM shows a high number of CTOs/CIOs and specialized AI/data leadership roles leading AI initiatives compared to other industries. This is helping to move AI beyond experimentation and embed it into core risk, operations, and customer workflows, providing a strong foundation for enterprise-scale adoption.
Advanced enterprise architecture and security foundations
BCM shows stronger adoption of cloud-native AI platforms, API-first integration, and zero-trust security architectures than many of the other industries included in the report. These capabilities are critical for operating AI in regulated, high-risk environments and enable AI systems to be scaled with greater control, resilience, and auditability.
Proven value from AI use cases
BCM organizations demonstrate relatively high adoption and effectiveness in proven AI domains, including predictive analytics, process automation, risk and fraud detection, and customer experience personalization. These strengths reflect a long history of data‑driven decisioning and regulatory discipline, positioning banks ahead of many industries in using AI to manage risk, improve efficiency, and tailor client interactions at scale.
Where BCM leaders outperform other industries
BCM leaders are extracting meaningfully greater value from AI than their peers, reflecting the industry's deeper structural readiness, stronger governance discipline, and faster operational integration. Specifically, BCM leaders report greater benefit than other industries in five critical areas:
Faster rollout of AI use cases
BCM institutions move from pilot to production more quickly because they have clearer risk thresholds, more mature data foundations, and tighter alignment between technology and compliance teams. This shortens approval cycles and accelerates time to value, allowing leaders to deploy AI into frontline operations ahead of their peers.
Stronger experimentation and sandboxing
The industry's long history of regulated testing environments means BCM is already adept at controlled experimentation. Leaders use structured sandboxes to validate models, assess bias and drift, and test explainability before deploying into regulated workflows. This creates a more reliable pipeline for innovation and ensures new AI capabilities can be adopted with confidence.
Better alignment of AI with risk rules and compliance frameworks
BCM shows higher effectiveness in embedding risk controls directly into AI workflows. Leaders are more likely to integrate rule‑based guardrails, automated audit trails, and explainability checks into AI systems – allowing models to operate more safely in high‑stakes environments like credit decisioning, fraud detection, know your customer (KYC), and anti-money laundering (AML). This alignment helps reduce friction between innovation and compliance, a challenge that slows many other industries.
Improved fraud detection and risk scoring
BCM organizations report some of the strongest gains in predictive analytics and monitoring. With richer historical datasets and mature decisioning frameworks, the industry is using AI to detect anomalies earlier, score risk more accurately, and reduce false positives. Leaders also combine AI‑driven signals with human expertise to build more adaptive risk models that respond in real time.
More empowered business teams with AI‑driven decisioning
BCM's focus on data literacy and cross‑functional squads means business teams are becoming active AI practitioners rather than passive users. Leaders provide frontline teams with AI‑assisted insights, guided decisioning tools, and configurable models – allowing them to respond to customers faster, detect risk earlier, and adapt decisions to local contexts. This shift is enabling more distributed, real‑time decisions without compromising control.
The AI of now: Barriers to scaling AI
Three core barriers prevent progress toward AI autonomy in the BCM industry:
- Regulatory, compliance, and governance complexity: BCM firms face the strongest regulatory hurdles of any sector, with 75% of respondents citing regulatory or compliance challenges as a main obstacle
- Skills gaps and organizational readiness constraints: 63% of BCM leaders still struggle with AI-related skills gaps and also highlight workforce resistance as a moderate readiness challenge
- Fragmented ownership and accountability challenges: 35% of respondents cite fragmented ownership and accountability as a significant barrier to scaling AI. This indicates that responsibility for AI decisions and outcomes is often unclear or distributed across multiple functions
The rise of the autonomous enterprise
Leaders are making real progress, unearthing four enabling themes that offer a working model of AI autonomy in practice:
A symphony of agents
As an enterprise deploys more AI agents, it risks having them work at cross-purposes without coordination across functions. Agentic orchestration is crucial, but so far, only 2% of BCM organizations are actively implementing this capability.
As BCM firms deploy more AI agents across functions, value depends on orchestration. Without coordination, agents optimize in silos, increasing risk and friction. Orchestrated agentic models connect KYC, AML, customer service, and operations into shared decision flows, aligning human oversight, compliance controls, and real‑time execution to enable safe, scalable autonomy across the enterprise.
The universal AI practitioner
Democratizing AI fluency and adoption across roles and functions empowers employees to act as AI practitioners. BCM organizations are reframing how humans and AI collaborate by managing change and reskilling.
Enterprise architecture redux
Technology complexity is a significant challenge. To scale autonomous AI, companies need a strong data-centric foundation, but this architectural backbone has yet to emerge for many firms. For BCM respondents, only 25% have fully adopted a real-time data infrastructure.
Governing at the speed of AI
Governance sets the pace – and limits – of AI innovation. But 98% of BCM executives indicate that their governance models are not yet adequate for autonomous or agentic AI systems. And the research shows that just over half of BCM organizations operate under a federated AI governance model, reflecting the need to balance enterprise‑wide control with domain‑level execution. This structure allows shared standards for risk, compliance, and accountability, while enabling business units to apply AI in context‑specific ways. The prevalence of federated models indicates their perceived suitability for scaling AI in complex, regulated banking environments.
The leaders’ playbook
Translating vision into action
Our playbook shares practical actions across the enabling themes for BCM organizations at any stage of their AI journeys:
Redefine processes to lead with AI. Build on process intelligence to stand up a unified orchestration layer
Elevate AI fluency across the organization, redesign roles to be AI first, and position employees as coauthors of AI change that help manage compliance and risk
Strengthen your existing data and integration foundation to support your continuous innovation successfully. And use an agentic development lifecycle to guide AI design decisions
Turn governance into an enabler by centralizing AI guardrails (accountability, risk tiers, regulatory standards) before federating execution, and embedding automated controls, monitoring, and explainability directly into BCM risk, compliance, and customer-impacting workflows
Move from AI pilots to AI platforms. Embed AI at the core of risk, compliance, customer, and revenue workflows – shifting from isolated use cases to scalable, orchestrated platforms that improve decision quality, resilience, and speed across the enterprise
Master the five enablers to help your business transition into an autonomous enterprise and turn AI into a lasting competitive advantage.
Explore the banking and capital markets report
Read the reportResearch methodology
The report drew insights from 500 senior executives across 13 business functions, 8 industries, and 13 countries, plus additional qualitative interviews with AI industry experts.
BCM findings are based on a subset of 60 senior banking and capital markets leaders.
