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Streamline the path to generative AI integration

Preparing for the next wave of innovation and cost savings in financial services

The financial industry is at an inflection point.

Financial institutions face a complex mix of economic volatility, rising customer expectations and tightening regulatory scrutiny. Each brings unique challenges organizations must address if they hope to remain competitive. As leaders pursue new strategies, a growing number are turning to generative AI (GenAI), making it a transformative force for modern financial firms.

For chief financial officers (CFOs) in the financial industry, the stakes have never been higher. They’re under increasing pressure to cut costs, enhance agility, improve forecasting, reduce manual workloads and base their decisions on real-time data. GenAI offers the potential to meet these challenges head-on. More than simply deploying an algorithm, success requires a holistic approach that integrates technology, processes and talent to drive meaningful transformation and sustained value.

Creating the future of financial services with AI

GenAI marks a significant leap forward for the financial industry, enabling organizations to not just analyze data but also generate content, simulate scenarios and make intelligent predictions based on real-time inputs. From automated financial close processes to dynamic customer communications, the scope of applications for the industry is vast. In a 2025 banking industry survey, 47% of respondents indicated having rolled out GenAI applications, compared to just 10% in 2023.¹ Additionally, 90% of banks were in the beta-testing stage or further of AI in 2025, compared to 64% in 2023.²

Proposed GenAI banking use cases:³

35% – Middle office (risk management, compliance, reporting)

33% – Front office (customer service, sales, marketing)

31% – Back office (IT support, human resources, accounting)

CFOs must lead the charge in adopting this technology securely, strategically and at scale. This means not only understanding the capabilities of GenAI but also ensuring the organization has the right infrastructure and partnerships to support long-term, sustainable benefits. Among banks already deploying GenAI or exploring opportunities, 43% planned to increase their investment in the technology in 2025 compared to 2024.⁴

81% of banking professionals say that banks will fall behind competitors if they do not implement AI.⁵

Why GenAI matters to financial institutions

As the financial services industry becomes more data-driven and dynamic, GenAI equips institutions with tools that can significantly reduce costs, improve operational efficiency and drive competitive agility. It also enables firms to transition from being reactive to being proactive: anticipating risks, responding to market changes and personalizing services in real time. The technology is improving business operations for firms, with 52% saying it has created operational efficiencies, 48% citing enhanced employee productivity and 37% achieving an improved customer experience.⁶

GenAI’s effects on business performance according to financial services professionals:⁷

64% increased revenue by more than 5% for their firm

61% reduced costs by more than 5% for their firm

77% of financial institutions are investing in data analytics and AI-driven insights.⁸

Here’s a closer look at some of the ways GenAI is reshaping financial services:

Cost reduction

By streamlining workflows and reducing time-intensive tasks, GenAI drives operational efficiency and allows financial institutions to lower costs and invest more in growth and innovation.

20% The average GenAI-powered productivity gain financial services firms experience across functions.⁹

Sophisticated data analysis

GenAI can extract actionable insights from massive, complex datasets, helping CFOs make more informed decisions, improve resource allocation and enhance risk management.

Regulatory compliance support

With the ability to parse complex regulatory updates and assist in preparing documentation, GenAI supports compliance efforts and reduces the risk of fines or oversights.

67% of financial services leaders are hesitant to adopt new technology due to regulatory uncertainties.¹⁰

Regulatory fines paid due to a data breach from 2024 to 2025:¹¹

70% paid over $50,000

48% paid over $100,000

23% paid over $250,000

Automation of routine processes

Tasks like accounts payable/receivable, reconciliation, financial close and report generation are repetitive, error-prone and resource-intensive. GenAI can automate these functions, empowering employees to focus on other strategic priorities and thrive in their roles.

Up to 39% of the work performed across capital markets, insurance and banking has high potential to be fully automated.¹²

Predictive modeling in real time

GenAI enhances forecasting for liquidity, credit risk and fraud detection by analyzing complex datasets in real time. This enables financial institutions to respond faster to market shifts and regulatory changes.

Personalized customer communication

GenAI can generate customized financial summaries, customer support messages or investment information based on user behavior, elevating the customer experience and deepening engagement.

61% of banking leaders report substantial impacts from their AI deployments.¹³

Laying the foundation: Four pillars of GenAI integration

To unlock the full value of GenAI, financial institutions must build a solid foundation across four core areas.

1. Data quality

GenAI is only as powerful as the data on which it is trained. Financial institutions must ensure their data is clean, unified and accessible across departments. This means breaking down silos and implementing strong data governance frameworks, lineage tracking and audit capabilities. Accurate, trustworthy data is essential not only for model performance but also for regulatory audits and stakeholder confidence.

98% of organizations have suffered with AI-related data quality issues.¹⁴

2. Infrastructure readiness

A modular, cloud-native infrastructure can support the demands of training and deploying AI models while offering the scalability needed for future innovation. Network infrastructure is especially critical. Financial institutions must support reliable, high-speed data transmission with low latency, enabling real-time processing and access to AI tools hosted in the cloud. A resilient and scalable network ensures seamless integration with third-party platforms and continuous availability for mission-critical applications.

3. Security and compliance

The average cost of a data breach in the financial services industry reached $5.56 million in 2025, second only to healthcare¹⁵ — despite financial organizations operating under some of the world’s strictest data privacy and security regulations. Failure can mean breaches, data leaks or fines. GenAI platforms must meet regulatory standards, particularly in areas involving sensitive customer data or high-stakes decisions, such as lending or fraud prevention. Transparency and explainability are crucial. AI models should provide clear, understandable justifications for their decisions to satisfy regulators and build internal trust.

43% of financial services professionals report that applying AI to security workflows is their top cybersecurity initiative.¹⁶

The state of data security according to financial services leaders:¹⁷

90% say data security is their top IT priority.

48% are highly confident they have the right security measures in place.

4. Organizational alignment

Adopting GenAI isn’t just a technical challenge, it’s a cultural one. Teams need training to interpret and use AI tools effectively. Leadership must communicate the value of GenAI clearly while setting realistic expectations and milestones. By forming AI task forces that include finance, IT, compliance and risk professionals, organizations can ensure alignment between AI solutions and business goals.

Addressing these foundational areas enables financial institutions to position themselves to unlock the transformative power of GenAI safely, strategically and responsibly.

Supporting connectivity, performance and reliability

AI adoption depends not only on powerful models but on achieving the requisite connectivity, performance and reliability. GenAI systems rely on real-time data access and processing. Consequently, latency, downtime and network failures are unacceptable, as they can disrupt crucial workflows like forecasting, reporting, real-time decision-making and customer engagement.

Most financial institutions now operate in hybrid environments that combine on-premises systems with cloud platforms. These environments require flexible movement of data and workloads, allowing organizations to scale AI initiatives as needed. Without seamless integration, AI adoption can be hindered by fragmented systems and data silos.

Equally important is undertaking ongoing network performance monitoring. Proactively tracking network infrastructure status helps prevent outages and enables AI applications to perform as intended. It also allows financial institutions to maintain optimal speeds, protect uptime and quickly resolve issues.

Financial institutions must ensure that their networks are not only AI-ready but also monitored and optimized continuously. Without this level of support, AI initiatives risk being slowed — or even derailed — by technical limitations.

Key focus areas for infrastructure success:

Low latency: Real-time data processing is essential for accurate forecasting and risk modeling.
High availability: Downtime can halt AI-powered decision-making, underscoring the need to pursue 100% uptime.
System flexibility: Workloads should move freely between environments to support agility.
Proactive monitoring: Continuous network and performance monitoring ensures issues are identified and resolved early.

Managed services providers: The strategic enabler of AI success

Given the complexity of integrating GenAI, many financial institutions are turning to managed services providers for help. A strong partnership brings the technical know-how, ongoing support and industry-specific expertise needed to implement GenAI at scale. The right provider enables organizations to focus on outcomes while accelerating innovation, reducing risk and maximizing the return on their AI investments.

Key benefits of partnering with experienced managed services providers include:

Reduced costs: Move away from expensive on-premises systems and data centers, lower staff and training costs and improve operational efficiency.
Expertise in AI infrastructure: Design systems that map AI use cases to on-premises and cloud platforms.
Security and compliance support: Ensure end-to-end adherence to financial regulations.
Managed connectivity solutions: Deliver secure, reliable network performance for AI workflows.
Ongoing model monitoring: Keep AI models aligned with changing data environments.
Scalability: Support the evolution from pilot programs to organization-wide deployment.

CFO action plan: A roadmap to GenAI integration

1. Assess current workflows

Start by identifying manual, repetitive tasks that could benefit from automation. Look for bottlenecks and areas where human error is common, turnaround times are slow or insights are limited. These areas are often strong candidates for enhancement through GenAI.

2. Evaluate data readiness

Assess whether the organization has the necessary data ecosystem to support AI tools. Address data centralization, quality, governance and accessibility. To help ensure the necessary AI performance, identify gaps such as siloed systems, lack of real-time data availability or weak data lineage practices.

3. Prioritize the right AI use cases

Choose initial projects that align with strategic goals and offer clear value. Consider ROI, ease of implementation, data availability and compliance implications. Start with low-risk, high-impact use cases to build confidence and gain early wins as proof to stakeholders. Good options include automating reconciliation, accelerating month-end close or improving liquidity forecasting.

75% of financial services companies report achieving or exceeding expected value from their generative AI initiatives.¹⁸

4. Partner with trusted providers

Collaborate with partners that offer deep experience in financial services. They can help design the right architecture, integrate AI into existing systems and manage infrastructure performance and cybersecurity. Their guidance helps ensure your systems meet regulatory requirements, integrate seamlessly and perform reliably, while their support brings peace of mind.

85% of financial services firms would use generative AI if regulators provided guidance on usage.¹⁹

5. Pilot, measure, scale

Use controlled pilots in select business units or process areas to test and validate models. Evaluate key performance indicators such as time saved, error reduction or customer satisfaction to measure success. Then, roll out more broadly, ensuring training, change management and performance tracking are in place.

Turn GenAI into a strategic advantage with Spectrum Business®

GenAI isn’t tomorrow’s technology — it’s today’s competitive differentiator. The opportunity is clear: Deploy AI to create smarter workflows, offer more personalized services and enable faster, more accurate decision-making. But the journey demands more than just ambition. Success depends on building the right foundation — uniting infrastructure, data and trusted expertise.

Spectrum Business provides the enterprise solutions, managed network services, enterprise cloud services and connectivity services financial institutions need to succeed in the AI era. Backed by a 100% uptime service-level agreement to the handoff point* and 100% US-based support, available 24/7, Spectrum Business ensures that your AI investments are supported by a foundation of reliability and performance. By working with Spectrum Business, financial institutions can unlock the full value of GenAI securely, efficiently and at scale.

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*100% uptime SLA guarantee applies only to Dedicated Fiber Internet, Secure Dedicated Fiber Internet, Ethernet Services, Cloud Connect and Enterprise Trunking.