Rackspace Email & Apps Blog

Financial Institutions Can't Fight Modern Financial Crime on Yesterday's AML Architecture

caro2698 — Mon, 01 Jun 2026 17:25:17 +0000

Financial Institutions Can't Fight Modern Financial Crime on Yesterday's AML Architecture caro2698 Mon, 06/01/2026 - 12:25

AI Insights

Cloud Insights

Financial Institutions Can't Fight Modern Financial Crime on Yesterday's AML Architecture

June 8, 2026

By Eddy Rodriguez, Sr. Director and Principal Architect, Financial Services and AI Enablement, Rackspace Technology

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Modern financial crime requires AML architecture built for behavioral intelligence, AI-assisted investigations and governed decisioning.

Traditional AML systems were built for a financial crime environment that no longer exists. BFSI leaders now need AML operations that can connect data, identify behavioral patterns and support AI-assisted investigations at scale.

Financial institutions are under more pressure than ever to detect, investigate and report suspicious activity. Compliance costs continue to rise. Alert volumes continue to grow. Analysts spend more time reviewing cases, gathering evidence, documenting decisions and preparing Suspicious Activity Reports (SARs).

Yet many AML environments still rely on architectures designed for a very different era of financial crime. For years, financial institutions have depended on rule-based transaction monitoring systems to identify potentially suspicious activity. These systems flag transactions based on thresholds, predefined scenarios, geographies, customer types or known typologies. That approach still serves an important purpose, but modern financial crime has evolved beyond what static rules alone were designed to handle.

Criminal networks now move faster, distribute activity across multiple entities and adapt behavior to avoid traditional controls. Transactions are structured to appear ordinary when viewed individually. Activity is spread across accounts, counterparties, payment rails and jurisdictions to avoid triggering predefined thresholds.

That is where traditional AML systems begin to lose context. A single transaction may not appear suspicious on its own. But when it is connected to a recently opened account, unusual counterparty activity, similar payments from unrelated senders or movement across related entities, the risk profile changes significantly. Risk becomes clearer when institutions can evaluate behavioral patterns across customers, entities, counterparties and transaction flows.

More alerts do not improve AML outcomes

For many financial institutions, AML operations have become a scale problem. More rules generate more alerts. More alerts require more analysts. More analysts create more review cycles, documentation requirements and operational overhead.

But adding people to a noisy system does not automatically improve detection quality. Instead, it often increases the cost of processing noise. That is why BFSI leaders need to rethink the AML challenge at the architectural level. Staffing, compliance workflows and transaction monitoring all play a role, but the larger challenge is how data, investigations and decision-making work together across the environment.

Most institutions already possess much of the data required to improve AML decisioning. Customer profiles, KYC records, transaction histories, sanctions data, beneficial ownership information, device signals, external risk indicators and historical investigation outcomes may already exist within the organization.

The challenge is that these datasets are often fragmented across systems that were never designed to work together in real time. Analysts may need to manually assemble context across multiple tools and workflows, slowing investigations and creating inconsistencies. When transactions are reviewed in isolation, institutions can miss broader behavioral patterns across accounts, customers and transaction activity.

AI-assisted workflows can help connect fragmented processes and reduce manual effort, but they also need to operate within the governance expectations of regulated financial services. In regulated financial services, AI cannot operate as a black box. AML decisions require oversight, evidence, explainability and accountability. That makes assisted intelligence a far more practical model than full automation.

AI can help prioritize alerts, summarize customer and transaction context, identify behavioral deviations, surface related entities and counterparties, compare historical cases, assemble supporting evidence and draft SAR narratives for analyst review. But human investigators and compliance leaders remain responsible for the final decision.

Compliance teams need visibility into how recommendations are generated. Model risk teams require validation and documentation. Regulators expect auditability and traceability. Security teams need control over sensitive financial data. AI creates value in AML by reducing manual friction and giving investigators more time to focus on higher-value analysis and risk evaluation.

The next phase of AML modernization is operational

Many financial institutions have already experimented with AI through pilots and proof-of-concept initiatives. They have seen models summarize cases, classify documents and improve alert triage in controlled environments.

But pilots do not solve production challenges. Production-grade AML AI must connect to live data sources, integrate with existing workflows, protect sensitive customer information and support human review. It must generate audit-ready evidence, perform consistently at scale and operate within the governance expectations of regulated financial services.

That is why the AML conversation is shifting away from standalone models and toward operating architecture. BFSI organizations need an approach that aligns intelligence, infrastructure, governance and operational workflows from the beginning.

Where Rackspace Technology and Uniphore fit

Rackspace Technology and Uniphore address different layers of that production challenge. Uniphore delivers the intelligence layer through AI agents, workflow automation, business-context models and intelligent engagement capabilities. Rackspace provides the infrastructure and operations layer through private and hybrid cloud expertise, AI-ready infrastructure, managed operations, security, governance discipline and forward-deployed engineering designed to help organizations move from AI pilots to production outcomes.

That combination is especially relevant for AML modernization. Financial institutions may need AI-enabled workflows that prioritize alerts by risk, gather evidence across multiple systems, identify behavioral patterns, generate draft narratives and route decisions for human approval. But those workflows also require a secure, governed and scalable operational foundation underneath them.

That foundation matters because AML is a high-scrutiny use case involving sensitive customer data, regulatory obligations, financial crime exposure and institutional reputation.

AI-enabled AML workflows must be explainable, auditable, resilient and operationally efficient enough to run at enterprise scale.

Better AML decisioning starts with better architecture

The business case for AI-enabled AML includes faster investigations, more consistent documentation, stronger risk visibility and more efficient operations.

When AI reduces false-positive noise, analysts can focus on higher-risk investigations.

When evidence gathering is automated, investigations move faster.

When SAR narratives are generated from structured data, documentation becomes more consistent.

When decisions are explainable and traceable, audit readiness improves.

When infrastructure is designed for regulated AI workloads, institutions gain greater control over performance, security and operational cost.

This is the shift BFSI leaders should prioritize: more connected intelligence, faster investigations and stronger operational consistency through an AML operating model built for behavioral intelligence, governed AI workflows and enterprise-scale decisioning.

Financial institutions need AML architecture built for what comes next

Financial crime will continue to evolve. Regulatory expectations will continue to increase. Compliance teams will continue to face pressure to deliver greater precision, transparency and operational efficiency.

The institutions that adapt successfully will modernize the architecture underneath AML operations. They will connect data across silos. They will use AI to identify patterns instead of isolated events. They will automate evidence gathering and documentation workflows. They will keep humans in the loop. They will embed governance directly into operational processes. And they will run AI workloads on infrastructure designed for regulated financial services environments.

That is the opportunity in front of financial institutions today. Modernizing AML helps compliance teams build the intelligence, and operational foundation architecture, i to identify real risk faster and operate with greater confidence.

Rackspace Technology and Uniphore can help BFSI organizations make that shift from alert overload to governed, AI-assisted AML decisioning.

Talk to a Rackspace AI Specialist today

Explore how infrastructure-to-agents architecture can support your AI roadmap or connect with the Rackspace Technology team to continue the conversation.

Learn more about the Rackspace Technology and Uniphore partnership!

Tags:

AI Insights

Cloud Insights

Financial Services/BFSI

Financial Institutions Can't Fight Modern Financial Crime on Yesterday's AML Architecture

caro2698 — Mon, 01 Jun 2026 17:25:17 +0000

Financial Institutions Can't Fight Modern Financial Crime on Yesterday's AML Architecture caro2698 Mon, 06/01/2026 - 12:25

AI Insights

Cloud Insights

Financial Institutions Can't Fight Modern Financial Crime on Yesterday's AML Architecture

June 8, 2026

By Eddy Rodriguez, Sr. Director and Principal Architect, Financial Services and AI Enablement, Rackspace Technology

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Research Shows Cyber Confidence in the NHS Remains Low as Regulation Increases

caro2698 — Tue, 05 May 2026 22:04:01 +0000

Research Shows Cyber Confidence in the NHS Remains Low as Regulation Increases caro2698 Tue, 05/05/2026 - 17:04

Cloud Insights

Research Shows Cyber Confidence in the NHS Remains Low as Regulation Increases

June 4, 2026

by Matt Moore, Director of Healthcare and Public Sector, Rackspace Technology

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New Rackspace Technology research shows how cyber resilience is developing across the NHS, where gaps remain and what is shaping the next phase of transformation.

As part of the 2026 Rackspace Healthcare study, we partnered with Coleman Parkes Research to survey 75 NHS IT and digital leaders across the UK, examining how organisations are advancing cloud, AI and cybersecurity capabilities.

In previous blog posts, we explored how cloud adoption across the NHS is progressing alongside integration challenges, and how AI is moving into operational use, shaped by security, governance and organisational readiness. Those findings point to a broader pattern.

Digital transformation is advancing, but it is doing so within environments where control, integration and oversight are still developing. The findings on cyber resilience bring that into sharper focus. Organisations are increasing investment and preparing for new regulatory requirements.

However, confidence in their ability to withstand and recover from cyber incidents remains low. That gap between rising expectations and limited confidence in resilience is where the next phase of transformation will be defined.

Cyber resilience across the NHS remains uneven

According to our research, only 12% of organisations describe themselves as cyber-resilient, with a fully integrated and regularly tested resilience strategy in place. At the same time, 44% report low confidence in their ability to protect patient data and critical systems from cyberattacks.

We see this reflected in how organisations describe their current security and recovery capabilities. Many have established security measures and recovery processes, but these are not always fully integrated or consistently tested across environments. As a result, resilience is still being built rather than fully embedded and ready to support critical systems under disruption.

This creates a clear operational gap. Organisations are continuing to advance cloud adoption and AI initiatives while strengthening the resilience needed to support those systems under disruption.

Regulatory pressure is accelerating the need for resilience

Regulatory pressure is increasing across the NHS as expectations around data protection, operational continuity and accountability continue to rise. This is bringing greater focus to how organisations prepare for and manage cyber risk.

According to our research, 56% of organisations are actively preparing for the UK Cyber Resilience Bill, while only 16% believe they are fully compliant, highlighting a clear gap between preparation and readiness. Organisations recognise the need to strengthen resilience, but many are still working to align their systems, processes and governance frameworks to meet the new requirements.

That readiness gap has practical consequences. As organisations work towards new requirements, resilience is coming under closer scrutiny across security, recovery and continuity of service. NHS organisations need to demonstrate that controls are in place, recovery processes are tested and critical services can continue operating when systems are disrupted.

Investment is increasing but capability is still developing

Organisations across the NHS are responding with increased investment in cybersecurity and resilience. According to our research, 72% expect to increase cybersecurity spending over the next 12 to 24 months, while 81% plan to increase investment specifically in cyber resilience initiatives.

This reflects a clear shift in priorities. Resilience is moving higher on the agenda, with organisations directing investment towards areas such as staff training, threat detection and access controls.

At the same time, investment alone does not immediately translate into capability. Many organisations are still building the processes, skills and operational discipline needed to turn that investment into consistently applied resilience across systems and environments.

Cyber resilience is now shaping broader digital decisions

Cyber resilience is increasingly influencing how organisations make decisions about cloud, data and AI. It is becoming a determining factor in where workloads are placed, how systems are integrated and how new technologies are introduced into existing environments.

We see this reflected in the data. Sixty-one percent of organisations report moving workloads between cloud providers due to data security concerns, while 41% have shifted workloads from public cloud back to on-premises environments for security and compliance reasons. In addition, 44% cite security risks as a barrier to AI adoption.

Taken together, these findings show how cyber maturity is shaping broader digital strategy. Decisions about where workloads run, how systems are integrated and how AI is deployed are all being influenced by the need to maintain control over data and reduce risk.

For NHS organisations, this brings resilience into the centre of digital planning. It is a factor that directly influences how and where transformation can progress.

Compliance and resilience are not the same

Meeting regulatory requirements is an important step, but it does not fully capture what resilience requires in practice.

Compliance focuses on whether specific controls, processes and policies are in place. Resilience is demonstrated through how those controls perform under pressure, how effectively organisations can recover from disruption and how consistently critical services can be maintained.

This distinction becomes more visible as organisations respond to new requirements. Preparing for regulation often involves establishing the right structures and documentation. Building resilience requires those structures to be tested, integrated and applied across real-world scenarios.

For NHS organisations, this comes down to operational capability. Resilience depends on how well systems, teams and processes work together during disruption, not just on whether the right controls have been defined.

Resilience is rooted in discipline and governance

Cyber resilience is the result of how effectively security, recovery and governance practices are integrated and maintained across systems and environments.

Across the NHS, organisations are investing in areas such as staff training, threat detection, access controls and recovery capabilities. The effectiveness of that investment depends on how well these capabilities are integrated into day-to-day operations and applied consistently across systems and teams.

For NHS organisations, this comes down to operational discipline. Resilience is strengthened through regular testing, clear accountability and coordination across teams responsible for security, infrastructure and clinical systems.

Over time, this creates a more consistent and reliable approach to managing risk. It allows organisations to respond to disruption with greater control and maintain continuity across critical services.

Cyber resilience will define how far transformation can scale

Cyber resilience is becoming a defining factor in how far digital transformation can progress across the NHS. As organisations continue to advance cloud adoption and expand the use of AI, resilience will determine how confidently those capabilities can be deployed and operated at scale. Where resilience is well established, organisations are better positioned to extend transformation across systems and services. Where it is still developing, progress is likely to remain more measured.

This points to a shift in how resilience is viewed. Resilience is evolving from a discrete area of focus within security to becoming a core component of how organisations plan, deliver and sustain digital services.

Across the NHS, digital transformation is advancing. The next phase will be shaped by how well organisations can strengthen resilience alongside cloud and AI adoption, ensuring that systems remain secure, available and responsive under pressure.

For a deeper view of the UK findings, including cloud maturity and AI adoption, explore the full NHS-focused Rackspace Healthcare survey report.

Tags:

Private Cloud

Cloud Insights

Healthcare

Why Migrate-then-Modernize Creates More Work Than Value

caro2698 — Thu, 28 May 2026 16:39:29 +0000

Why Migrate-then-Modernize Creates More Work Than Value caro2698 Thu, 05/28/2026 - 11:39

Cloud Insights

Why Migrate-then-Modernize Creates More Work Than Value

June 1, 2026

by Sriram Rajan, Senior Principal Architect - Managed Public Cloud, Rackspace Technology

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Separating migration and modernization creates hidden costs. A workload-aware approach reduces rework, accelerates value, and builds a stronger cloud foundation.

Why Migrate-then-Modernize Creates More Work Than Value

Cloud migration milestones often create a sense of completion, with applications running in cloud, infrastructure provisioned and the program declared a success.

But for many organizations, that milestone marks the start of a second, unplanned transformation.

The gap stems from how the work was structured. Migration and modernization are frequently treated as separate initiatives, executed in sequence, with the initial focus on moving everything quickly and the expectation that improvements will follow later. While this approach can simplify early planning, it often recreates legacy constraints in a new environment, delays meaningful outcomes and introduces avoidable rework.

The structure of the program shapes the result as much as the destination.

The hidden price of sequential transformation

A migrate-then-modernize strategy often appears efficient in the early stages because it reduces upfront decision-making and accelerates initial timelines. What it defers, however, tends to surface later in more complex and costly ways.

When workloads are lifted and shifted without deeper evaluation, existing inefficiencies move with them. Architecture limitations, tightly coupled dependencies and outdated data structures, remain intact. This means the environment changes but the operating model does not.

This dynamic creates compounding challenges over time. Teams frequently need to revisit the same applications to redesign, refactor or replatform them after migration, introducing avoidable duplication of effort. At the same time, value realization slows because cost optimization, performance improvements and scalability gains are delayed until modernization begins. As these issues accumulate, technical debt expands and makes future changes more difficult and expensive.

For organizations running revenue-critical workloads, these compounding challenges also carry operational risk. Revisiting an application post-migration — particularly one with unresolved dependencies — introduces periods of instability that a more deliberate, workload-aware approach can largely prevent. In practice, the second phase often becomes larger and more complex than expected, effectively turning what was planned as a single transformation into two.

Why infrastructure-led migration misses the real opportunity

Many cloud programs are structured around infrastructure first, with a primary focus on moving servers, storage and networks with minimal disruption to the business. While this approach supports continuity, it also narrows the scope of transformation.

Applications are not isolated systems; they carry dependencies, data flows and operational behaviors that define how the business runs. When migration is treated primarily as an infrastructure exercise, it overlooks the role these workloads play in delivering value.

A workload-aware migration strategy shifts the lens from how systems move to how each workload should evolve as part of that move. This perspective enables more deliberate decision-making, where some workloads are replatformed during migration, others are refactored or replaced and some remain largely unchanged but are optimized in targeted ways.

By evaluating workloads individually, organizations can avoid turning cloud into a new home for existing limitations and instead begin realizing value earlier in the journey.

What it means to modernize in the move

Modernization during migration does not require transforming every application at once. Instead, it requires making intentional decisions about where modernization will create the greatest impact and aligning those decisions to business priorities.

In practice, this approach often includes redesigning application architectures to improve scalability and resilience, modernizing data platforms to support analytics and AI use cases, enhancing security models to align with cloud-native practices and optimizing cost structures through right-sizing and managed services.

Organizations that adopt this model frequently avoid the need for a separate, large-scale modernization phase. Allcargo, for example, combined migration with optimization, security modernization and architecture redesign as part of its move, which reduced follow-on transformation work and improved operational performance earlier in the process.

Similarly, Switchfly modernized its data tier during migration, eliminating the need for a second transformation initiative tied to data platform redesign. These types of decisions shift effort forward, reduce duplication and create a stronger operational foundation from the outset.

Why this approach is more achievable now

In the past, combining migration and modernization required significant manual effort, particularly in areas such as discovery, dependency mapping and testing. These constraints made sequential models more practical, even if they introduced inefficiencies later.

That reality is changing as AI and automation improve how organizations plan and execute cloud transformations. Capabilities such as automated workload discovery, intelligent dependency mapping, AI-assisted recommendations for modernization paths and orchestrated testing and deployment workflows are reducing the operational burden of making workload-level decisions earlier in the process.

For most organizations, modernization is no longer a late-stage infrastructure decision. Teams need the capacity and expertise to evaluate platforms, prioritize tradeoffs and execute modernization work in parallel with migration activities, without pulling internal resources away from business priorities. A managed services partner can help bridge that gap by bringing the engineering depth, tooling and cloud-native operational experience required to support both assessment and execution at scale.

As a result, organizations can integrate modernization into migration without slowing progress, while also gaining better visibility to support more confident decision-making across stakeholders.

This shift has implications beyond the migration itself. A cloud environment built on modern architectures and well-structured data foundations is better positioned to support future innovation, including AI-driven use cases. Companies such as Infocare and Basware have demonstrated how stronger cloud foundations can translate into more effective data utilization and expanded automation capabilities.

The structure of your cloud program shapes the results you achieve

Cloud transformation is not defined by the act of migration, but by the state an organization reaches once it arrives.

When migration and modernization are treated as separate efforts, organizations often carry forward the limitations they intended to leave behind, which extends the path to value and increases the complexity of future transformation work.

A workload-aware approach that incorporates modernization where it makes sense changes that trajectory by reducing rework, accelerating benefits and establishing a stronger foundation for what comes next.

Modernization will happen. The priority is deciding when and how to do it.

If you are planning or executing a cloud migration, this is the right time to evaluate whether your current strategy is setting you up for a single transformation or unintentionally creating two.

Explore how a workload-aware approach can shape better outcomes with the AWS Cloud Discovery and Modernization Workshop.

Tags:

Public Cloud

Cloud Insights

Three Forces Reshaping U.S. Financial Services Software Vendors

caro2698 — Fri, 22 May 2026 15:36:43 +0000

Three Forces Reshaping U.S. Financial Services Software Vendors caro2698 Fri, 05/22/2026 - 10:36

Cloud Insights

Three Forces Reshaping U.S. Financial Services Software Vendors

May 29, 2026

by Mahesh Subramanium, BFSI ISV Lead, Rackspace Technology

Link Copied!

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U.S. financial services ISVs are navigating AI adoption, private-cloud demand and rising infrastructure costs that are reshaping growth and margins.

How U.S. banking, financial services and insurance software providers can accelerate AI, expand distribution, and protect margins in a structurally reset market.

In the United States, SaaS markets are normalizing after a period of elevated growth expectations, shifting toward a more balanced focus on efficient growth, margins, and governance. Public market multiples have declined significantly from 2021 peaks, according to reports from Aventis Advisors and SaaS Capital published between 2024 and 2026, while venture investment has increasingly favored software businesses with durable unit economics and differentiated market positions.

For U.S.-based financial services independent software vendors (ISVs), these market forces now intersect with tighter buyer scrutiny, evolving domestic regulation, rising expectations for AI-driven functionality and changing GPU economics. We’re seeing many software providers reassess how they differentiate, deploy and monetize AI capabilities in a market that looks materially different from even two years ago. Enterprise buyers across U.S. banks, insurers and asset managers increasingly expect capabilities that many incumbent ISV platforms were not originally designed to deliver.

We believe financial services ISVs that move early on three capabilities — a private-cloud distribution channel aligned to U.S. regulatory expectations, production-grade agentic AI, and governed GPU infrastructure — will be better positioned to grow through these structural shifts.

AI has become a procurement criterion

For U.S. financial institutions, AI has moved from an experimental initiative to a core purchase requirement. While most financial services firms report deploying AI, relatively few have realized measurable business value, according to research from McKinsey, Salesforce and KPMG published between 2024 and 2025. That gap creates both risk and opportunity for financial services software vendors.

If you’re operating in this market, you’re likely seeing the same shift: enterprise buyers increasingly expect AI functionality to arrive embedded in core workflows, not positioned as a future roadmap capability.

ISVs that close the implementation gap by delivering prebuilt, domain-specific, SR 11-7-aligned AI agents embedded directly into production workflows are positioned to strengthen retention and sustain pricing power as traditional SaaS differentiation narrows. Vendors that fail to evolve face increasing pressure from AI-native entrants building financial services workflows such as credit analysis, KYC/AML, underwriting and regulatory reporting on agent-first architectures.

Enterprise buyers are preferring private cloud

A significant procurement shift is underway across regulated U.S. financial institutions. A growing majority of IT leaders are evaluating private-cloud strategies, according to research from BCG, IDC and Flexera, with many identifying private cloud as critical to future operations and actively considering workload repatriation from public hyperscalers.

For financial services ISVs, this is no longer a future technology preference. We increasingly see it becoming a present-day revenue constraint. FedRAMP requirements and guidance from the OCC, FDIC and Federal Reserve have increased scrutiny around third-party risk, concentration risk and infrastructure governance, creating challenges for single-cloud, multi-tenant SaaS architectures. ISVs without a private-cloud deployment option are already excluded from portions of institutional procurement.

AI infrastructure is actively eroding gross margins

The compute intensity of AI workloads is reshaping the near-zero marginal-cost economics that historically defined SaaS. AI-enabled software often operates at materially lower gross margins than traditional SaaS, according to 2026 research from Monetizely, with many software companies reporting year-over-year margin erosion tied directly to AI infrastructure costs.

Agentic workflows — among the highest-value AI use cases in financial services — can increase token consumption by an order of magnitude or more per task. At the same time, shared public GPU infrastructure introduces data co-mingling concerns under SEC Regulation S-P, prompting institutional clients to impose stricter requirements around isolation, auditability and infrastructure governance.

Three Rackspace propositions for financial services ISVs

At Rackspace, we work with financial services ISVs that are trying to solve all three challenges simultaneously: accelerating AI adoption, expanding into more regulated buyer environments and managing the economics of GPU-intensive workloads. Our approach addresses these forces through an integrated infrastructure-to-AI offering rather than a series of disconnected procurement decisions.

Operate enterprise AI with governance built in

Rackspace operates enterprise AI environments for financial services ISVs that need to deploy, govern and scale AI workloads in regulated environments. The offering combines dedicated infrastructure, domain-optimized small language models (SLMs), governance tooling and operational controls designed for financial services use cases such as KYC/AML, underwriting, claims processing and regulatory reporting. These environments include domain-optimized models, tooling for fine-tuning, observability, governance and human-in-the-loop controls, along with forward-deployed engineers who work directly with product and engineering teams to operationalize production AI workloads.

Within that operating model, the Buy-Train-Extend framework gives ISVs a structured path to deploy production-ready agents, build internal AI capability alongside our engineers and ultimately assume greater operational ownership over time.

The result is a governed enterprise AI environment that helps financial services ISVs accelerate deployment while maintaining greater control over model governance, infrastructure economics, auditability and long-term operational resilience.

Private cloud distribution channel — serving markets that traditional SaaS cannot reach

Rackspace works with ISVs to add a private-cloud deployment option that aligns with U.S. regulatory and risk expectations while preserving a unified product codebase. In this model, the ISV platform is packaged and operated on dedicated infrastructure located in appropriate U.S. regions and architected with controls aligned to financial-regulatory requirements around data handling, segregation and resiliency.

For many software providers, this shift creates a difficult operational balance: maintaining a unified SaaS platform while supporting customers with increasingly strict infrastructure and governance requirements.

ISVs maintain a consistent product experience across existing SaaS offerings, while Rackspace operates the underlying infrastructure, observability and FinOps capabilities. This approach enables ISVs to support U.S. customers with restrictive cloud policies, public-sector or quasi-public financial entities dependent on FedRAMP-aligned infrastructure and enterprises requiring greater auditability and control over data processing — without maintaining multiple product variants.

The model also delivers lower total cost of ownership, more predictable billing, high-availability SLAs and integrated cyber-recovery capabilities.

Governed GPU services — protecting margins and creating new revenue streams

We’re also seeing growing concern around the long-term economics of AI inference as production workloads scale beyond pilot environments.

Rackspace delivers dedicated GPU infrastructure in fully isolated, financial services-compliant environments for both internal AI development and enterprise AI consumption. The approach provides more predictable inference economics for sustained workloads, multi-tenant isolation with auditable access controls and per-client infrastructure that keeps sensitive financial data out of shared compute environments — helping address SEC Regulation S-P and third-party risk requirements.

This model enables ISVs to stabilize AI-driven infrastructure costs and preserve healthier gross margins as adoption scales. It also creates opportunities to package AI capacity as a premium offering, including dedicated agent lanes, reserved GPU capacity or enhanced AI functionality that unlocks incremental revenue streams across the U.S. financial services market.

The strategic implication

For financial services ISVs, the central strategic questions now extend well beyond feature development. Leaders are increasingly evaluating which U.S. market segments to unlock, which AI-driven workflows to own and what long-term economics to build toward.

In our view, the vendors that move early to combine production-grade agentic AI, private-cloud deployment flexibility and governed GPU economics will be better positioned to differentiate as market expectations continue to rise.

Vendors that combine production-grade agentic AI, a private-cloud distribution channel and governed GPU economics are positioned to build competitive advantages that will become increasingly difficult to replicate. Rackspace advances all three capabilities through a single integrated offering designed to shorten time to market while improving cost efficiency and regulatory alignment.

Explore how Rackspace helps financial services ISVs operationalize AI, private cloud and governed GPU infrastructure.

Tags:

Cloud Insights

Financial Services/BFSI

Microsoft 365 E7 and the Shift to Operational AI

caro2698 — Fri, 22 May 2026 15:56:46 +0000

Microsoft 365 E7 and the Shift to Operational AI caro2698 Fri, 05/22/2026 - 10:56

Products

Microsoft 365 E7 and the Shift to Operational AI

May 27, 2026

by Adolfo Jaquez, Product Manager, Rackspace Technology

Link Copied!

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Microsoft 365 E7 marks the shift from AI tools to AI operations. The opportunity is real. So is the readiness gap.

Microsoft introduced Microsoft 365 E7 as organizations look for ways to move AI beyond individual productivity tools and into broader business operations. This release reflects a growing focus on how enterprises can scale AI adoption while maintaining the security, governance and operational control required across complex environments.

Over the past two years, enterprises have invested heavily in tools like Microsoft Copilot to improve productivity. According to Microsoft’s 2024 Work Trend Index, 90% of AI users say AI helps them save time, while 85% say it helps them focus on more important work. But for many organizations, the next challenge is scaling AI in a way that is secure, governed and manageable across the business.

Microsoft positions E7 as its new “Frontier Suite,” combining Microsoft 365 E5, Microsoft 365 Copilot, Agent 365 and Microsoft Entra into a more unified platform for enterprise AI. A major part of that shift is the introduction of AI agents, which are designed to support workflows and operational tasks across systems and business processes.

Microsoft describes this model as the “Frontier Firm,” where organizations combine human oversight with AI-assisted execution at scale. But for many enterprises, reaching that stage will require more than deploying new AI tools. Identity, governance, security and data management remain foundational requirements for scaling AI effectively across the organization.

From AI productivity to AI operations

Microsoft 365 E7 brings together capabilities that many enterprises have been trying to assemble independently. It combines Microsoft 365 E5, Copilot, AI agents and Microsoft Entra into a more unified framework for enterprise AI.

On the surface, that simplifies licensing. More importantly, it connects AI capabilities with the identity, governance and security controls required to scale AI across the organization. According to IDC, organizations that integrate AI with governance and security frameworks are significantly more likely to scale AI successfully.

The introduction of AI agents is another significant part of that shift. While Copilot focuses on improving individual productivity, agents are designed to execute tasks, support workflows and operate within defined identity and governance controls. This is where AI begins to move beyond productivity tools and into operational execution.

Why most AI initiatives still struggle to scale

As organizations move AI beyond individual productivity tools and into operational workflows, the requirements become significantly more complex. Scaling AI across systems, data and business processes requires stronger governance, security and identity controls than most pilot deployments were designed to support.

Data fragmentation continues to slow progress, particularly in large or regulated environments where governance and security requirements are more demanding. Many organizations can deploy AI tools, but scaling them across systems, workflows and teams is significantly more difficult. Microsoft 365 E7 is designed to address that challenge by connecting AI capabilities with identity, data and security from the start.

The business case for Microsoft 365 E7

Microsoft 365 E7 is expected to be priced at approximately $99 per user per month. Purchasing comparable capabilities separately would cost more, based on current Microsoft pricing estimates.

A comparable stack includes:

Microsoft 365 E5 at $57 to $60
Microsoft Copilot at $30
Agent 365 at approximately $15
Microsoft Entra Suite at approximately $12

That brings the total to roughly $115 to $117 per user per month.

The pricing advantage is relatively modest, but the larger shift is how Microsoft is packaging these capabilities together. E5 and Copilot primarily improve individual productivity, while E7 adds the identity, governance and agent capabilities required to support AI at an operational level.

If you’re looking to scale AI across workflows and business processes, that distinction is significant.

From Copilot to Frontier Firm

Microsoft uses the term “Frontier Firm” to describe organizations that combine human oversight with AI-assisted execution across business operations. In this model, employees continue leading decision-making while AI agents support repeatable tasks, workflows and operational processes.

These environments also depend on tighter integration between AI, identity, data and governance. The model is designed to support more scalable, consistent and responsive operations across the organization.

For many enterprises, however, reaching that level of operational maturity will require significant preparation in areas like governance, security and workflow design.

Where AI readiness gaps become visible

This is where the gap becomes clear. Many organizations are still working through foundational challenges in identity, data, security and workflow design that directly affect their ability to scale AI.

Identity is often fragmented across cloud and SaaS environments, while AI agents depend on identity controls to determine what they can access and execute. According to Verizon’s 2024 Data Breach Investigations Report, 80% of breaches involve compromised credentials.

Data presents a similar challenge. AI depends on governed, high-quality data, but Gartner estimated in 2023that poor data quality costs organizations an average of $12.9 million annually. Without clear ownership and access controls, AI can accelerate existing data issues instead of resolving them.

Security teams are also under increasing pressure as AI-driven environments operate at a speed and scale that traditional security models were not designed to support. IBM reported in 2024 that the average cost of a data breach reached $4.45 million globally.

Workflows create another obstacle. McKinsey estimated in 2023 that up to 60% of work activities could be partially automated, yet many business processes remain inconsistent or poorly structured. Without redesign and standardization, AI agents have limited ability to deliver meaningful operational value.

Building the foundation for operational AI

Organizations moving toward operational AI need stronger alignment between identity, data, security and operational workflows to scale AI effectively across the business. Identity plays a central role by helping manage access, governance and policy enforcement across environments. Data also needs to be treated as a managed asset, with clear ownership, classification and access controls that support secure AI adoption.

Security operations must continue evolving to monitor and respond to AI-driven activity in real time. Simultaneously, workflows often need to be redesigned to support automation with more consistent processes, inputs and decision points.

Most organizations approach these tasks incrementally, starting with high-impact use cases, measuring outcomes and expanding based on proven value. Organizations that follow phased AI adoption models are significantly more likely to achieve ROI from their AI investments.

How we help you move from Copilot to operational AI

As you expand AI adoption across workflows and business operations, identity, governance and security requirements become significantly more complex. Rackspace supports customers across Microsoft 365, Azure, identity and security, helping you scale AI adoption in a more structured and secure manner. Our focus extends beyond enabling Copilot to align AI initiatives with measurable business outcomes.

The process typically begins with the Copilot Business Value Workshop, where we identify high-impact use cases, align AI initiatives to business goals and evaluate potential ROI. From there, the Copilot Flight Plan provides a more structured path to deployment and scale through readiness assessments, governance planning, pilot programs and adoption support.

We also help customers take advantage of Microsoft funding programs that support envisioning, deployment and adoption initiatives.

Turn Microsoft Copilot into measurable business value

Microsoft 365 E7 reflects Microsoft’s broader push toward operational AI, where agents, governance and identity become part of day-to-day business execution. For organizations looking to scale AI securely and effectively, readiness across data, security and operational workflows will play a central role in long-term success.

To learn how we can help you move from Copilot adoption to operational AI at scale, contact the experts at Rackspace for additional guidance on planning and deployment.

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How a Unified AI Architecture Connects Systems, Data and Decisions Across Banking

caro2698 — Fri, 22 May 2026 14:34:53 +0000

How a Unified AI Architecture Connects Systems, Data and Decisions Across Banking caro2698 Fri, 05/22/2026 - 09:34

AI Insights

How a Unified AI Architecture Connects Systems, Data and Decisions Across Banking

May 26, 2026

By Eddy Rodriguez, Sr. Director and Principal Architect, Financial Services and AI Enablement, Rackspace Technology

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See how a unified architecture brings systems, data and decisions into alignment across the bank.

Over the last three posts, we’ve looked at how AI agent sprawl takes shape across the bank — from fragmented systems and inconsistent data (post 1), to the role of a shared knowledge layer in aligning context (post 2), and the impact that fragmentation has on cost visibility and governance (post 3). This final post focuses on how to connect these systems through a unified architecture that aligns data, decisions and oversight across the institution.

At its core, this comes down to establishing a shared foundation across data, decisions and infrastructure, so that each system can operate with consistent context while continuing to perform its specific function.

That foundation centers on a governed ontology, supported by infrastructure the institution controls. Rather than replacing existing systems, it connects them to align how data is interpreted, how decisions are produced and how governance is applied across the institution.

From there, the architecture can be understood as a set of capabilities that provide consistent context, controlled access and visibility across the full agent fleet.

A governed ontology creates the shared foundation

The ontology defines how data is understood across the institution. It maps entities, relationships and business rules across customers, accounts, transactions and regulatory requirements in a way that every system can reference. This is particularly important in banking, where customer relationships span lending, payments, wealth management and compliance functions.

The data itself remains where it resides, but the ontology governs how it is accessed, what it represents in context and how it can be used by both human users and AI systems. The result is a consistent view of the customer that every agent can rely on when making decisions.

Governed access connects agents to shared context

Each agent connects to that ontology through a governed access layer that controls how data is retrieved and used. Access controls, audit logging and data lineage are applied here, ensuring that every system operates within the same set of rules, regardless of which vendor provides the underlying agent.

When an agent queries customer data, it is drawing from the same defined context as every other system, even as different agents perform different functions across the bank.

Observability connects decisions across systems

A unified observability layer provides visibility into how decisions are made across the agent fleet.

It tracks how agents behave over time, identifies changes in model output and highlights differences in how similar data is interpreted across systems. This makes it easier to see how decisions evolve and where they begin to diverge.

Because this visibility spans across agents and lines of business, it supports a more complete audit trail that reflects how decisions are produced across the institution, supporting auditability in regulated banking environments.

Private infrastructure supports control and consistency

This architecture runs on infrastructure the institution controls, providing a consistent environment for ontology queries, agent inference, governance checks and observability.

Operating on private cloud or dedicated infrastructure gives institutions greater control over how data is accessed, where it is processed and how policies are applied across systems — particularly in banking environments where data sensitivity, regulatory requirements and operational consistency are critical.

It also brings cost into clearer focus at the level of individual decisions, making it easier to understand how AI usage translates into business outcomes over time.

Putting this architecture into practice

This is the type of architecture we’re building at Rackspace. It brings together a shared knowledge layer, governed access and observability on infrastructure designed for regulated environments, so that institutions can connect systems while maintaining control over how data is interpreted and how decisions are produced.

Rather than replacing existing vendors, each system continues to perform in its role while operating within a consistent framework for data, governance and cost visibility across the institution.

Bringing systems, data and decisions into alignment

As AI adoption continues to expand, the focus shifts from adding systems to understanding how those systems work together. A shared knowledge layer, consistent governance framework and controlled infrastructure provide the foundation for aligning data, decisions and outcomes across the bank, where decisions span multiple lines of business and regulatory expectations are high.

With that foundation in place, institutions can scale AI in a way that supports clearer visibility, more consistent governance and greater confidence in how decisions are produced.

Explore how Rackspace Technology helps financial institutions build and operate unified AI architectures that connect systems, align decisions and support governance across the bank.

Learn more about our AI solutions for financial services.

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Building a Secure and Resilient Healthcare Cloud for Operational Readiness

caro2698 — Fri, 15 May 2026 17:16:43 +0000

Building a Secure and Resilient Healthcare Cloud for Operational Readiness caro2698 Fri, 05/15/2026 - 12:16

Cloud Insights

Building a Secure and Resilient Healthcare Cloud for Operational Readiness

May 21, 2026

by Rich Fletcher, Global Marketing Director – Healthcare, Rackspace Technology

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Why leading healthcare teams improve uptime, strengthen defenses and support continuous operations in high-risk environments.

Healthcare organizations are adapting to a changing threat landscape. Cyberattacks are becoming more frequent and sophisticated, often targeting patient data and critical systems. At the same time, clinical operations depend on consistent system availability.

This creates a structural challenge: systems must remain secure while staying fully operational under pressure.

Yet readiness has not kept pace with risk. The 2026 Rackspace Healthcare Cloud Report, From Cloud Adoption to Cloud Advantage in Healthcare, shows that only 4% of organizations are extremely confident in their ability to protect patient data and systems from a cyberattack. Just 6% report a fully integrated and regularly tested cyber resilience posture.

This gap extends beyond security. When resilience breaks down, operations follow.

Security shapes cloud strategy

In healthcare, security and compliance requirements directly influence how cloud environments are designed and managed. Decisions about where workloads run often center on risk exposure and regulatory constraints.

More than half of organizations cite data security and compliance as the primary reason for moving or repatriating workloads across cloud environments or back on-premises.

These decisions are often made incrementally, without a unified strategy. Over time, that can lead to fragmented environments where workloads span multiple platforms with inconsistent controls.

The result is added complexity. Systems become harder to manage, visibility decreases and risk becomes more difficult to contain.

Fragmentation increases operational risk

Many healthcare organizations still rely on reactive security models that prioritize response over prevention and recovery.

The report highlights the impact:

Only 6% of organizations have a fully integrated resilience strategy
Many rely on partial or ad hoc recovery processes

These gaps show up operationally:

Longer recovery times after incidents
Increased downtime for critical systems
Greater financial and reputational exposure

In clinical environments, delays in system availability can affect care delivery. Resilience directly influences operational continuity.

What Cloud Leaders do differently

Cloud Leaders distinguish themselves by embedding resilience into daily operations rather than treating it as a separate recovery function. Their environments are designed to maintain continuity, reduce operational disruption and support faster recovery when incidents occur.

The report shows that 44% of Cloud Leaders have either a fully integrated cyber resilience strategy or coordinated recovery plans supported by regular testing.

These organizations strengthen operational readiness through four core resilience capabilities:

Proactive threat detection

Advanced tools, including AI-based monitoring, help identify anomalies early and reduce response time before issues escalate into larger operational disruptions.

Integrated resilience planning

Disaster recovery is built into cloud strategy through defined processes, regular testing and automation that support continuity during outages or cyber incidents.

Access control and governance

Identity, access and data protection controls are applied consistently across environments to reduce exposure and improve visibility across increasingly complex cloud estates.

Continuous evaluation

Security posture is continuously reviewed and adjusted as threats evolve, helping organizations adapt more effectively to changing operational and security demands.

Together, these capabilities help healthcare organizations maintain uptime, reduce operational strain during incidents and support more consistent continuity of care.

Resilience supports operational performance

Security is often evaluated through the lens of risk reduction. In healthcare environments, resilience plays a broader operational role. The ability to maintain continuity during cyber incidents, outages or infrastructure failures directly affects how consistently clinicians, staff and patients can rely on critical systems.

When resilience is built into cloud operations from the beginning, organizations are better positioned to sustain care delivery under pressure rather than simply recover after disruption occurs. That operational stability becomes increasingly important as healthcare environments grow more distributed and dependent on interconnected digital systems.

Organizations with mature resilience capabilities are typically better able to:

Reduce unplanned downtime across critical systems
Recover more quickly from incidents and operational disruptions
Maintain confidence in system availability during periods of stress
Limit the downstream operational impact of outages on clinical and administrative teams

The difference becomes especially visible during high-pressure events. Organizations with stronger resilience models can isolate issues, maintain essential operations and restore services with less disruption to care delivery. Organizations with weaker resilience strategies often face cascading operational strain, prolonged recovery timelines and growing financial and reputational exposure.

In this context, resilience supports more than infrastructure performance. It helps create the operational consistency healthcare organizations depend on to deliver care reliably.

The investment gap

Despite rising threats, investment levels do not always reflect urgency. The report shows that 41% of organizations expect cybersecurity investment to remain flat or decrease over the next 12-24 months.

This creates a mismatch between risk exposure and preparedness.

Cloud Leaders prioritize targeted investments that strengthen resilience at scale:

AI-driven threat detection
Regular disaster recovery testing
Identity and access management
Workforce training and awareness

These investments support continuity as much as protection.

Resilience supports operational performance

When resilience is built into cloud operations from the outset, organizations are better positioned to sustain care delivery under pressure rather than simply recover after a disruption. That operational stability becomes increasingly important as healthcare environments grow more distributed and dependent on interconnected digital systems.

Organizations with mature resilience capabilities are typically better able to:

Reduce unplanned downtime across critical systems
Recover more quickly from incidents and operational disruptions
Maintain confidence in system availability during periods of stress
Limit the downstream operational impact of outages on clinical and administrative teams

In this context, resilience supports more than infrastructure performance. It helps create the operational consistency healthcare organizations depend on to deliver care reliably.

What this means for healthcare leaders

Security and resilience now influence how effectively healthcare systems operate. The focus extends beyond risk reduction to maintaining continuity under pressure.

Organizations that integrate security into their cloud strategy improve their ability to withstand disruption and recover quickly when incidents occur.

Cloud Leaders show that this level of readiness is achievable. By aligning security, resilience and cloud strategy, they create more stable and predictable operating environments.

In healthcare, that stability supports both operational efficiency and patient care.

Take the next step

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AI Is Forcing Infrastructure, Security and Governance to Converge

caro2698 — Tue, 12 May 2026 19:22:47 +0000

AI Is Forcing Infrastructure, Security and Governance to Converge caro2698 Tue, 05/12/2026 - 14:22

AI Insights

AI Is Forcing Infrastructure, Security and Governance to Converge

May 18, 2026

by Vikram Reddy Kosanam, Practice Head, Data & AI Engineering, Rackspace Technology

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As AI moves into production, siloed operating models are breaking down, pushing enterprises to rethink how they run and manage technology.

Most enterprise conversations about AI still center on models, use cases and applications as those are areas where the impact is easiest to see. But what’s becoming clear is that the bigger shift is happening underneath. As AI moves into production and starts to become pervasive, it’s starting to change how infrastructure, security and governance actually operate, and how tightly they need to work together.

Separate cloud, security and compliance models are no longer effective

For the past decade, most enterprises built cloud, security and compliance as separate functions. Each grew in its own direction, with different tools, different teams and its own way of operating. For a while, that separation was manageable.

It held up because workloads were relatively stable, and regulatory requirements moved at a pace organizations could keep up with using periodic audits and manual processes.

AI changes that because AI workloads behave differently. They scale in less predictable ways, they are more sensitive to where data lives and how quickly it moves, and they introduce performance considerations that are harder to isolate. At the same time, the volume of security signals increases to a point where manual triage quickly becomes a bottleneck.

Governance is shifting as well. Expectations around model oversight, data lineage and continuous compliance are becoming more defined as we move through 2026. Those expectations do not fit neatly into annual audit cycles or after-the-fact validation.

This is where the old model starts to show strain. When infrastructure, security and compliance operate independently, it becomes harder to keep up with the speed and coordination required to manage AI in production.

What we are seeing is organizations being pushed to bring these functions closer together. Not because it is a cleaner design on paper, but because it is becoming the only way to operate at the pace AI demands.

AI is forcing closer coordination across operations

AI is starting to act as a connective layer across areas that have traditionally operated independently. The impact shows up differently depending on where you look, but the pattern is consistent.

Security teams have long dealt with alert volumes that outpace their ability to respond, often without enough context to distinguish false positives from real threats or assess impact and urgency. AI is helping connect signals across environments and prioritize the incidents that require immediate action. In some cases, it also reduces the manual effort required to investigate and contain issues, which shortens response times in a meaningful way.

Governance and compliance are moving in a similar direction. Instead of relying on periodic audits, organizations are establishing more continuous visibility into how controls are performing. When requirements change, the impact can be assessed more quickly, and gaps are easier to identify. Work that was once manual and time-consuming is becoming more consistent and easier to manage as an ongoing process.

In cloud operations, the challenges tend to center on cost, performance and reliability. Many environments have grown faster than they have been optimized, which creates inefficiencies that are difficult to track manually. AI is helping teams make better decisions about how workloads are sized, where they run and how they scale. It is also improving how issues are detected and resolved, reducing resolution times and easing the pressure on teams already stretched by on-call rotations.

What stands out across all of these areas is how much more connected they are becoming. Decisions in one domain increasingly depend on context from another, and AI is making that coordination possible at a scale that would be difficult to manage otherwise.

Manual workflows are giving way to AI-assisted operations

This shift shows up most clearly in how work gets done day to day. For a long time, the model was straightforward. People used tools to investigate issues, run queries, write scripts and document results. Much of that work depended on manual effort and individual expertise.

That model is starting to change. AI is taking on more of the continuous, high-volume work, while teams focus more on oversight, interpretation and decision-making.

The implications are practical. A security operations center that relies on AI-assisted triage operates differently from one built around manual investigation. A compliance function with continuous monitoring requires different skills and processes than one designed around periodic audits.

It also changes how organizations evaluate partners. Strength in a single domain is no longer enough when the work itself is becoming more connected. Infrastructure, security and compliance increasingly need to be managed together, with shared context across all three.

Assess, prioritize, align and evaluate as these functions come together

As these functions become more connected, most organizations find they are uneven in how far they have progressed. Some areas are already evolving, while others are still operating in older models.

The goal shouldn’t be to change everything at once, but to understand where to focus first and how to move forward in a sustainable manner. Start by focusing on four key areas.

Assess where you are: Most organizations are further along in some areas than others. Security tooling may be modern, while compliance processes are still largely manual. Cloud operations may be well automated, but disconnected from governance or security. Understanding those gaps is the first step.

Prioritize where it matters most: Trying to converge everything at once rarely works. It is more effective to start where the operational pressure is most visible, whether that is cloud cost, alert volume or another bottleneck that is already slowing teams down.

Align how work gets done: Adding new tools without changing workflows tends to create more complexity. Roles, processes and decision-making need to evolve alongside the technology if the benefits are going to show up in practice.

Evaluate partners with this in mind: As these areas become more connected, support limited to a single domain can create new gaps. What matters more is the ability to work across infrastructure, security and compliance with shared context.

As complexity grows, organizations that adapt will pull ahead

The way these functions come together will continue to evolve. Enterprise environments are becoming more complex, regulatory expectations are increasing and the economics of AI are pushing more organizations toward automation.

Organizations that recognize this early and adapt how they operate will be better positioned to manage complexity, reduce fragmentation and focus on performance, risk and outcomes. They will spend less time working around disconnected systems and more time driving measurable results.

For others, AI may still be introduced in more isolated ways, which can make it harder to move past the fragmentation many teams are already dealing with. The opportunity is in using AI to bring infrastructure, security and governance closer together so they can operate with shared context.

See how Rackspace Technology helps organizations operationalize AI across infrastructure, security and governance. Learn more →

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AI Insights

How AI Agent Sprawl Impacts Cost and Governance Across the Bank

caro2698 — Mon, 11 May 2026 17:47:28 +0000

How AI Agent Sprawl Impacts Cost and Governance Across the Bank caro2698 Mon, 05/11/2026 - 12:47

AI Insights

How AI Agent Sprawl Impacts Cost and Governance Across the Bank

May 14, 2026

By Eddy Rodriguez, Sr. Director and Principal Architect, Financial Services and AI Enablement, Rackspace Technology

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See how fragmented AI systems shape cost visibility, governance and decision consistency across the bank.

Across the banking industry, AI adoption is expanding within individual lines of business, often through vendors selected to solve specific problems. Each vendor brings its own pricing model and reporting structure. Usage may be measured in tokens, API calls, processed documents, active seats or proprietary units that don’t align with standard financial reporting.

Taken together, these differences make it harder to form a clear view of overall spend. As a result, it becomes difficult to answer a basic question: What is our total AI spend, and what is it producing?

The information is available, but it is captured in separate systems, each using its own usage model, timing and reporting format. A commercial lending platform may charge per document processed, while an AML solution charges per alert triaged and a fraud platform charges per transaction scored. These models reflect how each system operates, but they do not translate easily into a unified view of cost or business outcome.

As a result, understanding how AI investments perform across the institution requires pulling these inputs together across systems. Each vendor is optimizing its own model, while the broader picture of cost, value and efficiency remains distributed across the bank.

Cost visibility is difficult to establish

This fragmentation makes evaluating AI spend at the institutional level more complex. Each vendor uses a pricing model aligned to how its system operates, which means forming a unified view of cost, value and efficiency across the bank requires a consistent way to compare those inputs.

Cost, however, is only part of the picture. Governance introduces a second layer of complexity that becomes more apparent as these systems scale.

Governance is hard to apply across independent AI systems

As these systems scale, governance becomes more complex to apply consistently across the institution. When an examiner asks how AI systems are governed, the response often points to existing model risk management frameworks.

Those frameworks are effective for internally developed models, but they are not always designed for a distributed set of third-party agents operating across different lines of business, each with its own data access patterns, update cycles and explainability standards.

In practice, this creates gaps in the application of oversight. Its vendor may update a fraud model while an AML model continues to run on an earlier version. A mortgage processing system may be fine-tuned on institution-specific data and then remain unchanged for months.

This variation makes it more difficult to track model behavior across systems, understand how decisions evolve and maintain a consistent audit trail across the agent fleet.

Gaps in visibility can lead to inconsistent outcomes

As systems operate independently, differences in data timing, model behavior and system context can lead to inconsistent outcomes across the institution. In some cases, similar transactions may be evaluated differently by separate systems because they are operating on different data snapshots or interpreting customer activity in different ways. These variations are not always visible at the point of decision, which makes them harder to identify and reconcile across systems.

Over time, this lack of alignment can affect reporting, compliance and control processes. When decisions are not consistently connected across lines of business, it becomes more difficult to demonstrate how outcomes were produced and how they align with institutional policies.

Regulatory expectations continue to evolve in this area. Financial institutions are expected to maintain clear oversight of how models are used, how decisions are made and how controls are applied across systems. As AI adoption grows, these expectations extend across a broader set of technologies and decision points.

A connected foundation brings cost, governance and decisions into alignment

As AI systems continue to expand across the bank, the way they connect and operate together becomes increasingly important. Cost visibility, governance and decision consistency are all shaped by whether these systems share a common foundation for how data is interpreted and how outcomes are produced.

Without that alignment, each additional system introduces more variation in how data is used, how decisions are made and how results are measured across the institution. With the right foundation in place, these systems can operate with shared context, enabling a clearer view of cost, more consistent governance and greater confidence in how decisions are produced across the bank.

As you evaluate AI across your organization, consider how cost visibility, governance and decision-making come together across systems. We can help you build the foundation to support that alignment. Get started →

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Data

AI Insights

Financial Services/BFSI