Part 1: escaping the trap – data remediation disguised as transformation

Canadian financial institutions do not suffer from too little technology. They do not suffer from too few regulations and processes. They suffer from too many architectural promises. Between heightened regulatory pressure from the Office of the Superintendent of Financial Institutions (OSFI)^1,2 and mounting shareholder expectations, banks cannot afford another clean up disguised as transformation.

While our focus here is on the Canadian environment, financial institutions across the US and international markets face identical challenges. The governing regulatory bodies may differ, but their underlying intentions remain the same.

If financial institutions want their investments in platforms like ServiceNow Financial Services Operations to produce material business returns, they must escape the remediation trap. This includes addressing the frustrating customer experience that is a result of relying on legacy systems and processes.

Disguising remediation as transformation

Across major financial institutions, funding requests labeled “core database cleansing” or “configuration remediation” rarely survive scrutiny and approval processes. They sound defensive, slow, and expensive. Conversely, business cases promising “autonomous operations,” “agentic servicing,” and “enterprise AI” are viewed more positively by executives.

This capital dynamic has created an industry-wide open secret: most enterprise AI programs are legacy data remediation initiatives disguised as an AI transformation.

When an institution invests in enterprise platforms like ServiceNow, executive sponsors expect certain business outcomes – smooth processing for dispute management, automated loan servicing triage, and rapid onboarding for new clients.

However, when delivery squads begin deployment, they often collide with the reality of their data: duplicate catalog items, fragmented routing rules, broken service mappings, and orphaned configuration items across the enterprise Configuration Management Database (CMDB).

The delivery team quietly spends months remediating under the banner of an “AI transformation.” This is not an isolated organizational quirk. According to research by McKinsey & Company, technical debt amounts to 20% to 40% of the entire technology estate’s value across large enterprises. Worse, 30% of CIOs report that more than 20% of technical budgets ostensibly allocated to new capabilities is quietly diverted to resolving underlying technical debt.^3,4 In one major North American bank, accumulated technical debt across 1,000 systems generated more than $2 billion in unaccounted remediation costs.⁵

When the platform fails to deliver the expected outcomes on schedule, the initiative is labeled an AI failure. In truth, AI was never the problem; the organization simply used an AI business case to pay down a decade of deferred maintenance.

The remediation trap: measuring activity instead of value

Once banking delivery teams acknowledge their data challenges, they frequently swing to the opposite extreme: the remediation trap.

The remediation trap is an operational holding pattern driven by the belief that enterprise data must be pristine before any automated workflow or intelligent assistance can go live. When delivery milestones stall, the narrative pivots: “your common service data model (CSDM) is not mature enough. We need to pause rollout and conduct a 12-month foundational cleansing engagement.”

This is precisely why Gartner predicts that at least 30% of AI projects will be abandoned after proof of concept, explicitly citing poor data quality, escalating costs, and unclear business value as the primary culprits.⁶ This dynamic is sustained by tracking technology metrics (measuring volume of work) rather than business metrics (measuring operational value).

Measurement domain	Tech-centric metric (activity)	Business-centric metric (value)
Platform architecture	Percentage of payment-service CIs with validated ownership, service relationships, and support-group mappings	Reduction in payment exceptions requiring manual ownership investigation or ticket reassignment.
Workflow routing	Percentage of legacy scripts and unaligned service models systematically harmonized	Percentage change of eligible and complete commercial onboarding applications processed with zero manual touchpoints.
Intelligent assistance	Total tokens processed or prompt volume	First-contact resolution (FCR) rate in customer servicing

You can diagnose this trap in a single meeting using the 3-slide rule: If an implementation squad needs more than three slides of technical architecture to explain an AI or workflow deployment, they have not identified the operational decision the technology is supposed to solve. Teams often over-index on multi-layered diagrams of retrieval-augmented generation (RAG) pipelines and LLM wrappers to mask the fact that they cannot articulate what business outcome they are accelerating.⁴

The alternative: “decision-first” modernization

Escaping the remediation trap does not mean ignoring data hygiene; it means scoping data hygiene strictly to the perimeter of a single business decision. Banks do not need an enterprise-wide, pristine CMDB or a fully realized CSDM across every division before deploying intelligent workflows. You only need the data clean, governed, and structured enough to support one critical decision path.

Operating vector	The remediation trap	Decision-first modernization
Data scoping	Attempting to clean all configuration records across the enterprise before automating.	Identifying one high-friction workflow (e.g., mortgage discharge processing), investigating its dependencies, and cleaning only its supporting data model.
Success metrics	Data lake volume, table counts, and schema uniformity.	Cycle time reduction, duplicate identification, error reduction, and auditable operational resilience.
Delivery model	Multi-year roadmaps with back-loaded, theoretical value.	45-day (or less) production increments delivering demonstrable workflow improvements.
Platform role	AI deployed as an isolated pilot or generic enterprise chatbot.	Intelligent workflows embedded directly within ServiceNow, where business context already lives.

By anchoring platform investments to decision velocity, financial institutions satisfy the OSFI’s risk posture while demonstrating tangible operational returns to your executive committee.¹

The bridge to execution: what is the correct sequence?

Admitting that an AI program is caught in a data remediation trap is a critical first step. It strips away vendor rhetoric and refocuses the leadership team on business outcomes. However, recognizing the trap introduces an immediate operational question: if we shouldn’t automate everything at once, and we shouldn’t wait for perfect data, what is the exact order of operations?

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Part II: the order of operations for modernization

Consider a common scenario in retail banking: a customer applies for a mortgage application. On the surface, the bank has built a sleek digital portal for document intake. Behind that screen, however, the process remains entirely manual. Employees must still extract data, cross-reference it across multiple legacy systems, engage third parties, and route it through physical sign-off queues .

If you layer AI or automation on top of a workflow like this, you are not likely to improve operations. Instead, you may simply accumulate more technical debt.  This dynamic takes a heavy toll on the employee experience. Operational staff find themselves buried under repetitive, administrative work. Rather than empowering your team with modern tools, an un-optimized system increases operational frustration, leading to employee fatigue and elevated processing error rates.

To deliver operational returns without creating a patchwork of disconnected systems, financial services leaders can follow a structured four-phase modernization sequence. This sequence is widely recognized as an industry best practice; however, implementing these four steps successfully remains a challenge for many institutions:

1. Simplify processes to their minimal viable steps
2. Standardize workflows on a platform like ServiceNow
3. Leverage only the data you need for your specific use-case
4. Introduce automation and AI into your standardized workflows

Common pitfalls with modernization initiatives

Without a disciplined architectural roadmap, banks inevitably fall victim to transformation traps that drain budgets and degrade both the customer and employee experience:

Creating an attractive front-end experience, doesn’t compensate for legacy processes behind it

Financial institutions often confuse “digitization” with “optimization.” When modernizing, departments frequently build a polished digital intake form for an archaic, 15-step manual review process.

If a credit decision requires three separate manual signatures and redundant verification checks, a fast digital form simply hurries the document up to sit in an underwriter’s queue faster. You cannot automate operational waste out of a process; you must simplify the process first.

This operational friction is a primary focus for Canadian financial regulators and risk leaders. According to the Global Risk Institute and OSFI’s Joint 2026 report (FIFAI II: AI Risks and Opportunities), underinvestment in core operational workflows is a systemic bottleneck across Canadian financial institutions.²

The report explicitly warns that attempting to layer advanced AI or automated decision-making onto fragmented legacy processes and un-orchestrated data layers fails to deliver the expected productivity gains. Instead, it creates severe technical overhead, warning that institutions must establish solid, simplified process guardrails before deploying automation to free up actual operational capacity.²

The rise of siloed tools and shadow IT

When there is no enterprise-wide, sequenced blueprint, localized business units begin solving their pain points in isolation: a branch network manager purchases a standalone scheduling SaaS tool or the call center deploys an isolated, vendor-hosted chatbot.

Because these decisions bypass enterprise architecture and governance, they result in a fragmented maze of overlapping tools that cannot communicate with each other. Suddenly, the bank is paying for dozens of different point solutions, security risks rise, and the customer is forced to act as the human integration layer between disconnected systems.

To understand why this fragmentation occurs at your institution, consider these diagnostic questions:

• How does your business engage with enterprise architecture? Is your enterprise architecture team consulted early when a team identifies an operational need, or is the review treated as a late-stage approval hurdle?
• How is enterprise architecture governed and measured? Are your architects evaluated on simplifying and consolidating your technology stack over time, or are they measured solely on individual project delivery?
• Is there a clear understanding of your strategic platforms? Does business leadership have a clear view of which primary platforms are designated as your core systems, and do they know what capabilities those systems already provide?
• Do your technical teams understand the bank’s broader business goals? When business units select tools, are those choices aligned with the bank’s long-term technology consolidation strategy?

Iterate vs. sprint: it’s possible to sprint in the wrong direction

The agile movement taught technology teams to “move fast and break things.” However, in a highly regulated financial environment, sprinting without a baseline simply accumulates technical debt and creates risk without return.

Sprinting on the wrong sequence is not agility; it is simply moving faster in the wrong direction.

According to research from McKinsey & Company, technical debt consumes a significant amount of resource capacity, typically amounting to 20% to 40% of the entire technology estate’s value before depreciation. Furthermore, 30% of CIOs report that more than 20% of their new capability budgets are diverted to resolving legacy technical debt accumulated from past, uncoordinated sprints.^3,4

To avoid the “iteration trap,” teams must first understand their foundational architecture, document the current state, and define clear milestones. Too many project teams skip this groundwork, assuming experts will simply resolve architectural issues on the fly. However, establishing these baselines is exactly what empowers delivery squads to truly sprint in the right direction.

Creating a center of excellence and innovation (CoEI) that establishes guardrails and keeps you moving forward

Many banks build Centers of Excellence and Innovation that act as rigid committees, slowing down every request and forcing business units to wait months just to modify a simple service form. This is the wrong approach. A true CoEI is not an obstacle; it is an engine designed to establish guardrails that protect the enterprise while keeping the business moving forward.

For example, we worked with Desjardins to create and run their CoEI. Drawing directly from this experience, here are four best practices to guide your CoEI setup and maximize your investment:

1. Business-led, shared governance. Too often, technology implementations are driven solely by IT. For transformation to succeed, the initiative is best led by the business units identifying actual operational challenges. Establish common standards and shared capabilities so different business units do not build duplicate, isolated solutions.

• Do you really need customization? It is a common misconception that a bank’s operational needs are entirely unique and require extensive platform customization. Over-customization is a major trap that accelerates technical debt and limits your ability to adopt out-of-the-box innovations. Your system integration partner should understand banking objectives and proactively challenge internal demands for custom code.

• Governance is essential, but over-indexing too early stifles innovation. Start with a minimum viable governance framework focused on initial project delivery, then scale to robust demand intake processes as the platform matures. Regardless of your maturity level, documentation must be established at every stage. Because systems continuously evolve and teams shift, structured documentation prevents your platform architecture from becoming unmanageable.

• Measure and Communicate Value. Embrace an agile planning cadence to continuously review your automation roadmap. Define exactly how business value will be measured and communicate these Key Performance Indicators (KPIs) to stakeholders regularly to build a clear, ongoing business case for future investments.

The order of operations for modernization

A practical way to build operational agility is to sequence your roadmap in four phases, such as the ones below. This approach helps your teams deliver quick wins without getting bogged down in massive, company-wide database cleanups:

Phase 1: simplify processes to their minimal viable steps 

Map your target customer and employee journeys (e.g., mortgage discharge or commercial onboarding). Identify and reduce legacy compliance checks, manual sign-offs, or procedural steps that are not legally required and add no value to the workflow.

This exercise is also an extremely valuable starting point for your consulting partner. Handing your partner a simplified, pruned process map allows them to come in and immediately ask the right design questions.

Phase 2: standardize workflows on a platform like ServiceNow  

Standardize your highest-value core workflows on ServiceNow first to secure immediate business impact. Instead of a risky, all-at-once migration, start by prioritizing key workflows and ingesting your existing legacy data and spreadsheets. This approach allows you to consolidate disjointed ticketing systems into a unified service layer while making smart integration decisions.

Phase 3: leverage only the data you need for your specific use-case 

Avoid the temptation to fix your entire enterprise database at once. Instead, identify only the specific data tables, records, and system integrations required to execute the targeted workflow you simplified in Phase 1.

Phase 4: introduce automation and AI into your standardized workflows 

Once your workflows are clean and standardized, introduce automation, predictive intelligence, and generative AI capabilities (such as Now Assist or AI triage agents). This ensures you achieve measurable operational gains and a clear return on investment.

Following this order of operations resolves your strategic and architectural alignment. You have the correct mindset and a structured roadmap. Yet, as you prepare to execute, you may still encounter organizational and technical silos.

Your data team, your integration team, your AI team, and your business team often operate as separate kingdoms with disconnected backlogs. Forcing your customers to act as the manual integration layer to bridge these silos introduces human variability, which directly diminishes your project’s success and neutralizes your technology investments. 

In our third and final installment, we deliver the operational playbook to break down internal team silos, align platform engineering, and orchestrate your systems so your customers don’t have to.

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Part III: the orchestration imperative

In Part I, we looked at why banking AI initiatives stall: they are often legacy data cleanups disguised as transformation. In Part II, we laid out a practical sequence to fix that problem, showing why simplifying processes and standardizing workflows on a platform like ServiceNow must happen before introducing advanced automation and AI. Now we’re going to talk about execution.

Even with the right mindset and a sequenced roadmap, banks run into a familiar barrier: organizational and technical silos. The data team, security team, integration team, enterprise architecture team, and application team often work from separate backlogs with different priorities. When these systems and teams do not communicate, your customers and employees end up being the integration layer.

This final installment provides a practical playbook to connect those systems, align your teams, and orchestrate the platforms you already own to deliver the value you expect from them.

Disconnected systems: when people become the integration layer

When core operational systems cannot share data directly, people are forced to bridge the gap:

• For customers: a client updates their contact information on an online portal. Because that portal does not sync automatically with core lending databases, the customer is asked to provide the exact same information again during their next branch visit or credit application.
• For employees: a service specialist receives an incoming request via an email queue. To fulfill it, they must manually re-enter data into two separate backend systems, spending their day copy-pasting across screens instead of focusing on resolving complex cases.

This friction is a result of application sprawl. According to the MuleSoft Connectivity Benchmark Report, the average enterprise now manages 897 applications, yet only 29% are integrated. That leaves over 70% of software operating in functional silos, with 83% of IT leaders reporting that integration challenges are stalling their modernization efforts.⁷

When no single team takes responsibility for the end-to-end workflow, operational efficiency drops and costs rise. The solution is not to buy another standalone platform. The priority is orchestration – connecting your existing systems into a reliable, automated workflow.

The playbook: three practical steps to align platform engineering

To foster cross-functional alignment and naturally break down silos, we explore three hands-on plays:

Play	Operational challenge	The play (the how)	Operational ownership (the who)	Governance guardrails (the safety)	Key performance indicators
Play 1: evolve IT into value-stream “product squads”	Banks traditionally group engineers by horizontal technology layers (e.g., database, API, portal). Because no single team owns the end-to-end customer experience, hand-offs remain slow, manual, and uncoordinated.	Transition from horizontal technology queues to vertical, cross-functional product squads aligned directly to core business value streams (e.g., commercial loan onboarding or retail mortgage renewals).	• The journey owner (business-led): actively prioritizes the backlog and represents customer and operational requirements.   • Dedicated engineers (platform-scoped): a dedicated squad comprising one architect, one business process consultant, and one developer.	Each squad operates under a shared release cadence. To maintain compliance with OSFI Guideline B-13 (technology and cyber risk management), all code changes and configurations are peer-reviewed to prevent custom, un-governed scripts.	Straight-through processing (STP) rate: transition from measuring individual system uptime to tracking the STP rate of the end-to-end business journey.
Play 2: adopt an API-first integration architecture	Individual development squads frequently write custom, hardcoded point-to-point connections to solve immediate project needs. This creates a fragile, un-governed integration footprint that breaks during platform updates.	Establish a standardized approach where system interactions use standard, reusable APIs managed through a central gateway. Individual squads are encouraged to consume and publish data via pre-approved endpoints rather than writing direct database queries to core banking systems.	• The CoEI integration lead: governs the API lifecycle, approves new endpoint requests, and manages the API gateway.   • Integration specialists: responsible for configuring and mapping standard connectors between centralized platforms like ServiceNow and backend systems of record.	Implement an integration registry housing all pre-approved REST APIs and out-of-the-box ServiceNow connectors. If a squad requires a new connection, the CoEI reviews the security protocol (e.g., OAuth 2.0 validation) before publishing the new reusable API.	API & connector reuse rate: target 90%+ reuse of data flows utilizing standard, upgrade-safe registry connectors for new integrations.
Play 3: establish a unified backlog and coordinated sprint planning	The data, security, integration, enterprise architecture, and application teams manage independent backlogs and timelines. Because their priorities are uncoordinated, critical front-end features are often built months before backend APIs are ready, stalling project velocity.	Introduce shared sprint planning across all specialized teams. Under the guidance of the CoEI, the  squads synchronize their two-week development sprints to align dependencies.	• The CoEI delivery lead: facilitates joint planning sessions and resolves inter-team dependencies.   • Squad scrum masters: align their individual team velocity and task schedules to the shared sprint goals.	Establish a unified backlog prioritized around the current modernization blueprint. A front-end user experience task is scheduled only when its supporting backend database and API integration dependencies are committed to the same, or previous, sprint.	Time-to-value: accelerate delivery by aligning dependent technical tasks into a single release cycle, eliminating downstream idle time.

Orchestration in practice: National Bank of Canada

Operational orchestration is just as critical for internal bank staff as it is for customer-facing channels. When internal technology teams and operational staff rely on fragmented tools, the resulting delays directly affect service delivery across the entire institution.

National Bank of Canada addressed this challenge by partnering with Ateko to modernize its internal IT service management and operations infrastructure. They needed to replace their legacy service management platform to improve operational agility, cloud readiness, and system connectivity. Rather than adding yet another isolated tool to their environment, the bank focused on orchestrating its existing ecosystem on ServiceNow.

The practical value of this approach lies in how they connected their key systems to eliminate data silos:

• Access governance: integrating Microsoft Entra ID (Azure AD) established centralized single sign-on security, ensuring that access was handled automatically across the platform without manual IT interventions.

• Infrastructure visibility: connecting Microsoft System Center (SCCM) automated hardware and software configuration tracking. This fed clean, real-time data directly into the central database, removing the need for manual inventory audits.

• Incident Response: integrating Everbridge for incident alerting and Microsoft Teams for collaboration meant that when operational issues occurred, the right support squads were alerted automatically, with communication channels instantly tied to the active ticket.

By focusing on orchestration and choosing the right partner, they successfully onboarded and trained over 25,000 internal users while achieving zero P1/P2 incidents during the critical post-launch period.

“Ateko is an essential partner for us, helping us modernize our IT service management and operations processes with ServiceNow, while seamlessly integrating several critical systems. Thanks to Ateko, we are improving our operational efficiency, which contributes to enhanced user satisfaction and better service.”

Julie Lévesque, Executive Vice President of Technology and Operations at National Bank of Canada

The practical lesson from their success is clear: real operational efficiency is achieved not by replacing your entire software suite, but by orchestrating your existing core tools into unified, reliable workflows.

Conclusion: from high-level roadmaps to practical execution

A frequent pitfall for financial institutions is treating every internal department’s request as a core project requirement. When delivery teams simply fulfill individual wish lists without reviewing the broader enterprise architecture, they tend to build custom, one-off integrations that reintroduce technical debt and operational complexity.

Taking a pragmatic approach to demand management means prioritizing the end-to-end user journey over localized departmental preferences, which helps keep platform solutions standard, sustainable, and out-of-the-box.

This operational discipline is what separates theoretical roadmaps from successful execution. As we have outlined across this three-part series, modernizing your banking operations does not have to mean committing to multi-year system overhauls, expensive database cleanups, or unproven technology experiments. Instead, it is often a matter of:

1. Diagnosing the data remediation trap and adopting a “decision-first” lens (Part I).
2. Following a practical sequence to simplify processes before automating (Part II).
3. Aligning backlogs and integrating the software assets your institution already owns (Part III).

Ultimately, transformation is all about execution. Delivering meaningful operational improvements relies on practical capabilities, platform experience, and a clear understanding of the regulatory and operational realities within Canadian banking.

At Ateko, we partner with Canadian financial institutions to turn complex platform investments into reliable, orchestrated workflows that improve operational efficiency. Let’s discuss how we can help your teams transition from the planning phase and start orchestrating your platform for real returns.

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1 Technology and cyber risk management – Office of the Superintendent of Financial Institutions

2 Guideline E-23 – Model Risk Management (2027) – Office of the Superintendent of Financial Institutions

3 Tame tech debt to modernize your business | McKinsey

4 A new standard to measure and tame technical debt | McKinsey

5 30% of Generative AI Projects Will Be Abandoned After Proof of Concept By End of 2025 | MHL News

6 Technology Debt Is a Balance Sheet Item, Start Treating It Like One

7 Data Integration Statistics – you must know in 2026 – Peliqan