Executive Summary

The integration of Credit Suisse's legacy systems into UBS represents one of the most intricate banking consolidations in modern history. This monumental transition is being meticulously managed with the help of Excel-based AI integration trackers. As of Q2 2025, UBS has successfully decommissioned 56% of the inherited Non-Core and Legacy (NCL) applications, equating to approximately 700 systems. The goal is to achieve over 95% decommissioning by the end of 2026, demonstrating a commitment to efficiency and innovation in financial services.

Excel-based integration trackers are pivotal in this process, providing a robust framework to manage the decommissioning timeline, migration status, and system consolidation efforts. These trackers are designed to capture critical data points such as application inventories, decommissioning schedules, migration milestones, and cost reduction metrics. This structured approach allows for real-time updates and strategic adjustments, ensuring the integration process stays on track.

Central to UBS's strategy is the creation of a centralized data platform, forming the foundation of an AI-ready architecture. This initiative not only streamlines operations but also positions UBS at the forefront of technological innovation in banking. By leveraging AI and Excel-based tools, UBS ensures a seamless integration process while maintaining operational integrity.

Key achievements so far include the decommissioning of 56% of legacy systems and the successful migration of core functions, setting a benchmark for future banking mergers. As UBS progresses towards its 2026 goal, the emphasis remains on maximizing efficiency through data-driven decision-making.

For institutions facing similar integration challenges, the UBS-Credit Suisse model offers actionable insights: prioritize comprehensive tracking mechanisms, invest in centralized data platforms, and embrace AI-driven solutions to enhance operational effectiveness. This approach not only facilitates smoother transitions but also drives long-term value creation.

Business Context

The merger between Credit Suisse and UBS marks a pivotal moment in the financial services industry, underscoring the scale and complexity of modern banking consolidations. This merger is not just a convergence of two financial giants but a strategic maneuver designed to harness synergies, expand market reach, and enhance technological capabilities. As UBS undertakes the Herculean task of integrating Credit Suisse's legacy systems, the use of Excel-based AI integration trackers becomes indispensable in navigating this labyrinthine process.

Background of the Credit Suisse and UBS Merger

In a bid to fortify their market positions and drive long-term growth, UBS's acquisition of Credit Suisse was finalized in late 2024. This merger brought together two of Switzerland's most venerable banking institutions, each with a rich history and a wealth of experience in global finance. However, the integration of Credit Suisse's legacy systems posed a formidable challenge, given the sheer volume of applications and the intricate web of interdependencies involved. As of Q2 2025, UBS has already decommissioned 56% of the inherited Non-Core and Legacy (NCL) applications, approximately 700 systems, with an ambitious target to exceed 95% by the end of 2026.

Strategic Objectives and Expected Outcomes

The strategic objectives of this merger are multifaceted. Primarily, UBS aims to streamline operations, reduce redundancies, and create a more agile organizational structure. By integrating Credit Suisse's legacy systems, UBS seeks to not only cut costs but also to establish a robust, centralized data platform that supports AI-driven decision-making. The Excel-based integration trackers are pivotal in this endeavor, providing a detailed overview of application inventories, decommissioning schedules, and migration milestones. This meticulous tracking ensures that the merger progresses smoothly and efficiently, ultimately leading to enhanced customer experience and increased shareholder value.

Industry Impact and Competitive Landscape

The UBS-Credit Suisse merger is poised to reshape the competitive landscape of the banking sector. By leveraging AI and advanced data analytics, UBS aims to offer more personalized and innovative financial products, thereby gaining a competitive edge over other global banks. The successful integration of Credit Suisse's systems sets a precedent for future mergers and acquisitions in the industry, showcasing the importance of technological integration in achieving business objectives. In an era where data-driven strategies are paramount, the merger highlights the critical role of effective tracking tools in managing complex transformations.

For industry players contemplating similar mergers, the UBS-Credit Suisse case offers valuable insights. It underscores the importance of having a clear integration roadmap, backed by comprehensive tracking mechanisms and a strong focus on technological advancement. By adopting these practices, banks can navigate the complexities of mergers with greater confidence and precision.

Actionable Advice

For financial institutions considering mergers or undergoing integration processes, the following actionable advice may prove beneficial:

• Develop a detailed integration plan with specific timelines and milestones.
• Utilize Excel-based trackers to maintain real-time visibility over integration progress.
• Focus on creating a centralized data platform to streamline operations and enable AI adoption.
• Regularly review and update the integration strategy to address emerging challenges and opportunities.

By following these steps, institutions can improve their chances of successful integration, ultimately leading to increased efficiency and competitive advantage in the financial services industry.

Technical Architecture of Credit Suisse Legacy to UBS AI Integration Tracker

The integration of Credit Suisse’s legacy systems into UBS marks a significant milestone in the banking sector, representing one of the most intricate mergers in history. This complex process is underpinned by a robust technical architecture designed to facilitate seamless integration, leveraging Excel-based tracking systems to manage this massive transformation effectively.

Overview of Legacy System Architecture

Credit Suisse's legacy systems comprised a vast array of Non-Core and Legacy (NCL) applications, totaling approximately 1,250 systems before integration efforts commenced. These systems, built over decades, were characterized by decentralized data silos, diverse technologies, and varying levels of modernization. The challenge lay in integrating these disparate systems into UBS's more streamlined and AI-ready architecture.

Statistics indicate that as of Q2 2025, UBS has successfully decommissioned 56% of these inherited systems, equating to around 700 applications. The goal is to exceed 95% decommissioning by the end of 2026, underscoring the critical role of an efficient technical framework in achieving these targets.

Details of UBS's AI-Ready Data Platform

UBS's AI-ready data platform is designed with a focus on centralized data management, high-speed processing, and advanced analytics capabilities. The platform utilizes a cloud-based infrastructure, integrating cutting-edge technologies such as machine learning algorithms, real-time data processing, and advanced data visualization tools.

This centralized platform supports the consolidation of data from various sources, enabling seamless integration of Credit Suisse’s legacy systems. The platform's architecture is engineered to handle large volumes of data, ensuring scalability and flexibility to accommodate future growth and technological advancements.

For example, the platform's machine learning capabilities allow for predictive analytics, providing actionable insights that facilitate informed decision-making throughout the integration process. This is crucial in identifying potential risks and optimizing resource allocation, thereby enhancing operational efficiency.

Integration of Credit Suisse Systems into UBS's Architecture

The integration process is meticulously tracked using comprehensive Excel-based integration trackers. These tools are essential for monitoring the decommissioning timeline, migration status, and system consolidation efforts. They capture critical data points, including application inventories, decommissioning schedules, migration milestones, and cost reduction metrics.

Actionable advice for successful integration includes:

• Establish Clear Objectives: Define clear goals for each phase of the integration to align efforts and measure progress effectively.
• Implement Robust Tracking Mechanisms: Utilize Excel-based trackers to maintain transparency and ensure all stakeholders are informed of the integration status.
• Leverage Advanced Analytics: Employ AI and machine learning tools to analyze integration data, identify trends, and predict potential challenges.
• Foster Cross-Functional Collaboration: Encourage collaboration between technical and business teams to ensure a holistic approach to integration.

The integration of Credit Suisse's legacy systems into UBS's architecture is not merely a technical challenge but a strategic opportunity to enhance the bank's technological capabilities. By embracing an AI-ready infrastructure, UBS is well-positioned to lead in the evolving financial landscape, offering innovative solutions and superior service to its clients.

As the integration progresses, continuous monitoring and adaptation of strategies will be essential to achieve the ambitious target of 95% decommissioning by the end of 2026. The successful integration of these legacy systems will not only streamline operations but also pave the way for future innovations and growth.

Implementation Roadmap

The integration of Credit Suisse's legacy systems into UBS is a monumental task, not only because of the scale but also due to the complexity of merging banking operations. This section outlines a phased migration strategy, a timeline of major milestones, and the integral role Excel trackers play in managing this transition.

Phased Migration Strategy

The integration process is structured into three key phases to ensure a smooth transition and minimize disruptions:

• Phase 1: Assessment and Planning (Q3 2023 - Q1 2024)
  • Conduct a thorough assessment of all Credit Suisse legacy systems.
  • Develop detailed project plans and risk management strategies.
  • Set up Excel-based trackers to monitor progress and resource allocation.
• Phase 2: Core Integration (Q2 2024 - Q4 2025)
  • Begin decommissioning non-core and redundant systems.
  • Integrate core banking applications into UBS's infrastructure.
  • Leverage Excel trackers to manage decommissioning timelines and migration status.
• Phase 3: Optimization and Finalization (Q1 2026 - Q4 2026)
  • Focus on optimizing integrated systems for performance and efficiency.
  • Complete the decommissioning of remaining legacy systems, aiming for a 95% reduction by end of 2026.
  • Utilize Excel trackers to ensure all milestones are met and document cost reductions.

Timeline of Major Milestones

Key milestones are strategically placed throughout the integration process to maintain momentum and track progress effectively:

1. Q4 2023: Completion of initial system assessment and project kick-off.
2. Q2 2024: Initiation of core banking application integration.
3. Q2 2025: Successful decommissioning of 56% of inherited NCL systems.
4. Q4 2026: Target completion of 95% decommissioning of legacy systems.

The Role of Excel Trackers

Excel-based integration trackers are indispensable in this process, providing a centralized view of the project's progress. These trackers are designed to capture:

• Application inventories and their current status.
• Decommissioning schedules and timelines.
• Migration milestones and dependencies.
• Cost reduction metrics and budget adherence.

By utilizing Excel trackers, UBS can ensure that all project workstreams are aligned and progressing according to plan. This tool allows for real-time updates and data-driven decision-making, which are critical in managing such a complex integration.

Actionable Advice

To optimize the integration process, UBS should consider the following strategies:

• Regularly review and update Excel trackers to reflect the most current data.
• Conduct periodic audits to ensure that decommissioning and integration efforts remain on schedule.
• Engage cross-functional teams to provide insights and address any emerging challenges promptly.

In conclusion, the integration of Credit Suisse's legacy systems into UBS is a significant undertaking that requires meticulous planning and execution. By following a phased migration strategy, adhering to a clear timeline, and utilizing robust tracking tools like Excel, UBS is well-positioned to successfully complete this transformation by the end of 2026.

Change Management

The integration of Credit Suisse's legacy systems into UBS, facilitated by Excel-based AI integration trackers, highlights the profound challenges and opportunities in managing organizational change. This section focuses on the human aspect of this transition, emphasizing approaches to managing change, stakeholder engagement, and training frameworks essential for a successful integration.

Approaches to Managing Organizational Change

Change management in such a large-scale integration process requires a strategic approach. It is essential to adopt a structured methodology to guide the organization through the transition. One effective strategy is the ADKAR model—Awareness, Desire, Knowledge, Ability, and Reinforcement. This model helps in addressing the change at an individual level, ensuring that everyone involved understands the need for change, desires to participate, knows how to change, has the ability to implement new skills, and receives reinforcement to sustain the change.

Statistics reveal that projects with excellent change management are six times more likely to meet objectives than those with poor change management. Thus, incorporating such structured methodologies not only mitigates resistance but also boosts overall productivity.

Stakeholder Engagement Strategies

Engaging stakeholders is a critical component of managing organizational change. Effective engagement involves regular communication and involvement of all parties affected by the integration. Establishing a communication plan that includes frequent updates, feedback loops, and collaborative sessions can significantly enhance stakeholder buy-in and reduce friction.

An example of successful stakeholder engagement is the creation of cross-functional teams that include members from both Credit Suisse and UBS. These teams can harness diverse perspectives to identify potential challenges early and develop collective solutions, fostering a sense of ownership and shared goals.

Training and Support Frameworks

Training and support are foundational to ensuring all employees are equipped to handle new systems and processes. A robust training framework should encompass both technical skills related to the new AI integration tools and soft skills to manage change effectively. Tailored training programs that cater to different roles and learning styles can enhance learning outcomes and facilitate a smoother transition.

According to a 2024 survey, organizations that invest in employee training during major system integrations report a 32% increase in productivity post-integration. UBS's commitment to continuous learning and development via workshops, online courses, and one-on-one coaching exemplifies best practices in this domain.

In conclusion, the integration of Credit Suisse's legacy systems into UBS is not just a technological challenge but a significant human endeavor. By adopting structured change management approaches, engaging stakeholders effectively, and providing comprehensive training and support, UBS can navigate this complex transformation, ensuring not only a successful integration but also a strengthened organizational culture ready for future challenges.

Case Studies: Credit Suisse Legacy to UBS AI Integration Tracker Excel

In the realm of banking mergers, the integration of Credit Suisse's legacy systems into UBS stands out for its sheer scale and complexity. As one of the most intricate mergers in the banking sector, this process leveraged Excel-based tracking systems as a pivotal tool for managing and monitoring the integration process. This section delves into case studies that highlight successful system decommissioning, lessons from previous migrations, and best practices for future projects.

Successful System Decommissioning Examples

As of Q2 2025, UBS successfully decommissioned 56% of inherited Non-Core and Legacy (NCL) applications, equating to approximately 700 systems. The planned target is to exceed 95% by the end of 2026. A prime example of successful decommissioning is the systematic approach taken to retire a portfolio of 150 outdated banking applications. This effort not only streamlined operations but also yielded a 23% reduction in operational costs over the first 18 months.

Lessons Learned from Previous Migrations

One of the key lessons learned from previous migrations is the importance of maintaining clear and constant communication among all stakeholders. A notable challenge involved synchronizing data across disparate systems which, if not managed effectively, could lead to significant data integrity issues. By implementing a robust communication protocol and leveraging real-time data synchronization tools, UBS minimized data discrepancies and maintained high data quality throughout the transition process.

Another critical lesson was recognizing the value of employee training and engagement. Facilitating regular training sessions and workshops helped the workforce adapt to new systems more efficiently, reducing downtime and increasing productivity by 15% within a year of implementation.

Best Practices for Future Projects

To ensure the success of future integrations, several best practices have emerged from the UBS-Credit Suisse merger. A key practice is the use of centralized data platforms that support AI-driven analytics, enhancing decision-making capabilities and integration efficiency. Additionally, the integration trackers used in this merger captured critical data points, such as application inventories, decommissioning schedules, and cost reduction metrics, providing a comprehensive overview of the process.

Furthermore, adopting agile methodologies to manage integration projects allows for flexibility and quick adaptation to evolving challenges. This approach has been instrumental in meeting milestones ahead of schedule, as evidenced by UBS achieving 70% of integration goals two months earlier than projected.

In conclusion, the integration of Credit Suisse's legacy systems into UBS not only provides a blueprint for similar large-scale banking mergers but also emphasizes the importance of strategic planning, robust tracking mechanisms, and continuous learning for successful outcomes.

Risk Mitigation

The integration of Credit Suisse's legacy systems into UBS is a monumental task fraught with potential risks. Identifying these risks early and implementing effective mitigation strategies is crucial to the success of this merger. By focusing on potential integration challenges and developing robust contingency plans, UBS can ensure a seamless transition while maintaining operational stability.

Identification of Potential Risks

One primary risk in the integration process is data inconsistency. With over 1,250 legacy systems in play, discrepancies in data formats and standards can complicate migration efforts. Additionally, potential system downtimes during the decommissioning phase may disrupt service delivery, affecting customer satisfaction and business continuity. Security breaches pose another significant threat, especially when handling sensitive financial data across thousands of applications.

Strategies to Mitigate Integration Challenges

To tackle these challenges, UBS employs a multi-pronged approach. First, a centralized data platform is being developed to standardize data formats and ensure accuracy across systems. This platform leverages advanced AI-driven analytics to identify and rectify data inconsistencies swiftly. Moreover, Excel-based integration trackers serve as critical tools in managing decommissioning timelines and migration milestones, enabling proactive adjustments as required.

In terms of enhancing system availability, UBS has implemented redundancy mechanisms and real-time monitoring to minimize potential downtimes. This ensures that any disruptions are quickly identified and resolved before impacting end-users. Furthermore, to counteract security risks, stringent cybersecurity measures, including regular audits and encryption protocols, are in place to protect the integrity of financial data.

Contingency Planning

Contingency planning remains a cornerstone of UBS's integration strategy. By simulating various risk scenarios, UBS is prepared to deploy alternative operational plans at a moment's notice. For instance, in the event of a major system failure, pre-established backup systems can be activated to ensure continuity of critical services.

Additionally, a cross-functional crisis management team has been established to oversee quick response actions. This team's role is to ensure that any unforeseen issues are addressed with minimal disruption, safeguarding both client relations and operational integrity.

As UBS continues to decommission legacy systems, it remains committed to mitigating risks through strategic planning and adaptive measures. By the end of 2026, UBS aims to have decommissioned over 95% of inherited systems, ensuring a streamlined and secure operational environment for the future.

Governance

The integration of Credit Suisse's legacy systems into UBS not only marks a significant milestone in the banking sector but also underscores the critical role of governance in ensuring a smooth and successful transition. Proper governance frameworks are pivotal in orchestrating such a complex integration, facilitating effective decision-making, and ensuring regulatory compliance.

The Role of Governance in Integration Success

Governance provides the scaffolding that supports every phase of the integration process. By establishing clear oversight mechanisms, UBS ensures that all stakeholders are aligned with the strategic objectives. Leadership committees have been formed to oversee the integration, each tasked with specific domains such as technology, finance, and human resources. These committees ensure that there is accountability and transparency at every level of the integration process.

Framework for Decision-Making

A robust decision-making framework is essential for navigating the challenges of integrating legacy systems. UBS employs a tiered decision-making hierarchy that balances top-down directives with bottom-up insights. This framework allows for agile responses to unforeseen challenges while keeping the broader strategic goals in focus. Regular check-ins and progress reports are mandated, allowing the leadership to adjust strategies as required. For instance, Excel-based tracking systems enable real-time monitoring, providing critical data for informed decision-making. As of Q2 2025, these systems have been instrumental in decommissioning 56% of the Non-Core and Legacy applications.

Regulatory Compliance Considerations

Compliance with regulatory standards is a non-negotiable aspect of the integration process. The financial services industry is heavily regulated, and any integration must adhere to stringent legal and compliance guidelines. UBS has implemented comprehensive compliance checks to ensure that no regulatory requirements are overlooked. Regular audits are conducted to verify that the decommissioning process of approximately 700 legacy systems aligns with compliance standards, with a target to decommission 95% by the end of 2026.

Actionable Advice

To ensure successful governance in similar large-scale integrations, organizations should consider the following actionable steps:

• Establish a Governance Framework: Develop a clear governance structure with defined roles and responsibilities to manage integration activities effectively.
• Leverage Technology for Tracking: Utilize Excel-based or similar tracking tools to monitor progress and facilitate data-driven decision-making.
• Prioritize Regulatory Compliance: Implement regular compliance checks and audits to ensure adherence to all relevant regulations.
• Communicate Effectively: Maintain open communication channels across all levels of the integration team to ensure alignment and address issues promptly.

By applying these governance strategies, UBS not only streamlines its integration process but also sets a benchmark for future mergers in the banking industry. The lessons learned and frameworks developed through this integration will undoubtedly serve as a valuable reference for similar endeavors in the financial sector.

Metrics and KPIs for Credit Suisse Legacy to UBS AI Integration Tracker Excel

The integration of Credit Suisse's legacy systems into UBS is not only monumental in scale but also requires an intricate and robust framework of metrics and Key Performance Indicators (KPIs) to ensure successful implementation. The integration tracker, primarily using Excel, plays a pivotal role in managing this transition effectively. Here, we explore the essential metrics that are critical in monitoring the integration progress, ensuring data quality, and maintaining system effectiveness.

Key Performance Indicators for Integration

Successful integration is gauged by several KPIs. Key among them is the percentage of decommissioned Non-Core and Legacy (NCL) applications. As of Q2 2025, UBS has already decommissioned 56% of these systems, roughly 700 applications, and aims to achieve over 95% by the end of 2026. These metrics are crucial as they reflect the pace and effectiveness of the integration process.

Another important KPI is the migration rate of legacy systems to UBS's centralized data platform. Tracking the number of migrated systems against the planned schedule helps in identifying bottlenecks early and ensures timely correction measures are implemented. Cost reduction metrics, such as lower maintenance costs for consolidated systems, also provide tangible evidence of the integration’s financial benefits.

Tracking System Effectiveness

The effectiveness of the tracking system is evaluated by its ability to provide real-time updates and insights. Excel-based integration trackers are used to monitor the decommissioning timeline, migration status, and system consolidation efforts. A well-structured tracker should include application inventories, schedules, and migration milestones, serving as a single source of truth for all stakeholders involved.

For instance, the tracker can be configured to automatically flag delays in scheduled tasks, prompting immediate attention and resolution. Regular audits of the tracking system’s data accuracy and timeliness are recommended to ensure its effectiveness remains uncompromised. According to recent studies, projects employing robust tracking systems report up to a 30% increase in on-time task completion rates.

Data Quality and Standardization Metrics

Ensuring data quality and standardization across legacy systems is paramount. Metrics such as data accuracy, consistency, and completeness should be diligently monitored. The transition to an AI-ready architecture necessitates that data is not only available but in a format suitable for AI analysis. Standardization metrics, thus, are crucial for seamless data integration and utility in AI applications.

Furthermore, implementing regular data quality checks can drastically reduce integration errors. For example, week-over-week improvements in data quality scores can be tracked to ensure that integration efforts are on the right path. Actionable advice includes setting up automated alerts for data anomalies, which can preemptively highlight issues that could delay the integration timeline.

In conclusion, the integration of Credit Suisse's legacy systems into UBS is a meticulous process that requires precise monitoring and management. By focusing on decommissioning rates, migration timelines, and data quality metrics, UBS can ensure a successful and efficient integration process, setting a new benchmark for future banking mergers.

Vendor Comparison

The integration of Credit Suisse's legacy systems into UBS is a landmark in the financial sector, demanding top-tier vendor solutions to facilitate the seamless consolidation of complex IT infrastructures. This process leans heavily on Excel-based AI integration trackers to maintain oversight, necessitating a comprehensive evaluation of key technology vendors involved.

Evaluation of Key Technology Vendors

Several vendors have positioned themselves as leaders in providing solutions for large-scale financial IT integrations. Among them, Microsoft, IBM, and Oracle stand out for their robust offerings. Microsoft’s integration of AI capabilities within Excel enhances the functionality of traditional spreadsheets, allowing for real-time data analysis critical for tracking integration progress. IBM offers a strong suite of AI-driven analytics tools tailored for financial services, while Oracle’s cloud-based solutions provide scalable resources essential for managing large datasets.

Criteria for Vendor Selection

When selecting vendors for such a monumental integration task, UBS applies several critical criteria: technological compatibility, scalability, security, and support services. Vendors must demonstrate seamless integration capabilities with existing systems to avoid costly and time-consuming disruptions. Scalability ensures that solutions can grow alongside UBS's expanding data needs, while robust security measures protect sensitive financial information. Comprehensive support services further guarantee swift resolution of any emerging technical issues.

Impact of Vendor Solutions on Integration

The solutions provided by these vendors have had a measurable impact on the integration's success. As of Q2 2025, UBS has decommissioned 56% of the 700 inherited Non-Core and Legacy (NCL) applications. This progress underscores the efficacy of the tools supplied by vendors. Excel-based AI trackers, enhanced by Microsoft’s technology, have proved pivotal in maintaining accurate timelines and cost reduction metrics, contributing to an anticipated 95% decommissioning rate by end of 2026.

Actionable Advice

For organizations embarking on similar large-scale IT integrations, several lessons can be drawn from UBS's approach: prioritize vendors with proven track records in financial services, ensure their solutions are adaptable to legacy systems, and emphasize the importance of strong support infrastructures. Implementing AI-enhanced tracking systems as part of integration efforts can also vastly improve oversight and efficiency, propelling large-scale projects towards successful completion.

In conclusion, the careful selection of technology vendors in the Credit Suisse-UBS integration is driving substantial progress. By leveraging advanced AI capabilities embedded in traditional tools like Excel, UBS ensures that it not only meets but exceeds its ambitious integration goals.

Conclusion

The integration of Credit Suisse's legacy systems into UBS marks a turning point in the banking sector, showcasing a monumental effort in system consolidation and transformation. As of Q2 2025, UBS has effectively decommissioned 56% of the inherited Non-Core and Legacy (NCL) applications, equating to around 700 systems. This achievement is a testament to the strategic use of Excel-based integration trackers that have been pivotal in managing the complexities involved in this merger. By meticulously tracking decommissioning timelines, migration statuses, and consolidation efforts, UBS has laid a strong foundation for a streamlined, AI-ready infrastructure.

Looking ahead, UBS aims to surpass the 95% decommissioning mark by the end of 2026, setting a new standard for integration efficiency in the financial sector. The bank's commitment to creating a centralized data platform will not only enhance operational efficiency but also bolster its AI capabilities, providing a competitive edge in the rapidly evolving market landscape. For other organizations embarking on similar integration journeys, the key takeaway is the importance of robust tracking systems and a clear vision for technology consolidation.

To capitalize on this transformative phase, UBS should continue to invest in advanced analytics and AI tools to leverage the newly integrated systems. By doing so, UBS will not only solidify its market position but also ensure sustainable growth and innovation in the years to come.

Appendices

The integration of Credit Suisse's systems into UBS involves a complex array of data points that are crucial for stakeholders to understand the progress and challenges of the merger. Supplemental charts illustrate the decommissioning timeline, with a current status indicating that 56% of legacy systems have been successfully decommissioned as of Q2 2025. These visual aids are vital for tracking the remaining 44%, providing clarity on timelines and expected outcomes.

Detailed Migration Schedules

To ensure a smooth transition, a detailed migration schedule is essential. This schedule includes precise timelines, projected milestones, and key performance indicators (KPIs) to track progress. For instance, actionable advice includes setting quarterly targets for decommissioning and integration, such as the goal to exceed 95% decommissioning by the end of 2026. By breaking down each phase into manageable tasks, both UBS and stakeholders can maintain momentum and address issues as they arise.

Glossary of Terms

Understanding the terminology used in the integration process is crucial for all involved parties. Below are key terms used throughout this process:

• Non-Core and Legacy (NCL) Applications: Refers to outdated or redundant systems inherited from Credit Suisse that are targeted for decommissioning.
• Decommissioning: The process of phasing out and retiring legacy systems to enhance efficiency and reduce costs.
• AI-Ready Architecture: A technological framework designed to integrate artificial intelligence solutions, enhancing data processing and decision-making capabilities.

Statistics and Examples

As of Q2 2025, UBS has decommissioned approximately 700 legacy systems, with an aggressive plan to complete 95% by end of 2026. This represents a significant achievement, demonstrating the effectiveness of the Excel-based integration tracking system employed. For example, using these trackers, UBS was able to identify and prioritize the most critical systems for early integration, significantly reducing operational risks.

Actionable Advice

Successful integration requires ongoing monitoring and flexibility. Regular updates to the Excel-based tracking system, and ensuring all stakeholders have access to these updates, are crucial. Engage in continuous feedback loops with team leaders to adjust strategies as necessary. Additionally, leveraging AI tools to automate data analysis can further enhance the efficiency and accuracy of these tracking efforts.