Navigating the Landscape: The Cost and Benefits of Generative AI Implementation | Blog

Generative AI (gen AI) can significantly benefit the BFSI industry. However, it can be an expensive investment, making it critical for enterprises to conduct a cost-benefit analysis before implementation. Explore the various costs and advantages associated with this technology in this blog, or get in touch to find out more. 

Gen AI has recently gained considerable attention in the banking, financial services, and insurance (BFSI) industry. Many use cases that go beyond creating or summarizing content are being explored throughout the value chain.

Implementing gen AI can improve the velocity of change, increasing the overall efficiency of existing tasks. This technology can streamline operational processes, automate tasks, and enhance customer experience by fostering engagement through tailored experiences. Moreover, it can potentially drive innovation to create change or transformation by generating unexplored ideas, optimizing products, and identifying new market opportunities. Ultimately, this positions enterprises for continuous evolution and success.

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BFSI enterprises have recognized the transformative potential of adopting gen AI, which undoubtedly can disrupt existing enterprise models. In the race to get the early advantage, enterprises face challenges as they reallocate funds from other projects and seek to secure new investments to finance new AI and gen AI initiatives.

Concurrently, cloud costs emerge as a significant concern that can potentially escalate when training AI models. However, the overall cloud cost impact from gen AI hinges on specific use cases and model architecture.

A cost-benefit analysis becomes imperative as gen AI-driven use cases are limited, and most can be explored through other AI technologies. This is particularly important because other relatively less expensive technologies can achieve comparable outcomes with similar efficiency.

While gen AI has generated a lot of hype and rapid investment, it’s not currently viable to implement the technology almost everywhere without understanding the cost implications for achieving the potential gen AI benefits. Let’s explore this further.

Exploring Cost and Generative AI Benefits

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Below are some of the high-cost categories across the value chain to consider:

Infrastructure and compute

The computational backbone, encompassing graphic processing units (GPUs), tensor processing units (TPUs), and energy consumption, constitutes a substantial investment. Building and maintaining a powerful infrastructure is pivotal for running complex algorithms and training sophisticated models.

Model training or fine-tuning

Gen AI implementation comes with many fixed and variable costs. Training or fine-tuning gen AI models to meet specific requirements is intricate, involving significant computational resources, expert oversight, and time. These costs are substantial but also the foundation for the gen AI model’s efficacy and adaptability.

Data acquisition, preparation, and processing

Performance is heavily influenced by the data quality on which these models are trained. Collecting, cleaning, and storing data can come with high costs to acquire, prepare, and process diverse and high-quality datasets. Ensuring diverse and representative datasets while maintaining data quality standards can be challenging, ultimately impacting the accuracy and reliability of gen AI outputs. At the same time, acquiring high-quality data for training gen AI models and holistic data readiness initiatives can be expensive and require significant capital investments, especially if specialized or proprietary datasets are required.

Security measures

In a highly regulated industry like BFSI, where data and security are imperative, meticulous attention to security and regulatory compliance is critical. Implementing robust security measures cannot be compromised.

However, this adds costs for deploying cybersecurity measures, encryption protocols, and access controls to protect sensitive financial data, notwithstanding increased investments in security technologies, routine audits, and adherence to industry standards.

Considering that gen AI often relies on large datasets, managing personally identifiable information (PII) necessitates strict adherence to data privacy regulations.

Privacy-preserving techniques, anonymization processes, and implementing consent management systems to meet compliance requirements can be costly. On top of that, continuous monitoring and regular audits are essential to maintain compliance and security standards, contributing to ongoing operational expenses.

Integration and service 

Not all models run independently and often require integration with existing systems. Seamlessly integrating gen AI into existing workflows and providing continuous support have financial implications. The processes of customization, compatibility checks, and uninterrupted service provision collectively contribute to the overall expenditure.

Regulatory compliance

Operating within a highly regulated BFSI industry with standards such as the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and industry-specific regulations necessitates additional investments in compliance monitoring, data governance, and legal counsel.

Non-compliance with these regulations may lead to fines and legal consequences. As the regulatory laws for gen AI are still evolving, enterprises must be vigilant.

In light of the dynamic regulatory landscape, remaining flexible to accommodate incoming regulations is crucial.


Following deployment, continuous monitoring and proactive maintenance of the systems are demanded to ensure gen AI’s sustained performance. Although this is an ongoing expense, these measures are pivotal for adaptability and longevity.

Talent related costs

Enterprises may incur expenses related to recruitment efforts, training programs, certification courses, and retention strategies to attract and retain top talent in the competitive gen AI landscape. As gen AI continues to evolve and play a pivotal role in digital transformation initiatives, businesses must carefully consider and budget for talent costs to ensure successful implementation and utilization of advanced AI technologies.

While investments in gen AI and related technologies are crucial, enterprises must also invest in their human capital by empowering employees with the skills and knowledge needed to thrive in today’s digital age. Effective leadership and a commitment to upskilling and reskilling will drive successful technology adoption and foster an organizational culture of innovation and agility.

The outlook for cost reduction efforts

While gen AI comes with a high cost, the landscape is evolving daily. Technology companies are substantially investing in developing proprietary AI chips and more efficient architectures, a strategic shift that aims to diminish reliance on expensive alternatives.

Enterprises can also explore a micro use case-led approach to implementing gen AI, deploying small, focused areas where gen AI can deliver clear and measurable benefits. Targeting smaller tasks allows for quicker development and deployment of gen AI solutions, leading to faster ROI (Return on Investment). Micro use cases provide opportunities to test and learn from gen AI implementations, enabling continuous improvement and informing future deployments. Smaller projects require less time and resources compared to developing a large, complex gen AI system.

Moreover, the gen AI domain is experiencing a notable training cost reduction, with some solutions claiming a remarkable 50% reduction. These advancements signal a significant stride toward enhancing AI’s capability and affordability, marking a pivotal turning point in the technology’s ongoing evolution.

While the costs associated with gen AI implementation are evident, the benefits in specific uses can significantly outweigh the expenses. Balancing financial considerations and the innovation potential is key. Enterprises must align their AI strategy with business objectives to position themselves at the forefront of innovation and competitiveness.

To discuss gen AI in BFSI, please reach out to [email protected], [email protected], and [email protected].

Looking for use cases for gen AI? Check out our LinkedIn Live on Distinguishing Gen AI Hype from Real Application, or read our latest research on generative AI and its adoption potential.

Unlocking Enterprise Preparedness for T+1 Settlement: The Crucial Role of IT and Technology Services Providers | Blog

By partnering with IT and technology services providers, banks and financial institutions can prepare for the new T+1 settlement. This security trade rule change to shorten the order finalization date by a day is expected to enhance operational efficiencies and reduce risk. Read on to understand how this updated regulation will impact the industry landscape and rapidly transform critical areas. Reach out to discuss the topic with us.

In today’s ever-evolving financial industry, the shift to T+1 settlement aims to enhance market efficiency, reduce counterparty risk, and align North American markets with global standards.

The transition to a T+1 settlement cycle represents a monumental shift for banks and financial institutions that will impact trade management, resource allocation, and risk mitigation.

Scheduled to go into effect this May in the US and Canada, the amended rule would require trades to be settled just one day after the transaction date, marking a significant departure from the current two-day cycle.

Let’s explore its ramifications further.

Impact on investors 

Shifting toward instantaneous or faster settlements is a remarkable milestone that will streamline operations, improve risk management, boost liquidity, and provide better counterparty risk management. This will further lead broker-dealers to reduce margins and collateral requirements.

Accelerating settlement cycles will save buyers and sellers time and increase trading volume. The positive impact will vary by the investor type. Large institutional investors like corporations will benefit from more liquidity and reduced margin requirements. Meanwhile, small or retail investors, who contribute significantly to the daily exchange trading volumes, will receive funds or assets post-execution faster. This will bring various operational benefits and improve market risk mitigation.

However, the proposed shift will require significant investor education and resilience to overcome the negative market sentiment caused by affected broker businesses. Investors will grapple with the shorter period for trade settlement, and brokers will need substantial investments to update front-to-back-office systems. Moreover, the higher settlement costs could potentially disappoint investors.

Implications for banks and financial services institutions

Transition to T1

The shift to T+1 settlement presents a significant opportunity for the financial industry to enhance operational efficiencies and reduce risk. As Martin Palivec, Head of Securities Services, Canada, Citi, has highlighted, many post-trade processes still rely heavily on manual intervention, indicating a clear need for automation and streamlining. Settlement compression will drive the industry to strengthen and automate these processes, leading to more efficient and reliable operations.

Moreover, improving reporting capabilities is becoming increasingly critical, with clients expecting timeliness and accuracy in trade status updates. This underscores the urgency for organizations to adopt advanced technologies and modernize their post-trade infrastructure.

Role of IT and technology service providers

By partnering with IT and technology services providers, banks and financial institutions can navigate the complexities of T+1 settlement and position themselves for success in the evolving financial landscape.

IT and technology services providers can play a crucial role in assisting banks and financial services institutions in their T+1 journey in the following ways:

  • System Upgrades and Integration: Technology providers can help firms upgrade their existing systems or integrate new systems to support T+1 settlement. This includes implementing real-time processing capabilities, enhancing data management systems, and ensuring interoperability with other market participants
  • Automation and Straight-Through Processing: Automation is key to achieving operational efficiency in a T+1 settlement environment. Technology providers can assist firms in automating trade interfaces, matching and affirming trades in real time, and streamlining settlement workflows to minimize the risk of failed settlements
  • Data Management and Reporting: Accurate and timely data management is critical with the compressed settlement timeline. Technology providers can help firms improve data quality, implement real-time reporting capabilities, and enhance communication with counterparties to reduce the risk of errors and delays
  • Regulatory Compliance and Risk Management: Technology providers can assist firms in ensuring compliance with T+1-related regulatory requirements, including implementing systems to monitor and report trade status and manage risk factors associated with the new settlement cycle

The move to the T+1 settlement represents a significant advancement in the financial industry, necessitating operational and technological adjustments for banks and financial institutions.

As this transition unfolds, the prospect of T+0 settlement looms, with countries like India already making strides in this direction. The adoption of distributed ledger technology (DLT) is expected to be pivotal in enabling T+0 settlement and offering real-time, secure, and transparent transaction processing.

Organizations preparing for T+1 should also consider the potential shift to T+0 in their strategic planning. By embracing technologies and practices supporting T+1 and T+0 settlement, businesses can streamline operations, enhance efficiency, and stay ahead of the curve in an evolving financial landscape.

To learn more about the impact of T+1 settlements in the financial services industry, contact Abhinav Rathaur, [email protected], Kriti Seth, [email protected], and Pranati Dave, [email protected].

Read the blog, Beyond the Hype: Approaching Gen AI in BFSI Enterprises with the Generative AI-EXCEL Framework, to learn about successful gen AI adoption in the BFSI sector.

Beyond the Hype: Approaching Gen AI in BFSI Enterprises with the Generative AI-EXCEL Framework | Blog

To successfully adopt Gen AI in BFSI, enterprises need to consider four fundamental aspects that can lead to responsible and effective deployment. Carefully evaluating each framework component is essential to ensure a positive Gen AI journey. Read on to learn about the Generative AI-EXCEL Framework and the importance of each element, or get in touch.

As there is urgency to embrace Generative Artificial Intelligence (Gen AI) across all industries – the BFSI industry is no exception given its prevalence. However, a thoughtful approach is required to fully reap the benefits of Gen AI.

Before immersing themselves in various use cases and integrating Gen AI into their operating structure, BFSI enterprises should strategically examine four fundamental components along the Gen AI value chain:

Generative AI-EXCEL framework

  • Enable AI
  • Execute AI
  • Champion AI Operations
  • Lead AI Change Management and Governance

These elements can guide enterprises toward harnessing the full potential of Gen AI in BFSI while ensuring responsible and effective deployment.

Beyond the Hype Approaching Gen AI in BFSI Enterprises with a Generative AI EXCEL Framework pdf

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Enable AI

Embarking on AI initiatives demands the expertise of AI experts to define a clear vision and strategy. Seeking guidance from Gen AI experts is essential in laying a solid foundation for successful implementation. Assessing organizational readiness through an AI maturity and readiness assessment is recommended as this can provide insights into preparedness levels and potential challenges.

Developing a Gen AI roadmap and conducting a Return on Investment (ROI) analysis further ensures a well-structured approach, allowing organizations to navigate the complexities of integrating Gen AI effectively in their operations. A thoughtful approach is essential for consulting and enabling generative AI across the value chain before delving into specific use cases, relying on AI technology partners, and tool selection advisory services to ensure that organizations secure the right resources for success.

Adequate resources are crucial to ensure scalability, allowing Gen AI systems to manage increasing workloads efficiently. There is a lot of demand for talent, skills, and domain expertise, especially in Gen AI that needs to be plugged.

Moreover, hardware and infrastructure compatibility and version compatibility among different Gen AI models and frameworks are essential for seamless operations. Massive datasets play a pivotal role in training large-scale AI models, demanding significant computational power from specialized hardware such as graphics processing units (GPUs) and tensor processing units (TPUs). Balancing these elements is vital to harness the potential of Generative AI effectively.

Execute AI

When developing AI systems, some essential steps include preparing the data, refining features, utilizing and fine-tuning pre-built models, integrating AI with existing systems, creating custom models as needed, and conducting thorough testing to ensure reliability.

The increasing complexity of Gen AI models has led to the emergence of Machine Learning Operations (MLOps) and Large Language Model Operations (LLMOps) as services. These can play a pivotal role in easing the efficient deployment, orchestration, and monitoring of AI models.

Given the possibility of potential biases introduced by Gen AI, it becomes imperative for BFSI enterprises to ensure fairness. Vigilant model monitoring and drift analysis are some ways to achieve this. In addition, optimized performance can be achieved by incorporating accelerators.

Champion AI Operations

A robust change management strategy is essential for navigating a smooth transition. Leadership communication about AI’s benefits can set a positive tone for adoption. Equipping workforce with the necessary skills through comprehensive training and upskilling is essential. Developing a streamlined process for Gen AI adoption can enhance its acceptance rate. Recognizing and reinforcing Gen AI’s contributions can motivate the workforce, ensuring effective and sustainable AI integration.

Lead AI Change Management and Governance

Strong data governance can help address some of the concerns related to source attribution and confidence levels in data and foster trust in Gen AI outcomes.

Gen AI can generate content that is low in authenticity. Model explainability can help make AI decisions more understandable and traceable, boosting user confidence. Furthermore, enforcing compliance, validation, and auditing mechanisms can reinforce AI solutions’ reliability and ethical deployment.

The Gen AI model can potentially produce biased or dangerous results. Other AI models can be used to test results for risky outputs. Enterprises can also use data loss prevention and other security tools to prevent users from inputting sensitive data into prompts in the first place. Maintaining control over data is essential, and multiple levels of security are required.

In an industry where data security and privacy are paramount, governance becomes a linchpin for safeguarding sensitive information. Beyond regulatory compliance, governance can address critical aspects such as risk management, fairness, transparency, and accountability. With ongoing regulatory uncertainty and evolving laws, it is critically important to exercise caution about data breaches, privacy violations, or biased or discriminatory decisions that can create regulatory liabilities.

By following this Generative AI-EXCEL framework, BFSI enterprises can ensure they have addressed all essential aspects of enabling Gen AI. From identifying the right infrastructure and resources to developing and testing models and ensuring proper change management and governance, thoroughly evaluating each component guarantees a smooth AI transition. This approach will allow BFSI enterprises to harness Gen AI’s power fully.

To discuss Gen AI in BFSI, please reach out to [email protected], [email protected], and [email protected]. Learn more about how we can help your enterprise to leverage Gen AI, or read our report on revolutionizing BFSI workflows with Gen AI.

Generative AI and Cloud Integration Keep Mainframes Alive in the BFSI Industry | Blog

Though a recent Everest Group survey revealed the pressing need for mainframe modernization, the technology is far from dead in the banking, financial services, and insurance (BFSI) industry. The rise of generative AI (gen AI) will encourage more BFSI firms to adopt a comprehensive technical architecture, integrating cloud and mainframe technology at its core. Read on for insights from the survey or get in touch. 

Are BFSI firms really ditching mainframes? The BFSI industry is indeed grappling with the prospect of abandoning this approach. According to an Everest Group survey, about half of the respondent firms have shifted their peripheral tasks away from mainframes.  

Concerns about mainframe system scalability loom large for more than 50% of sizable BFSI firms, while about 60% of smaller firms struggle primarily with finding talent skilled in older programming languages such as COBOL.  

Operational complexity with mainframe systems is also reported as a challenge by over 90% of BFSI respondents, underscoring the pressing need for mainframe modernization. Evolving priorities such as building data-driven workflows, digitalization, and enhancing customer experiences further fuel this urgency. 

Cost efficiency and talent unavailability are the main drivers for mainframe modernization, closely followed by the imperative for innovation. North American firms prioritize core banking and CRM workloads over modernization, while European players emphasize digital channels and payment infrastructure. 

Despite these challenges, mainframes are expected to remain integral to BFSI operations. A significant majority, about 60% of the respondent firms, have not yet started modernizing their core systems. In the coming years, non-core applications will continue to have a higher migration rate than core applications.  

However, industry research underscores that BFSI enterprises optimize and enhance their mainframe ecosystems, presenting a promising opportunity for service providers to assist. Let’s explore this further.  

Capturing cloud value through a hybrid infrastructure

With mainframes here to stay in the BFSI industry, enterprises can gain a competitive advantage by investing in the private cloud to capture the underserved and large demand for hybrid IT. Hybrid cloud is a constant across all our BFSI industry enterprise conversations.  

Of the respondent BFSI firms, 35% utilize private cloud for their modernization initiatives — mainly in a hybrid cloud environment — while 65% rely on a multi-cloud strategy.  

IBM’s focus on transforming the mainframe’s interface to other environments validates this trend. The launch of z15 and z16 is the company’s answer to the age of cloud computing. It is an evolution to meet the needs of hybrid cloud deployments, leveraging investment in data, generative Artificial Intelligence (gen AI), and applications, adding features and functionality to complement this strategy. IBM is focusing its messaging on rightsizing over downsizing. The strategy to provide more flexibility, predictability, and cost-effectiveness is evident in the company’s push for tailored fit pricing.   

The survey reveals many firms believe the disconnection between mainframe environments and new cloud-native systems and applications is a big challenge. Further investments in technologies like application programming interfaces (APIs), in collaboration with technology and service providers, will help bridge this gap in the coming years. 

Will banking’s AI revolution enable cloud-based modernization? 

We expect a symbiotic relationship between gen AI and IT modernization, each complementing the other’s growth. Cloud computing is the foundational block providing the right computational power to run AI applications, while AI’s enhanced speed and efficiency will support cloud migrations.  

BFSI firms are channeling investments into gen AI, crafting use cases to support their modernization initiatives and business operations. The survey found that 40% of the BFSI firms have proof of concepts or use cases for gen AI to support mainframe modernization.  

Firms have moved beyond experimenting with gen AI. Goldman Sachs and Deutsch Bank have started using gen AI to generate code and refactor their modernization initiatives, closely watching the impact. They are building and rolling out use cases to improve operational efficiency. We believe that the banks poised for future success are identifying use cases that solve specific business problems aligned with their organization’s strategy. This can enable them to measure the results easily and encourage leadership buy-in.  

With mainframe modernization services growing at a steady 4-5%, the ability to adapt and innovate using newer technologies such as gen AI will drive more BFSI firms to adopt a more robust and holistic technical architecture with both cloud and mainframes at its core. 

However, the question remains: will gen AI bring exponential change in the next three years? There is one certainty: the need for a strong IBM, service provider, and hyperscaler value proposition will continue to grow for BFSI clients. 

To discuss mainframe modernization further, please contact [email protected] and [email protected]. Understand more about the future of the enterprise mainframe, or watch our on-demand webinar on the future of generative AI implementation at enterprise level.

Wipro Acquires Capco Creating End-to-End Digital Consulting Services | Blog

Since Wipro’s March 4, 2021, announcement to acquire Capco, the London-based global management and technology consultancy that provides digital, consulting, and technology services to financial institutions, for US$1.45 billion, reaction has been mixed as to whether it will deliver the synergies and earnings growth Wipro expects. However, Wipro’s consulting-led offerings matched with Capco’s digital capabilities appear to be poised to deliver a powerful, end-to-end service for clients.

Here’s our take.

What’s in it for Wipro?

Wipro, a leading, India-based global IT, consulting, and business process services company, has acquired numerous companies in the last few years, such as Appirio for cloud services, Opus CMC for the mortgage industry, Designit, Syfte, and Cooper for design thinking and strategy, and International TechneGroup (ITI) for its industrial and engineering services. The Capco deal, which is expected to close at the end of June, stands apart from the other acquisitions not only because it’s Wipro’s largest to date but because it will greatly improve Wipro’s digital offerings in the BFS space, Wipro’s largest business unit. This will narrow the gap between Cognizant, Infosys, and TCS, Wipro’s three biggest competitors in the BFS arena.

Also, in 2020, digital contributed to nearly 40 percent of Wipro’s total revenue, making Capco’s digital capabilities integral in positioning Wipro as one of the market leaders.

The Wipro/Capco acquisition will deliver improved benefits to clients, including:

  • Superior capabilities in consulting and advisory: With this deal, Wipro will join a small group of service providers that bring integrated end-to-end solutions at scale to their customers. Wipro and Capco’s collective capabilities include high-value, upstream activities like consulting and advisory, and design and build, as well as downstream activities such as implement and manage
  • Better access to newer geographies and clients: With more than 40 percent of Capco’s US$700 million revenue coming from Europe, this acquisition will help Wipro strengthen its foothold in that market. In addition to the larger strategic benefits that the deal aims to provide, it will also add 30 new BFS logos to Wipro’s portfolio and could bring in more business from the company’s existing clients
  • Balanced shoring mix: Capco’s high leverage of onshore and nearshore delivery centers nicely complements Wipro’s offshore-heavy delivery footprint, which will give Wipro the opportunity to handle judgment-intensive work for onshore-heavy clients
  • Technology synergies: A blend of Capco’s multiple point solutions and Wipro’s digital investments will help Wipro strengthen its asset management, custody, and prime brokerage offerings, and develop niche, targeted next-gen solutions for its client base
  • Domain depth: Capco will deepen Wipro’s capabilities in digital banking and payments, as well as the asset management, custody, and prime brokerage spaces. This is critical at a time when financial services firms are looking to engage with providers with more domain depth
  • Augmented risk and compliance offerings: Wipro will be able to augment its current risk and regulatory compliance offerings on its Wipro Holmes platform by leveraging Capco’s extensive Finance, Risk, and Compliance (FRC) offerings and solutions portfolio

Large-scale acquisitions are not new to Wipro or the BFS industry; however, success from such high-value acquisitions are not always guaranteed. It is, therefore, no surprise that this announcement was received with mixed reactions and speculation from the market as to whether it will deliver the synergies and earnings growth that Wipro has promised.

Overall, we remain optimistic about the deal and believe this acquisition will equip Wipro to better solve BFS clients’ challenges through Capco’s future-ready digital capabilities. Most importantly, the acquisition is complementary in nature and will help Wipro gain scale, speed, and stature.

How will unities like consulting and digital end-to end services affect the broader BFS market?

Wipro’s consulting-led services, together with Capco’s digital capabilities, will provide more meaningful end-to-end long-term support to clients. It would not be surprising to see similar deals coming up across various segments of BFS with the aim of providing bundled offerings, as products lose their charm when offered on a standalone basis. Complementing service offerings with consulting-led delivery capabilities is being seen across various BFS industries, including mortgage and FCC. These capabilities are being acquired not just through acquisitions but also through partnerships, such as the recent one between Genpact and Deloitte in the financial risk and compliance domain.

Though this trend witnessed a slow start, especially for the Indian IT firms, it looks promising and rewarding in the long run and is only expected to gain momentum. Through end-to-end consulting and digital service offerings, enterprises get access to a compelling combination of digital talent at scale with a consulting-led delivery approach, which helps achieve greater business value and gains.

Success hinges on successfully executing consulting-led digital transformation

Generally, the value addition to enterprises entirely depends on the speed at which service providers can utilize the enhanced breadth and depth of their offerings post-acquisition. Having said this, the key to achieving significant value addition for both Wipro and Capco’s clients would eventually lie on smooth integration and flawless execution.

For the returns to outweigh the risks, a superior execution policy needs to be in place. The key to inspiring its BFS clients will be to align consulting, design, build and operate capabilities around solving some of the industry’s biggest challenges. For BFS clients, this is namely modernizing legacy systems, providing innovative product and service offerings, ensuring a delightful customer experience, and effectively managing the ever-evolving regulatory landscape. For Wipro’s enhanced capabilities to be successful, it should help reinvent the client’s journey through a rare combination of consulting-led digital transformation.

We would love to hear your thoughts on this acquisition and or others that are following the same trend, reach out to [email protected].

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