Digitization must enable new value creation for customers in BFSI market, delivering on expectations across the value chain.
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Digital Transformation in BFSI
How We Made It?
Wondering how we turn technology into practical values for businesses? Explore the success stories here!
05 Mar 2025
T Dao
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Following the previous discussion of AI Transformation’s future outlook & impacts, this article addresses key aspects that businesses should keep in mind, ensuring a successful AI Transformation journey.
Refer to this article for the insights of AI Transformation’s potential impacts & future outlooks
From my point of view, these adoptions have immersive potentials that leverage business productivity to the next levels while posing significant challenges in many aspects.
One of the most noticeable impacts is that the new AI Innovations are designed to maximize cost optimization, increase efficiency with reduced manual labor, and improve risk management through better analytic solutions. At NTQ, we harness the power of AI to maximize productivity through self-developed tools, including NxDev for Developers, NxTrans for translators & NxNote for efficient meeting minutes.
Beyond operational benefits, AI also fosters a stronger competitive edge and a more engaging customer experience. Businesses are increasingly deploying AI-driven personalization to tailor services to individual customer needs, ensuring a highly interactive and responsive engagement model. From AI-powered chatbots to intelligent recommendation engines, AI is transforming customer interaction from reactive to proactive, anticipating user needs and delivering personalized solutions in real time. At NTQ, we have successfully deployed NxChat, an advanced AI chatbot that supports multilingual, real-time interactions, helping businesses scale customer support while reducing operational costs.
AI is also unlocking new business growth and innovation by automating complex decision-making processes in sales, marketing, and software development. With AI-powered analytics, businesses can gain deeper insights into customer behavior, market trends, and sales performance, enabling data-driven strategies that drive revenue. Furthermore, AI is redefining content creation, product design, and software development by enabling intelligent automation in these domains. At NTQ, we are at the forefront of this shift, integrating AI Agentic technology into NxUniverse, transforming products like NxSales, NxTrans, and NxKMS into autonomous AI agents capable of handling customer interactions, streamlining business processes, and automating development workflows.
One of the most pressing challenges is the high cost of AI implementation and infrastructure. Training AI models requires expensive GPUs and cloud computing resources from providers like AWS, Azure, and Google Cloud, leading to substantial financial strain, especially for large-scale AI applications. Additionally, maintaining high-performance AI infrastructure demands ongoing investment in hardware, storage, and computational power. To overcome this challenge, NTQ is actively optimizing AI deployment costs by building its own AI infrastructure within its Data Center and exploring Edge AI solutions that reduce dependency on costly cloud services while maintaining high processing efficiency.
Another major hurdle is the global shortage of skilled AI talent, particularly AI engineers and data scientists. The demand for AI expertise is outpacing supply, making it difficult for businesses to recruit, train, and retain top-tier talent. Internal AI training programs require extensive time and resources, further slowing AI adoption. To bridge this gap, NTQ has taken a proactive approach by partnering with universities and research institutions under the AKD initiative, allowing the company to identify and nurture young AI talent early in their careers. This strategy not only strengthens NTQ’s AI capabilities but also contributes to the broader AI ecosystem.
Integrating AI into legacy systems remains another significant challenge, particularly for enterprises that rely on outdated technologies such as COBOL, PHP 5.6, or on-premise infrastructure. Many traditional business systems were not designed to support AI-driven processes, making seamless integration complex and resource-intensive. Successful AI adoption requires businesses to rethink their digital transformation strategy, ensuring that AI solutions can be effectively embedded into existing workflows. NTQ is actively assisting clients in modernizing their IT infrastructure by migrating from COBOL to C# or Java, while integrating AI-powered testing and code analysis tools to accelerate the transition.
Beyond technical and financial barriers, AI security and ethical risks are emerging as critical concerns. As AI becomes more powerful, threats such as deepfake fraud, data privacy violations, and AI-driven cyberattacks pose serious risks to businesses. Moreover, regulatory uncertainty surrounding AI remains a challenge, as many governments are still developing AI governance frameworks. Companies must navigate compliance complexities to ensure their AI applications adhere to global security standards and legal requirements. At NTQ, we are committed to deploying AI securely and responsibly, focusing on compliance with GDPR, ISO 27001, and other international AI security regulations. This ensures that our AI solutions not only drive innovation but also uphold trust, transparency, and data integrity.
AI Transformation is a long-term strategy requiring businesses to have a careful preparation, define clear goals & objectives to ensure generating valuable outcomes. Here is how you should break down these aspects:
AI should not be seen as an end goal but rather as a means to enhance business objectives. One of the most common reasons for AI failure is the lack of a clear strategy—many businesses implement AI without a defined purpose, leading to wasted resources and unmet expectations. To avoid this, companies must identify specific areas where AI can drive measurable value, whether in operations, customer engagement, or product innovation. Instead of implementing AI across all functions at once, organizations should start small with high-impact use cases, measure their effectiveness, and scale accordingly.
AI is only as effective as the quality of the data it processes. Poorly structured, inconsistent, or biased data can significantly reduce AI accuracy and reliability, leading to flawed predictions and ineffective automation. Companies must invest in data standardization, cleaning, and continuous updates to ensure AI systems function optimally.
A strong data infrastructure is essential for AI to deliver real value. Organizations should implement data pipelines that allow AI models to continuously access and learn from the latest data. Additionally, data security and compliance with regulations such as GDPR and ISO 27001 must be prioritized to protect sensitive information.
AI transformation is not just about technology—it also requires highly skilled professionals to develop, optimize, and maintain AI models. AI engineers, data scientists, and machine learning specialists are essential for ensuring that AI solutions operate efficiently, scale effectively, and remain free from critical errors. However, the demand for AI talent far exceeds supply, making recruitment and retention a major challenge.
To address this gap, businesses should invest in long-term AI talent development by establishing internal training programs and collaborating with universities. By fostering a pipeline of AI professionals, companies can future-proof their workforce and maintain a competitive edge.
For businesses new to AI, one of the biggest pitfalls is trying to implement AI everywhere at once. A more effective approach is to start with focused, high-impact use cases, refine them, and expand AI adoption gradually.
For instance, AI chatbots can be introduced to enhance customer service efficiency by automating responses and reducing the workload for human agents. AI-powered data analytics can be used to optimize marketing strategies and increase revenue. Once these initial implementations prove successful, consider extending AI applications to other operational areas.
One of the biggest challenges in AI adoption is employee resistance. Many workers fear that AI will replace their jobs, leading to a negative mindset toward AI integration. To overcome this, businesses must position AI as a collaborative tool rather than a replacement. AI should augment human capabilities, allowing employees to focus on higher-value tasks rather than repetitive processes.
Companies should also provide AI training programs to help employees understand and embrace AI as a productivity booster. Instead of eliminating jobs, AI can enhance efficiency, enabling employees to work smarter and faster.
AI brings significant risks in areas such as data security, ethical concerns, and regulatory compliance. Without proper safeguards, businesses risk data breaches, biased decision-making, and legal violations. Organizations must implement strict AI governance frameworks to ensure AI is transparent, accountable, and compliant with global security standards.
Security measures such as Explainable AI (xAI) can improve transparency by ensuring AI decisions are understandable and controllable. Additionally, AI models must be regularly audited to detect biases and prevent unethical AI behavior.
Building AI solutions from scratch is costly and time-consuming. Instead, businesses can accelerate AI adoption by leveraging existing AI platforms and partnering with AI specialists. By integrating pre-built AI models through APIs and collaborating with AI technology providers, companies can reduce costs and accelerate time-to-market.
Rather than reinventing the wheel, companies can work with AI industry leaders to deploy AI solutions more efficiently. This approach not only reduces development efforts but also provides access to state-of-the-art AI innovations.
As a Global IT Service Provider, technology innovation is at the core of our key strategies. Realizing the potential of AI in reshaping the future landscape, we have made relentless efforts in the research and development of new AI implementations to leverage productivity and enhance service offerings for global companies.
A notable example that showcases our key capabilities in turning AI Innovation into various use cases, generating breakthrough values that boost businesses’ productivity & automated process. NxUniverse ecosystem features a series of specialized tools for specific tasks that connect & synergize with each other, forming a comprehensive all-in-one solution tackling business challenges, notably including:
As global businesses seek alternatives to reduce their reliance on China, the “China Exit” trend presents a unique opportunity for NTQ to leverage its AI practices for global enterprises. With increasing demand for cost-effective, high-performance IT solutions, NTQ is utilizing AI to offer faster, more efficient, and scalable services, strengthening its competitive edge against other IT providers.
AI-powered tools such as NxKMS facilitate knowledge transfer, allowing teams to onboard and share expertise more efficiently. NxTrans accelerates translation processes while reducing manual effort, ensuring seamless communication across international teams. Meanwhile, NxNote enhances the quality of meeting documentation, streamlining workflow and decision-making. These innovations position NTQ as a strategic partner for global enterprises, gradually replacing traditional Chinese IT service providers with AI-powered solutions that offer higher efficiency, speed, and precision.
NTQ is not just an IT service provider—it is pioneering the AI-driven transformation of software development. By integrating AI across the entire software development lifecycle, NTQ is reducing development time, lowering costs, and enhancing quality assurance.
AI-powered software development tools such as NxDev enable NTQ’s developers to code faster, reduce errors, and optimize security standards, ensuring robust solutions for clients. AI is also revolutionizing automation testing, significantly increasing testing speed while minimizing costs. Additionally, AI-assisted requirement analysis improves project estimation and development planning, leading to more accurate pricing and timelines.
Beyond sales and production, NTQ is integrating AI into internal business operations to enhance resource management and cost optimization.
For instance, NxFinance applies AI to financial data analysis, enabling NTQ to predict profitability and optimize budget allocation. In HR, AI-powered solutions assist in performance evaluation and training recommendations through NxHR, ensuring employees receive personalized development plans. AI is also being applied in recruitment through NxInterview, which accelerates candidate screening and selection, improving hiring efficiency.
By leveraging AI for internal operations, NTQ can reduce operational costs, streamline workflows, and scale faster without a significant increase in headcount. These improvements enhance overall productivity, allowing NTQ to maintain agility in a rapidly evolving IT landscape.
NTQ is actively developing NxEdge, an Edge AI solution that enables AI processing to be performed on IoT devices rather than relying solely on cloud or on-premise GPU servers. This significantly reduces infrastructure costs, making AI deployment more scalable and energy-efficient. Additionally, NTQ is utilizing AI to optimize server management, ensuring lower energy consumption and cost savings across its IT infrastructure.
By adopting Green AI and Edge AI, NTQ is not only reducing operational expenses but also enhancing its competitive positioning. These initiatives attract enterprises seeking cost-effective AI solutions while aligning with the global push for sustainable technology.
With a focus on turning emerging technology into practical use cases, NTQ has implemented NxUniverse – AI Ecosystem to maximize workflow efficiency for diverse positions, including programmers, engineers, sales executives, and translators
By T Dao , 25 Mar 2025
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