Why AI Alone Won’t Sustain Your Competitive Edge - But It Can Amplify What You Already Have

The Double-Edged Sword of Generative AI

The rise of generative artificial intelligence (gen AI) is causing a seismic shift across industries, with the Banking, Financial Services, and Insurance (BFSI) sectors at the forefront. From automating mundane tasks to revolutionising customer service and financial modelling, gen AI is being hailed as a game-changer. However, while gen AI can create immense value, it does not necessarily provide a sustainable competitive advantage. The technology is widely accessible, and what differentiates organisations in the long run is not the AI itself but how it is leveraged to enhance existing strengths.

As an IT leader with over 30 years of experience in digital transformation, I’ve witnessed how the right technology, applied strategically, can amplify a company’s unique capabilities. This article explores the nuanced role of Gen AI in the BFSI sector, offering insights on how to turn this powerful tool into a lasting advantage.

The Myth of Sustainable Advantage Through AI

1. AI’s Revolutionary Impact - But Not a Long-Term Differentiator Generative AI is already delivering substantial efficiency gains across the BFSI sector. Financial institutions use it to automate routine customer interactions, enhance fraud detection, and develop personalised investment strategies. For example, central banks have incorporated AI to streamline credit decision-making processes, reducing the time required to assess risk and approve loans. This has resulted in improved customer experiences and reduced operational costs.
   
   However, the challenge lies in the fact that these AI capabilities are not proprietary - they can be implemented by any organisation with the resources to do so. This democratisation of AI technology means that the competitive edge gained from AI is often short-lived. As more companies adopt the same AI tools and techniques, the playing field levels and the initial advantages dissipate.
   • Example from BFSI: Consider using AI-driven chatbots in customer service. With its virtual assistant, a leading Global Bank pioneered and gained an early lead by using AI to provide customers with instant, 24/7 support. However, competitors' rapid adoption of similar AI tools quickly diluted this advantage. Today, AI-powered customer service is a standard offering, with little to differentiate one bank’s service from another’s.
   • Actionable Insight: While adopting AI is essential for staying competitive, BFSI leaders should focus on how AI can enhance and integrate with their organisation’s unique strengths - those that are difficult for competitors to replicate. This requires a strategic approach, where AI follows industry trends and reinforces them used to follow industry trends and reinforce core capabilities.


2. The Pitfall of Imitation: How AI Levels the Playing Field One of the most significant challenges with generative AI is that it thrives on data - including data generated by early adopters. This creates a scenario where innovative AI applications can quickly be mimicked by competitors, eroding any first-mover advantage.
   • Example from BFSI: Suppose a bank uses gen AI to develop a new financial product, such as a dynamic savings plan that adjusts based on real-time market conditions and customer spending behaviour. Initially, this innovation could attract new customers and set the bank apart. However, as competitors observe this success, they may develop similar products, especially if they can access identical data sources. Over time, the unique advantage of the original product diminishes, and the market becomes saturated with comparable offerings.
     
     This dynamic was evident in the adoption of AI for fraud detection. Early on, firms like HSBC and Citi implemented sophisticated AI algorithms to detect and prevent fraudulent transactions, gaining a competitive edge in risk management. However, as other financial institutions followed suit, the differentiation faded, and AI-driven fraud detection became a standard feature across the industry.
   • Actionable Insight: BFSI companies should consider how AI can be integrated into proprietary processes and data sources unique to their organisation to maintain a competitive edge. This approach ensures that while competitors may imitate the technology, they cannot replicate the specific value generated by your AI application.

Amplifying Existing Advantages with Generative AI

1. Leveraging Proprietary Data for Sustained Advantage One potential source of sustained competitive advantage lies in applying gen AI to proprietary datasets. Unlike publicly available data, proprietary data is unique to your organisation and built over years of customer interactions, market research, and operational insights. Combined with gen AI, this data can unlock insights that are inaccessible to your competitors.
   • Example from BFSI: A sizeable multinational bank with a vast repository of transactional data spanning decades applies gen AI to uncover deep patterns in customer behaviour. These patterns reveal insights into customer preferences, creditworthiness, and potential financial needs that are not apparent through traditional analysis. With these insights, the bank develops highly personalised financial products and services, offering customers tailored solutions that competitors cannot easily replicate due to their lack of access to similar data.
     
     However, it’s crucial to recognise that the competitive advantage gained from proprietary data is not permanent. As AI technology evolves, it may become more accessible for competitors to infer or replicate your data-driven strategies. Therefore, it’s essential to continuously innovate and update your AI applications to stay ahead of the curve.
   • Actionable Insight: BFSI organisations should focus on building and protecting their proprietary datasets while ensuring their AI systems are continuously updated with new data and insights. This approach helps maintain a competitive edge by providing you with AI-driven innovations that ensure your AI-driven innovations remain relevant and valuable.


2. Enhancing Customer Relationships with AI-Driven Insights Another way to use gen AI to amplify existing advantages is by deepening customer relationships. In the BFSI sector, trust and customer loyalty are critical. Generative AI can help by providing deeper insights into customer needs and preferences, enabling more personalised and responsive service.
   • Example from BFSI: A major insurance company uses gen AI to analyse customer data, including claims history, lifestyle choices, and social media activity. The AI identifies customers who may benefit from additional coverage options or wellness programs and sends personalised recommendations through the company’s mobile app. This proactive approach increases customer satisfaction and fosters long-term loyalty, as customers feel their insurer understands their needs.
     
     Unlike AI-driven efficiencies, which competitors can quickly replicate, the deep, trust-based relationships you build with customers through personalised service are much more complex to duplicate. This can be a powerful source of competitive advantage, especially in an industry where customer trust is paramount.
   • Actionable Insight: BFSI companies should use gen AI to deepen their understanding of customer behaviour and preferences. By leveraging AI-driven insights to enhance the customer experience, organisations can build stronger, more enduring relationships that competitors will find difficult to replicate.


3. Optimising Risk Management with Predictive AI Models Risk management is at the core of BFSI operations, and generative AI offers a new dimension of predictive accuracy that can significantly enhance this function. By analysing vast amounts of historical and real-time data, AI can identify emerging risks and potential threats more quickly and accurately than traditional methods.
   • Example from BFSI: A global investment bank uses gen AI to predict market trends and assess the risk associated with various financial instruments. The AI model analyses historical market data, macroeconomic indicators, and geopolitical events to forecast potential market shifts. Based on these predictions, the bank adjusts its investment strategies in real time, mitigating risks and optimising returns. This level of agility and foresight gives the bank a significant competitive advantage, as it can navigate volatile markets more effectively than its peers.
     
     However, even in risk management, AI's advantage is not absolute. As competitors develop similar predictive models, the differentiation lies in AI and its integration into the bank’s broader risk management strategy, including human oversight, regulatory compliance, and continuous model refinement.
   • Actionable Insight: BFSI organisations should integrate AI-driven insights into a comprehensive risk management framework that combines AI’s predictive power with human judgment and strategic oversight to sustain a competitive edge in risk management. Continuous model refinement and scenario analysis can further enhance AI's effectiveness in managing risk.

The Role of Culture and Leadership in AI Adoption

1. Creating a Culture of Continuous Innovation Organisations must foster a culture of continuous innovation to leverage Gen AI's potential. This means encouraging employees at all levels to experiment with AI, explore new applications, and think creatively about how AI can solve business challenges.
   • Example from BFSI: At a multinational bank's leading Global Capability Centre (GCC), the CIO implements an “AI innovation lab” where teams from different departments can collaborate on AI-driven projects. These projects range from optimising back-office operations to developing new customer-facing applications. By creating a space where employees are encouraged to experiment with AI, the bank fosters a culture of innovation that keeps it at the forefront of AI adoption.
     
     This culture of innovation is crucial because it ensures that the organisation remains agile and responsive to changes in technology and market conditions. It also helps attract and retain top talent, often drawn to companies prioritising innovation and offering opportunities to work with cutting-edge technology.
   • Actionable Insight: BFSI leaders should invest in creating a culture that supports continuous innovation. This can be achieved by providing employees with the tools and resources to experiment with AI, offering training and development opportunities, and recognising and rewarding innovative ideas.


2. Inclusive Leadership: Driving AI Adoption with a People-First Approach While AI is a powerful tool, its success ultimately depends on how it is integrated into the organisation. Inclusive leadership is critical in ensuring that AI adoption is not just about technology but also about people. Leaders must ensure that AI empowers employees, enhances their skills, and contributes to the organisation’s mission.
   • Example from BFSI: I have always championed a people-first approach to AI adoption in my assignments. I ensure that employees are involved in the AI implementation, offering training programs that help them understand how AI can enhance their work. By fostering a sense of ownership and engagement, I ensure that AI is seen not as a threat but a tool that empowers employees and drives the organisation forward.
     
     Inclusive leadership also involves ensuring that AI is used ethically and responsibly. This includes addressing issues such as bias in AI algorithms, protecting customer data, and ensuring that AI-driven decisions are transparent and fair.
   • Actionable Insight: BFSI leaders should adopt an inclusive approach to AI adoption, ensuring employees are engaged and empowered. This includes providing training and support, promoting ethical AI practices, and fostering a culture of collaboration and innovation.

The Future of AI in BFSI: Strategic Considerations

As we look to the future, it’s clear that generative AI will continue to play a significant role in the BFSI sector. However, its value will not come from the technology itself but from how it is integrated into the organisation’s strengths and capabilities.

1. Embrace AI as a Tool, Not a Solution Generative AI should be seen as a tool to enhance your organisation’s capabilities, not as a standalone solution. Integrating AI into your existing processes, data, and culture can amplify your competitive advantages and drive sustainable growth.
   • Example from BFSI: A regional bank uses AI to streamline its loan approval process, reducing the time from application to approval from days to hours. However, rather than relying solely on AI, the bank integrates the technology into its broader customer service strategy, ensuring that human advisors can provide personalised guidance alongside the automated process. This hybrid approach allows the bank to offer a faster, more efficient service while maintaining the personal touch that sets it apart from larger competitors.


2. Focus on Continuous Improvement Rapid AI development means organisations must be committed to continuous improvement. This involves regularly updating AI systems, exploring new applications, and staying informed about the latest advancements in AI technology.
   • Example from BFSI: A significant insurance company continually refines its AI-driven underwriting models based on new data and insights. This commitment to continuous improvement ensures that the company’s risk assessment processes remain cutting-edge, providing more accurate pricing and reducing the risk of adverse selection. The company maintains a competitive edge in the highly competitive insurance market by staying ahead of the curve.


3. Prioritise Ethical AI Practices As AI becomes more integrated into decision-making processes, it’s essential to prioritise ethical AI practices. This includes addressing bias, transparency, data protection and ensuring that AI-driven decisions are fair and responsible.
   • Example from BFSI: A leading financial institution implements a comprehensive AI ethics framework, including regular audits of AI algorithms for bias, transparency in AI-driven decisions, and robust data protection measures. By prioritising ethical AI practices, the institution complies with regulatory requirements and builds trust with customers and stakeholders, reinforcing its reputation as a responsible and moral organisation.

Conclusion: The True Power of Generative AI

In conclusion, while generative AI is undoubtedly a powerful tool, its value lies in its ability to amplify your organisation's strengths. By integrating AI into your proprietary processes, deepening customer relationships, and fostering a culture of continuous innovation, you can leverage AI to drive sustainable competitive advantage.

As we move forward, we must remember that AI is not a magic bullet. Its success depends on how it is used, the data it is fed, and the culture in which it operates. By taking a strategic, people-first approach to AI adoption, BFSI leaders can ensure that AI supports their business goals and drives long-term value for their organisations and stakeholders.