Varun Shankarnarayan
Engagement Leader, Practus
As technology evolves at breakneck speed, it’s evident that it alone isn’t sufficient to drive genuine business transformation. While generative AI (gen AI) may be the latest buzz, the real catalyst for change lies in how businesses integrate these tools into their processes. True transformation happens when organizations rethink their operations, invest in reskilling their workforce, and redesign their business models to leverage new technology.
Navigating the Productivity J-Curve
History has shown that new technology adoption doesn’t always lead to immediate productivity boosts. Economists call this the “productivity paradox,” where early investments in tech often don’t deliver quick returns. However, as companies adapt their processes and retrain their teams, the long-term benefits become more significant, following what’s known as the “productivity J-curve.”
Generative AI is accelerating this shift, thanks to its ease of implementation. Tools like ChatGPT, for example, can be learned and integrated within hours, speeding up the transformation process and allowing businesses to see productivity gains sooner.
Augmenting, Not Replacing
A major concern about AI adoption is its potential impact on jobs. However, research suggests that AI is more likely to enhance roles rather than eliminate them. By enabling employees to perform tasks more efficiently, AI raises the quality and effectiveness of work. A study by the Stanford Digital Economy Lab, led by Erik Brynjolfsson, Lindsey Raymond, and Danielle Li, where a large language model (LLM) was implemented in a call center to help, rather than replace operators, demonstrated productivity gains of up to 35% in just a few months. The AI not only boosted productivity but also improved customer satisfaction and employee morale. Less-experienced agents benefited significantly, with the AI coaching them and sharing best practices. Notably, these improvements persisted even during system outages.
The Human Element in Business Transformation
The transformative power of AI and other technologies doesn’t come from the software itself but from how businesses evolve around it. While technology can automate tasks and streamline processes, substantial productivity gains come from rethinking business models. Rather than using technology to merely replicate current workflows, organizations should explore ways to reshape how work is done entirely.
Forward-looking CEOs and managers know that to maximize AI’s potential, they must promote a culture of continuous learning, upskilling, and collaboration between humans and technology.
AI’s greatest promise lies in its potential to act as a critic, collaborator, and creative ally. Whether assisting in data analysis, customer service, accelerated learning, or strategy development, AI can enhance tasks when humans remain actively involved in the process. The goal is to find the optimal balance where technology complements human skills, allowing businesses to achieve more than they could otherwise.
Avoiding the Turing Trap
Although AI offers immense potential, there’s a risk of over-relying on technology to replace human roles. Brynjolfsson warns of the “Turing Trap,” where companies focus on substituting human labor with machines, leading to lower wages and increased concentration of wealth. Instead, AI should be used to amplify human capabilities, creating shared prosperity and greater value.
The future of AI and technology should prioritize extending human potential rather than replicating it. By using AI to support and enhance human skills, businesses can unlock new avenues for growth and innovation.
Conclusion: Technology as a Tool, not a Solution
Ultimately, technology is an enabler of transformation, not the solution itself. To harness its full power, businesses need to rethink their strategies, invest in employee training, and integrate technology in ways that create fresh value. It’s only by blending human creativity with advanced tools like AI that companies can truly achieve sustainable growth in today’s digital age.
The bottom line: Technology alone cannot drive transformation. It’s the integrated approach involving people, processes, and technology that brings about real and lasting change.
The Role of Regulation in Paving the Way
Regulation is playing a crucial role in paving the way to a greener future. The EU’s Corporate Sustainability Reporting Directive (CSRD), which will require nearly 50,000 companies in Europe to disclose ESG information by 2025, marks a significant milestone in ESG reporting. The European Commission’s plans to create reporting standards for SMEs and regulate ESG ratings are geared toward enhancing transparency and comparability.
The Basel Committee’s proposed changes to the Basel 3 framework, including climate disclosure rules for larger banks, are set to be implemented from January 2025. These changes aim to improve risk management related to climate risks. The European Green Bond legislation, which came into effect recently, seeks to promote sustainable project funding and market coherence.
ESG Integration in Indian Banking
India’s financial sector is also embracing sustainable finance. The government has allocated significant funds for the net-zero transition, and bodies like the Securities and Exchange Board of India (SEBI) and the Reserve Bank of India (RBI) have introduced draft frameworks to promote green finance.
Indian banks have been actively supporting green projects and initiatives, such as sustainable agriculture, energy efficiency, and renewable energy. The RBI’s Framework for Acceptance of Green Deposits has further incentivized banks to offer green deposit options, channeling funds towards environmentally sustainable projects.
Generative AI’s Potential in ESG Reporting
Generative AI has the potential to revolutionize ESG reporting by enabling companies and financial institutions to harness data, streamline processes, and provide a seamless experience for stakeholders. According to Finastra’s State of The Nation Report, 36% of respondents want to use Gen AI to collect, process, and analyze data for ESG criteria.
AI applications in sustainable finance include risk management and ESG integration, climate risk modeling, impact investing, sustainable supply chain management, and identifying greenwashing risks. These applications help financial institutions and investors drive positive environmental and social impact while generating financial returns.
Challenges and Opportunities
While progress is being made, the transition to net-zero banking presents significant challenges. Banks must navigate regulatory complexities, incorporate ESG factors into their risk management frameworks, and develop robust data analytics capabilities to assess climate-related risks. Concerns about greenwashing and inflexible structures have also dampened enthusiasm for products like sustainability-linked loans (SLLs), which offer lower interest rates to borrowers meeting certain ESG criteria but face resistance from those hesitant to disclose their ESG targets.
At the same time, sustainable finance offers immense opportunities. By aligning their business models with sustainability goals, banks can enhance their reputation, attract ESG-conscious investors, and contribute to a more resilient financial system.
Conclusion
The integration of ESG factors into financial services is no longer optional but imperative for the long-term success of the banking industry. By embracing sustainable finance, banks can play a crucial role in mitigating climate change, creating shared value, and building a more resilient and equitable future. Through comprehensive strategies, regulatory support, and the innovative use of technology, the financial sector can drive the transition to a sustainable global economy.