AI Adoption in Business: Navigating Hesitations and Opportunities
The world is slowly waking up to the transformative potential of artificial intelligence (AI) in business. Yet, a palpable hesitation lingers, particularly within the finance sector. A recent survey by IBM illustrates this duality: while an impressive 94% of CIOs are leveraging AI in various business functions, it’s concerning that just over 50% anticipate broad adoption by 2025 in key sectors, notably IT (71%), supply chain (68%), and product development (67%). The finance function, however, remains a bastion of skepticism.
CFOs Demand Assurance: The Call for ROI
In discussions among financial leaders, it’s clear that Chief Financial Officers (CFOs) are taking a cautious stance on the adoption of AI technologies, especially the more advanced forms known as agentic AI. A recent roundtable organized by IBM and FinancialExpress.com echoed a unified focus on accuracy. Many CFOs conveyed that their trepidation stems from the inability of AI to ensure 100% reliability, crucial in financial operations.
Hemant Kumar Jain, the CFO of Reliance General Insurance, articulated this concern discussing the reliability of technologies like Optical Character Recognition (OCR) in invoicing. “The accuracy caps at 95-96%, leaving a margin that requires manual verification,” he noted, reflecting the broader hesitance among CFOs.
Amit Malpani, CFO at Edelweiss Asset Management, was equally candid, stating, “Even a 5% inaccuracy presents a significant concern.” Addressing the need for human validation, Siddesh Naik, IBM’s Country Leader for Data & AI Technology Sales, emphasized: “Accurate OCR requires minimum 300-dpi resolution. A human-in-the-loop approach is necessary but adds costs that many CFOs prefer to avoid.”
The ROI Dilemma: Unpacking the Cost-Benefit Analysis
Beyond accuracy concerns, the return on investment (ROI) factor significantly influences the apprehensive stance of finance leaders. Swayam Saurabh, CFO of JSW Steel, articulated that while AI finds application primarily in back-office operations, including robotic process automation, it’s hardly more than a buzzword today. “It must drive down fixed costs, which remains unproven,” he lamented.
Kapish Jain, President and Group CFO of IIFL Finance, succinctly encapsulated the central challenge: “The crux lies in reconciliation—what are the true costs, and how can AI be employed to mitigate these?” Similarly, Yogish Udhoji, CFO of Muthoot Housing Finance, revealed that their focus remains limited to basic automation, indicating that they have yet to identify a suitable AI application.
Meanwhile, SBI General Insurance is trialing AI applications in GST compliance and underwriting. However, Jitendra Attra, their CFO, confirmed that, “the core finance function has yet to see AI implementations.”
Towards a Future of AI in Finance
Looking forward, Pavan Jain, CFO of Grasim Industries Limited, acknowledged potential for AI in regulatory compliance automation, but stressed that any widespread adoption within core finance tasks hinges on improved accuracy. To earn CFOs’ confidence, companies must reevaluate their workflows, aiming not just for automation but also for verifiable cost reductions and enhanced value, shifting focus from mere adoption to tangible financial outcomes.
AI’s Growing Role in Customer Experience: A Distinct Landscape
Interestingly, while finance remains cautious, AI is carving out significant advancements in areas like customer experience. In today’s saturated marketplace, where service differentiation is minimal and customers can easily switch providers, exceptional customer engagement becomes the ultimate competitive advantage.
AI has redefined customer interaction, from efficiently identifying high-intent prospects to automating workflows and personalizing offers tailored to customer needs. However, successfully leveraging AI goes beyond merely accessing cutting-edge technology; it also involves harmonizing convenience with privacy, and efficiency with empathy, particularly in sensitive sectors such as wealth management, insurance, and trading.
Behind the Scenes: AI in Wealth Management
Notably, even if invisible to the end user, AI is working diligently in the background, optimizing customer engagement. In wealth management, firms are increasingly employing AI to enhance both customer interactions and their research capabilities, striving to be more in tune with client expectations.
Additionally, insurers are utilizing this technology to streamline client journeys efficiently. Girish J Kalra, Chief Marketing Officer at Tata AIA Life Insurance, mentions that AI is instrumental in cutting costs related to creative marketing initiatives, evaluating customer eligibility for policies, and significantly expediting the insurance purchase process—each stage of which alleviates burdens on the consumer.
Challenges in Scaling AI: A Multifaceted Journey
Nevertheless, as Kunal Sanghavi, Chief Strategy and Transformation Officer at HDFC Securities, pointed out, scaling AI implementation can be fraught with challenges. “When multiple stakeholders are involved, gauging AI’s impact and effectiveness becomes challenging,” he said.
Regulatory compliance adds further layers of complexity; overcoming these hurdles will require a meticulous hands-on approach. Sanghavi acknowledged that adapting the latest large language models (LLM) within major institutions will involve a gradual learning curve.
Personalization: Balancing Act in Customer Engagement
A crucial aspect of customer loyalty arises from personalization. However, navigating this balance demands caution—being overly intrusive risks alienating customers, while a too-cautious approach might lead to lost opportunities. “Finding the right level of involvement is critical for offering personalized solutions,” remarked Santoshi Kittur, CTO of 360 One.
It becomes imperative to empower ground-level personnel to deliver fast solutions, ensuring empathy remains a cornerstone of customer service.
The Bottom Line: AI’s Financial Impact
The ramifications of AI stretch far beyond customer satisfaction; they visibly enhance the bottom line. Specifically, cost savings from acquiring new customers and the retention of existing ones are quantifiable benefits. In fiercely competitive markets, accurately targeting high-conversion leads can yield significant savings.
“AI aids in differentiating between intent and non-intent customers, enabling a sharper focus on the right audience,” noted Sanghavi. This capability to measure customer-related ROI can drastically cut acquisition and retention costs, ultimately enabling firms to acquire quality clientele over mere quantity.
Optimism Amid Challenges: The Road Ahead
While the enthusiasm for AI across various sectors is palpable, the road to widespread adoption is still under construction. Adhering to regulations, establishing robust data governance, and ethically managing customer information are central priorities for businesses keen on thriving in this landscape.
For companies seeking to enhance their customer experience, achieving the right blend of intelligent automation and emotional intelligence may prove invaluable. In the finance arena, however, the critical pathway lies in ensuring that AI solutions can deliver consistent accuracy.
Conclusion: A Pivotal Moment for AI in Business
The hesitation surrounding AI adoption within finance illuminates the compelling need for enhanced accuracy and clarity on ROI. While AI is already reshaping customer experiences across sectors, the finance function lags behind, constrained by concerns over reliability and validation. As businesses reassess their approaches to AI, unlocking its true potential will require a meticulous blend of innovation, verification, and tangible outcomes to earn the trust of financial leaders. Embracing this challenge could reshape the landscape of finance, heralding a new era of efficiency and effectiveness fueled by AI.
