AI in all sectors
There is no shortage of AI use cases across industries. Retailers are tailoring shopping experiences to individual preferences by leveraging customer behavior data and advanced machine learning models. Traditional AI models can deliver personalized offers. However, with generative AI, these personalized offers are enhanced by incorporating tailored communication that takes into account the customer’s personality, behavior, and past interactions. In insurance, by leveraging generative AI, companies can identify subrogation recovery opportunities that a manual manager might overlook, improving efficiency and maximizing recovery potential. Banking and financial services institutions are leveraging AI to strengthen customer due diligence and improve anti-money laundering efforts by leveraging AI-driven credit risk management practices. AI technologies improve diagnostic accuracy through sophisticated image recognition in radiology, enabling earlier and more accurate disease detection, while predictive analytics enables personalized treatment plans.
The core of a successful AI implementation lies in understanding its business value, building a strong evidence base, aligning with the organization’s strategic goals, and embedding skilled expertise at all levels of the business.
- “I think we should also ask ourselves what we’re going to stop doing if we succeed. Because when we empower our colleagues with AI, we’re giving them new capabilities and ways to do things faster and more efficiently. So we have to stay true to the design of the organization. Often, an AI program doesn’t work, not because the technology doesn’t work, but because downstream business processes or organizational structures remain unchanged.” —Shan Lodh, Director of Data Platforms, Shawbrook Bank
Whether it’s automating routine tasks, improving customer experience, or providing deeper insights through data analysis, it’s essential to define what AI can do for a business in specific terms. AI’s popularity and broad promise are not enough reasons to dive headfirst into enterprise-wide adoption.
“AI projects need to be value-driven rather than technology-driven,” Sidgreaves says. “The key is to always make sure you know what value AI brings to the business or the customer. And always ask yourself: do we really need AI to solve this problem?”
Having a good technology partner is essential to ensure value creation. Gautam Singh, Head of Data, Analytics and AI at WNS, says, “At WNS Analytics, we keep the clients’ organizational goals at the center of our focus. We have focused and strengthened on core productized services that go a long way in driving value for our clients.” Singh explains their approach: “We achieve this by leveraging our unique AI and human resources. interaction approach to develop personalized services and deliver differentiated results.
Adoption of any cutting-edge technology is driven by data and AI is no exception. Singh explains, “Advanced technologies like AI and generative AI are not always the right choice. That’s why we work with our clients to understand the needs and develop the right solution for each situation.” With data volumes becoming increasingly large and complex, it is essential to effectively manage and modernize data infrastructure to provide the foundation for AI tools.
This means that breaking down silos and maximizing the impact of AI involves regular communication and collaboration across departments, from marketing teams working with data scientists to understand customer behavior patterns to IT teams ensuring their infrastructure supports AI initiatives.
- “I want to highlight the growing expectations of customers in terms of what they expect from our businesses and what they expect from us to provide us with quality and timely service. At Animal Friends, we see the potential of generative AI to be greatest with sophisticated chatbots and voice robots that can serve our customers 24/7 and provide the right level of service, while being cost-effective for our customers. — Bogdan Szostek, data director, Animal Friends
Investing in domain experts with deep knowledge of regulations, operations, and industry practices is just as necessary to the successful deployment of AI systems as good databases and strategy. Continuous training and upskilling are essential to keep pace with evolving AI technologies.
Ensuring trust and transparency in AI
Building trust in the implementation of generative AI requires the same mechanisms as for all emerging technologies: accountability, security, and ethical standards. Transparency about how AI systems are used, what data they rely on, and the decision-making processes they use can go a long way toward building trust among stakeholders. In fact, The Future of Enterprise Data & AI report states that 55% of organizations consider “building trust among stakeholders in AI systems” to be the biggest challenge when scaling AI initiatives.
