Prepare your business for AI
AI across industries
There is no shortage of AI use cases across sectors. Retailers tailor shopping experiences based on individual preferences using advanced machine learning models and data from customer behavior. The traditional AI models are capable of delivering personalized offerings. With generative AI however, these personalized offers are elevated through tailored communication that takes into account the customer’s persona and behavior as well as past interactions. By leveraging generative AI in insurance, companies can identify opportunities for subrogation recovery that a human handler may overlook. This increases efficiency and maximizes recovery potential. AI is being used by financial institutions to enhance their anti-money-laundering efforts and bolster due diligence for customers. AI technologies are enhancing diagnostic accuracy through sophisticated image recognition in radiology, allowing for earlier and more precise detection of diseases while predictive analytics enable personalized treatment plans.
The core of successful AI implementation lies in understanding its business value, building a robust data foundation, aligning with the strategic goals of the organization, and infusing skilled expertise across every level of an enterprise.
- “I think we should also be asking ourselves, if we do succeed, what are we going to stop doing? When we empower our colleagues with AI, we give them new abilities [and] faster and leaner ways to do things. We need to think about the organization design in a true way. “A lot of times, AI programs fail not because they aren’t working, but because the downstream processes or organizational structures remain the same.” –Shan Lodh director of data platforms at Shawbrook Bank
Whether it’s automating routine tasks, improving customer experiences, providing deeper insights via data analysis, or automating routine tasks. It’s important to define exactly what AI can offer an enterprise. AI’s widespread popularity and promises aren’t enough to justify a rush into enterprise adoption. Sidgreaves says that AI projects should be driven by value, rather than technology. The key is knowing what value AI can bring to a business or customer. Ask yourself: Do we really need AI to solve this problem? “
Having the right technology partner to help you realize value is essential. Gautam Singh is the head of data analytics and AI at WNS. He says: “At WNS Analytics we put our clients’ goals first.” We have focused and strengthened around core productized services that go deep in generating value for our clients.” Singh explains their approach, “We do this by leveraging our unique AI and human interaction approach to develop custom services and deliver differentiated outcomes.”
The foundation of any advanced technology adoption is data and AI is no exception. Singh says, “Advanced technologies such as AI and generative AI are not always the best choice. We work with our customers to understand their needs and develop the right solutions for each situation.” Effectively managing and modernizing the data infrastructure will be essential for AI tools, as data volumes are becoming increasingly complex and large. This means that maximizing AI’s potential requires regular communication and collaboration between departments, from marketing teams working closely with data scientists to better understand customer behavior patterns, to IT teams making sure their infrastructure supports AI initiatives. “I would like to emphasize that customers have higher expectations of our companies in terms of the services they expect and their quality. We at Animal Friends believe that the most promising potential of generative AI is the use of sophisticated chatbots or voice bots to serve our customers 24 hours a day, deliver the best level service and be cost-effective for our customers. — Bogdan Szostek, chief data officer, Animal Friends
Investing in domain experts with insight into the regulations, operations, and industry practices is just as necessary in the success of deploying AI systems as the right data foundations and strategy. Continuous training and upskilling are essential to keep pace with evolving AI technologies.
Ensuring AI trust and transparency
- Creating trust in generative AI implementation requires the same mechanisms employed for all emerging technologies: accountability, security, and ethical standards. Transparency about AI systems, data that they use, and decision-making processes can help to build trust between stakeholders. According to The Future of Enterprise Data & AI Report, 55% of organizations cite “building trust in AI among stakeholders” as their biggest challenge for scaling AI initiatives.
