AI across industries
There is no shortage of AI use cases across sectors. 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 elevated by including tailored communication that takes into account the customer’s personality, behavior and past interactions. In insurance, using generative artificial intelligence, companies can identify replacement recovery opportunities that a manual handler might bypass, increasing efficiency and maximizing recovery potential. Banking and financial services institutions are using AI to strengthen customer due diligence and enhance anti-money laundering efforts using AI-driven credit risk management practices. AI technologies are improving diagnostic accuracy through sophisticated image recognition in radiology, allowing earlier and more accurate disease detection while predictive analytics enable personalized treatment plans.
The essence of successful AI implementation lies in understanding its business value, building a strong data foundation, aligning with the organization’s strategic goals, and embedding skilled expertise at every level of an enterprise. .
- “I think we have to ask ourselves, if we succeed, what will we stop doing? Because when we empower colleagues through AI, we’re giving them new capabilities [and] faster, faster, easier ways to do things. So we must also be true to thinking about the design of org. Often, an AI program does not work, not because the technology does not work, but because the downstream business processes or organizational structures are still maintained as before.” –Shan Lodh, director of data platforms, Shawbrook Bank
Whether it’s automating routine tasks, improving customer experiences, or providing deeper insights through data analysis, it’s essential to define what AI can do for an enterprise in specific terms. AI’s popularity and sweeping promises aren’t good enough reasons to jump headlong into enterprise-wide adoption.
“AI projects need to come from a value-led position rather than technology-led,” says Sidgreaves. “The key is to always make sure you know what value you’re bringing to the business or customer with AI. And actually always ask yourself the question, do we need AI to solve that problem?”
Having a good technology partner is essential to ensure that value is realized. Gautam Singh, head of data, analytics and AI at WNS, says: “At WNS Analytics, we keep our clients’ organizational goals at the center. We are focused and strengthened around the core services produced that go deep into value generation for our customers.” Singh explains their approach, “We do this by leveraging AI and our unique human interactive approach to develop bespoke services and deliver differentiated results.”
The foundation of any advanced technology adoption is data, and AI is no exception. Singh explains, “Advanced technologies like AI and generative AI may not always be the right solution, and that’s why we work with our clients to understand the need, to develop the right solution for each situation.” With increasingly large and complex data volumes, effectively managing and modernizing data infrastructure is essential to provide the foundation for AI tools.
This means 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 that their infrastructure supports AI initiatives.
- “I would highlight the growing expectations of customers in terms of what they expect our businesses to offer them and provide us with quality and speed of service. At Animal Friends, we see the generative potential of AI to be the greatest with sophisticated chatbots and voice bots that can serve our customers 24/7 and provide the right level of service and be cost effective for customers ours. – Bogdan Szostek, chief data officer, Animal Friends
Investing in domain experts with knowledge of regulations, operations and industry practices is as necessary to the success of deploying AI systems as the right data base and strategy. Continuous training and upskilling are essential to keep pace with emerging AI technologies.
Ensuring trust and transparency of AI
Building trust in the generative application of AI requires the same mechanisms used for all emerging technologies: accountability, security, and ethical standards. Being transparent about how AI systems are used, the 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 cites 55% of organizations that identify “building trust in AI systems among stakeholders” as the biggest challenge when scaling AI initiatives.