“LLM (Large Language Models) here and LLM there, but not a use case to fit”, this may be a rather cynical yet widely prevalent experience of businesses trying to use AI in the last few months. Things took-off with the launch of ChatGPT and the mindless competition in this space gave birth to Bert, Claude, Cohere, Gemini and other fancy named LLM’s trained on hours of Nvidia GPU using enormous amount of energy! Whilst the outputs of these models seem jaw-droppingly human, embedding them in business operations remains largely elusive. In search of more meaningful business applications, the AI pundits jumped on the next bandwagon of AI agents and Co-pilots. This is a more useful version of AI for business; however, the key point of inflexion being the ability to build custom AI co-pilots and agents with relatively narrower business applications. These domain specific AI agents and co-pilots could then transform the business landscape by enabling hyper-personalized customer experiences through advanced decision optimization. These intelligent systems could be designed to support human decision-making, streamline operations, and enhance customer engagement. As businesses become more sophisticated in using these custom AI agents and co-pilots, they revolutionize the way they comprehend and cater to individual customers, paving the way for a new era of data-driven, personalized service.
The Role of domain specific AI Agents and Co-Pilots in Business
AI agents and co-pilots are software tools or virtual assistants that use artificial intelligence to support various tasks within an organization. Unlike traditional automation, which relies on static rules and pre-defined processes, AI agents and co-pilots leverage machine learning, natural language processing, and real-time data to learn, adapt, and respond to complex situations dynamically. This makes them highly effective in assisting with customer service, sales, marketing, and decision-making across industries.
For instance, co-pilots embedded within CRM (Customer Relationship Management) platforms can analyse customer data to suggest personalised product recommendations, while others in finance might help optimise investment portfolios or identify high-risk transactions. Their role is not just to execute commands but to work alongside human professionals, enhancing insights and guiding actions. As a result, businesses can provide faster and more accurate responses to customers, leading to more satisfying and tailored experiences.
Let us consider the use of AI agents and Co-pilots is a debt servicing operation, to further understand the nature of these domain specific AI agents and Co-pilots.
Unlike the rigid, rule-based systems of the past, these intelligent systems learn and adapt based on each debtor’s behaviour, financial situation, and past interactions. AI agents can autonomously handle straightforward queries, such as explaining balances or clarifying payment options, while co-pilots work alongside human agents to provide deeper insights and suggest optimal approaches for managing accounts.
For example, an AI-driven co-pilot in debt collections can analyse historical data and payment patterns to determine the best times to contact customers, recommend optimal communication channels (e.g., SMS, email, or phone), and even suggest specific language likely to resonate based on prior interactions. These insights create a less intrusive, more customer-focused experience, improving collection rates while preserving the relationship between the customer and the lender.
Hyper-Personalization and Decision Optimisation with AI Agents and Co-pilots in Debt Servicing
From a business perspective, the end goal is a tailor-made customer experience, which is beyond the hitherto segmentation-based customer strategies. Hyper-personalization, made possible by AI, is reshaping debt collection practices by focusing on each individual debtor rather than relying on generalized strategies. Traditionally, debt collection involved mass communication and standardized messages, often leading to poor engagement and low success rates. AI technology, however, can assess a debtor’s financial history, behaviour patterns, and responsiveness to tailor interactions that feel more relevant and considerate.
For instance, if a customer has a history of responding to email communications but ignoring phone calls, AI agents can prioritize email as the primary channel. If the customer has recently encountered financial hardship, an AI co-pilot can suggest offering a flexible repayment plan or reduced settlement options that consider their specific circumstances. This type of hyper-personalized outreach not only increases the likelihood of successful repayment but also demonstrates empathy, which can encourage debtors to engage more positively with collectors.
Decision optimization technology is a powerful tool for enhancing debt collection by evaluating numerous variables to determine the most effective actions. Using advanced algorithms, decision optimization can analyse factors such as the debtor’s credit score, payment history, financial standing, and even broader economic indicators to recommend the best course of action for each account.
For example, decision optimization can predict the likelihood of a customer making a payment based on their recent transactions and suggest when a follow-up might be most effective. In cases where the debtor might be struggling, the system can recommend a payment plan or deferment option that increases the chances of eventual repayment. These personalized strategies allow lenders to recover debts more efficiently and ethically, supporting financial health for both the debtor and the institution.
Ethical and Privacy Considerations
While AI in debt collection offers clear benefits, it also raises important ethical and privacy concerns. Collecting and analysing personal data for decision-making requires strict adherence to privacy laws and transparent data use practices. Businesses must ensure that AI-driven systems respect customer data rights and maintain transparency about how decisions are made. By prioritizing ethical AI practices, lenders can build and maintain trust, which is crucial in sensitive areas like debt servicing.
By A Mishra, CEO – Zinia AI, 08/11/2024
