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Re-imagining CX with AI - a fireside chat with Microsoft

girl with ipad customer experience

In a recent session of Microsoft’s Innovation Fireside Chat series, Deb Oberly, Sales Manager at Microsoft was joined by Mona Champaneri, Head of Experience and Product Service Line and Cameron Turner, VP of Data Science - both of Kin + Carta. They shared insights on leveraging AI to enhance customer interactions, discussed emerging trends and provided industry examples where AI is reshaping the customer experience.

Creating personalization and loyalty through data and AI

Personalization plays a crucial role in moving customers from anonymous browsers to advocates. The power of data and AI allows us to connect with customers on a deeper level, bringing a human touch to critical moments in the customer experience - and by tailoring experiences to individual preferences, businesses can build loyalty and deliver value at each stage of the customer journey.
Tailor experiences so that everyone feels like someone. That’s where the value of AI is really going to come into play.
Mona Champaneri, Head of Experience and Product Service Line, Kin + Carta

AI is getting smarter about everyone

AI enables true hyper-personalization, where technology adapts to users' needs and preferences in real time. Unlike traditional segmentation and targeted advertising, AI-driven personalization goes beyond predicting user behavior. It creates adaptive experiences based on real-time data, continuously redefines itself in real time based on what we're doing, who we are and what's known about us and others like us. AI's ability to leverage collective user experiences enhances the customer journey and fosters a deeper connection between brands and customers.
Take healthcare as an analogy. Wouldn't you rather go to the doctor who's seen every patient in the world versus the doctor that's seen a dozen patients? AI has that capability. It can roll together everyone’s experiences in order to create a personalized experience just for you
Cameron Turner, VP Data Science, Kin + Carta

Moving from predictive to prescriptive AI

While predictive models can anticipate outcomes, prescriptive AI takes it a step further by recommending actions to prevent undesired outcomes. For example, businesses can predict customer churn and tailor responses to retain at-risk customers. Prescriptive AI transforms customer experience by providing valuable recommendations that drive engagement, loyalty and revenue.

How do you make the leap from predictive to prescriptive AI? AI and machine learning can be leveraged for explanatory purposes to answer questions like:

  • What are the drivers that are converting my customers?
  • What are the drivers that are retaining my customers?
  • How can I recommend certain products?

For example, customer churn. You can build a model without much effort - and there’s even pre-built models inside Azure ML - that will predict which customers are at risk so you can tailor responses to those individuals. Being able to predict churn is not nearly as valuable as being able to prevent it.

Prescriptive recommendations are where the money’s at.
Cameron Turner, VP Data Science, Kin + Carta

Achieving a 360-degree view of the customer

To deliver a comprehensive customer experience and exceed expectations, organizations need to synthesize data from various sources and create a unified view of the customer. This 360-degree approach involves integrating customer service, marketing, advertising and product telemetry data.
If a customer purchases a certain item, there’s a strong likelihood they’ll need a specific second item soon after; that omnipresence within behavior and habits is critical and helps to build trust as a brand.
Mona Champaneri, Head of Experience and Product Service Line, Kin + Carta

Industry examples of AI

AI in Retail

The use of bots powered by natural language processing (NLP) has gained momentum. Customers are becoming more receptive to leveraging AI-based solutions as they offer increasingly efficient and personalized customer service experiences. Bots can handle routine queries, and the human element is introduced when necessary, resulting in enhanced customer satisfaction.

AI in Agriculture

AI assists sales teams in making data-driven predictions for seed purchases based on factors such as weather, season and competitor activities. By leveraging AI, agricultural businesses can streamline sales processes and enhance decision-making, ultimately maximizing productivity and profitability.


AI in Healthcare

AI-powered chatbots are being adopted to improve patient interactions. Research has shown that patients feel more comfortable sharing information with chatbots than with live physicians during intake processes. This preference for AI-based interactions presents scalability advantages for healthcare providers and enables exponential growth in personalized care.


AI in Manufacturing

Integrating AI into existing workflows and processes is a complex task. Language-based AI solutions enable seamless integration and continuous learning, facilitating effective communication and optimization within manufacturing environments.

Real-world applications for Generative AI

Generative AI, with its ability to produce content based on user input, has sparked immense excitement across various industries. Initially, the focus was on simple interactions with language models like ChatGPT, but organizations are now exploring the business value and potential use cases of Generative AI.

Examples include leveraging sophisticated chatbots for enhanced customer experiences, summarizing financial reports, automating claims processing, and even generating personalized stories and recipes in a personal context.

Applications of Generative AI are expanding far beyond chatbots. By fine-tuning base models with proprietary data, organizations can derive unique value and provide domain-specific answers. This integration of corporate data with the power of large language models offers a novel way to unlock enterprise data and generate tailored insights. Companies that quickly embrace this approach are likely to gain a competitive edge by leveraging the best of the internet and their own proprietary data.

AI can be daunting - so start backwards

One common concern among organizations considering AI adoption is the daunting nature of the technology. A recommendation is to start with the business value and design backwards. That is, identify the desired business outcomes and treat them as hypotheses - then start small with proof of concept projects and iterate from there.
Align your investment in data integration and platform building with specific value points, ensuring a clear path to success and avoiding unnecessary technical debt
Cameron Turner, VP Data Science, Kin + Carta

By holistically analyzing these three essential considerations, organizations can establish a strong foundation for their AI journey:

1. Determine the problem AI will solve

2. Select the appropriate AI type

3. Decide how to leverage the data collected for empowering experiences

AI itself should never be the headline

The true value lies in the products and experiences it enables. Rather than focusing solely on the technical aspects, organizations should prioritize enhancing customer experiences and providing value through the integration of AI technologies.
The most successful projects are those in which AI teams support the work of product and experience teams, creating a seamless human plus AI collaboration.
Cameron Turner, VP Data Science, Kin + Carta

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