Hyper-Personalising Customer Interactions: In highly competitive B2C sectors, optimising every interaction with a customer is essential. But, in a high pressure call centre environment even the most experienced service agents can struggle to immediately assimilate all the available information about this particular customer and process that mentally in real time to come up with the next best action on every occasion. Using AI to give your agents smart prompts on next best action based on all available data is a classic use case for AI and will significantly increase the likelihood of full customer satisfaction with every interaction.

Automating High Volume Processes: Service-led organisations - in both the private and public sectors - typically have to handle large volumes of requests that arrive as emails with completed forms as attachments. Reading, assimilating, actioning and responding to these has traditionally been a time consuming and labour-intensive process. Using Natural Language Processing (NLP) and Text Analytics to read, “understand” and process the emails and their attachments, AI-driven email bots can triage these high volumes of requests, extract all the necessary information, create a “case” and route it to the right responder for immediate resolution. Everybody wins: customers enjoy faster service, organisations reduce costs per transaction, and staff spend more time focusing on more satisfying non-routine cases.

Conversational AI Bots: Chatbots have been around for a number of years now, and have been deployed with mixed results. But not all Chatbots are equal. The vast majority of Chatbots deployed to date have been “linguistic” or “rule based” (using if/then logic): whilst these can work well where the context is effectively an FAQ type of scenario with a predictable set of queries, they struggle if someone goes slightly off-topic or uses alternative terminology. By contrast, AI Chatbots are based mainly on machine learning and draw on other advanced AI features such as sentiment analysis and memory regarding each individual customer, enabling much more flexible, personalised communication. Getting it right requires experience and know how - and that’s where we come in.

Predictive Modelling: AI-driven Predictive Modelling can be used to drive smarter and better informed decisions in a broad range of areas across many different types of organisation. For example, energy businesses are dependent on the ongoing availability and high performance of critical assets such as pipelines and generators: timely intervention, maintenance and repair is essential to prevent interruption or degrading of service delivery. Using a combination of historic data, the practical knowledge of your engineers and machine learning platforms (e.g. Amazon Sagemaker or Google AI) to build, train and deploy predictive models of your assets, anomalies and threats can be identified and actioned with greater speed and accuracy. The same approach applies to predicting customer churn in B2C businesses, and credit risks for financial services businesses. And so on. Please contact us to discuss the possibilities for your organisation.