Data-driven and AI-assisted customer guidance development partnership

… the utilisation of customer understanding as part of the development of services, operating models and customer relationships. Customer experience, the effectiveness of customer guidance and the use of services are systematically monitored and measured so that operations can be continuously developed in a data-driven manner. The success of the transformation will be assessed on the basis of indicators such as: • customer experience and recommendation • relevance of customer guidance • redirection to services • utilisation rate of services • success in identifying customer needs • effectiveness of customer activation • development of customer relationships and utilization of services • efficiency of expert work and utilization of automation • degree of utilization of data, analytics and artificial intelligence • effectiveness indicators supporting the achievement of Business Finland's strategic goals. The challenges of the current situation and the need for change Business Finland has carried out a preliminary study on customer guidance, which is based on data, and in connection with this a number of development areas and bottlenecks in the current situation have been identified. To solve these, a long-term development partner is needed to support the organisation's change. The main challenges identified are: 1. Fragmented information and limited customer understanding: Information related to customers, markets, services, transactions, interactions and networks is fragmented into several systems, data repositories and external data sources. As a result, the organisation does not have a consistent and up-to-date view of customers, customer needs, potential, use of services, transaction history or the current state of the customer relationship. This complicates customer identification, targeting, prioritisation and timely service guidance. 2. Lack of proactivity and limited use of AI-assisted customer guidance: Business Finland lacks the systematic ability to identify, prioritise and direct the right customers to the right services at the right time. The use of AI-assisted analytics, targeting and service management is still limited, which weakens the effectiveness and scalability of customer management. 3. Manual customer guidance and identification of experts: Combining customer needs with the right services, experts, networks and ecosystems is now largely based on manual data retrieval and the experience of individual experts. The organisation lacks the AI-assisted ability to identify the services, experts and cooperation opportunities best suited to the customer's situation and to support experts with timely recommendations. This slows down the service process, complicates the utilisation of data and reduces the scalability of customer guidance. Target state: The long-term vision is to build an operating model based on a unified customer image, data-driven decision-making, AI-assisted customer guidance and modern data and AI capabilities. The aim is to provide a holistic view of customers.