iA Financial Group is one of North America’s largest insurance and wealth management conglomerates, publicly traded on the Toronto Stock Exchange. Its core sectors include individual insurance, individual wealth management, group insurance, and group savings and retirement.
In a data-rich environment spread across multiple departments and systems, it is challenging to select and prioritize AI projects that offer the best return on investment. Establishing a clear direction for integrating AI to maximize data value is also difficult.
Videns was tasked with supporting the individual sector of its subsidiary, Industrial Alliance, in developing a strategic roadmap tailored to the sector and aligning it with corporate ambitions.
Implementation of a Data Train in an agile delivery mode to sequence the execution of data-related initiatives and enhance the visibility of analytics within the organization.
The main objective for iA Financial Group was to develop a two-year roadmap that would provide a logical, beneficial, and strategically aligned course of action for the company.
As part of the various workshops, Videns interacted with different leadership groups such as risk selection, actuarial, marketing, sales, and product development. Coordinating teams and individuals from multiple sectors of the organization was a significant challenge.
The developed roadmap delivered an improved return on investment and expedited alignment with strategic initiatives.
Videns worked with multiple leadership teams through workshops and interviews, enriching the overall perspective on information gathering.
The development of the comprehensive roadmap facilitated the identification and prioritization of more than 280 AI initiatives.
Through interactive workshops and individual meetings, data valuation maturity assessments, and an in-depth SWOT analysis, we developed and deployed a detailed roadmap comprising over 280 initiatives. This roadmap included timelines, effort/expectations, responsibilities, tasks, as well as dependencies and constraints. The various use cases were prioritized based on business value, internal value, and various feasibility criteria.
