Technological development makes it possible to collect a wide range of useful data. Thanks to artificial intelligence (AI), businesses can capitalize on a focussed and relevant analysis of this information.
The answer to tangible and complex business issues can be found in corporate data. Analytical solutions to problems are a means of optimizing both finances and productivity.
The productivity of onsite resources can be increased by including algorithms in the decision-making processes. In the manufacturing sector, for example, algorithms make the advanced detection of breakdowns in the production chain possible or can be used for the automated sorting of compliant products.
These solutions rely on data provided by industrial sensors that are currently in place or by low-cost devices, such as cameras or specific sensors that can be installed after the fact. Other applications, such as predictive maintenance, make it possible to boost the efficiency of physical and financial resources used in a manufacturing environment.
Clients in the financial and professional services sectors use algorithms to automate low added-value processes. For example, character recognition can be used to automate data entry.
Algorithmic analyses of financial market results, news and annual reports make it possible for a portfolio management team to prioritize its research work. In the logistics and distribution sector, analysing changes in inventory levels and client demands helps organizations use their storage space more efficiently to reduce inventory shortages and customer dissatisfaction.
The data produced can be used to develop solutions that assist departments making strategic and tactical decisions to generate new income. Improved knowledge about clients and their behaviour can be used, for example, to develop initiatives to get their attention and thus meet their needs.
Our AI expertise is leveraged by manufacturing entities to improve their knowledge of clients and detect business opportunities by analyzing past activities and the history of interactions with clients. Analytics are then used to pinpoint where irritants are generated in the client experience or to detect opportunities for cross-sales or supplementary services.
Additionally, thanks to transaction histories and external data cross-checking, clients can be targeted individually during their life cycle. Predictive models can then be used to increase the conversion rate for acquisitions and cross-sales, foster client engagement and reduce attrition rates.
Depending on the available data, these models will identify clients who require special attention. If you know which clients have a high risk of leaving the organizations in the coming weeks, what could be better than preparing a retention offer and acting before they make their final decision?
With this income protection initiative, supported by the analytical component, retention efforts can be invested in the most strategic areas. Similarly, predictive models for product and service cross-sales can be developed to target clients with a strong propensity to convert and thus increase revenues.
Improved client experience
AI can also be applied to improve the client experience. In recent years, the digitized client relationship has significantly changed client expectations in terms of service time, quality and accessibility. Clients want products and services more quickly, they want businesses to be attentive to their needs at all times and they want quality at a lower cost.
In the manufacturing sector, an optimized production line can then reduce production times by anticipating equipment or resource issues, reduce client wait times by anticipating demand and optimizing procurement and improve quality by automating parts of the quality assurance process and controlling input factors. Naturally, similar applications can be implemented in other industries.
AI allows a business to exploit the quantity of data it generates in order to optimize and meet specific business needs. Although it can be used in various situations, it’s important to use it, first and foremost, to support the entity’s mission and bottom line, that is, increase revenues, reduce costs and improve the client experience. Integrating it into a business strategy calls for vigilance.
Did you know?
There are scientific research and experimental development investment tax credits available. The scope of these programs may have been curtailed in recent budgets, but they are still one of the most generous sources of financial assistance in the country. In some cases, combined Canada/Quebec credits can be as much as 70% of expenses.