Artificial Intelligence: Numerous Benefits for Businesses

Artificial intelligence in business can leverage targeted and relevant analysis of your data. What are the benefits?

The answer to tangible and complex business issues can be found in corporate data. Analytical solutions to these issues enable you to optimize your financial plan or productivity.

Improve productivity

The productivity of onsite resources can be increased by including algorithms in the decision-making processes.

Manufacturer

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.

Finance

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.

Services

Increasingly powerful conversational tools multiply points of contact with customers and free up talent within the company to make the most of human skills.

Increase your income

The data produced can be used to develop solutions that assist departments making strategic and tactical decisions to generate new income. Among other things, improved knowledge about clients and their behaviour can be used to develop initiatives to get their attention and thus meet their needs.

For example, manufacturing companies can gain a better understanding of their customer base and identify business opportunities by analyzing past activities and the history of interactions with their customers. This allows them to analytically identify the stages of the customer journey that generate irritants, or to detect business opportunities for cross-selling or additional services.

Predictive models

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.

Improve client experience

Artificial intelligence (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.

The rise of AI in all sectors of activity requires companies to develop a well-thought-out strategy to evolve with their market and remain competitive. By surrounding yourself with a competent team, you will be guided in the right direction to gradually achieve your goals, according to your resources and needs.