Identify the business processes in the finance function that would benefit most from digitization

The digitalization of companies impacts the finance function. The need to conduct a digitalization project is now a given for the vast majority of CFOs. To carry out this mission, it is necessary to identify the data, business processes and procedures that are most sensitive to digital transformation, before implementing the vast possibilities offered by new technologies: dematerialization, robotization, artificial intelligence, etc.

Strategy FINANCE

Develop a strategic vision of the digitalization of finance

If there is unanimous support for digitalization today, it’s because of the realization of the considerable potential made available through new tools such as the cloud, as well as the precision of advanced analyses of the exponential number of data available.

It is no longer just a matter of achieving a few minor productivity gains or improving risk prevention by reducing the probability of human error. On the contrary, the digitalization of the finance function is now a means of creating value and strengthening the company’s position in a competitive market.

New analytical technologies make it possible to process large volumes of data, leading to increasingly precise analyses that can be updated in near-real time.

This transformation concerns all of the company’s departments, the business processes, the sharing of information to reinforce its reliability and its mobilization by the departments concerned. By having key information about, for example, changes in consumer behavior, CFOs will be able to simulate development models that will help the company’s management make decisions.

The earlier opportunities are identified, the more effective and potentially value-creating decisions will be. The finance function is now mobilizing proactively in its digital transformation and is leading all other departments with its dynamism. A real race to digitalization is underway between companies in the same sector of activity, in order to obtain decisive competitive advantages.

Optimize data and transactional processes

Digitization enables significant productivity gains to be achieved internally.

Dematerialization, supported by various communication and sharing tools (cloud, intranet, social networks, etc.), improves the circulation of data and digital documents. Dashboards and forecasting studies can be generated almost immediately, thanks to data optimization.

However, the multiplicity of servers and the fragmentation of the IT landscape within the company represent major obstacles to accelerating processes. The reliability of information is also affected.

That’s why it’s often preferable to choose a global business platform and ERP suite capable of processing a considerable amount of data, to deliver consistent information across all departments of the company.

However, this choice is sometimes costly and requires a deployment over several years. Also, this approach to optimizing data and transactional processes is mostly favored by large groups. Externally, digital transactional platforms have an interest in improving trust and customer relations, particularly with regard to billing management.

Set up pilots to obtain quick wins

Quick wins” solutions are designed to respond to a given situation in a short space of time, at limited cost and risk.

They are particularly suitable for starting a digital transformation project. Achieving quick wins will convince management of the benefits of digitalization, and give company staff the opportunity to perfect their practical skills.

This may involve automating dashboards, sales planning, anti-fraud checks, or even facilitating account reconciliation for accounting purposes.

While quick wins can address certain process improvement needs on a case-by-case basis, they are not sufficient on their own to deploy digitization on a large scale. They are often limited to data entry and data storage automation operations. Moreover, their operational character is largely conditioned by the quality of the basic data.

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