AI's impact on digital transformation and digital adoption

A new chapter in the information age, this technology promises unprecedented productivity gains. Here, data takes on an unprecedented dimension, feeding algorithms eager for continuous learning. In this way, AI offers the opportunity to reinvent oneself, to gain in agility and, ultimately, to secure the future of an organization in the digital age.

AI and digital transformation: what are we talking about?

Artificial intelligence and digital transformation are two closely related concepts. The former refers to technologies that enable machines to mimic certain human cognitive capabilities, such as learning or problem-solving. The latter refers to the integration of digital technologies by companies to improve their business processes, customer relations and, ultimately, their business performance.

If we go into a little more detail, we realize that digital transformation involves not only the dematerialization of internal processes and greater empowerment of employees, but also the deployment of innovative customer journeys based on self-care and online interactions.

It's easy to see the link between these two concepts: AI is in fact the driving force behind the digital transformation of businesses at various levels:

• Intelligent automation of back-office processes to boost productivity.
• Fine-tuned personalization of user paths thanks to predictive analysis and BPMN process analysis
• Enhancing employee skills with conversational agents.
• Data-driven decision-making for business managers.

From a disruptive technology just 5 years ago, AI is now emerging as the key ingredient in the winning recipe for digital transformation.

How does AI facilitate digital transformation efforts?

AI automates processes

Automation is one of the main use cases for AI in the digital transformation of companies. In practice, machine learning (ML) and natural language processing (NLP) algorithms can imitate human reasoning to perform tedious, repetitive tasks.

One example is process robotization [link to RPA and AI article when published] (RPA), which uses software bots to carry out tedious manual operations such as data entry, accounting transactions or e-mail management. These digital robots analyze and reproduce human actions in a chain to automate a whole range of critical procedures.

Another example is chatbots , which assist or even replace agents in customer support. Equipped with NLP, they converse with users in everyday language to solve their technical problems or answer their questions. Behind the scenes, they also provide relevant insights to support teams.

Finally, there's the analysis of business processes using BPMN (Business Process Model and Notation). AI is able to automate this sequence to identify the steps in a business process.

Ultimately, it is estimated that the intelligent automation enabled by AI can speed up employees' work by around 35%. By freeing them from low-value-added tasks, it enables them to concentrate on strategic missions that cannot be entrusted to algorithms.

Better decision-making

Beyond automating operational tasks, AI also optimizes decision-making within digitized organizations. Indeed, machine learning algorithms analyze colossal volumes of structured and unstructured data to extract actionable insights.

One example is predictive maintenance in industry, which uses real-time sensor data to detect equipment at risk of failure. Machine stoppages are anticipated and asset availability optimized.

Better customer knowledge and experience

By exploiting the data collected on user journeys, AI enables us to better understand needs and personalize digital experiences.

Predictive marketing is an excellent example: thanks to machine learning, it models the behavior of B2C prospects and customers to anticipate their needs. As a result, campaigns are hyper-personalized and addressed at the right time according to the predefined customer journey.

Competitive advantage  

It's by enabling operations to be optimized, decisions to be streamlined and customer journeys to be made more fluid that AI offers a definite competitive advantage to companies pioneering its adoption.

Insurers, for example, optimize the targeting of potential customers and personalize their offers to boost sales.

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The same is true of the retail sector, where pioneers in data intelligence analytics are seeing their average basket grow faster than laggards, thanks to better customer segmentation and optimized promotional campaigns.

Attracting talent

Finally, by embracing AI and the latest technological innovations, companies are also boosting their attractiveness to talent, those famous "millenials" in search of meaning and agility at work.

They prefer organizations that offer modern working environments where new technologies play a central role.

And AI is precisely what they're looking for, offering them the opportunity to upgrade their skills in disruptive technologies that are set to revolutionize their business. In the future, few jobs will be unaffected by intelligent automation and predictive analysis.

AI is also having a major impact on employer branding, and on companies' ability to attract the skills of tomorrow. Companies that have succeeded in making augmented intelligence an integral part of their information systems will also be able to secure the talent they need to maintain their competitive edge.

How to integrate AI into digital transformation?

The democratization of AI within the information system represents a large-scale project requiring an appropriate strategy. A strategy that involves :

1 - Choosing the right AI technology

The first step is to clarify the priority use cases according to their complexity and potential business impact. Different technologies are required for different use cases.

Once the use cases have been identified, it's a matter of selecting the right technological building blocks from among the many existing solutions.

2 - Feed AI with data

Data is the essential fuel for AI engines. Without massive, high-quality data, it's impossible to train algorithms and make them autonomous in their decision-making.

It is therefore unavoidable to plan ahead for the datasets required for machine learning, according to the use cases envisaged: customer data, sales history, application logs, open data... The challenge is also to keep them up to date, to optimize the reliability of predictions.

3 - Training and adapting employees to AI

Introducing AI inevitably leads to business and organizational transformations that need to be supported. Rethinking jobs in terms of the skills required, or developing a data culture within the company, is essential to its adoption.

Appropriate interactive guidance must be deployed to help employeesassimilation these technologies, a sine qua non for their success. They will be all the more inclined to adopt AI tools if they have the necessary skills to use them and measure their value on a daily basis.

AI is accelerating the digital transformation of companies and impacting their competitiveness in the short term. However, its adoption requires certain technological and human challenges to be met within the IS. If they fail to do so, organizations risk falling behind competitors who are quick to take advantage of AI's potential.

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