Understanding the use of AI in business to boost efficiency and innovation

Today, it's impossible for an organisation to envisage its future without thinking about how it will benefit from artificial intelligence. And that goes for all sectors of activity. At the same time, in the face of competitive pressure, it would be a shame not to exploit the potential of this technology, which is helping us to work faster and more skilfully!

How is AI impacting businesses in concrete terms? How can it be integrated into day-to-day processes, and using which tools?

Automation, text generation, facial recognition, personalisation... there are a whole host of use cases, covering a whole range of different professions. But we still need to use artificial intelligence properly, with full awareness of its limitations and the issues surrounding it...

... and to understand what we mean by AI in business. That's why we need to start with a definition.

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Definition of artificial intelligence

What is AI, and AI in business?

Artificial intelligence defines a set of technologies capable of reproducing human behaviour and cognitive abilities (planning, decision-making, creation, etc.). Whether in our private or professional lives, AI is steadily gaining ground and is increasingly seen as the obvious way to support us in our day-to-day tasks and decisions.

On the pro side (since that's what we're interested in today), the use cases for AI in business are numerous, ranging from text generation to automation, via image analysis and the Internet of Things.

💡 There are several types of artificial intelligence:

• weak AI, specialised in one task (voice assistants, for example) ;
• strong, hypothetical AI, capable of reasoning like an individual.

The different artificial intelligence technologies

Depending on your point of view, there are many different artificial intelligence technologies.

Let's focus on three of them that come up quite often in debates, because understanding them will help you to visualise the range of possibilities that arise from them:

• Machine Learning: this is the famous automatic learning. Here, machines are able to learn by themselves from data, without first being programmed to perform a specific task. The more they analyse, the more accurate they become.
  
  
• Deep Learning: this is a branch of Machine Learning. What makes it special? It processes complex information (images, text, sound, etc.). Inspired by the human brain, it is based on artificial neural networks composed of large volumes of data.
  
  
• Generative AI: we've heard a lot about this in recent years, as it is the technology that enables us, among other things, to produce content (text, images, video and even music). To achieve this, it exploits a large amount of data, including language models (LLMs).

How can AI be used in business? 10 examples

#1 Virtual assistants for everyone

When the subject of artificial intelligence in business is raised, the example that often springs to mind first is that of virtual assistants.

In this case, we are dealing with generative AI. Microsoft Copilot, Google Gemini, ChatGPT... these tools, widely used within organisations, support employees in carrying out their daily tasks.

We can imagine, for example, an HR department using them to draft its job offers, or a sales department to develop its sales pitch.

#2 Personalised recommendations

Well-known to marketing and sales departments, this facet of artificial intelligence relies on recommendation algorithms to analyse users' habits, with the aim of suggesting products, content or services that are tailored to them. The aim: to improve the customer experience even further. 🤩

This is, for example, the strategy employed by Netflix, which suggests films based on your preferences and history.

#3 Intelligent decision-making

Whether carried out by the highest levels of the company, with strong decision-making powers, or by the more operational profiles, the necessary analysis of the company's data would take, a la mano, an incalculable amount of time.

Thanks to machines, large quantities of historical data can be examined in record time, so that the best conclusions can be drawn...

... but also to project into the future, as demonstrated by predictive sales analysis, for example!

#4 Automating administrative tasks

When we think about the use cases for AI in business, we often think about " automating processes and repetitive tasks ".

And this applies to most professions: the accounts department uses AI to process invoices, the HR department to sort out CVs, etc.

#5 Robotisation at the service of industry and logistics

Artificial intelligence and robotisation form a powerful alliance to save time in industrial and logistics operations. Thanks to AI, robots no longer simply execute programmed movements: they are able to adapt and make decisions based on their environment. 🤖

Examples include the machines used by Tesla to assemble car parts, or by Amazon to sort and transport parcels in warehouses.

#6 Facial recognition for surveillance

Facial recognition, which consists of identifying an individual from an image or video, is based on artificial intelligence and computer vision.

In fact, this technology involves studying the features of a face and then comparing them with a database in order to validate (or not) a match. It is used extensively in the field of security, for everything from simple validation of access to a machine or premises, to the identification of criminals by defence bodies.

💡Note that marketing departments also use facial recognition, with the aim of understanding people's emotions when faced with an advertisement.

#7 Real-time fraud detection

It is now common practice for financial institutions to use artificial intelligence to analyse banking transactions and detect suspicious behaviour in real time.

More specifically, AI compares each transaction with the customer's habits (amount, location, type of purchase, etc.). If an anomaly is detected, the system blocks the transaction or sends an alert.

#8 Enhanced cyber security

Cyber security threats are constantly evolving, and attacks are becoming increasingly complex. Artificial intelligence is a real asset for businesses here, as it enables them to identify and respond to attacks in real time.

Using Machine Learning and deep learning algorithms, AI can detect suspicious behaviour... even before a risk has been fully validated! 🛡️

#9 Image analysis for medical diagnosis

The medical field, like all others, is increasingly seeing artificial intelligence at the heart of its processes.

The technology excels, in particular, in the analysis of X-rays, MRIs, scans and other medical images. Using computer vision algorithms, it is able to detect anomalies and diseases much more quickly than the human eye!

#10 Predictive machine maintenance

Finally, let's talk about predictive maintenance, which is used to anticipate equipment breakdowns before they happen.

Here, artificial intelligence is used to process real-time data from sensors installed on machines. In this way, it detects signs of wear and tear or malfunction, to reduce downtime.

Artificial intelligence: advantages and disadvantages for businesses

The benefits of artificial intelligence for businesses

Productivity gains

The first advantage that appeals to businesses is, of course, the promise of time savings and productivity gains.

Indeed, there are many examples of machines performing tasks in place of humans: answering emails, entering invoicing data, sorting CVs, and so on.

As a result, AI frees up time for employees, who can then focus their efforts on higher added-value tasks. The company becomes more efficient, and reduces its costs!

A more refined strategy

Artificial intelligence supports businesses in their prediction and analysis work, thanks to the mass processing of data (Big Data).

As a result, organisations are able to fine-tune their strategy, and even stay one step ahead of their competitors by being able to react quickly to future consumer trends and changes in consumer behaviour.

Improving the customer experience

In marketing and customer relationship management, artificial intelligence provides a response to this major challenge: how can we move towards ever greater personalisation, at a time when interactions are taking place less and less in person?

As you might expect, AI is once again emerging as the solution. And with good reason, it can personalise the customer experience based on their preferences and buying behaviour.

💡 Let's not forget that, at the same time, increased consumer satisfaction also means saving the time mentioned above! Working faster means irrevocably satisfying their demands for speed and responsiveness.

Greater agility and innovation

Artificial intelligence enhances the agility of businesses, making them more ready to adapt quickly to changes in the market. Organisations can now adjust their strategies in real time, without delay (price optimisation, production, marketing actions, etc.), and react swiftly to changes in their environment.

In short, AI stimulates innovation by offering the flexibility required for competitiveness. 💪

Limits and issues surrounding AI in business

Of course, such an upheaval in our professional and private lives is not without raising certain questions and fanning a few fears!

First of all, every company needs to consider the ethical issues involved in using consumers' personal data. For example, when they collect data for their systems, they need to ensure that they comply with the requirements of the RGPD.

But ethics also means not getting caught up in a vicious circle, in which a self-perpetuating system risks reinforcing a single way of thinking and discrimination that is already (unfortunately!) entrenched. Let's take the case of recruiters: they need to find the right balance between human intervention and automation, otherwise they run the risk of missing out on certain "atypical" profiles that don't fit the statistical standards.

Finally, there is undoubtedly the issue of business transformation. Let's face it, AI is scary. However, more than the disappearance of certain jobs, it is their transformation that is at stake. As a result, organisations need to take this issue seriously, supporting their employees as much as possible with training and a solid change management approach.

Implementing AI in companies: an example

Faced with the challenges posed by AI, implementing it within companies is no mean feat! You need to be able to deploy systems that are both robust (to support large amounts of data) and secure.

👉 Here's an example of a process for a smooth and successful integration.

• Define your objectives. Artificial intelligence takes on many faces. And given how cumbersome it is to implement, it's hard to instil it uniformly throughout all the strata of your organisation. So define your priorities, the objectives you want to achieve using artificial intelligence. More productivity? More personalisation? Better decision-making?
  
  
• Select the appropriate technologies. AI solutions vary depending on the use case. You can go for Machine Learning, Deep Learning, or even NLP for tasks related to textual data.
  💡If you prefer turnkey, turn to a global business management solution that will harness the potential of AI for you. Such is the case with SAP S/4HANA, an innovative ERP developed to optimise numerous processes thanks, among other things, to artificial intelligence. As well as automating a multitude of tasks, it provides powerful real-time analyses, even of large amounts of data, for better decision-making.
  
  
• Rely on the right people. Implementing an impactful AI strategy requires a wide range of skills. An interdisciplinary team is essential: data experts, artificial intelligence scientists and project managers need to work hand in hand. Not forgetting the commitment of management, which is essential if the technology is to be adopted within the organisation!
  
  
• Collect and prepare the data. AI relies on quality data. Identify internal and external data sources, then ensure that they are clean, complete and well structured.
  
  
• Integrate artificial intelligence into business processes. Once the AI models have been validated, it's time to integrate them into existing processes, via software platforms (cloud or otherwise) or internal applications. The aim? Make AI accessible to all users, without disrupting day-to-day activity.
  
  
• Test and iterate. The AI deployment process never stands still. Regularly test the performance of AI and adjust the models according to the results. Also set up a technology watch procedure to maintain the relevance of your models over time.

💡 As a reminder, it's important to get employees on board when faced with such an upheaval. We therefore advise you to set up training sessions, which will help teams to understand AI and its impact on their work. In fact, we invite you to visit the economie.gouv.fr page to discover examples of quality training (theoretical, practical, more targeted on ChatGPT for example, etc.).

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What does the future hold for AI in business?

Artificial intelligence is profoundly transforming businesses. To remain competitive and innovative, they now understand how important it is to deal with these new technologies.

However, as AI evolves, new challenges are emerging, particularly in terms of data security, ethics and the impact on jobs. This is why the future of artificial intelligence must be based more than ever on human-machine collaboration.

This synergy will open up new opportunities, enabling employees to take advantage of the analytical capabilities of artificial intelligence while preserving their creativity and critical thinking. Companies that master this interaction will have a decisive advantage in tomorrow's digital economy. Are you ready for it?