Why Should Your Organization use AI?

When artificial intelligence is used correctly, it can help you to work more efficiently, make better decisions and create value.

Artificial intelligence (AI) is no longer an emerging technology exclusively used by certain large organizations. It has become a tangible tool for improving efficiency, service quality and competitiveness.

It has vast potential, but one factor must be considered. AI is worthwhile only where it meets a demonstrable need.

A performance lever

AI can help you to improve efficiency. It can facilitate the automation of certain repetitive tasks, speed up information processing and free up time for activities that require decision-making and a human touch.

For example, AI can be efficient when:

• processing documents;
• sorting requests;
• answering common questions;
• analyzing a large amount of information.

The intention is not to replace a team, but to allow your talent to concentrate on tasks where their contribution is more important.

AI can also support your decision-making process. In contrast to a dashboard that shows past events, AI can pinpoint trends and anomalies and quickly focus on what deserves attention.

These benefits can result in:

• improved resource allocation;
• a reduction of certain inefficiencies;
• an enhanced client experience;
• support with certain cybersecurity challenges.

Different AI models have different uses

We often refer to artificial intelligence as a single tool. However, the term encompasses several approaches that meet different needs.

Machine learning

Machine learning is used to plan, file information or make recommendations based on historical data. For example, it can be used to anticipate demand and adjust stock levels in retail stores or forecast equipment maintenance in manufacturing settings.

Deep learning

Deep learning can be useful when the data is complex and in various formats (images, text, audio and signals, for example). Among other things, it can be used to analyze medical imaging files and detect subtle anomalies using industrial sensors.

Generative AI

Generative AI, which includes large language models (LLMs), is useful for understanding, summarizing and drafting content and replying in a more natural manner in addition to generating audio and visual formats. In particular, it can summarize legislation, reports and public consultations in the municipal sector, draft an initial response to a frequently asked question or summarize the history of a customer service file. As a result, businesses can improve efficiency.

Agentic AI systems

Agentic AI systems harness the power of generative AI and take it even further. These systems can consult several sources, link steps and use tools to trigger certain actions. For example, you can leverage agentic AI to receive a request, verify the applicable rules, direct the file to the relevant team in an internal department, alert the right people and initiate an initial tracking process.

Focus on a tangible need

This is often the critical phase. Before launching a project, you should ask yourself which issue you’d like to resolve. Is the process in question recurring? Is it time-consuming? Are there frequent careless mistakes? Does it create irritants for your teams or clients?

AI can also improve existing activities that could generate better results. For example, you could:

• optimize product recommendations for your clients;
• better anticipate maintenance needs for your equipment;
• detect critical signs in your clinical data or client satisfaction rates earlier.

In other words, the right question is not simply “Which technology should I use?” but rather “Where can AI create the most value for my organization?”

This reflection process helps you to avoid scattered initiatives, implementing tools without a clear vision, and projects that generate few tangible results.

Responsible use of AI

AI can deliver tangible opportunities. However, it also presents risks that users must consider.

An AI system can generate a plausible, but not accurate answer. It can also reproduce bias and deliver poor results where roles, rules and validation are not clarified.

From the outset, certain benchmarks must be put in place:

• keeping a human in the loop for making important decisions;
• protecting sensitive and confidential information;
• validating results before using them;
• clearly defining responsibilities and usage rules.

The goal is not to slow down innovation, but rather to take a responsible approach.

Create value by taking a methodical approach

For several organizations, the challenge goes beyond simply understanding what AI can do. It also involves knowing where to begin, what to prioritize and how to make realistic progress.

When AI is used purposefully, it can lead to tangible and sustainable benefits. However, you must define the relevant usage rules, ask pertinent questions and take a methodical approach. During this process, structured support can make all the difference.

AI does not have to be spectacular to be useful. However, you have to provide clear instructions.