Since ChatGPT's arrival in 2022, AI adoption has accelerated at breakneck speed. But for the majority of executives, its concrete use in business remains unclear. We talk about it a lot; we understand its potential impact... but we don't really know where to start.
The aim of this article is to help you understand artificial intelligence and, above all, to see how it can become a concrete lever for your organization.
Artificial intelligence (AI) is neither a magic wand, nor a conscious machine, nor an autonomous entity. It's a set of technologies that enable computer systems to learn and reason from data.
In practice, AI is used, among other things, to :
You're already using AI, even without knowing it: spam filters, Netflix recommendations, voice recognition, search engines... AI is everywhere. What's changing today is that these capabilities are becoming accessible to companies of all sizes, including SMEs. They can be more easily integrated with business tools (CRM, ERP, support, office suites) and automate business processes such as invoicing and quote generation.
The history of AI dates back to the 1950s, when British mathematician and cryptanalyst Alan Turing proposed that a machine could simulate any human reasoning process. This idea led to the creation of the Turing Test, a method of determining whether a machine could think like a human. In 1956, the term "artificial intelligence" was first used at a conference at Dartmouth College, marking the official beginning of the field of AI.
The first decades of AI research were marked by considerable optimism, but also by technical challenges. Researchers quickly realized that certain tasks, such as recognizing objects in an image or understanding natural language, were far more complex than expected. In the 1970s and 1980s, initial enthusiasm was followed by periods of stagnation, sometimes referred to as "AI winters", when progress was slow, and funding was limited.
However, from the 1990s onwards, advances in hardware and algorithms led to a resurgence of interest in AI. Breakthroughs in machine learning and deep learning, fueled by increasing data and computing power, led to practical applications and notable successes, such as IBM's Deep Blue program defeating world chess champion Garry Kasparov in 1997.
Three accelerators transformed the destiny of AI:
As a result, AI has gone from being a primarily academic subject to an operational lever for businesses everywhere.
In 2026, AI continues to develop at an impressive pace. Advances in machine learning, deep learning and computer vision mean that complex data can be processed and decisions made with greater precision.
Quebec is world-renowned for its research in artificial intelligence. Montreal has established itself as an international hub, thanks in particular to institutions like Mila, the presence of leading universities, a pool of machine learning talent and a rapidly evolving community of technology companies.
But this scientific leadership does not always translate into rapid corporate adoption. On the field, there is a mismatch between the strength of the ecosystem and the speed of deployment within organizations, particularly in SMEs and highly regulated or operational sectors (manufacturing, insurance, healthcare, finance).
So the issue isn't "Is Quebec ready for AI?" Rather, it's: does your organization know where to start turning a good ecosystem into measurable value?
If AI is advancing more slowly on the field, it's not for lack of interest. It's because the obstacles are often organizational, not technological:
This gap creates a very interesting window of opportunity: organizations that start now with a structured approach can gain a real advantage.
Behind the word "AI" lie several technological bricks. For an organization, the challenge is not to become an expert in each technology. Rather, it's a question of understanding the range of new opportunities that arise from these bricks.
Machine learning enables you to make better decisions based on your historical data. It's used when you want to automate logic based on real patterns, rather than hand-written rules.
What does it do?
Deep learning is a sub-category of machine learning, particularly effective when data is complex or unstructured.
What does it do?
What this means for the company :
LLMs (Large Language Models) are at the origin of tools such as ChatGPT. They are designed to understand and produce text, making them extremely relevant for organizations where work is carried out via documents, e-mails, procedures and ticketing systems.
What it's really good for
To be useful in a company, an LLM often needs to be connected to the company's knowledge (internal documents, databases) via software development. Otherwise, it remains generic.
Generative AI refers to systems capable of creating content: text, images, code, audio and so on. In business, its value lies mainly in its ability to accelerate and standardize production, while keeping a human in the loop to validate.
What does it actually do?
To deploy generative AI in the enterprise, you need to frame it from the outset:
It's this trio that transforms an impressive AI demo into a reliable, governed tool that can be deployed at scale.
With agentic AI, AI doesn't just suggest: it acts in your systems by chaining steps together within a defined framework (e.g., search for info, decide, execute an action, then confirm).
What does it actually do?
Software engineering is the foundation on which artificial intelligence can truly create value. Without it, AI remains an isolated capability; with it, it integrates with tools, data, and business rules to become a reliable, secure, and cost-effective solution.
In concrete terms, engineering serves to:
While many companies are still hesitant to embark on an artificial intelligence project for fear it will be complex and expensive, others believe a few ChatGPT licenses will be enough to revolutionize their operations.
The reality lies somewhere between the two perceptions. The real value comes when AI changes a process, not just when it delivers isolated gains to a few individuals. AI solutions can be grouped into four categories: from the simplest to deploy to the most structured.
Generic "plug-and-play" tools (often licensed) that improve individual or team productivity, e.g. for writing, summarizing, searching or generating drafts, with little integration with internal systems.
Solutions designed for a specific industry or business process (e.g. insurance, support, finance) are generally quicker to deploy because they already integrate business logic and common scenarios.
Technology bricks that provide access to ready-to-use AI models from major suppliers such as Google, Microsoft, and Amazon. They enable you to reduce costs and accelerate deployment by building on proven models, then integrating them into your applications and workflows (sites, intranets, CRM/ERP, customer portals) by connecting them to your data, business rules and systems. The result: software engineering firms can develop reliable, secure and rapidly deployable solutions, without having to train a model from scratch.
Solutions developed specifically for your organization to automate or optimize an end-to-end process, taking into account your data, integrations, security constraints and governance requirements.
The table below summarizes the differences between these types of solutions to help you select the level best suited to your context.
|
Out-of-the-box AI tools |
Vertical AI solutions (industry/business) |
Platforms and APIs |
Customized AI solutions |
|
|
Typical value |
Individual efficiency (people work faster and better) |
Organizational gains on a targeted business process |
Organizational gains through multi-system integration and automation |
Organizational gains and competitive advantage (differentiation) |
|
Who benefits? |
An employee (or small team) |
A team, a role, a business line (e.g. support, HR, finance) |
A department or several departments; sometimes the entire company |
The business (operations, service, compliance, revenue) |
|
What it improves |
Writing, synthesis, research, ideation, and office productivity |
Standard process execution: triage, processing, qualification, compliance, reporting |
Cross-functional processes: orchestration between tools (CRM/ERP/helpdesk), customer portal, intranet, automation |
Critical processes specific to your organization (data + rules + integrations + governance) |
|
Complexity |
Low |
Low to medium |
Medium |
Medium to high |
|
Cost |
$ - $$ |
$$ |
$$ - $$$ |
$$ - $$$$ |
|
Deployment |
Fast (days/weeks) |
Rapid short-term (weeks) |
Short to medium term (weeks/months) |
Medium to long term (months) |
|
Tools |
Generic (general public/company) |
Industry-specific, customizable |
Technology bricks (LLM, OCR, speech, vision, RAG, orchestrators) to be integrated |
Customized (built around your data/processes) |
|
Risk |
Mainly usage/confidentiality (safeguards, internal policy) |
Operational (poor configuration, supplier dependency, data) |
Integration/security (access, permissions, traceability, compliance) |
Project/governance (scope, data quality, adoption, security) |
|
Example |
ChatGPT/Copilot license to help an employee write e-mails, summarize documents or prepare a presentation |
AI tool for insurance claims processing (classification, extraction, prioritization) |
Integrate an AI assistant on your website/intranet connected to your knowledge base + CRM via API |
Automate end-to-end invoicing (extraction, checks, exceptions, ERP updates, auditing) |
When an organization wants to get started with artificial intelligence, there is often confusion about the different AI service providers. Indeed, developing AI models is not the same thing as deploying AI within enterprise systems. Both are complementary, but address different needs.
These are organizations specializing in data science and model training (machine learning and deep learning). They excel when the main issue is model performance on a specific problem.
What they typically do:
When it's the right choice:
A frequently observed limitation is that even an excellent model can remain unused if:
In short: they make AI smarter, but they don't always make the solution operable at scale within your organization.
A software engineering firm focuses on transforming an AI capability (existing model, LLM, OCR, search engine, agent, etc.) into an operational product integrated into your enterprise and business processes.
What they typically do:
When it's the right choice:
For the majority of companies, the value of AI does not lie in creating a new model, but rather depends on :
Important: A good software engineering firm is not a coding factory. It's a partner who, before writing a single line of code, helps customers identify their best opportunities and seize them as simply as possible to accelerate and maximize the impact of their technology investments.
To make a decision, reduce it to a simple question: where is your main challenge? If your challenge is to obtain a high-performance, specific model (for complex scoring, for example), an AI company specializing in model creation and training is often the best option. If your challenge is to put AI into production in your operations by connecting it to your data, integrating it with your systems, and making sure it's secure, traceable, and usable by your teams, then a software engineering firm is the right choice.
And in many cases, the most effective strategy is to build on existing models and technologies and invest in solid integration to turn this capability into measurable results.
Artificial intelligence has applications across a multitude of sectors, transforming how companies and individuals operate. The fastest gains rarely come from "science fiction" projects. They come from very concrete use cases, such as the following.
AI is used for medical diagnosis, disease prediction and treatment personalization. AI systems can analyze medical images, detect anomalies and suggest diagnoses with an accuracy comparable to, or even superior to, that of human experts. What's more, AI enables drugs to be developed more rapidly by analyzing vast sets of genomic and clinical data.
In the financial sector, AI improves fraud detection, risk analysis and investment management. Machine learning algorithms can identify suspicious transactions in real time, minimizing losses due to fraud. AI systems also help financial institutions assess credit risks and optimize investment portfolios by analyzing complex economic data and predicting market trends.
Education also benefits from advances in artificial intelligence. AI systems can tailor learning experiences to students' individual needs, adapting content and teaching methods. E-learning platforms use AI to provide course recommendations, assess student performance and offer personalized support. In addition, AI enables the development of adaptive learning tools that help teachers identify students' knowledge gaps and adjust their teaching strategies accordingly.
For Collège Sainte-Anne, Nexapp has developed an AI-powered application that reduces correction time for French teachers by up to 75%.Emilia's algorithm corrects with great precision, following the teachers' correction grid, while leaving them full control to adjust the correction to the notions taught, add audio comments and approve the final mark. What's more, the teacher has an overview of the skills of his or her class, and Emilia suggests sub-groups according to the skills to be worked on, thus freeing up more time.
In the insurance sector, artificial intelligence creates value especially where large volumes of information need to be processed quickly, such as e-mails, forms, PDFs, supporting documents, file notes, and exchanges with third parties. In concrete terms, AI can speed up the processing of a claim by summarizing a file, automatically extracting key fields, classifying and prioritizing requests, and then preparing responses or communications consistent with internal policies. It is also used to better manage risk, notably by spotting inconsistencies or atypical patterns that may indicate fraud, helping direct investigations to the most relevant files without replacing teams' judgment.
Nexapp recently supported Azimut Lab, an InsurTech specializing in the development of tools for insurance brokers, in the development of an AI-powered solution for end-to-end billing automation. From strategy workshop to implementation, initial results were achieved in just two months, resulting in cost savings of $700,000 over 5 years.
In manufacturing, one of the most common uses is analyzing equipment and sensor data to anticipate failures, plan maintenance at the right time, and avoid costly interruptions. AI is also highly effective in quality control, particularly through computer vision, which can detect defects, anomalies, or variations that are difficult to spot with the human eye while standardizing inspection at scale. Beyond production, it can improve planning, inventory management and procurement by identifying trends and recommending adjustments, thereby reducing waste and increasing yield.
Vibrotech, a manufacturer of vibratory handling equipment offering over thirty configurable products, was facing a long and complex estimating process, with critical knowledge concentrated among a few experts. Nexapp implemented a customized GPT agent, controlled by a dedicated server, capable of generating a flat-rate estimating grid for its best-selling equipment (around 40% of estimates), based on project history, materials table, design data and business rules.
The result: faster, standardized estimates, fewer calculation errors, more files processed without increasing the team, and experts freed up for higher value-added tasks. The project resulted in a 30% reduction in time per estimation, and a 20% increase in the annual volume of estimation cases processed.
In the environmental field, artificial intelligence is used to detect anomalies in a series of measurements (sensors, consumption, air or water quality) to identify deviations that indicate a potential problem and trigger alerts or interventions earlier. It is also used for forecasting, for example, by modelling trends and anticipating variations linked to weather, energy demand or operational conditions, helping to improve planning and reduce waste.
One of the main benefits of artificial intelligence for businesses is improved efficiency and productivity. AI systems can automate repetitive, tedious tasks, allowing employees to focus on higher-value activities.
Another major advantage of AI is its ability to process large amounts of data and extract valuable information. Machine learning algorithms can analyze massive datasets and identify hidden trends and patterns, enabling companies to make more informed decisions.
Artificial intelligence also helps to improve the quality of products and services. AI systems can monitor and control production processes in real time, detecting and correcting errors before they become major problems. What's more, AI enables the development of more personalized products and services, which better meet customer needs and preferences.
In short, AI becomes a lever when you link it to measurable results:
Investing in artificial intelligence is not just a technological project. It's a business decision.
Despite its advantages, artificial intelligence has its limits. And it's precisely for this reason that it should be approached as a management and engineering issue, not simply as a feature to be activated.
The quality of results depends directly on the quality of data. Yet in many organizations, data is incomplete, scattered between several systems and often unstructured (PDFs, e-mails, notes, scanned documents). Added to this is the presence of sensitive data, which requires rules for access, storage and processing. Without clarification of what data is reliable, where it resides and who can access it, even the best AI will produce inconsistent results. Another challenge comes from bias: AI learns from historical data, and if this data reflects imbalances or imperfect decisions, AI can reproduce or even amplify them.
Modern models, especially language models, can produce very convincing answers... even when they're wrong. These errors (often referred to as hallucinations) make validation mechanisms essential, especially when AI is used for customer service, compliance or business-critical decisions. Reliability also depends on traceability: it must be possible to know which sources an answer is based on, so as to quickly verify it and improve the system. Finally, we must not confuse power with autonomy: today's systems are very powerful, but remain specialized and context-sensitive, which calls for framing, validation and realistic expectations regarding the accuracy of information generated by AI.
Deploying AI means managing access to your systems and information. This raises concrete issues: control of permissions, choice of hosting, secure management of keys and secret DES, prevention of data leakage and separation of environments (test/production). Useful AI is connected AI, and anything that's connected needs to be secure, monitored and auditable.
Many projects fail not because the AI is "bad", but because it's not in the right place. If AI isn't integrated with the tools your teams actually use (CRM, ERP, support, intranet, customer portal), it remains a demo or an isolated tool. Integration also means managing rights, processes, audit logs and ease of use: if the flow is heavy, adoption drops and value disappears.
Finally, AI must be governed as a corporate asset. This means defining usage rules (what is allowed, what is forbidden), clarifying responsibilities (who validates, who owns the system, who responds in the event of an incident), monitoring performance (quality, adoption, ROI) and managing risks over time. Without governance, we often observe two extremes: either AI is blocked out of caution, or it is used without control, leading to inevitable drifts.
Remember: AI is not inherently dangerous. The risks come mainly from poor governance, insufficient data and unrealistic expectations. Here are the five areas to watch out for when deploying AI in business.
The impact of artificial intelligence on employment is a subject of debate and concern. On the one hand, AI has the potential to create new job opportunities and stimulate economic growth. AI technologies can generate jobs in areas such as software development, data analysis and cybersecurity. In addition, AI can improve worker productivity by automating repetitive tasks and providing support tools.
On the other hand, AI could lead to job losses in certain sectors. Routine and manual tasks are particularly vulnerable to automation. For example, jobs in manufacturing, logistics and customer support services could be replaced by robots and AI systems. This automation could cause economic and social disruption, particularly for low-skilled workers.
To mitigate the negative effects of AI on employment, it is essential to implement training and retraining strategies. Governments, businesses and educational institutions must work together to offer continuous education and skills development programs. The goal is to prepare workers for the jobs of tomorrow and help them adapt to technological change. By investing in education and training, we can maximize the benefits of AI while minimizing its negative impacts on employment.
Key takeaway: In most cases, AI transforms roles rather than eliminating them. It enhances capabilities, automates certain tasks and increases the value of human labour. Specifically, it changes:
the nature of the work (less execution, more supervision/exception management),
skills (writing, analysis, validation, tool management),
processes (review, approval, quality control).
For an executive or manager, the goal is not to become a data scientist. The goal is to make good decisions and turn AI into measurable results. In concrete terms, this means knowing how to identify a profitable use case, frame the risks (data, security, compliance), ask the right questions of suppliers and steer a deployment without losing control over quality, costs and governance.
Online training courses are often the best starting point for rapidly acquiring a common vocabulary and understanding key concepts (AI, generative AI, limits, best practices). They also enable you to test tools and understand what is realistic without mobilizing a technical team. The important thing is to choose training that focuses on use and decision-making, rather than overly theoretical content: you're looking to understand how AI applies to processes, how to measure value, and how to avoid common mistakes.
Most Canadian programs and management training courses offer online versions of their activities.
If you want to structure your learning within a recognized framework rooted in local reality, Canadian programs are an excellent option. They often have the advantage of addressing concrete issues encountered by organizations here: business adoption, skills, process transformation, and security and compliance constraints. They are also a good lever for aligning several managers around a common understanding and accelerating decision-making.
Here are a few examples:
Training courses for managers generate the most value in the short term, because they address the questions that block execution: "Which use case should I choose?", "How do I calculate ROI?", "What risks should I manage?", "What does a successful AI project look like?". They help you build a realistic roadmap, define governance rules, frame the use of generative AI and avoid the trap of projects that are too big or poorly prioritized.
Nexapp is also a partner in the InnovIA program, which supports managers in addressing IT issues and structuring decisions related to the adoption of new technologies, including artificial intelligence.
Here is a non-exhaustive list of organizations and events that deal with artificial intelligence, keeping in mind the context of managers:
Would you like to accelerate your deliveries by adopting AI in your software development teams?
Nexapp can help you deploy AI in your engineering practices to make it a real performance driver in your development teams. Find out how.
The ethics of artificial intelligence are a growing concern as the technology becomes integrated into our daily lives. One of the main ethical issues is data confidentiality. AI systems often require large amounts of data to operate effectively, raising questions about how this data is collected, stored and used. It is crucial to guarantee the protection of personal data and user control over their information.
Another ethical issue is autonomous decision-making by AI systems. When machines make decisions that affect human beings, it is essential to ensure that they are fair and transparent. For example, in criminal justice, AI algorithms are used to assess the risk of recidivism. If these algorithms are biased, this could lead to unfair and discriminatory decisions. It is therefore important to put in place mechanisms for hearing and regulating AI systems.
AI ethics also involves considerations of social and economic impacts. Automation and AI can exacerbate existing inequalities and create new forms of disparity. For example, low-skilled workers are more likely to be affected by automation, while the economic gains of AI may be concentrated in the hands of a few technology companies. To promote the fair and responsible adoption of AI, policies and regulatory frameworks that address these ethical issues are needed.
To remember: AI must be governed like any strategic asset. And well-governed AI protects your brand, your customers and your teams. This means: transparency, data protection, explainability, human accountability and use case guidance.
In our view, the future of artificial intelligence will be less spectacular than imagined... and much more concrete. For the majority of organizations, the next phase will not be to adopt an AI tool, but to live in a world where AI becomes a standard layer of software, on par with cloud computing or cybersecurity. In other words, it will become less visible but increasingly ubiquitous. The companies that will derive the most value from this will not be those that test the most novelties, but those that learn to integrate AI into their processes in a reliable, secure and measurable way.
One structuring trend is the rise of agents and end-to-end automation. We are gradually moving from AI that assists (summarizing, writing, suggesting) to AI that performs tasks within a defined framework: opening a ticket, classifying a request, retrieving information from multiple systems, preparing a response, triggering a workflow step, then requesting human validation when required. This approach promises significant gains, but also increases engineering requirements: permissions, validations, auditing, security and monitoring.
At the same time, AI will increasingly combine with connected systems and field data, notably via the Internet of Things (IoT). In manufacturing, logistics or energy, value will increasingly come from continuous analysis (sensors, events, anomalies), incident prevention and operational optimization. In these contexts, AI does not replace human decision-making: it enhances the ability to detect, prioritize and act quickly.
Explainable AI (XAI) will also see progress, driven by a growing need for transparency, particularly in sensitive sectors (healthcare, finance, insurance, public services). The challenge is not just to produce a result, but to be able to answer questions such as: "Why this recommendation?", "On what data?", "What limits?", "Who validated?". This transparency is a key factor in trust and adoption, and becomes a competitive advantage when AI is integrated into critical processes.
Finally, like any major technology, AI is going through a period of over-mediatization: some promises are exaggerated, some projects will be disappointing, and there will be adjustments. But the underlying trend (AI integrated everywhere, automation and the transformation of work) is here to stay. The best posture is neither naive enthusiasm nor paralyzing skepticism: it's a pragmatic, value-oriented approach, with measurable use cases and solid governance.
In Quebec, this dynamic is particularly important: the ecosystem is strong, and support options are numerous, but the advantage will go to those organizations that transform this favourable context into execution. In the medium term, the main risk is not making a mistake when testing a project. The real risk is doing nothing, accumulating operational delays and letting others standardize gains you could have captured earlier.
For many organizations, the primary obstacle to adopting artificial intelligence is not interest: it's the perceived cost and risk associated with a poorly framed project. The good news is that in Quebec and Canada, there are levers available to reduce this financial risk and accelerate the transition to action. Depending on your sector, the size of your company, and the nature of your project, you may have access to subsidy programs, government support programs, tax credits for innovation and development, as well as specialized support resources to structure and deliver your initiatives
Going from "we should be doing AI" to "we have a project that delivers" doesn't require a huge leap in technology. What works is a simple approach: target what will really pay off, build quickly and well, validate early with users, then adjust quickly. This approach enables us to bring AI projects to fruition quickly, with measurable and rapid ROI, while limiting risk.
The starting point is not technology: it's a concrete business problem. A good AI use case has three characteristics: it affects a process with a real irritant (time, costs, quality, deadlines), it is frequent enough to have an impact, and it is measurable. In practice, the best first projects are often linked to information processing (documents, e-mails, tickets), internal research, support, compliance or the preparation of answers and reports. The aim is to choose a case where AI can produce a tangible gain in a matter of weeks, not years.
Before you start building anything, you need to define your success criteria. How many minutes are saved per file? How much time was saved? What error rate will be reduced? What additional volume will be processed with the same number of staff? This step avoids the "impressive demo" effect, which does not translate into impact. It also enables you to prioritize correctly: if you can't measure it, you shouldn't embark on the project.
A useful first deliverable beats a perfect project that never happens. The approach is to deliver an initial version that works within a clear scope, with the right safeguards, and then improve it. This means integrating, from the outset, what makes the solution deployable: data access, permissions, validation, traceability, and a simple user experience.
Adoption is often the real limiting factor. We quickly involve the relevant teams (support, sales, operations, finance) to test with real cases, understand irritants, adjust the results format and clarify when AI should help... and when it should pass the baton to a human. The shorter the feedback loop between users and the team developing the solution, the more certain we are of building a solution that will be adopted and have the desired impact.
Once the value has been demonstrated, we expand: more volume, more cases, more integrations. The important thing is to maintain the original discipline: measure, adjust, secure. This is how AI becomes a sustainable lever rather than a one-off experiment.
In short, the best way to succeed with AI is not to launch a major project. It's to string together short, focused, profitable projects with solid integration and clear metrics. This is exactly the kind of approach Nexapp implements to help organizations turn AI into measurable results, fast.
A set of techniques enabling systems to learn from data to predict, recommend, rank or generate content.
AI that generates content (text, images, code). Widely used to speed up writing, support, documentary research and the creation of summaries.
It has developed in stages since the 1950s, with major advances enabled by greater access to data, more computing power, and better architectures.
It improves productivity, quality, and operational speed, and reduces the costs associated with repetitive or cumbersome information processing.
There is no such thing as a universal "best" AI. The right choice depends on the use case, the data, the security requirements and the level of integration required.
The major cloud platforms and a number of specialized providers offer services tailored to businesses. The choice depends on your constraints (data, compliance, integrations, costs).
Start with a priority use case, validate security and confidentiality, and prioritize simple integration with your existing tools and processes.
The most common: an assistant (FAQ/agent) connected to your knowledge base and systems (CRM, support), as well as intelligent recommendations.