Artificial intelligence is already helping businesses automate tasks, analyze information faster and improve efficiency. But businesses are obviously not the only ones taking advantage of these new capabilities.
Cybercriminals are using them too.
AI can help attackers create more convincing phishing campaigns, look for vulnerabilities, gather information about their targets or accelerate malware development. One development deserves particular attention: the use of AI to create polymorphic viruses and other types of malware that can modify their code to make detection more difficult.
The concept itself is not new. Polymorphic malware has been around for a long time. What AI changes is primarily the speed and ease with which some of these techniques can now evolve.
What Is a Polymorphic Virus?
A polymorphic virus is a type of malware that can modify certain characteristics of its code without changing what it is designed to do.
Think of a burglar who changes their appearance every time they try to break into a building. The objective remains the same, but recognizing them becomes more difficult.
In cybersecurity, this ability can be used to change a piece of malware’s digital signature. This matters because some traditional detection methods rely on recognizing signatures associated with known threats.
Polymorphism therefore allows the same malware to appear in different forms. Artificial intelligence did not invent this technique. It has existed for decades.
AI, however, can make the process faster and more automated.
How Is Artificial Intelligence Changing Malware?
Until recently, when we talked about cybercriminals using AI, we were mostly referring to relatively straightforward applications: writing phishing emails, translating messages, conducting research or getting help with programming.
We’re now beginning to see something different.
In 2025, Google Threat Intelligence Group documented experimental malware that used AI models directly during execution. One example, called PROMPTFLUX, was designed to communicate with an AI model to obtain new techniques for modifying and obfuscating its code.
It’s important to keep this in perspective. At the time, Google specified that the malware was still under development and, in the form observed by its researchers, was not capable of compromising a device.
Still, the experiment gives us an indication of where some cyber threats could be heading.
In 2026, Google also reported that malicious actors were experimenting with AI to accelerate the development of polymorphic malware and generate code designed to make detection more difficult.
This doesn’t mean we’re suddenly facing autonomous viruses that are impossible to stop. The issue is much more practical: AI allows attackers to automate certain tasks and work faster.
Cyberattacks and AI: Speed Is the Real Change
This is probably where the biggest concern lies for businesses.
Cybercriminals didn’t wait for artificial intelligence to search for vulnerabilities, send fraudulent emails or develop malware. They were already doing all of that.
AI simply allows them to do more, faster.
A phishing campaign can be adapted for different targets. Information about a company can be analyzed more easily. Code can be generated or modified faster. Tasks that once required considerable time and effort can now be accelerated.
In the case of polymorphic malware, this could make it easier to produce multiple variations of the same threat.
And when a threat can continually change its appearance, simply looking for a file that has already been identified as malicious is no longer enough.
How Can a Polymorphic Virus Be Detected?
For years, a large part of antivirus protection was based on a relatively simple principle: once malware was discovered, its signature could be added to security databases so that it could be recognized the next time it appeared.
That approach is still useful, but it can no longer be the only line of defence.
Modern cybersecurity solutions also look at what is actually happening within an IT environment.
Has a device suddenly started communicating with an unusual destination? Is an account attempting to access resources it doesn’t normally use? Is an application modifying a large number of files within seconds?
Even if the malware responsible has never been seen in exactly that form before, its behaviour can reveal that something isn’t right.
This is one of the roles of detection and response solutions such as EDR and XDR.
As cyber threats become capable of evolving more quickly, this type of behavioural detection becomes increasingly important.
Is Antivirus Still Enough to Protect Your Business?
No, but that doesn’t mean antivirus is no longer useful.
Antivirus remains an important part of a company’s security. The problem arises when it represents almost the entire cybersecurity strategy.
Today, effective protection depends on several layers working together.
Multi-factor authentication and proper access management can help limit the impact of compromised credentials. Updates and patch management reduce the number of vulnerabilities attackers can exploit. Continuous monitoring makes it easier to identify unusual activity quickly, while backups provide another layer of protection when an incident does occur.
And employees can’t be overlooked.
AI can also be used to create fraudulent communications that are much more convincing than they used to be. The obvious spelling mistakes and awkward wording that once made some phishing emails easy to recognize aren’t always there anymore.
Cybersecurity awareness therefore remains essential.
Artificial Intelligence Is Also Strengthening Cybersecurity
It would be misleading to present artificial intelligence only as a new tool for cybercriminals.
Cybersecurity teams are using it too.
AI can help analyze enormous volumes of signals, identify unusual behaviour and accelerate incident analysis. It can also help cybersecurity specialists make connections between different activities that would be much more difficult to identify manually.
In June 2026, for example, Microsoft reported using AI as part of an operation targeting cybercriminal infrastructure. AI-assisted analysis accelerated the investigation of malware and helped identify connections between different infrastructures, contributing to the disruption of more than 200 command-and-control servers.
The tools are evolving on both sides.
And that’s probably the best way to look at what is happening today: AI isn’t changing the fundamentals of cybersecurity, but it is dramatically accelerating the game.
How Can You Protect Your Business Against AI-Driven Cyber Threats?
Polymorphic viruses existed before AI. So did phishing, vulnerability exploitation and credential theft.
What is changing is the ability of cybercriminals to automate certain tasks, adapt their methods and operate more quickly.
For businesses, the answer isn’t to look for a new solution that promises to stop every “AI-powered attack.” It starts with making sure the foundations of their cybersecurity strategy are solid.
Are access rights properly controlled? Are systems up to date? Are devices being monitored? Can unusual behaviour be detected quickly? Do employees know how to recognize a fraudulent request that looks completely legitimate? And, perhaps most importantly, would the organization know what to do tomorrow morning if an attack succeeded despite its protections?
These questions were already important. With AI, they’re becoming even more so.
Want to know whether your IT environment is adequately protected against today’s cyber threats? Kezber’s experts can help you assess your current situation, identify your main risks and determine which measures to prioritize to better protect your business. Contact our team today!
Artificial intelligence is evolving rapidly. Following the first wave of generative AI, which helped businesses accelerate content creation, research and information analysis, a new stage is emerging with agentic AI.
This evolution is changing the way AI can be integrated into business operations. It is no longer simply about asking a tool to produce something. AI agents can take on specific steps within a process, interact with different systems and execute actions based on a defined objective.
For businesses, the possibilities are significant. But creating real value requires understanding what agentic AI actually changes, identifying where it can have the greatest impact and selecting the right projects to get started.
In this series, we explore agentic AI from three complementary perspectives.
1. Beyond Generative AI: How Agentic AI Is Transforming Business Processes
What is the real difference between generative AI and agentic AI? This first article explores the next evolution of artificial intelligence and, more importantly, what it means for business processes. Discover how AI agents can go beyond generating content to analyze situations, interact with systems and take action.
Read the article: Beyond Generative AI: How Agentic AI Is Transforming Business Processes
2. Doing More With the Same Team: The Challenge Businesses Need to Solve
Teams are already stretched, while expectations continue to grow and organizations look for ways to increase their capacity. Yet a significant portion of their time is still spent on repetitive tasks, approvals, follow-ups and searching for information. This second article explores how agentic AI can help free up that capacity without simply asking employees to do more.
Read the article: Doing More With the Same Team: The Challenge Businesses Need to Solve
3. Agentic AI: Why Your First Project Should Be Simple, Measurable and Low-Risk
Once the opportunities have been identified, one question remains: where should you start? The answer usually isn’t with a project designed to transform the entire organization. This third article presents a more pragmatic approach: start with a clearly defined business process, target a specific pain point and choose a first project whose results can be measured quickly.
Read the article: Agentic AI: Why Your First Project Should Be Simple, Measurable and Low-Risk
From Exploration to Action
Agentic AI opens the door to new possibilities, but its value does not depend on technology alone. It also depends on an organization’s ability to look at its processes differently and identify where AI can genuinely improve the way work gets done.
You don’t need to transform everything at once. A repetitive process, a task that unnecessarily takes up several people’s time or a recurring operational pain point can already represent a valuable opportunity for AI and business process automation.
At Kezber, we help organizations identify these opportunities, assess their potential and progressively implement automation and AI solutions that fit their business reality.
Wondering where agentic AI could create value in your organization? Let’s talk about your processes.
After discovering the potential of agentic AI and understanding how it can help organizations increase their capacity, one question almost always comes up: where should you start?
For many business leaders, the question is no longer whether artificial intelligence will have an impact on their industry. They are already seeing its influence on operations, customer service, sales, and administrative functions. The real challenge is determining how to integrate agentic AI into the business and choose a first use case that will deliver real value.
This is often where organizations make their first mistake.
Agentic AI in Business: Avoid the Trap of an Overly Ambitious Project
When a new technology generates significant interest, it can be tempting to transform several processes at once. Some businesses are already imagining an AI agent capable of managing all customer requests, coordinating multiple systems, or automating an entire department.
While that vision may eventually become a reality, it is rarely the best place to start.
The most relevant first projects are generally much simpler. They address a known pain point, a repetitive task, or a process that has been slowing operations down for some time.
During his presentation at Salon Connexion, Eric Murray emphasized a fundamental principle: the first gains should be quick, measurable, and relatively easy to achieve. These small wins make it possible to gradually expand the use of AI and build the confidence needed to go further.
Start with a Business Process, Not the Technology
One of the key statements from the presentation sums up this philosophy well:
“We start with a process, not the technology.”
This distinction may seem simple, but it completely changes how an AI project is approached.
Too often, organizations begin by comparing platforms, artificial intelligence models, or the features available on the market. But technology does not create value on its own. Value is created when technology is applied to a real business problem.
Before discussing tools, organizations should look at their operations. Which processes are slowing teams down? Where do delays occur most frequently? Which activities are repeated several times a day? Where are employees spending time searching for, transferring, or validating information?
This is generally where the best process automation opportunities and the most relevant use cases for agentic AI can be found.
How Do You Choose a First Agentic AI Use Case?
The best first projects are not necessarily the most impressive. The initial processing of requests submitted through a web form, preliminary invoice analysis, extracting information from documents, or preparing certain administrative follow-ups can all be excellent starting points.
A strong first use case should ideally meet a few criteria:
- the process occurs frequently;
- it takes enough time for the improvement to be measurable;
- its rules are relatively well defined;
- potential errors can be detected and corrected;
- results can be compared with a clear baseline.
These projects can generate tangible gains while giving teams an opportunity to better understand what AI agents can actually bring to their operations.
Why Start with a Low-Risk AI Project?
The level of autonomy given to an agent is another important consideration when choosing a first project.
There is no need to begin with an agent that independently makes important decisions or automatically executes every step of a process. A first AI agent can analyze a request, classify information, gather data, prepare a recommendation, or suggest an action while keeping human validation in place when necessary.
This approach makes it possible to test the solution in a controlled environment and assess the quality of its results before gradually increasing its level of autonomy.
It also gives teams an opportunity to understand how the agent responds to less predictable situations and identify the exceptions that need to be addressed.
The first project therefore becomes both an opportunity to generate value and a way to learn how to integrate AI into business processes progressively and responsibly.
How Do You Measure the Results of an AI Project?
The enthusiasm surrounding artificial intelligence can sometimes overshadow a fundamental principle: without a point of comparison, it is difficult to demonstrate real gains.
Before deploying a solution, it is important to document the current situation. How long does the task take? How many people are involved? What volume is processed each week? What delays or errors are currently being observed?
Eric Murray also emphasized the importance of measuring the situation before and after deployment.
Does a process that previously required ten hours of work per week now take four? Has a two-day processing time been reduced to a few hours? Can a team handle more requests without increasing its workload?
These results help determine whether the project is actually creating value and make it easier to decide whether to go further with automation or agentic AI.
Involve the People Who Really Know the Process
A process that seems simple on paper often includes exceptions that only the employees who perform it every day are aware of.
An invoice from a particular supplier may need to be handled differently. Certain types of customer requests may always require validation. Missing information may trigger an additional step.
These details matter when part of a process is being entrusted to an AI agent.
Frontline teams can identify these exceptions as well as the real pain points within the process. Their involvement therefore helps improve the solution while making adoption easier.
Rather than automating a process as it is documented, the goal is to automate the process as it actually works.
How Do You Get Started with Agentic AI?
Agentic AI should not be approached as one large technology project with a defined beginning and end.
Early deployments are as much about learning as they are about generating gains. They help teams understand the possibilities and limitations of the technology, adjust their ways of working, and gradually identify additional automation opportunities.
A simple, well-chosen first project can therefore become the starting point for a much broader initiative.
You do not need an AI strategy that covers your entire organization before getting started. You need a relevant first use case, a way to measure its impact, and a framework that is safe enough to learn from. The rest can be built from there.
At Kezber, we help organizations identify their first agentic AI use cases, assess potential gains, and progressively implement solutions tailored to their business reality.
Wondering where to start with agentic AI? Let’s identify a first project that is practical, measurable, and suited to your organization.
For many businesses, the biggest barrier to growth is no longer a lack of opportunities. The markets are there, and so are the projects. The real challenge is often finding the capacity needed to support that growth.
Teams are already stretched. Customer expectations are increasing, timelines are getting shorter, and recruitment remains difficult across many industries. In this context, many organizations are facing a paradox: they have the potential to do more, but they don’t always have the resources to handle the additional workload.
During his presentation at Salon Connexion, Eric Murray pointed out that labour shortages remain one of the major challenges businesses face today. The question, then, is no longer simply how to hire more people. It’s also about determining how to make better use of the expertise already within the organization.
The Real Problem Isn’t Strategic Work
When we look at how teams spend their days, one thing quickly becomes clear: employees rarely spend all their time focused solely on their area of expertise.
A significant portion of their time goes toward administrative work, validations, follow-ups, searching for information, or coordinating between different systems. These tasks are necessary to keep operations running, but they don’t always make the best use of the skills of the people performing them.
Think of a sales representative who spends part of the day qualifying requests before even getting the chance to speak with a prospect. A finance team that has to manually validate information from several sources. Or an HR team answering the same administrative questions week after week.
The problem isn’t that these activities exist. It’s that they often take up valuable capacity that could be used elsewhere.
The Hidden Cost of Low-Value Tasks
On their own, a few minutes lost here and there may seem insignificant. But when these activities are added up across a team or an entire organization, their impact becomes much greater.
In his presentation, Eric Murray used the example of processing customer service emails. Reading the request, understanding the context, checking internal systems, retrieving the right information, and preparing a response may take only a few minutes per interaction. Repeated dozens or hundreds of times each week, however, the process quickly adds up to hours of work.
The same thing happens across many departments. Finance teams process invoices and perform validations. Sales teams qualify prospects and update records. Human resources teams answer recurring questions. Operations teams transfer, verify, and organize information.
None of these activities is a problem in itself. But as their volume increases, they gradually limit the organization’s ability to focus on higher-value work.
That’s also what makes these inefficiencies difficult to see. They don’t necessarily appear as one major problem. Instead, they accumulate a few minutes at a time until they represent a significant portion of the workload.
How Can Agentic AI Help Teams Do More with the Same Resources?
Traditionally, when a business reached its operational capacity, the solution was to hire.
Today, that approach isn’t always realistic—or even necessary.
Organizations now have new ways to absorb part of their operational workload without systematically increasing headcount. This is one of the reasons agentic AI is generating so much interest.
Unlike generative AI tools, which primarily respond when prompted, AI agents can be integrated directly into a process and take responsibility for certain steps: analyzing a request, retrieving information, applying rules, preparing a response, performing validations, or triggering an action in another system.
Let’s go back to the customer service example. An AI agent could analyze an incoming email, determine the nature of the request, retrieve relevant information from the company’s systems, validate certain details, and prepare a response. When the situation falls within established parameters, part of the process can be completed automatically. When a decision, exception, or human judgment is required, the case can be handed over to an employee with the relevant context already gathered.
At that point, it’s no longer simply about generating a response faster. Part of the process itself is being handled.
And when a process that takes a few minutes is repeated hundreds of times, the capacity gained can become significant.
The Real Question: Which Tasks Actually Require a Human?
This is probably one of the most useful questions an organization can ask when looking to increase its capacity.
Some tasks require judgment, experience, creativity, empathy, or a deep understanding of context. Others are primarily based on rules, validations, and the transfer of information.
It’s the latter that are worth examining more closely.
An HR manager who spends less time answering the same administrative requests can devote more energy to talent development and supporting managers. A sales team that automates certain validations can focus more on conversations and customer relationships. A finance team that reduces manual information processing can spend more time on analysis.
The goal, then, isn’t simply to automate more. It’s to determine where human involvement truly adds value and where technology can support the process.
That’s often where some of the most meaningful productivity gains can be found.
A Competitive Advantage That Goes Beyond Technology
The next few years probably won’t be defined by the organizations that simply adopted artificial intelligence. They’ll be defined by those that successfully integrate it into their operations and use it to increase their ability to execute.
In an environment where resources remain limited, doing more with the same team becomes an important competitive advantage.
This doesn’t necessarily mean asking employees to do more. Quite the opposite. The goal is to remove some of the work from their day-to-day responsibilities that can be simplified, automated, or handled differently.
The organizations that succeed will be those that know how to identify these activities, rethink their processes, and use technology where it can make a real difference.
How to Identify the Best Automation Opportunities
Most organizations already have several processes that could be optimized. But there’s no need to start with the most complex process or transform the entire business at once.
The best starting points are often much simpler.
A process may be worth examining when it occurs frequently, involves several people, requires information to be searched for or transferred between different systems, or when a large portion of the decisions involved are based on relatively predictable rules.
A few questions can already help uncover opportunities:
- Which tasks are repeated several times a day or week?
- Where do employees have to copy, search for, or validate information?
- Which activities increase quickly as business volume grows?
- Which processes regularly create delays or frustrations for teams?
A process analysis can then help determine which opportunities have real potential, which can be automated, and where agentic AI can provide additional value.
Before Hiring, Take a Look at Your Processes
Increasing an organization’s capacity no longer necessarily means increasing the number of people on the team.
Before adding another resource to absorb a growing workload, it’s worth asking whether all the work currently being done by the team really needs to continue being done the same way.
Some tasks will always need to remain in the hands of employees. Others can be simplified. Some can be automated. And others can now be partially handled by AI agents capable of interacting with the company’s systems and processes.
At Kezber, we help organizations identify these opportunities, assess their potential, and progressively implement solutions adapted to their business reality.
Wondering where your organization could gain capacity without adding to your team’s workload? Let’s take a closer look at your processes and the opportunities that may already be there.
Jonathan Wilson has been with Kezber for nearly three years, helping businesses across a wide range of industries with their technology projects. We wanted to introduce you to a different side of Jonathan by sharing his background, what drives him, and how he approaches each client relationship. We asked him a few questions to help you get to know your account manager.
Your career path is a little unconventional. How did you get into technology?
I got into the industry somewhat by chance. I’ve always been passionate about technology. When I was younger, I was already building computers with my father, and my grandfather shared that same passion. Technology was part of my life long before I realized it could become a career.
After earning my bachelor’s degree in international business from Bishop’s University, a former acquaintance told me about an opportunity in the United States selling accounting and management software. I was ready for the experience. I didn’t have many ties keeping me here, so I moved to Michigan, where I worked for nearly five years.
That’s where I really discovered the world of business technology. At first, I was selling very specific solutions, but when the pandemic hit, the conversations completely changed. Clients were asking me how they could enable their teams to work remotely, secure access to their systems, or modernize their infrastructure. Even though those areas weren’t directly within my expertise, I enjoyed helping them and connecting them with the right resources. That’s when I realized I wanted to do much more than sell software: I wanted to be part of the conversation and help build solutions tailored to each company’s needs. That’s what naturally led me to Kezber.
What matters most to you when meeting a client for the first time?
First and foremost, I want to know whether there’s a good fit. I’m not talking about a personal fit, but a business fit. I want to understand the company, its goals, its challenges, its technology, and what it wants to accomplish. To me, asking the right questions is much more important than jumping straight to a solution.
At Kezber, we don’t pretend to be experts in every technology. If I realize that another approach or another partner would better meet a client’s needs, I’d rather be transparent about it. I want to build a relationship that will last for years, not simply get a signature.
What are the essential qualities of a good account manager?
The first is knowing how to surround yourself with the right people. No one succeeds alone. I’m fortunate to have specialists in many different areas at Kezber, and it’s by working together that we’re able to offer the best solutions.
You also need to understand technology well enough to properly guide your clients. I’m not the person handling every technical aspect, but I need to understand the issues, ask the right questions, and quickly identify which experts to bring in.
Another important quality is really knowing your clients: how the company operates, who is involved in making decisions, and what its processes look like. The better you understand the organization, the more effective you can be.
Finally, I strongly believe in staying close to clients. With remote work, we sometimes lose that human connection. I like visiting my clients’ facilities and getting to know the people they work with. Those face-to-face interactions are often where trust is built and where you really come to understand their reality.
What motivates you most about working with your clients?
One client comes to mind. They came to Kezber after having a difficult experience with another provider. At first, there was a lot of mistrust, and we had to rebuild that relationship. When a plant has to stop production every two weeks because its systems can’t keep up, the consequences are very real, and I completely understood their concerns.
By taking the time to understand their pain points, being present, and bringing in the right expertise at Kezber, we were able to turn the situation around. Today, when that client calls me, it’s often simply to wish me a good weekend or tell me that everything is going well.
That’s the kind of project that reminds me why I love what I do. It’s not just about implementing a solution or delivering a project. We’re making a real difference in a company’s operations while also building a relationship based on trust.
In your opinion, what sets Kezber apart?
I would say our transparency. We don’t want a client at any cost. If I feel that we’re not the right fit for a project, I’d rather be honest from the start. That might mean recommending a different approach or even referring the client to another provider.
And above all, I’d say our team. We can rely on specialists in many different areas and bring in the right expertise at the right time. I don’t need to have all the answers myself. I need to know who to bring in so the client gets the best possible answer.
In the long run, that’s how you build a genuine relationship based on trust.
From a client’s perspective, what is it like to work with Jonathan?
We asked Karine Martineau, Director of Performance at Granit Design, to share her experience.
“Available, efficient, personable, and always business-focused, Jonathan isn’t the type of account manager who tries to sell at all costs. Instead, he takes the time to understand what truly makes sense for your organization and ensures that everything aligns with your strategic objectives.
He’s an excellent translator: he makes highly technical concepts simple, concrete, and easy to understand, even for less technical people. Jonathan is truly an extension of your team. He’s the technology ‘jack of all trades’ who understands the realities of an SME and knows how to bridge the gap between business challenges and technology solutions.”
— Karine Martineau
Director of Performance, Granit Design
Businesses today are facing several pressures at once. Customers expect faster responses, operating costs continue to rise, and teams are often expected to accomplish more with the same resources. In this context, improving productivity is no longer simply a matter of efficiency—it has become a growth imperative.
Over the past few years, generative AI has helped many organizations save time on tasks such as writing, information research, and content creation. For many businesses, this first wave of adoption confirmed that artificial intelligence could deliver tangible benefits in day-to-day operations.
But a new stage is already emerging: agentic AI.
What Is Agentic AI?
Agentic AI refers to a form of artificial intelligence that can not only generate content, but also take ownership of an objective, execute actions, and interact with different systems to move a business process forward.
Unlike generative AI, which responds to a request made by a user, an AI agent can analyze a situation, plan the necessary steps, access external tools, and perform certain tasks autonomously based on predefined rules.
As Eric Murray, Eng., Director of Innovation at Kezber, explained at Salon Connexion 2026:
“Automation follows fixed rules. Generative AI acts as a personal assistant. Agentic AI can execute tasks, take action, and work toward an objective.”
Generative AI vs. Agentic AI: What’s the Difference?
Generative AI is primarily used to create content: emails, reports, presentations, summaries, or computer code.
Agentic AI, on the other hand, focuses more on processes. For example, generative AI can draft a response to a customer. An AI agent can analyze the request, retrieve relevant information from an internal system, prepare the response, and trigger the actions required to process the request.
The difference may seem subtle, but it fundamentally changes how organizations can use artificial intelligence.
Why Are Businesses Interested in Agentic AI?
The growing interest in agentic AI can be explained by several challenges organizations are currently facing:
- rising operating costs;
- labour shortages;
- increasing customer expectations;
- pressure to improve productivity;
- growing volumes of information to process.
For many business leaders, the challenge is no longer simply to work faster. It is about finding ways to support growth without necessarily increasing headcount at the same pace.
Where Does Agentic AI Create the Most Value?
The first use cases are often simpler than you might think.
Businesses are already using AI agents to:
- handle customer requests;
- qualify leads;
- analyze invoices;
- classify documents;
- prepare administrative follow-ups;
- synchronize information across different systems;
- assist HR teams with certain repetitive tasks.
Individually, these activities may seem minor. Collectively, however, they represent a significant portion of the time spent on day-to-day operations.
People Remain at the Heart of Business Processes
One of the most common myths about AI is that it will completely replace employees.
In reality, the most successful projects are generally those in which people continue to play a central role in supervision, validation, and decision-making.
AI agents handle repetitive tasks. Employees remain responsible for business judgment, exceptions, customer relationships, and strategic decisions.
The goal is therefore not to replace human expertise, but to allow people to focus on the activities where their expertise creates the most value.
Agentic AI: A Natural Evolution of Business Processes
Generative AI has demonstrated that there are new ways to work. Agentic AI now enables organizations to go further by directly improving their business processes.
For businesses looking to increase operational efficiency, reduce certain delays, and make better use of the resources they already have, this evolution represents a tangible opportunity for improvement.
The question is no longer whether artificial intelligence will have an impact on organizations. It is about identifying which processes could benefit from this new generation of tools.
Where Should You Start?
Adopting agentic AI does not require transforming the entire organization overnight.
The most effective projects often begin with a process that is simple, repetitive, and easy to measure. This approach makes it possible to achieve results quickly while limiting risk.
Wondering which processes in your organization could benefit from agentic AI? At Kezber, we help businesses identify opportunities, assess potential gains, and progressively implement automation and artificial intelligence solutions aligned with their business objectives.
Contact our team to learn more.
A new role to better support you
Edward Kezber has recently taken on the role of Customer Success Manager at Kezber. This role was created with a clear intention. To better support you, in a context where IT environments are becoming increasingly complex and every decision matters.
Over the past few years, one reality has become clear. It is no longer just about delivering services, but about ensuring they have a real impact on operations, performance, and decision-making. That is exactly what this role is designed to address.
After nearly 10 years at Kezber, Edward has a strong understanding of your realities, your challenges, and the pressures that come with them. He has seen how needs and priorities have evolved, and how important it is to make the right decisions at the right time. This new role is built directly on that hands-on experience.
In practical terms, it means you have someone who keeps a clear, overall view of your situation. Someone who supports your thinking, helps you see things more clearly, connect the dots, and make decisions aligned with your business objectives.
“What matters to me is that every client understands where they’re going, why they’re doing it, and what it actually brings them.”
This role strengthens the way Kezber supports you. It allows for more structured follow-ups, better anticipation, and ensures that the efforts invested truly translate into results. The objective is clear. To help you stay on track and move forward with confidence in an environment that is not slowing down.
3 questions for Edward
What motivates you the most in your new role?
What motivates me most in my new role is the opportunity to build long-term relationships with clients by truly understanding their business reality. I have always had a long-term vision when it comes to partnerships, and this role allows me to stay close to the field, have direct conversations, and fully grasp their challenges. Being able to bring clarity to their technology decisions and see real improvements take shape makes a meaningful difference every day, and that is what drives me the most.
In your opinion, what makes a truly successful client collaboration?
In my view, a truly successful collaboration starts with honest and open communication. Whether it is with client teams, partners, or internally, everything starts there. Being able to speak openly about both successes and challenges allows us to move forward more effectively and build trust. When that level of transparency is there, there is very little that cannot be solved together.
What do you see most often that slows organizations down in their IT decisions?
What slows organizations down the most is not one single thing. It is the overall weight of technology decisions. Between costs, choices to make, implementation, usage, and sometimes even training, it requires much more than just budget. It takes time and energy.
Day to day, teams are already fully occupied with operations. Finding the space to build new systems or evolve what is already in place becomes a real challenge. It is rarely a matter of willingness. It is more about the capacity to do it alongside everything else.
In my role, this is exactly where I step in. I help organizations structure their thinking, prioritize, and reduce that mental load by putting the right tools and processes in place. That is what I find especially rewarding. Seeing things become clearer, decisions align, and teams move forward with more confidence.
If you are asked directly how your IT management is currently performing, many leaders will say it is going well. Operations are running, issues are resolved when they arise, and no major crisis has brought the organization to a halt.
In most SMEs, IT management is based on a largely reactive model. When an incident occurs, action is taken. When access becomes problematic, it is corrected. When equipment reaches end of life, it is replaced. This approach can create the impression of a controlled environment because the organization continues to move forward.
What is far less visible is the scale of the costs associated with this posture. Reactive management does not necessarily cause a dramatic outage. Instead, it leads to an accumulation of productivity losses, decisions made under pressure, fixes applied too late, and technology investments executed without an overall vision.
The company does not stop, but it moves forward without a structured framework, without formalized indicators, and without proactive planning. It is within this gap between the perception of stability and actual control that the true cost is hidden.
When urgency drives priorities
No company would manage its finances solely in reaction. Financial statements are not produced only when a problem arises. Cash flow is not planned after a deficit occurs. Margins are not reviewed only when a client disputes an invoice. Finances are monitored, analyzed, and projected because they are strategic.
IT management should be approached with the same level of rigor.
When a technology environment is driven by urgency, the organization acts after the fact. An incident triggers action. An outage triggers an investment decision. An intrusion attempt triggers a security review. This model may appear efficient because it delivers quick solutions, but it keeps the organization in a defensive posture.
By contrast, a structured approach is built on anticipation. Fixes are planned. Risks are assessed before they materialize. Technology investments are aligned with growth objectives. The difference may not always be visible in daily operations, but it becomes evident in stability, scalability, and risk control.
Few organizations would accept improvisation in financial management. Yet many still accept that their IT operates this way.
Reactivity versus proactivity: two models, two impacts
The distinction between reactive IT management and structured managed services is not based on the tools being used, but on the method and discipline applied every day. Here is what this means in practical terms for an organization.
| Dimension | Gestion TI en mode réactif | Infogérance structurée et proactive |
| Gestion des incidents | Intervention après la panne ou la plainte | Surveillance continue et prévention des incidents |
| Mises à jour et correctifs | Appliqués selon les urgences | Planifiés, automatisés et suivis |
| Cybersécurité | Mesures en place mais rarement testées systématiquement | Révision régulière des accès, tests de sauvegarde, suivi documenté |
| Productivité | Interruptions tolérées comme normales | Stabilité recherchée et optimisée |
| Documentation | Fragmentée ou dépendante d’une personne clé | Centralisée, à jour et accessible |
| Planification budgétaire | Dépenses imprévues liées aux urgences | Investissements planifiés et prévisibles |
| Vision stratégique | Décisions prises sous pression | Alignement technologique avec les objectifs d’affaires |
| Continuité des opérations | Plan implicite ou non testé | Plan de reprise formel et validé |
What managed services change for leadership
Managed services are not simply about outsourcing support or transferring operational responsibilities. They introduce a structured governance framework that transforms how technology is managed within the organization.
For leadership, this first means greater visibility. The real state of the infrastructure, priority risks, upcoming investments, and critical dependencies are documented and monitored. Decisions are no longer made solely in reaction to incidents, but based on clear indicators and a comprehensive understanding of the technology environment.
It also means greater predictability. Budgets no longer fluctuate with every emergency. Equipment replacements are planned. Updates follow a defined cadence. Technology stops being a source of surprises and becomes an integrated component of strategic planning.
Finally, it reduces dependency on key individuals. When information is centralized, processes are documented, and monitoring is continuous, the organization is no longer vulnerable to the departure of an internal resource or the absence of a vendor.
Managed services do not remove control from leadership. They provide the tools to exercise it fully.
Your team deserves better than urgency
If this reflection leads you to question your organization’s true posture toward its IT, it is likely the right time to assess your governance framework. Not because a crisis is imminent, but because a company striving for sustainable performance cannot afford to operate solely in reaction mode.
Moving to structured managed services does not mean losing control. It means establishing a method, discipline, and visibility that allow leadership to manage its technology environment with the same rigor applied to finances and operations. At Kezber, our managed IT services are designed to support leadership teams that want to move from a reactive model to proactive, documented management aligned with their business objectives.