In a landscape where artificial intelligence often feels like a complex, futuristic concept reserved for corporate giants, Laurent Giraid, a technologist specializing in applied AI, is focused on the here and now. He champions the idea of “practical AI”—solutions that deliver immediate, measurable results without the friction of lengthy implementations. We sat down with Laurent to discuss how AI agents are evolving from experimental tools into indispensable digital team members for small and mid-sized businesses. Our conversation explored the mechanics behind rapid deployment, the real-world impact on workflows from e-commerce to healthcare, and how integrated AI can provide leaders with the crucial data needed to drive growth and efficiency.
Many businesses find AI adoption complex and time-consuming. How does a platform like this achieve deployment in minutes, not months, and what are the first steps a company in the professional services or healthcare industry would take to get an agent operational?
The magic isn’t in some lengthy, custom-coded project; it’s in leveraging pre-built, intelligent frameworks that are trained on specific business contexts. Think of it less like building a car from scratch and more like a high-tech pit stop. For a healthcare clinic, the first step is simply connecting the AI agent to their existing scheduling and patient communication systems. There’s no need to rip and replace anything. The agent can then be configured in a few clicks to manage appointment reminders, answer common insurance questions, and handle rescheduling requests, freeing up the front desk staff almost instantly. The system is designed to understand the core operational workflows of these industries from day one, which eliminates the months-long learning curve you see with more generalized, experimental AI.
You’ve emphasized the importance of “practical AI.” Could you describe a scenario where an AI agent acts as a virtual team member for a mid-sized e-commerce business? What specific lead conversion or customer support workflows would it automate, and how does it deliver measurable results?
Absolutely. Imagine a mid-sized online retailer that feels like they’re constantly playing catch-up. Their new AI agent, acting as a digital team member, is instantly on the front lines. When a potential customer asks a question via chat about product specifications or shipping, the agent provides an immediate, accurate answer, 24/7. More importantly, it can qualify that prospect against the company’s ideal customer profile. If the inquiry is a high-value lead, the agent can trigger a real-time follow-up, either by scheduling a call for a human sales rep or sending a targeted discount code. This transforms customer support from a cost center into a revenue generator, directly impacting lead conversion and reducing the churn that comes from slow response times. It’s about creating a seamless, responsive experience that makes customers feel seen and valued.
The ability to integrate with over 1,300 platforms like Salesforce and HubSpot is significant. Could you explain how “memory context protocols” allow an agent to work across these systems, remember past activities, and respond to changing situations in real time?
“Memory context protocols” are the agent’s long-term memory and its ability to connect the dots. In a typical business, a customer’s history is scattered—a support ticket is in one system, their purchase history in another, and their sales inquiries live in a CRM like Salesforce. The agent uses these protocols to access all of that information simultaneously. So, when a customer reaches out, the agent doesn’t just see a single question; it sees their entire journey. It remembers they had a shipping issue two months ago and recently viewed a new product. This allows the agent to have an incredibly intelligent, contextual conversation, responding to the situation with full awareness rather than as a disconnected, one-off interaction. It’s what allows the AI to feel less like a robot and more like a helpful team member who actually knows the customer.
A client reportedly reduced manual effort by over 60% in less than two weeks. Could you walk us through the specific operational pains that client was facing and how AI agents were deployed to achieve such a dramatic and rapid improvement?
That particular client, a financial services company, was drowning in what they called “reactive back-and-forth.” Their team spent most of their day manually answering repetitive inbound questions, following up on service requests, and trying to identify expansion opportunities from a sea of emails. It was a huge drain on resources and a frustrating experience for everyone. By deploying AI agents, they created a “safety net.” The agents immediately took over handling those initial inquiries, qualifying them, and routing only the most complex or high-value interactions to the human team. This single change eliminated the constant, low-value interruptions, allowing the team to focus on strategic growth. The 60% reduction wasn’t about replacing people; it was about removing the friction and noise so the team could finally do the work that truly mattered.
Beyond simple task automation, how do AI agents provide leaders with performance metrics and analytics? Can you share an example of a key insight a business owner could gain from this data to improve customer retention or reduce operational costs?
This is where AI becomes a strategic partner, not just a tool. The platform doesn’t just automate tasks; it tracks every interaction and analyzes the outcomes. A business owner can log in and see a dashboard showing which types of customer inquiries are most common, how quickly issues are resolved, and even which leads are converting most effectively. For example, a leader might discover that 40% of their customer support inquiries are related to a specific, confusing feature in their product. That’s a powerful insight. They can then use that data to improve their product documentation or create a tutorial video, which in turn reduces support tickets, lowers operational costs, and improves customer satisfaction and retention. The analytics turn raw operational data into a clear roadmap for business improvement.
What is your forecast for the role of AI agents in small to mid-sized businesses over the next five years?
Over the next five years, I believe AI agents will become as fundamental to SMBs as a website or a CRM is today. We will move away from the idea of AI as a separate, complex project and see it as an integrated, intelligent layer across all business operations. For smaller businesses, this will be a massive competitive equalizer. They will be able to offer the kind of 24/7, personalized customer service and operational efficiency that was once only possible for large enterprises. The focus will shift from just automating tasks to using AI for predictive insights—identifying at-risk customers before they churn and spotting sales opportunities before a competitor does. AI agents will become the digital backbone of the business, enabling leaders to spend less time on manual work and more time on vision and strategy.
