On June 11, 2026, a roundtable hosted by MC Factory will put the topic back in the spotlight: how are brands reinventing customer and employee experience with AI agents? Behind the jargon lies a very practical question for SMEs and mid-market enterprises: how do you automate the tasks that frustrate your teams and slow down your customers without compromising service quality?
The answer comes down to a simple concept worth remembering: Total Experience. The idea is no longer to treat the customer on one side, the employee on the other, and the channels in between. The goal is to orchestrate everything with journeys that are smoother, faster, and more consistent. And AI agents are quickly becoming the toolkit driving that transformation.
The SME Opportunity: Time Saved, Lighter Support, Cleaner Journeys
For an SME, the point is not to “do AI” for the sake of it. The point is to reclaim time from repetitive tasks and reinvest it where it creates value. Example: a self-service agent can answer frequently asked questions, route users to the right resource, or provide 24/7 after-sales support. The result: fewer simple tickets for support teams and more availability for high-stakes cases.
On the sales side, an agent can qualify a lead, draft a follow-up email, or maintain omnichannel follow-up without losing a prospect along the way. Internally, HR or IT assistants can streamline routine requests and reduce the burden on support teams. The real upside? Faster response times, fewer errors in standard processes, and a more consistent experience for both customers and employees.
The Warning Sign: The Trap of the Expensive Gimmick and Vendor Lock-In
The risk is just as familiar: launching a flashy AI agent without a business framework. In that case, you end up with a nice demo object that is hard to maintain, poorly measured, and sometimes connected too quickly to critical systems. Two pitfalls come up again and again: technology lock-in, when everything depends on a single ecosystem, and weak governance, with unversioned prompts, minimal quality control, and no clear escalation path to a human.
Another sensitive issue: an agent that gives the wrong answer, hallucinates, or traps a customer in an automated loop can damage the experience instead of improving it. Automation must therefore remain incremental, with prioritized use cases, clear KPIs, and built-in safeguards.
The Compliance Angle
If your agents process customer or employee data, you are operating in a regulated environment: GDPR in the EU, and Switzerland’s nLPD on the Swiss side. You need to document the purpose of processing, limit the data sent to models, define log retention rules, and govern your processors and vendors. For sensitive data, it is better to choose a controlled hosting model, ideally regional or sovereign depending on the stakes.
From an operational standpoint, a minimal AI governance framework is essential: one accountable owner, a steering committee, human review for high-impact use cases, and traceability of responses. The goal is not to slow the project down, but to prevent a “useful” agent from becoming a compliance or reputational risk.
Conclusion & Cohesium Support
This roundtable confirms a clear trend: AI agents are no longer a concept; they are execution building blocks. For an SME, the right approach is not to rebuild everything, but to choose the right journeys to enhance, at the right pace, with a clean architecture.
Rather than piecing things together, Cohesium AI can audit your customer and employee journeys, prioritize your AI use cases, define governance, and deploy custom AI agents connected to your existing tools, with real performance and compliance monitoring. Contact us