The Productivity Paradox Hiding in Your AI Stack
AI was supposed to make customer service leaner, faster, and more scalable. For many organisations, the opposite is quietly happening. New research from Freshworks, surfaced this week by CX Today, shows that AI complexity is increasing IT workloads, eroding budgets, and pulling technical teams away from the strategic work that actually drives CX improvement. The phenomenon even has a name now: "botsitting" — the unglamorous but time-consuming practice of manually supervising, correcting, and babysitting AI systems that were sold as self-sufficient.
This is not a fringe finding. Wider 2026 research echoes the same pattern: governance gaps are widening, AI tools are multiplying faster than the internal capacity to manage them, and CX leaders are discovering that deploying an AI agent is only the beginning of the operational burden — not the end of it.
What "Botsitting" Actually Looks Like on the Ground
For operations leaders, botsitting is probably already familiar, even if you have not called it that. It shows up as a senior agent spending two hours a day reviewing chatbot transcripts for quality failures. It is the IT ticket that gets raised every time the knowledge base updates and the bot starts giving wrong answers. It is the escalation path that was never properly designed, so frustrated customers end up in a queue anyway — having wasted time on an interaction that resolved nothing.
The Freshworks data points to a structural problem: organisations are layering AI tools onto existing operations without the governance frameworks or the skilled human oversight to make those tools reliable. The result is a hidden tax on productivity. Budget that was meant to fund CX improvement is being consumed by AI maintenance. Headcount that was meant to handle complex customer needs is being diverted into error-correction loops.
There is also a reputational dimension. When AI fails visibly — wrong information, looping menus, tone-deaf responses — customers do not blame the bot. They blame the brand. Every botsitting failure is a potential CX failure waiting to surface in a review, a churn statistic, or a regulatory flag.
The Governance Gap Is the Real Urgency
What the research makes plain is that AI deployment without governance is not a shortcut — it is a liability. Governance in this context means clear ownership of AI outputs, defined escalation protocols, regular performance audits, and a coherent feedback loop between what the AI does and what human teams learn from it. Most contact centre AI stacks currently lack at least two of those four elements.
This is not an argument against AI in customer service. The efficiency gains from well-implemented automation are real and measurable. But the keyword is "well-implemented," and that qualifier is doing enormous work. Implementation quality depends almost entirely on the human layer: the people who configure, monitor, calibrate, and continuously improve the AI system in production.
Why Hybrid Intelligence Is the Operational Answer
The instinct of many organisations facing AI complexity is to hire more technical staff or buy yet another monitoring tool. Both responses treat the symptom. The structural answer is a hybrid model in which AI handles repeatable, high-volume, low-ambiguity interactions, while skilled human operators own quality oversight, complex case handling, and the continuous improvement of AI behaviour.
This is precisely the model Conveneo is built around. Multilingual human talent embedded in AI-assisted workflows does not just improve customer outcomes — it resolves the botsitting problem at its root. When experienced agents are part of the operational loop, AI errors are caught early, escalation paths are intelligent rather than accidental, and the feedback that makes AI systems smarter actually reaches the people who can act on it.
The lesson from this week's research is uncomfortable but clarifying: AI complexity is not a technology problem waiting for a better technology solution. It is a people-and-process problem that demands human expertise at its centre. Organisations that treat AI as a replacement for operational judgment will keep botsitting. Those that treat it as a tool that skilled humans direct and govern will build something that actually scales.
The human edge is not in spite of AI. Right now, it is what makes AI work.