Exploring AI-driven automation to reduce operational friction

The challenge was not a lack of data. Gridserve already had strong monitoring, analytics, and operational tooling in place. The real problem was what happened next. Too much of the response process relied on manual triage, human judgement, and repetitive handoffs between systems and teams. Engineers were spending time classifying issues, restarting services, copying information between tools, and managing long-running support conversations with external partners.

This created three very real business pains. Resolution times were slower than they needed to be. Skilled people were tied up doing repetitive, low-value work. And it was hard to explore automation safely without committing to long discovery phases or heavyweight delivery projects.

Gridserve wanted to understand whether modern workflow automation, combined with AI, could meaningfully reduce this operational drag and accelerate time to fix.

Our approach

We ran a focused, hands-on hack day with Gridserve’s engineering, data, and leadership teams to explore real operational processes in a practical way. Rather than producing slides or abstract recommendations, the goal was to prototype working automation against live patterns from their business.

We used n8n, an open, enterprise-ready workflow automation platform, as the backbone for the day. n8n allowed us to rapidly connect existing systems, orchestrate logic, and embed AI decision-making without heavy upfront engineering.

Together with Gridserve, we explored several operational workflows in parallel:

Some flows focused on classification and triage. Incoming operational signals and alerts were analysed using AI models to determine whether an issue required human intervention or could be handled automatically. In simple cases, this meant triggering automated remediation actions such as restarting a charger. In others, it meant routing work to the right team with far better context than before.

Other flows focused on external support ticket automation. Gridserve often needs to raise and manage tickets with hardware and software partners. Using n8n and AI, we prototyped an automated workflow that could raise tickets, monitor responses, gather additional data when requested, and continue the conversation automatically. Human escalation only happened when the system genuinely needed it.

A key theme across the day was working with human language. Support tickets, operational notes, and partner communications are not clean data structures. They are written by people. AI was used where it made sense, particularly to interpret intent, summarise context, and decide next actions, while n8n handled the deterministic workflow logic around it.

Why n8n mattered

n8n played a critical role in making this work at speed. It provided a mature, production-grade automation layer that could orchestrate APIs, handle branching logic, loop over long-running processes, and integrate AI models without custom infrastructure.

This was not experimentation for experimentation’s sake. The same workflows built during the hack day followed patterns that could be hardened and taken into production. The platform made it possible to prototype, test, and iterate in hours rather than weeks, while still respecting enterprise concerns around control, observability, and extensibility.

The outcome

In a single day, Gridserve moved from abstract questions about AI and automation to concrete, working examples tied directly to operational pain.

The team was able to see how automation could remove significant amounts of repetitive work, shorten resolution cycles, and allow engineers to focus on genuinely complex problems. Just as importantly, the hack day surfaced where data and APIs needed to evolve to become more “AI-ready”, providing clear direction for future improvement.

Perhaps the biggest outcome was confidence. Confidence that modern workflow automation is no longer brittle or experimental. Confidence that AI can be safely embedded where it adds value. And confidence that meaningful automation does not require long, speculative projects to get started.

What this demonstrates

This engagement shows how enterprise automation, when approached pragmatically, can deliver value quickly. By combining a mature orchestration platform like n8n with targeted use of AI, organisations can prototype and deploy automated workflows that reduce operational friction, improve responsiveness, and scale without increasing headcount.

For Gridserve, it was the first step toward a more automated, resilient operational model. For us, it reinforced the power of hands-on, outcome-led automation work grounded in real business processes rather than theory.