In an industry facing relentless cost pressures and shrinking teams, publishers are losing more than headcount—they are losing critical operational knowledge. As in-house expertise disappears and budgets tighten, dependence on Professional Services grows, creating friction, inefficiencies, and escalating support demands. This case study explores how an AI-driven PS assistant helps close that gap—restoring lost knowledge, reducing reliance on costly interventions, and enabling both teams and customers to operate with greater speed, confidence, and autonomy.
Learn how reclaiming knowledge through AI can turn a growing operational challenge into a sustainable advantage.
Background
Across the publishing and media sector, cost‑cutting initiatives have become the norm. Many customers have reduced staff numbers, and with those departures, a significant amount of operational and system knowledge has disappeared.
At the same time, these customers are under pressure to keep expenses low, which has led to a declining willingness to pay for Professional Services (PS) hours—even as their internal capability to manage the system has weakened.
This combination has created a structural gap: less knowledge, more dependency, but lower budgets.
Current State
The loss of in‑house expertise has shifted responsibility for previously internal tasks onto our PS team. Customers now rely on us to fill knowledge gaps that used to be handled by their own staff.
This has created friction on both sides:
- Customers feel frustrated because they need help but want to avoid additional PS costs.
- Our PS team feels pressure because they are spending time on low‑value, repetitive tasks instead of strategic work.
- Developers are frequently pulled into PS cases to explain system behaviour or historical decisions, slowing down product development.
The result is a cycle of inefficiency, rising support volumes, and strained relationships.
Solution
To break this cycle, we developed an AI‑powered PS Agent designed to augment our internal teams and eventually support customers directly.
The agent is built on three core data sources:
- AI‑generated system documentation Automatically created based on our source code and continuously updated to reflect real system behaviour.
- Historical case tickets Thousands of resolved cases provide patterns, explanations, and proven solutions.
- System architecture and configuration knowledge: Deep technical context that typically requires developer involvement.
By combining these sources, the AI agent can:
- Answer complex system questions
- Explain configuration logic
- Suggest solutions based on historical patterns
- Guide PS staff through troubleshooting steps
- Reduce the need for developer escalation
Internally, the agent has already demonstrated measurable impact: faster case resolution, fewer escalations, and more consistent answers.
The next step is to adapt the agent for client‑facing use, giving customers direct access to the same knowledge our PS team uses.
Desired Outcome
Based on the strong results from internal usage, we expect the client‑facing version of the agent to deliver:
- Fewer support tickets: Customers get immediate answers to common questions without opening a case.
- Reduced friction: Customers regain autonomy and confidence, even with smaller teams.
- Happier clients: Faster answers, fewer delays, and less back‑and‑forth with PS.
- More efficient PS operations: PS staff can focus on high‑value work instead of filling knowledge gaps.
- Less dependency on developers: The agent absorbs much of the architectural and historical knowledge that previously required engineering involvement.
Ultimately, the AI agent helps reverse the negative effects of cost‑cutting by rebuilding lost knowledge, reducing operational friction, and strengthening customer relationships—without increasing costs.
