Hidden manual tasks drain resources in publishing operations. Learn how to identify, measure, and prioritize automation and AI investments for maximum impact.
Publishing businesses often overlook the true cost of manual checking tasks that never appear on a balance sheet. These hidden activities-like verifying ad campaign pacing, reconciling conflicting data, or manually updating reports-consume time and resources without formal recognition in job descriptions or budgets. For leaders managing editorial operations, understanding these costs is essential before investing in automation or AI solutions.
Jeremy Thorburn, founder of LatenZ and former group COO overseeing operations across six countries, argues that the most persistent operational waste comes from work created by disconnected systems. Employees bridge these gaps by checking, reconciling, and re-entering data, often because existing tools fail to alert them when something goes wrong. This manual oversight is not only common but also measurable, making it a prime target for efficiency improvements.
Identifying the Real Work
Thorburn recommends starting with what actually happens, not what process maps suggest. Instead of asking how a workflow should function, managers should observe staff as they perform routine tasks. Key areas to watch include polling (checking systems for updates), reconciliation (making data sources agree), and re-keying (entering the same data in multiple places). Each signals a breakdown in system integration, with people filling the gaps.
To quantify the impact, Thorburn suggests calculating both the labor involved and the cost of delayed detection. For example, if two employees spend five hours each week on manual checks at a loaded rate of £40 per hour, the annual labor cost reaches £20,000. However, the bigger expense often comes from discovering problems too late. If delayed detection leads to costly make-goods or lost inventory-say, six incidents per year at £1,500 each-the annual cost of lag can add another £9,000. Many organizations underestimate these figures because the work is scattered and rarely tracked.
Ranking and Prioritizing Automation
Once all manual checking tasks are identified and costed, Thorburn advises ranking them by annual expense. This ranking transforms a vague challenge into a clear action plan, showing exactly where automation or AI could deliver the greatest return. The process also clarifies which tasks require advanced AI and which can be solved with simpler automation.
Most manual checking can be replaced by exception alerting-automated scripts that monitor systems and notify staff only when issues arise. This approach eliminates both labor and detection delays. AI becomes valuable for more complex needs, such as predicting under-delivery before it happens, extracting structured data from unstructured sources, or drafting narrative reports from reconciled numbers. Thorburn cautions that seeking AI solutions for problems that automation can handle leads to unnecessary costs.
Addressing Organizational Resistance
Manual checking is often performed by experienced staff who have learned not to trust system alerts. The real issue is not whether their work is valuable, but why senior employees are compensating for system gaps. Framing the conversation around closing these gaps can turn these staff members into allies for change, as many are eager to eliminate repetitive checking tasks.
Before making decisions about automation or AI, Thorburn emphasizes the importance of accurately measuring current costs. Only with clear data can organizations prioritize investments and avoid disappointment when automation fails to deliver expected savings.
For those interested in the broader implications of automation and AI in publishing, a related analysis explores how trust and standards remain critical as technology advances: the role of standards in building trust as automation grows.
Jeremy Thorburn is the founder of LatenZ, an independent operations and AI advisory. He does not represent any vendor or platform.
