When a “people problem” isn’t one
If you manage customer service through numbers on a dashboard, it’s easy to draw the wrong conclusion.
- Backlog keeps growing
- Handle times creep up
- First response time slips
- Customer complaints increase
On paper, it looks like a performance issue: “The team isn’t efficient enough.”
In practice, what I see in most organizations is different: the team is stuck inside a system that makes good performance hard.
This article walks through a pragmatic way to look at customer service productivity from a COO’s angle, without defaulting to “let’s add training” or “let’s add headcount”.
What “stuck” looks like from the inside of a Customer Service team
If you sit next to agents for a day, “stuck” has a very recognizable pattern:
- They answer the same questions all day (status, policy, simple how-tos)
- Every second or third case requires jumping between three or more tools
- Simple cases become complex because data is missing or conflicting
- Escalations pile up because no one owns cross-team issues
- New hires feel overwhelmed, seniors feel like they’re always in fire-fighting mode
As a COO, it helps to separate capacity problems (not enough people for the volume) from system problems (too much friction per case, unclear ownership, bad tools). Most CS teams have some of both, but the second category is where there is leverage.
Three root causes that masquerade as “people issues”
1. Workload mix: too much routine and not enough leverage
In many teams, a big share of time is spent on:
- repetitive, low-judgment queries (“Where is my order?”, “How do I reset my password?”)
- simple updates across systems (copying IDs, changing statuses)
If 40-60% of your team’s time goes to work that follows the same steps most of the time, and adds little value per extra human you throw at it, it safe to say you have a work design problem.
What to check:
- Ask for a breakdown of time by intent (top 10 reasons customers contact you).
- Ask two senior agents: “What percentage of your day feels truly repetitive?”
If the number is high, the team is stuck doing work that should be standardized or automated.
2. Tooling and process friction
When people talk about “bad tools”, they usually mean friction:
- Agents open 4-6 systems to resolve a single issue.
- Data doesn’t match between tools (CRM vs billing vs logistics).
- There are no clear playbooks for cross-team issues.
- Macros and templates exist, but they’re outdated or incomplete.
Symptoms you might see in reports:
- high handle times for what appear to be simple cases
- “internal comment” threads dragging on
- escalations bouncing between teams
What to check:
- Sit with an agent and watch them handle 5 real cases.
- Count the number of systems they touch per case.
- Count the number of times they have to ask someone else for help.
- Ask: “Which step in a typical case feels the most pointless or slow?”
You’ll usually discover a lot of manual orchestration that is invisible in dashboards.
3. Ownership gaps and decision fatigue
Another common pattern: the team knows something is broken, but they don’t have the mandate to fix it.
Examples:
- Pricing or policy created outside CS, but CS takes the heat.
- No clear owner for recurring issues (e.g. a specific carrier, region, or feature).
- Agents are expected to “do what’s right for the customer” without clear boundaries.
Over time this creates:
- inconsistent decisions (customer experience varies by agent)
- escalations “just to be safe”
- slower handling because people hesitate
What to check:
- For your top 5 recurring issues, ask: “Who owns fixing the root cause?”
- Review a sample of escalations: are they about exceptions, or about unclear rules?
When the rules are fuzzy or change often, even a great team will feel stuck.
A COO’s diagnostic: four simple questions
You don’t need a 3‑month assessment to see if your CS team has a people problem or a system problem. Try these four questions:
- If volume stayed the same, could this team hit your targets with the current setup?
- If yes, you may have a pure capacity issue
- If no, there’s structural friction
- How many systems does an agent touch for a “typical” case?
- 1-2 is healthy
- 4-6 is a red flag
- What percentage of contacts are truly repetitive?
- If nobody knows, that’s a sign in itself
- If senior agents say “more than half”, you have leverage
- For your top 3 issues, do clear rules exist?
- For example: refunds, exceptions, SLAs across channels
- If decisions depend on “who you ask”, it’s not a people problem
What to fix first when your CS team is stuck
1. Clean up the work before you add headcount
Adding people can be necessary, but if the work itself is poorly designed, extra capacity hides the problem for a while.
A more durable sequence:
- Map your top 5 intents (by volume and cost).
- For each, document:
- resolution steps
- systems touched
- decision rules
- escalation paths
- Ask: “Which of these steps are:
- necessary?
- automatable?
- in the wrong place (owned by CS but should be upstream)?”
You’ll often find:
- steps that can be removed or simplified,
- steps that can be automated with existing tools,
- steps that really belong to product, logistics, or finance.
Cleaning that up makes any later investment (training, tools, or AI) much more effective.
2. Reduce tool-switching and manual updates
Tool-switching is one of the biggest invisible productivity drains.
Pragmatic actions:
- Consolidate where possible (fewer tabs, fewer systems per case)
- Standardize where data should be updated (one system as the source of truth)
- Introduce small automations or integrations that pre-fill data, sync statuses, trigger internal tasks automatically
You don’t need to jump to advanced solutions on day one! Even basic API-based integrations can free up hours per agent per week.
3. Clarify rules and escalation criteria
Agents move faster when they know hat they are allowed to decide on the spot, and when they must escalate.
Practical steps:
- For refunds, exceptions, and goodwill gestures, create simple thresholds and examples.
- Make escalation rules explicit:
- high value orders
- high-risk patterns
- regulatory constraints
This lowers decision fatigue, reduces unnecessary escalations, and makes your future automation roadmap much clearer.
Where agentic AI fits (once the basics are in place)
Once you’ve identified high-volume, repetitive work, reduced avoidable tool-switching, clarified decision rules, you’re in a good position to consider agentic AI, specifically AI agents that can:
- pull data from multiple systems,
- apply your rules,
- execute routine actions end-to-end,
- and escalate exceptions with full context
It is important to note that AI agents won’t replace your team, they only change the mix of work. Machines will handle the predictable, multi-step tasks and humans will handle exceptions, relationships, and continuous improvement.
From a COO’s perspective, it’s about freeing up capacity without linear hiring, and making the job more sustainable for the people you already have.
Conclusion
When a customer service team feels stuck, the root cause is almost always structural: undeflected routine work, fragmented systems, and missing rules for triage and escalation.
These are solvable problems. The first step is a clear diagnostic of your work mix, tooling, and decision rules. Once that picture is sharp, the right automation opportunities surface quickly, and today’s AI agent technology can resolve 40 to 60% of routine inquiries autonomously within weeks.
A structured maturity assessment is the fastest way to get there: mapping your readiness, identifying the highest-value use cases, and building a phased roadmap with measurable targets.
