u/lorikeet-cx

Something I keep seeing: teams deploy AI support, it works great for a week, then customers start getting contradictory or weird answers.

The AI isn't broken. The knowledge base is.

Human agents are unbelievably good at compensating for bad docs. They see "refer to manager for approval" and know that policy changed 6 months ago. They see "use your judgment" and actually have judgment. They see three articles covering the same topic with different details and know which one's canonical.

AI reads everything at face value. Every article is equally authoritative. Every instruction is meant to be followed literally. "See Sarah in billing for exceptions" becomes the AI telling a customer to contact Sarah.

The problems aren't dramatic — they're subtle, accumulated over years, and completely invisible to humans who work around them every day. That's what makes them dangerous.

Wrote up the full pattern — what "bad" actually looks like and what to audit before deploying AI: https://www.lorikeetcx.ai/blog/knowledge-base-ready-for-ai?utm_source=reddit&utm_medium=social&utm_campaign=toolshed-blog

Noticed a pattern in this week's CX+AI job scrape: enterprise companies are now hiring people whose explicit mandate is to deploy AI agents *into* CX — not just "use AI tools" as a buzzword bullet. The seniority is also climbing — these are Director/Manager-level mandates, not entry-level chatbot maintenance.

A few that stood out from this week's 23 new additions:

- Clio, Senior Director, Customer Support (Remote, North America) — https://www.lorikeetcx.ai/cx-jobs/senior-director-customer-support-clio

- AUTO1 Group, Senior PM, Conversational AI for Customer Service (Berlin) — https://www.lorikeetcx.ai/cx-jobs/senior-product-manager-conversational-ai-for-customer-service-f-m-d-auto1-group

- Wallapop, CS Transformation & Automation Lead (Barcelona) — https://www.lorikeetcx.ai/cx-jobs/cs-transformation-automation-lead-wallapop

- Sana, Director of Customer Support (Remote, US) — https://www.lorikeetcx.ai/cx-jobs/director-of-customer-support-sana

- Bumble, Integrity Operations Specialist (London) — https://www.lorikeetcx.ai/cx-jobs/integrity-operations-specialist-bumble-inc

- AI Rudder, Implementation Engineer (SEA) — https://www.lorikeetcx.ai/cx-jobs/implementation-engineer-ai-rudder

330 total roles on the board now, across 17 regions — https://www.lorikeetcx.ai/cx-jobs?utm_source=reddit&utm_medium=social&utm_campaign=cx-ai-jobs

Anyone here making this jump (CX leadership → AI-deployment-heavy CX role)? Curious whether you're learning this on the job or whether you came in already knowing prompt engineering / agent design.

One thing that bugs me: support backlogs live in ops dashboards, measured in ticket counts. They get discussed in standups, not finance reviews.

But a 500-ticket backlog with a 4-day average wait isn't a throughput problem. Each ticket represents a customer waiting, and waiting customers do predictable things — they evaluate alternatives, tell colleagues, and churn.

The really non-obvious part is the escalation multiplier. A billing question on day 1 takes 6 minutes. That same ticket on day 4 has a customer who's called back twice, filed a social complaint, and wants a manager. Original issue hasn't changed. Resolution cost has tripled.

Forrester found tickets older than 72 hours cost 2.4x more to resolve. Factor in churn risk and it's 3-4x.

A support director who says "our backlog is growing" gets a different reaction than one who says "our backlog is costing $340K/quarter in preventable churn." Same problem, different language, different budget response.

Longer piece with the full framework: https://www.lorikeetcx.ai/blog/support-backlog-cost?utm_source=reddit&utm_medium=social&utm_campaign=toolshed-blog

Klarna's 2024 earnings call claimed their AI was doing the work of 700 agents. Stock jumped. Six months later they quietly started rehiring because resolution quality had cratered.

The tech worked fine. The organisation wasn't ready.

Been thinking about this pattern a lot. The teams that succeed with AI tend to be strong across six dimensions — and technology is the least predictive one. Knowledge management, process maturity, data readiness, team structure, and change management all matter more.

The weird thing is how little assessment happens before the purchase. In other enterprise categories — ERP, CRM, security — nobody deploys without a readiness audit. With AI, teams skip straight to vendor demos because the board deck needs an "AI strategy" slide.

Wrote a longer piece on what actually matters: https://www.lorikeetcx.ai/blog/ai-readiness-checklist-cx-teams?utm_source=reddit&utm_medium=social&utm_campaign=toolshed-blog

What dimension would you say your team is weakest on?

Title: "Director, AI Strategy" + Customer Experience is becoming its own job category

Went through this week's CX+AI postings on the board and noticed something I hadn't really clocked before: a bunch of companies that previously didn't have anyone with "AI" in their CX leadership are now hiring exactly that.

Some of the ones that stood out from this week's batch:

• Strava: Manager, CX AI Strategy (SF)
• Public Storage: Sr Manager of AI Strategy & Customer Experience (Plano, TX)
• Inner Balance: Director, AI Enablement & Applications – Customer Experience (Remote)
• Hello Heart: Director, Customer Success Operations (with AI ownership baked into the JD)
• Concentra: VP, Artificial Intelligence (with CX scope)

A year ago these would've been "VP of Support" or "Head of CS Ops" with AI buried as one bullet point. Now AI is the noun, customer experience is the modifier.

Felt like a real signal of where mid-market and enterprise CX is going — orgs that already have a head of support are layering an AI lead on top, not replacing.

Full list (no email gate, no signup, just the board): https://www.lorikeetcx.ai/cx-jobs?utm_source=reddit&utm_medium=social&utm_campaign=cx-ai-jobs

Anyone else seeing these titles pop up in your network? Curious whether they're landing under CX or under a separate AI/data org in your companies.

Deployed an AI support agent last quarter. First week looked great. Then customers started getting contradictory answers. One asked about reimbursement timelines and got two different answers in the same conversation — pulled from a 2022 policy article and an updated FAQ that covered the same topic with different numbers.

The AI wasn't broken. The knowledge base was telling it two different things.

Did an audit. Out of 340 articles: 40% referenced outdated policies. A bunch had internal language like "check with Sarah in billing" or "use your judgment" — fine for human agents who know what that means, bizarre when an AI reads it literally. And we had duplicate coverage everywhere — the refund policy lived in three different articles with slightly different details.

Human agents compensate for all of this unconsciously. AI takes it at face value.

Built a tool that scans for these patterns if anyone wants to check their own KB before deploying AI: https://lorikeet.tools/kb-evaluator?utm_source=reddit&utm_medium=social&utm_campaign=toolshed