AI services
Objective. Explain how AI shows up across Time Champ - as assistance and suggestion with human review and inspectable evidence - so you position it honestly as a helper, never as an autonomous authority, and route model, data-handling and accuracy specifics to product and security.
AI is both a strong differentiator and the easiest place to over-claim. This teaches the honest framing - AI assists, humans decide, evidence is inspectable - what is safe to say, and the hard line at model detail, data handling and accuracy guarantees.
See it in the demo
What AI services are in Time Champ terms
AI appears across the suite as assistance that helps a person get to an answer or an action faster, with the human staying in control. The demos show several forms: RCM Insights has Ask (ask a question across data and inspect the evidence); Quality and Call QA has Ask Quality AI and positions AI output with human review; Intraday Management has a Copilot for the day; several apps expose Recommendations and AI-impact areas. RCM Operations uses AI-native briefings that move from signal to evidence to proposed action.
The consistent, honest pattern is: AI SUGGESTS, a human REVIEWS and DECIDES, and the EVIDENCE is inspectable. Quality's own guardrail is explicit - position AI output with human review and evidence - and RCM Insights' rule is that an answer without suitable data coverage or evidence should not become a customer commitment. That pattern is what you sell and teach, not autonomy.
What a Sales or CSM person can say confidently
Safe to say: 'AI helps you get to an answer and an action faster - ask a question and inspect the evidence, get a suggested evaluation or a recommended intervention - and a person always reviews and decides. It is a copilot, not an autopilot.'
Safe to say (Quality guardrail): 'AI-assisted evaluation is positioned with human review and the underlying evidence, so it speeds up quality work without replacing the reviewer's judgement.'
Safe for interpretation (rcm-insights rule): a fluent AI answer is not automatically a correct one - check data coverage and evidence before it becomes a commitment.
Do not describe which model is used, how customer data is handled for AI, whether data trains any model, or assert an accuracy or automation level. Those are product and security facts, and AI data-handling is squarely a PLAT-06 concern.
What always escalates to security or product
Route to a specialist:
- Which AI model or provider is used, and any model detail.
- How customer data is handled by AI services - what is sent, where, whether it is retained or used for training (security - PLAT-06).
- Accuracy, error-rate or performance guarantees for any AI feature.
- Whether any AI step is autonomous for a given customer, and the controls around it.
- Regional, regulatory or contractual constraints on AI use.
From F09 and the Quality guardrail: never present AI as an unreviewed authority, and never guess on AI data handling. 'AI assists and a person decides' is safe; model and data specifics escalate.
The honest limit
Demo-supported: AI assists across the suite (Ask and evidence inspection, Ask Quality AI with human review, Copilot, Recommendations, AI-native briefings), always with human review and inspectable evidence.
Confirm with product/security: the model/provider, AI data handling and training, accuracy or performance guarantees, autonomy and controls, and regulatory constraints.
Separate cleanly: 'AI helps people decide faster and the evidence is inspectable' (safe) from 'it uses model X, does not train on your data, and is accurate to Y%' (confirm with product/security). Sell the assist-with-evidence pattern; escalate the model and data specifics, every time.
Interpretation check
The honest AI pattern is suggest-review-decide with inspectable evidence; a fluent AI answer is not automatically correct and must not become a commitment without adequate data coverage and evidence. Model, data-handling and accuracy specifics are always product/security confirmations.
In the field
In an RCM account like KnackGlobal, AI-native briefings move a leader from signal to evidence to a proposed action, but the credible pitch keeps the human deciding and the evidence inspectable - and routes any question about the model or how claim data is handled to product and security.
Practical task
Using the RCM Insights Ask and Quality Command Center demos, script a two-minute AI demonstration that shows a suggestion, the human review step, and the inspectable evidence. Write the one limitation you will state out loud, and list the AI questions (model, data handling, accuracy) you will route to a specialist.
Self-check
How do you position AI in a demo without over-claiming?
As assistance that speeds up getting to an answer or action with the human reviewing and deciding and the evidence inspectable - a copilot, not an autopilot - never as an autonomous authority.
A customer treats a fluent AI answer as a committed figure. What is the correct interpretation rule?
A fluent answer is not automatically correct; check data coverage and evidence first, because an answer without suitable coverage should not become a customer commitment.
A security reviewer asks which model is used and whether their data trains it. What do you do?
Route the model/provider and all AI data-handling and training questions to a specialist - these are security facts (PLAT-06) you never assert.
Which AI facts are safe and which escalate?
Safe: the assist-review-decide pattern, evidence inspection, and the Quality human-review guardrail. Escalate: model/provider, AI data handling and training, accuracy guarantees, autonomy level and regulatory constraints.
Verified vs demo. AI assistance with human review and inspectable evidence (Ask, Ask Quality AI, Copilot, Recommendations, AI-native briefings) is shown in the demos; the model/provider, AI data handling and training, accuracy guarantees and autonomy level are product/security facts to confirm.