A consultant is coaching a sales team on how to use AI-generated next-best-action suggestions in Dynamics 365 Sales responsibly, rather than following them mechanically without judgment. Which TWO practices should the consultant recommend? (Select TWO.)
Select all correct answers, then click Submit.
Short Explanation
The whole point of a suggestion engine like this is that it is working from patterns across a lot of deals, it has no idea about the side conversation a rep had last week, or the history with a particular stakeholder that never made it into a note field. So the healthy way to use it is to treat it as one more input a rep weighs against what they actually know, not a command to execute on faith. That same instinct scales to the team level: watching how often people accept versus push back on suggestions, and why, tells a manager something real, and a pattern clustering around one kind of deal is worth digging into rather than shrugging off. What does not hold up is the idea that ignoring a suggestion costs the account something on paper, nothing about overriding a recommendation quietly works against the rep. And stripping away the ability to push back does not make the tool smarter, it just removes the one thing that would ever reveal when the model has gotten something wrong.
Full Explanation
The correct answers are C and D. Next-best-action suggestions are generated from patterns across many opportunities and do not have access to context a rep may have from a private conversation, a personal relationship with a stakeholder, or knowledge of circumstances the system was never told about, so a rep weighing a suggestion against that firsthand knowledge before acting on it is exactly the kind of judgment that keeps the tool useful rather than misleading. At the team level, a manager tracking how often suggestions are accepted versus overridden, and why, surfaces patterns worth acting on: if reps consistently override suggestions for a particular deal type or industry, that is a signal the model may be systematically missing something worth investigating, not noise to ignore. Option A is incorrect because it invents a mechanical penalty, overriding a suggestion does not lower an account's relationship-health score, and treating the ranking as an instruction to follow without judgment is the opposite of responsible use. Option B is incorrect because removing the ability to dismiss or override suggestions strips out the human judgment that catches cases where the model is wrong, turning a decision-support tool into a rigid mandate and eliminating the feedback that would otherwise show where the model needs improvement. Responsible use pairs the AI suggestion with human context, at both the individual and team level.