The automation of business is accelerating—but are we also automating deception? Businesses love to measure Customer Experience (CX)—yet most CX metrics don’t predict profitability or long-term success. They measure sentiment, not reality.
Consider these two categories of measurement:
Metrics Based on Real Numbers & Facts:
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CAC (Customer Acquisition Cost) – What it truly costs to win a customer.
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RPR (Repeat Purchase Rate) – Whether customers actually come back.
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OEE (Overall Equipment Effectiveness) – Tangible data exposing operational inefficiencies.
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SWOT (Strengths, Weaknesses, Opportunities, Threats) – A structured, fact-based strategy assessment.
Metrics Based on Sentiment & Assumptions:
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NPS (Net Promoter Score) – How customers feel, not what they do.
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CLV (Customer Lifetime Value) – A forecast built on assumptions about the future.
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Most CX Metrics – Vanity measures designed to look impressive, not drive real outcomes.
The Real Problem: Feeding AI the Wrong Metrics
Dashboards can be reworked. Metrics can be reset. But AI remembers. When we train AI on these distorted KPIs, we automate bias. We hardwire selective truths into systems that learn, adapt, and replicate at scale. Over time, this doesn’t just create blind spots—it cements them as operational reality.
We are at risk of building AI that optimizes for sentiment, not outcomes. AI that reinforces organizational bias, not business reality.
What happens when truth is no longer part of the equation?
The damage isn’t theoretical. Optimizing for the wrong things wastes time, misguides strategy, and when automated through AI, becomes self-perpetuating. Correcting it later won’t be simple.
Time to Rethink Success
If your AI, dashboards, and strategies aren’t built on profitability drivers grounded in reality, you’re not optimizing—you’re automating blind spots.
Consider this:
If you stopped tracking NPS tomorrow, how would your CX strategy actually change?
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