Blue Sage Data Systems
A real concern Lincoln leaders raise

We can't tell if AI is actually working for us

Real concern. McKinsey 2025 found only 39% of organizations report any EBIT impact at the enterprise level, and only ~6% qualify as high performers. The answer isn't a better dashboard — it's better metrics.

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Common questions from Lincoln leaders

Why is AI ROI so hard to measure?
Most teams measure activity (token spend, login frequency) instead of outcomes (cycle time, error rate, customer outcomes). Activity gets gamed; outcome metrics produce signal.
What's the realistic ROI range?
Wide. Deloitte Q4 2024 found 74% report meeting or exceeding ROI on advanced initiatives, 20% above 30%. McKinsey 2025: 39% report any EBIT impact, ~6% are 'AI high performers'.
Should we kill our AI initiative if we can't measure ROI?
Almost never. Switch to workflow-level outcome metrics. If those don't move after 90 days, then yes — but the diagnostic usually finds 'we measured the wrong thing.'
When does hard EBIT impact show up?
Usually only after workflow redesign at scale. McKinsey 2025: AI high performers are nearly 3x more likely to have fundamentally redesigned workflows.
How do we set realistic expectations with leadership?
Three tiers: 3 months for one workflow, 12 months for cumulative gains, 24+ months for EBIT-level impact. Promising tier 3 in 90 days is how trust erodes.

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