VTechFusion Team
VTechFusion Technologies
Pharma has invested roughly $60 billion in AI drug discovery over eight years with zero approved products yet — and largely isn't treating that as evidence of failure, because the timeline was set against the industry's actual, known regulatory reality from the start, not against hype-driven expectations. That discipline is worth applying directly to enterprise ERP AI investment.
Why Most Enterprise AI ROI Timelines Are Actually Miscalibrated
Much of the disappointment behind statistics like "95% of AI pilots fail to reach production" (covered elsewhere on this site) likely traces to timelines set against marketing-driven expectations rather than the actual, known complexity of the specific business process being transformed. Pharma's patience isn't lower standards — it's calibrated against a genuinely understood, long, regulation-driven development cycle, which most enterprise software projects simply don't share.
A Practical Recalibration Exercise
- Before launching an ERP AI initiative, honestly map the actual complexity and change-management scope of the specific process being transformed — a financial close automation project and a customer-service chatbot deployment have genuinely different realistic timelines, and treating them the same is a setup for premature disappointment
- Set ROI measurement checkpoints calibrated to the specific initiative's realistic complexity, not a generic "AI should show ROI within two quarters" default applied uniformly across genuinely different project types
- Distinguish between "this specific pilot failed because of a real, correctable problem" and "this initiative simply needs more time given its actual complexity" — conflating the two leads either to abandoning genuinely promising initiatives too early, or persisting with genuinely failed ones too long
Frequently Asked Questions
Why hasn't the lack of approved AI-discovered drugs after 8 years been treated as evidence AI drug discovery failed?
Because the industry's timeline expectations were calibrated against pharma's actual, known regulatory reality (a decade-plus from discovery to approval is normal even without AI) from the start, not against hype-driven expectations — a different discipline than much of enterprise AI adoption has applied.
How should an ERP team set more realistic AI ROI timelines?
Honestly map the actual complexity and change-management scope of the specific process being transformed before launch, and set ROI checkpoints calibrated to that specific initiative's realistic timeline — rather than applying a generic, uniform ROI expectation across genuinely different project types.
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