80% of AI projects fail to deliver business value.
Not because the technology doesn’t work. Because organisations spray technology at problems instead of aiming it at specific outcomes.
The spray-and-pray approach
“We need AI.” “We need machine learning.” “We need advanced analytics.”
These statements sound strategic. They’re actually symptoms of unclear thinking.
AI is a capability, not a strategy. Machine learning is a tool, not an outcome. Asking for “advanced analytics” without specifying what decisions need to improve is like asking for “more power” without knowing what you’re trying to move.
The spray approach: buy AI platforms, hire data scientists, build models, hope something valuable emerges.
The result: expensive infrastructure producing insights nobody uses.
Why precision matters
The organisations that extract value from AI start differently.
They don’t start with “we need AI.” They start with “we need to solve X problem, and here’s how we’ll measure success.”
Precise: “Our reps spend 40% of their time on customers who won’t grow. We need to identify which customers have genuine potential before each call.”
Imprecise: “We need AI to optimise our sales force.”
The precise version defines success criteria. The imprecise version hopes for magic.
The governance prerequisite
Here’s what most AI vendors won’t tell you: AI quality depends entirely on data quality.
Models trained on bad data produce confident bad answers. Models trained on inconsistent data produce inconsistent results. Models trained on incomplete data miss patterns that matter.
Data governance isn’t a bureaucratic checkbox. It’s the foundation that determines whether AI investments produce value or waste.
Before AI: Can you trust your customer data? Are your sales records complete? Do different systems agree on basic facts?
If the answer is “sort of” or “we’re working on it,” you’re not ready for AI. You’re ready for data governance.
The 80% failure pattern
Failed AI projects share common characteristics:
Unclear outcomes. The project was funded because AI seemed important, not because a specific business problem demanded a solution.
Data denial. Known data quality issues were minimised. “We’ll clean it up as we go.” (Nobody ever does.)
Model obsession. Resources focused on model sophistication rather than deployment practicality. A brilliant model that nobody uses is worthless.
Integration avoidance. The AI system was built alongside existing workflows rather than within them. Adoption required users to change behaviour, and they didn’t.
Success metrics missing. Nobody defined what success looked like before starting, so nobody could tell whether the project succeeded.
What precision execution looks like
Step 1: Problem definition. What specific decision are we trying to improve? Who makes that decision? What information would change how they decide?
Step 2: Data assessment. Do we have the data to answer this question? Is it accurate? Is it accessible? Is it current?
Step 3: Simple first. Can we solve this problem with basic analysis before building complex models? Often, simple logic rules outperform sophisticated AI.
Step 4: Integration design. How will insights reach decision-makers? In their existing workflow? At the moment of decision? Without requiring extra steps?
Step 5: Success measurement. How will we know this worked? What metrics will change? Over what timeframe?
The uncomfortable truth
Most organisations don’t need AI. They need cleaner data and clearer thinking.
AI amplifies what exists. If your data is a mess, AI will amplify the mess. If your decision-making is vague, AI will produce vague recommendations.
The organisations that succeed with AI don’t succeed because of AI. They succeed because they’ve done the hard work of governance and precision that makes AI valuable.
Stop spraying technology at problems. Start aiming at outcomes.
The 20% of AI projects that succeed share one trait: they knew what they were aiming for before they fired.
Written by
Dieter Herbst
CEO & Founder at Herbst Group. Working with pharmaceutical commercial leaders across South Africa, Kenya, and Brazil to transform sales force effectiveness through evidence-based approaches.
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