Even if 70% of AI Projects Fail, AI Will Not

AI projects often fail not because the technology doesn’t work, but because the incentives inside the institution are misaligned.

And yet, despite all that, failure might not even matter. Most information technology initiatives (up to 70 percent) seem to fail, and none of that diminishes the ultimate value.

And there are lots of institutional behaviors that might help explain risk aversion, not only IT project failure.

When risk is localized and visible while benefits are diffuse and hard to attribute, rational managers avoid experimentation even when the enterprise would benefit.

If an AI pilot fails, the sponsoring department (IT, operations, marketing) is clearly identifiable.

If it succeeds, gains often show up in ways that do not proportionally reward taking the risk:

• company-wide cost reductions

• improved customer experience

• better decision quality

• long-term competitiveness. 

Also, what is hard to measure is hard to reward. But failure or investment arguably often is measurable:

• budget overruns

• model bias incidents

• downtime

• compliance issues

• higher departmental spending

Successes, on the other hand, often are ambiguous or show benefit only at the “whole enterprise” level:

• “better decisions”

• “higher productivity”

• “reduced churn risk.”

Also, employee and manager rewards tend to be short term oriented, while AI improvements tend to be long-term.

Innovation	Who Bears the Risk	Who Receives the Benefit	How Rewards Are Structured	Likely Behavior
AI customer service chatbot	Customer support leadership	Entire company (lower costs, better customer experience)	Support measured on call resolution, customer satisfaction	Avoid deployment if early errors hurt scores
Predictive maintenance AI	Plant operations	Enterprise (reduced downtime, capex deferral)	Ops measured on uptime, safety	Resist pilot that might trigger false alarms
AI sales forecasting	Sales ops team	Finance, supply chain, executive planning	Sales measured on quota attainment	Ignore tool that might challenge forecasts
AI-assisted coding tools	IT budget owner	All product teams	IT measured on cost control & system stability	Delay rollout due to security concerns
AI fraud detection	Risk/compliance team	Entire firm (loss reduction)	Compliance measured on zero failures	Avoid model with false positives risk
AI hiring screening	HR	All departments (better hires)	HR measured on time-to-fill and legal risk	Reject tool due to bias concerns
AI pricing optimization	Pricing team	Company-wide margin gains	Team measured on revenue stability	Avoid experimentation that could reduce short-term revenue
AI knowledge management	IT, knowledge management  team	Entire workforce	KM measured on system uptime and adoption	Avoid radical change to workflows

The contrast between “fear” and “greed” explains quite a lot of human behavior, in personal life or in business. We might say the dichotomy is between expectations about “downside” and “upside” as well as the consequences of either happening. 

If the impact or results of a negative realized outcome happen right away, and are quantifiable, while the results of a positive outcome are delayed, intangible in the moment or hard to quantify, then risks might generally exceed rewards in many instances. 

All of which might help explain why artificial intelligence adoption seems to produce fewer immediate gains than we might expect. 

For individual decision-makers in business, downside risk from a model-driven decision, such as credit losses, customer complaints, or operational disruptions, is immediate and personally attributable. We know quickly what we have lost. 

Upside value, by contrast, is probabilistic, delayed, and diffused across the enterprise.

Rational actors therefore discount model recommendations. 

To increase the perceived margin of safety, adoption thresholds rise, discretionary review layers proliferate, and precedent asserts itself when change is called for. 

This behavior is often misinterpreted as cultural resistance or distrust in AI, but it reflects rational responses to incentive structures that punish mistakes more visibly and directly than rewarding successes.

In fact, where AI is able to better quantify risk, it might actually encourage inaction, especially if it is possible to surface specific entities, departments or functions where losses could occur, while benefits might accrue more broadly, and be harder to identify. 

This is analogous to the problem of cutting any government spending: the losers are easily identifiable (people who lose jobs) while the winners (taxpayers) are diffuse and anonymous. 

So AI return on investment results are the predictable outcome of deploying probabilistic technologies into institutions optimized for deterministic control, loss avoidance, and fragmented accountability. 

In other words, AI might create, highlight or accentuate a greater degree of uncertainty; allocate potential losses in clear ways while producing benefits that are more diffuse. 

The example is AI quantifying costs in specific departments, run by specific individuals, while producing enterprise-level profits or revenues that are not clearly attributable to any specific department or person. 

The risks of failure are clear and personal; the benefits non-attributable in the same way. 

So to increase the chances that AI adoption will work, entities can change incentives:

• Share credit across departments

• Tie success metrics to enterprise outcomes, not just local KPIs.

• Create “safe-to-fail” zones (innovation budgets and sandboxes reduce career risk)

• Centralize funding for cross-functional AI to avoid penalizing the initiating department

• Reward experimentation, not just outcomes

• Attribute value explicitly (use internal accounting to show which teams enabled enterprise gains).