90% Of AI Projects Fail: Use This One Simple Trick To Reap Massive Returns

90% of AI projects deliver no value

Executives are making unprecedented investments in artificial intelligence. McKinsey estimates that almost 80% of large enterprises are using some form of AI across their functions. In general, CEOs seem increasingly worried they will get left behind if they don't embrace this technology revolution.

Yet a startling reality persists: 90% of AI projects fail to deliver any meaningful value.

This isn't just wasted technology spend. These initiatives consume executive attention, drain talent from other priorities, and create organizational friction. Most concerningly, many "successful" implementations merely shift costs from operational teams to technology maintenance, with minimal impact on enterprise performance metrics.

Your AI isn’t saving money, it’s shifting costs

Most AI initiatives focus on replacing headcount in high-volume, low-complexity functions. This approach seems rational – if AI can perform tasks previously done by humans, surely cost savings will follow?

Reality tells a different story.

One financial institution spent $3M rolling out an AI chatbot to reduce call center volume. Six months later, they had reduced call center staff by just 8 employees ($600K annual savings) while adding 6 new AI specialists ($900K annual cost) to maintain the system. Net result: negative ROI with diminished service quality.

We see this pattern repeatedly across industries:

• A retail chain implemented AI-powered inventory management but needed twice as many data engineers to maintain the system

• A healthcare provider's AI diagnostic tool required more clinical validation than the manual process it replaced

• A manufacturer's predictive maintenance system created more alerts than their technicians could investigate

The fundamental error? Treating AI as a direct labor substitute.

In our experience, the best AI currently only replaces, at most, 1/3 of a given human’s job. Usually that’s the easiest or most menial part of the job. One major issue is that the most menial work was probably already automated to some degree. So you end up replacing one automation with another. Another issue is that, to make the AI as robust as the human requires a lot of tuning around prompts and integrations. Every time the process changes, tuning has to be redone by very expensive engineers.

All this leads to a situation where the majority of today’s AI projects are shifting costs from low-cost, adaptable humans to inflexible systems that need constant tweaking from expensive engineers.

Focusing on constraints delivers 50x greater ROI

Elite performers in the AI space take a fundamentally different approach: they deploy AI specifically at business constraints – the critical bottlenecks blocking end-to-end value delivery.

A global bank we worked with identified that their penetration testing team was a critical constraint, delaying digital deployments by an average of 17 days. Instead of using AI as a replacement for customer service, the company deployed AI to ease the burden on overloaded pen testers. As a result, the entire org was poised to release deployments weeks faster, for an estimated $28M in accelerated revenue.

When you target constraints, ROI calculations shift from modest cost-replacement to massive value-acceleration:

• Traditional AI Business Case: Replace 10 low-cost workers ($1M) with automation ($300K annual cost) = $700K annual savings

• Constraint-Based AI Business Case: Accelerate revenue recognition by removing delays at a constraint yields $28M ($2M per day * 17 days).

In one manufacturing plant, AI-assisted quality control at a production bottleneck increased throughput by 15%. This didn't save labor costs but, instead, enabled the factory to fulfill orders 2 weeks faster, with much lower levels of WIP.

The math is simple: improvements anywhere except at the constraint can reduce costs in that silo. Unlocking a constraint creates value that spans the entire enterprise.

Use simple counting to find high-yield targets

A great irony arises when organizations start hunting for constraints: they spend months mapping processes before implementing any changes. Constraint-finding, in other words, is the first constraint.

In reality, you can identify constraints in hours, not months, using these simple steps:

Identify Your Value-Critical Flows: Which processes directly impact revenue recognition or cost realization? Client onboarding is an obvious one. So is project delivery.

Measure Lead Times with Simple Counts:

1) Count # of items entering a process each month

2) Count # of items exiting (completions) each month

3) Count the current open items

4) Calculate the delivery lead time: # open items ÷ completions

5) Constraint => Anywhere with high lead time or incoming items > completions

Deploy AI Against Constraints: Use AI to augment to reduce the burden on human decision-making at that step. Maybe you accelerate a search task. Maybe you automate checks and validation. Maybe you have the AI schedule their meetings. Whatever you can do to help those people process more work, adds value.

A logistics company found that route optimization during peak periods was their critical constraint. Their AI solution didn't replace drivers – it helped dispatchers make better decisions in real-time, increasing delivery capacity by 22% during peak hours.

Tools like Enthoosa AI can accelerate this process even further, helping you identify constraints and quantify their financial impact. Our approach gets you an answer within hours rather than the weeks or months required by traditional process mapping approaches. Click here to receive a free trial.

AI works best when you point it at constraints

AI itself doesn't create value – it's just a tool. What creates transformative value is applying that tool precisely where it matters most: at the constraints that govern value-delivery in your organization.

The most successful executives aren't asking "What jobs can AI replace?" They're asking "Where are our constraints, and how can AI help us break them?"

This fundamentally reframes AI from a cost-reduction exercise to a strategic capability that accelerates your entire business.

The quality of your constraints analysis determines the quality of your AI strategy. Everything else is AI theater.

Looking to identify constraints in your organization within hours instead of months? Explore how Enthoosa AI has helped enterprises unlock $2B+ in accelerated value through constraint-focused transformation. Massive Returns