Thought Leaders
Why Enterprise AI Is Failing at the Finish Line — and How to Fix It
Despite the buzz around AI, most enterprise AI projects never make it past the experiment stage. According to recent IDC research, 88% of AI proof-of-concept (POC) projects fail to scale into full production. That’s a massive drop-off, and a clear sign that something’s not working. Many of these projects get close to the finish line, with a trained model that meets the benchmarks set by the team, and then end up not being launched or adopted by end users.
So, what’s going wrong? In many cases, it comes down to three big issues:
- Enterprise AI teams are relying on surface-level diagnostic tools and benchmarks that don’t catch key performance gaps
- Models are trained to standard benchmarks instead of solving real-world problems
- The cost of scaling up model usage ends up being too high for company-wide adoption
In this article, we’ll unpack each of these pitfalls—and what it takes to get AI projects across the finish line and into the hands of users at scale.
Problem #1: Standard diagnostics that miss key performance issues
One major reason that AI projects stumble after the proof-of-concept phase is that internal benchmarks and diagnostics often don’t drill deep enough into model performance and tend to miss issues that crater usability, trust, and adoption. Teams might check all the boxes on paper, but those checks don’t always reflect how the model will perform in the real world.
Take this example: One AI team had a model that passed every internal test with flying colors. It hit all their accuracy metrics and safety thresholds, and they were gearing up for release. But when they had a third party evaluate the model for their intended use case to mirror how actual users would interact with the system, they found a major blind spot. The model was nine times more likely to give evasive answers when asked questions a certain way. For example, it would respond correctly to “Who is the president of the US?” but treated “Can you tell me about the president?” as a safety risk and refused to answer.
The issue wasn’t with the model’s core knowledge—it was with how it interpreted intent based on phrasing. The team had optimized for safety so much that they accidentally blocked normal, reasonable questions.
Problem #2: Models are fine-tuned to benchmarks that don’t reflect the real world
Another common stumbling block for enterprise AI is that AI teams train models to meet industry-standard benchmarks rather than real-world needs. On paper, a model might look top-tier, scoring high on standard evaluations for accuracy, relevance, or safety. But in practice, it may struggle to deliver consistent, useful results without heavy user intervention.
This happens when teams optimize models to perform well on narrow, benchmark-specific tasks. The model ends up excelling at those test cases but falters when it encounters less structured, more varied real-world inputs. As a result, users need to “speak the model’s language” through prompt engineering just to get the right answers. If your AI product depends on end users crafting precise prompts, you’ve introduced friction that slows adoption and undermines its usefulness.
This kind of benchmark-focused training can also lead to overfitting. The model gets so fine-tuned to perform well on evaluation datasets that it loses generalizability. It might pass every internal test but still fall short when deployed in the wild, especially if the actual use cases differ even slightly from those it was trained on.
If you want an enterprise AI solution that succeeds, your model needs to work in the real world—not just in the lab.
Problem #3: Scaling AI adoption means scaling compute costs
The third reason many AI POCs fail to scale is financial: teams often underestimate the cost of running and maintaining the model in production. During development, it’s easy to overlook the compute demands of a large model, especially when testing is done on small datasets or in limited-use environments. But once deployed, those costs can skyrocket.
Enterprise-grade AI requires significant computational resources, not just to serve responses in real time, but also for ongoing fine-tuning, monitoring, logging, and retraining. If these costs aren’t factored in early, the business case for the solution can collapse once real-world usage begins. What seemed like a promising model in a controlled test can quickly become unsustainable when thousands of users start hitting the system daily.
Overcoming last-mile hurdles to successful enterprise AI
To avoid the common pitfalls that derail so many enterprise AI projects, teams need to go beyond the usual playbook. Here’s how your AI team can build something that actually works—and scales.
First, bring in a third party to evaluate your model. Internal testing is important, but it’s often too broad. A fresh set of eyes, paired with a custom evaluation framework tailored to your use case, can surface issues your team might miss, especially when it comes to how real users will actually interact with the system.
Second, make sure you’re testing with real-world prompts. Most benchmarks test on “clean” data that don’t reflect the real world, much less how your specific end users will be prompting your model. Testing your model on the messy, vague, or oddly phrased inputs will go a long way in showing how your model will actually perform after deployment and let you catch issues that might otherwise fall through the cracks and end up affecting adoption.
Third, revisit your safety protocols. It’s easy to go overboard on guardrails, and while safety matters, it shouldn’t make your model frustrating to use. If the model shuts down on simple, harmless questions, you’re trading usability for a false sense of security.
Finally, watch your compute costs. If your adoption goals include thousands of users and millions of requests, those expenses can balloon fast. One solution is to consider smaller models. Boosted.ai did just that—they switched to a custom small language model and cut their compute costs by 90% while improving speed and performance. Real-time results, better user experience, and no need for expensive hardware.
By tackling evaluation, usability, and scalability from the start, teams can give their AI project a real shot at long-term success. It’s not just about making it work in a lab—it’s about making it work in the world.
