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Artificial Intelligence is transformative, but most companies struggle to turn pilots into production. A recent MIT study found that 95% of generative AI projects fail to deliver measurable business impact, often because of flawed integration and unclear value.
At Invisible AI, we’ve seen these same barriers firsthand while helping manufacturers deploy edge AI and computer vision at scale. The good news: the obstacles are predictable and avoidable. If your project isn’t gaining traction, here are nine common reasons why, along with lessons from our work on the factory floor.
1. Undefined Business Case or Use Case
Too many projects start as “AI for AI’s sake.” Without a clear objective—like reducing downtime or detecting defects—it’s hard to gain momentum. Gartner has reported that only 48% of AI projects actually make it into production, largely due to unclear objectives.
The MIT study found that the most successful AI projects began with narrow, well-defined use cases that demonstrated value before scaling. We’ve seen this repeatedly in the field: a leading automotive OEM partnered with Invisible AI to target root cause analysis, ultimately saving millions. Similarly, an automotive supplier started with a single deployment focused on ergonomic risk detection. In both cases, clarity of purpose turned small wins into large-scale momentum.
2. Data Isn’t Ready for AI
AI runs on data, but most organizations underestimate the importance of data quality. Gartner estimates that 99% of AI projects encounter data issues, and 93% of executives admit poor data quality is a major blocker. Without clean, consistent, and accessible data, models can’t perform as expected.
Invisible AI helps manufacturers overcome this barrier by generating structured, high-quality datasets directly from their factory floors with our vision AI platform. By automating data collection at the edge, companies avoid the “data readiness” trap that stalls most pilots.
3. Misaligned Stakeholders
AI projects shouldn’t live in silos. When IT, operations, and leadership aren’t aligned, projects stall. A RAND Corporation report highlighted that communication breakdowns and unclear accountability between technical and business leaders are a top cause of failure.
Invisible AI avoids this by working directly with both operations leaders and IT teams to ensure everyone’s priorities are aligned. In one OEM partnership, alignment between leadership and plant-level teams was critical to moving quickly from pilot to production.
4. Unrealistic Expectations
AI isn’t magic. Many companies overestimate what AI can do in the short term and underestimate what it can do in the long term. Gartner predicts that at least 30% of GenAI projects will be abandoned even after proof-of-concept due to poor data and misaligned value expectations. Setting achievable milestones keeps stakeholders engaged.
5. Lack of Internal Expertise
Talent shortages remain a huge challenge. Specialized skills in data science, machine learning, and AI operations are scarce, making it difficult to build in-house capabilities. A global survey by AIMultiple found that “lack of talent” was consistently cited as one of the top three reasons AI deployments fail.
This is why many manufacturers choose to partner with Invisible AI. Our root cause analysis case study shows how we delivered turnkey expertise to an automotive OEM, enabling them to gain insights without needing to build a large internal AI team.
6. Change Management Resistance
Even if AI works technically, it can fail socially. Workers may resist new tools due to fear, distrust, or lack of clarity. A Stanford/BCG analysis found that companies investing 70% of their AI budgets into change management, training, and adoption programs—rather than purely on algorithms—were more likely to succeed. Adoption is as much a human challenge as it is a technical one.
7. Ignoring Edge Deployment Challenges
Many AI solutions are designed with the cloud in mind, but real-world industrial environments often require edge-based, low-latency processing. Neglecting infrastructure planning leads to endless pilot tests with no production rollout. Edge readiness—robust hardware, reliable networks, and security—is often the hidden hurdle.
8. Poor Integration with Existing Systems
AI cannot operate in isolation. For it to create value, it must integrate with ERPs, MES platforms, and shop-floor workflows. The MIT report found that poor integration, not algorithmic failure, was the leading reason AI failed to impact profit and loss statements. Integration strategy should be planned from the beginning, not bolted on at the end.
9. Technology Before Outcomes
Too many organizations get caught up in experimenting with models and tools rather than focusing on the outcomes they want to achieve. AIMultiple noted that unclear business objectives lead to wasted investments and failed pilots. The most successful projects treat AI as a tool to solve specific problems—not as an innovation experiment.
At Invisible AI, we always start with the outcome in mind — whether reducing downtime, improving quality, or lowering ergonomic risk. That approach ensures our technology drives measurable business value, not just technical exploration.
The Bottom Line
Getting AI off the ground requires more than the latest algorithms. As MIT Sloan and BCG research show, success depends on clear objectives, clean data, organizational alignment, proper integration, and strong change management.
At Invisible AI, we help organizations overcome these hurdles with computer vision and edge AI solutions that deliver measurable results on the factory floor.
If your AI project is stuck in “pilot purgatory,” it might be time to refocus on fundamentals and we can help you take the next step.
