Why Do AI Projects Fail?
Billions of dollars are poured into AI every year, yet most projects never make it out of the lab. With little to show for such a substantial investment, everyonefrom engineers and executives to investors is rightfully asking:
What’s holding AI back?
Edge Cases – A Critical Point of AI Failure
The real world is infinitely complex and constantly changing. This makes it difficult to build and commercialize AI products –robotic or otherwise – that perform accurately and reliably.
Every company doing anything interesting with AI, from warehouse automation to robotic delivery, faces this same reality: AI is hard to perfect.
Though AI products are trained for a variety of circumstances, the unstructured environments in which they operate – farm fields, warehouses, sidewalks, or inside homes – are inherently unpredictable. This makes it impossible to train for the long-tail of all potential scenarios, known as edge cases.
As a result, even the most advanced AI systems struggle to reason through the unexpected. At its core, the barrier is that AI is missing cognition. There is a fundamental cognitive gap in how AI models are built and trained, and we are several groundbreaking, once-in-a-decade innovations away from solving the edge case challenge. With safety, scalability, and reliability at stake, it represents a critical barrier to launch & scale.
Commercializing AI products today is about assembling building blocks. One of those building blocks is a solution for overcoming edge cases. AI cannot survive in the real-world without it.
The right edge case resolution strategy enables you to:
- Launch & scale your products today, with AI that’s still imperfect
- Overcome performance gaps preventing your product’s commercial viability
- Introduce new competitive features earlier than otherwise possible
- Offset the need for operationally intensive and expensive on-site human support
- Keep your engineering teams focused on highest value activities
- Accelerate performance of existing products to deliver more value to clients
What Are the Approaches to Edge Cases?
You cannot launch or scale a product that can’t deal with unexpected situations. Bringing AI products to life means filling this critical gap.
For AI innovators, then the question becomes: Which edge case approach should we use?
While there are several ways to approach edge cases, selecting the wrong one can:
- Degrade customer confidence and your brand reputation
- Delay your deployment – sacrificing revenue and ROI
- Negatively impact system performance
- Drain your resources and R&D spend
- Lead to potential safety and security issues
- Limit scale and business growth
- Create a competitive disadvantage
It's critical to understand the benefits and drawbacks of each approach.
Take a look at the free ebook we created to help you navigate through the six approaches to edge cases, and decide for yourself which is best for your business: