The hype for AI is well-documented.
And, the AI launch and ROI statistics confirm the hype:
- Only 53% of projects make it from prototype to production. Source: Gartner 2020
- “7 out of 10 companies reported minimal or no value from their AI investments" Source: Sloan Review MIT
Edge Cases: The Critical Point of Failure
A key reason for AI failure is that the real world is full of unexpected situations (“edge cases”), and AI simply isn’t trained to handle them all. This makes it hard to build AI that works perfectly, always. And in the real-world, AI needs to work perfectly, always.
You have to solve critical AI edge cases if you want to launch a successful automation product. This was true for:
- John Deere launching an autonomous tractor
- A Fortune 500 industrial robotics OEM scaling a picking arm
- And, any other automation product where uptime, accuracy, or safety matter
Put a Real-time Edge Case Resolution Strategy in Place
To overcome the hype and failure associated with AI deployment, it's critical to put an edge case resolution strategy in place early in AI development and launch planning.
There are two viable options that will both a) accelerate launch and scale so that you can realize your vision of a successful AI product, and b) deliver edge case resolutions in real-time in production:
- Buy SparkAI and work with us.
- Or, attempt to build something like it internally
Let's talk briefly about the “build” option and show how SparkAI compares.
Building an Edge Case Resolution Platform
Building something like SparkAI in-house requires a very serious commitment to creating a new set of people, process, and technology dedicated to edge case resolution – i.e., building things that are entirely non-core to your business or product’s purpose.
To resolve edge cases in real-time in production, you will need to build out an intelligent dispatch algorithm, real-time QA systems, workflow and decision tools, a robust and highly available API, a whole new operations team with a scalable workforce, modular GUIs and workflows, an efficient means for staffing and payment, and a great deal more.
Building may appeal to you if…
- You don’t mind diverting your AI engineering team from building your core product in order to build something entirely non-core: a platform and operations team to chase the edge case problem
- You’re building AI to do a simple task, but people are firmly in charge of making decisions and taking actions, e.g. AI that gives an alert to on-site, human-run security operations
- Your edge case resolution requires niche experts with high levels of education, e.g., doctors, lawyers, etc.
- At your beck and call, you have a very specialized edge case resolution workforce that you can easily train, manage, and scale up or down
- You have abnormally strict data privacy requirements that force all of your data to stay in-house
Drawbacks to Building a Real-time Edge Case Resolution Solution In-House
Simple Build vs.Buy Breakdown
While building an in-house platform (coupled with an operations team) might sound appealing in limited scenarios, it will always be an extremely challenging approach that involves enormous investment in people, process, and new technology.
Here’s what that tradeoff looks like:
Creating viable AI products requires an edge case solution.
The question this ebook answers is whether such an edge case solution should be bought from a third-party or built within your organization.
