October 5, 2017

7 Challenges of Building Industrial AI Applications

7 Challenges of Building Industrial AI Applications

A big part of what makes Industrial AI different from consumer and other business applications of AI is the fact that the stakes are much higher.

(See our blog “What is Industrial AI” for a refresher on how we define this and other key enterprise AI terms.)

The tools best suited for recommendation systems or data analytics won’t suffice for the unique requirements of Industrial AI applications. Here are 7 reasons organizations must look to leverage domain expertise and novel technologies to program intelligent control into  enterprise systems:

  1. Data acquisition and storage: Industrial AI systems often rely on sensor data. The sheer amount of collected sensor data can present noisy datasets as well as storage and analysis challenges
  2. Training challenges: Amassing enough data is challenging for all organizations, but physical systems also require data on part failures or unanticipated events - rare and costly occurrences - so they can learn how to respond when things go wrong.
  3. Testing costs and complexity: Testing AI systems on real systems - operating production lines, industrial equipment, warehouses - takes time and money. Simulation environments must be implemented for adequate training and testing.
  4. High regulatory requirements: Industrial environments are often subject to compliance statutes. Regulatory controls, which often require that changes to industrial processes be extensively validated and verified, can be at odd with the goals of automation via AI.
  5. High cost of failure and change: Quite simply, the cost of failure and change at the scale of many industrial systems is extraordinarily high.
  6. Large state spaces: Modern industrial systems are extremely complex, often offering tens or hundreds of inputs over which machine learning algorithms may optimize. This can make for more complex development and training routines.
  7. Cost of talent: Data scientists, data engineers and subject matter experts are required to implement industrial AI solutions. The talent in today’s market is rare and expensive.

While challenging, the right AI strategy can turn even minor improvements to the control or optimization of a system into massive returns.

You can learn more about how to get started building and industrial AI strategy, and the best-fit use cases, by downloading our free whitepaper, “Artificial Intelligence for Industrial Applications”.

Head over to https://bons.ai/ or tweet us at @BonsaiAI to let us know how you want to leverage Industrial AI in your own organization.

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