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paper robots

Pragmatic AI in the enterprise

The AI hype is so pervasive that most senior executives at any enterprise have AI in their radar. Every company today is thinking about AI or has some AI initiative in place. The current conversation on everybody’s mind right now is how to use AI and what more can be done. Most people treat AI as a black box, almost of a promised land. Expectations are big. Everyone has seen the power of Siri and Alexa and are hearing of self-drive cars.

There are some amazing AI powered applications that we have witnessed in the marketplace such as the mobile robots that go around the store aisles and collect inventory data automatically, or the warehouse robots that auto fetch products for shipment.

The latest entrant are the delivery robots as seen on the streets of Palo Alto or like the driverless delivery vehicles Kroger is testing out.

How does AI function?

AI is basically getting machines to see patterns in data. This process is called training or learning. This learning can be done in two ways:

Supervised learning

This is the process of training the machine by showing it large amounts of data of particular type. The machine looks at this data and learns its patterns. Once trained, the machine can consistently detect the same patterns in any new data. A simple example would be if we show a machine 100 images of a chair. Now when a machine is shown a new, unseen image of a chair, it would automatically map the patterns it has learnt, to say that it must be a chair.

Unsupervised learning

In this approach we allow the machine to automatically start finding patterns in the data and then based on these patterns pooling the data in to different buckets. This process is called clustering. The machine basically clusters the data based on patterns it sees in the data.

A good example for this would be if a machine is shown a mix of chairs and tables, the machine would create clusters of chairs and tables. Although, here it may not be able to indicate which cluster is a table or chair, but can group them separately.

The application of detecting patterns is where the most interesting part lies. We can use this ability to get machines to:

  1. Find errors or anomalies in the data like identifying an irregular transaction in a bank statement by scanning through the transaction or detecting wrong customer data.
  2. Label or classify data like training a machine to automatically classify product images into chairs, tables, etc or getting a machine to look at customer transaction data and creating customer personas. In our retail example we train models to look at pierce of furniture or clothing and automatically identify characteristics of the product like colours, shapes, patterns etc.
  3. Generating data This is the frontier of AI where machines can learn patterns and use these patterns to generate new data or content. The best example would be that of machines learning painting styles of masters like Van Gogh and Monet and repainting a picture in the same style. There are models that can learn writing styles an reproduce new product descriptions based on these styles.

While these examples are not large scale business applications, but they can be leveraged to solve real problems.

The beauty of AI is that it can consistently perform its tasks.

Our approach has been to identify problems that don’t have any alternate solution, and is also something that can be done using AI in days or weeks rather than months or years.

A good example is looking at large volume product images and automatically getting all the useful information from it as text attributes. This comes in handy when consumers search for products. Another application would be searching for products that look similar.

AI can be easily applied to accounts receivable and payable reconciliation, cleansing and augmenting customer data, creating personalized shopping experiences for consumers, automatically creating custom product bundles for every consumer, automating team schedules, automatically identifying best candidates from a pool of resumes. The list can go on and on.

The key takeaway is while there are game changing applications of AI like self-drive cars, there are far greater applications that can have immediate impact. Our belief is that the impact of AI will be far greater in solving day to day problems and improve people lives.