(excerpt from RD Perspectives video with Jen Stirrup interviewing Dan Hermes)
We’re hearing a lot about AI. Will it bring about a new kind of business? First it will help us to do our existing business better. Here’s how: We’re parsing the business decisions within each business process and determining which ones are best modeled by machine learning. Then we use machine learning to make those decisions faster and better.
The first step in any new technology is helping us do things we already do, but better. Any decision that a business makes, AI may be able to help them make it more efficiently and accurately. Take insurance claims, for instance, because those model well. A customer submits a claim. Then the customer must wait for the adjuster to get through their stack of claims and reach theirs. When the adjuster reviews the claim, they follow policies, but the ultimate decision lies with the adjuster. Give the claim to a well-trained AI and the company policies and actuarial tables are internalized by the AI, so the claim can be processed in a minimal period of time. If the AI requires human intervention, it asks for it, but otherwise, machine learning can process many insurance claims.
Will AIs replace employees? They already are, but not yet at a fast pace. Certain tasks (like processing insurance claims) lend themselves well to machine learning while many other tasks are still too complex to be handled by a machine alone. This is leading us into the age of AI-aided decisions, or expert assistance.
Haven’t computers aided decisions for a long time? Of course computers, reports, data, knowledge-based systems have assisted in business decisions for years. Eventually however, most major decisions will be accompanied by a “suggestion program”, borne of an AI that has models of many situations relevant to the decision at hand. Doctors have AIs recommending and aiding diagnosis. Stock brokers are guided by AIs modeling the market. In these cases of AI-aided decisions, the AI reaches its own conclusions but the business decision is typically left to the human being, which is why it’s called expert assistance and not fully automated.
In training models, what do we need to look out for? Training a machine learning model is as much an art as it is a science. You want enough data but not too much. You want to be certain that the training data applies to the specific types of scenarios you’re interested in. Data can be copious but flawed, missing key elements, and having elements irrelevant to the task at hand. If you train your model too much it becomes too specialized to particular situations, and this is called overfitting. If you don’t train it enough the model may not be sufficiently accurate, and that is underfitting.
Where do I get started with AI in my business? To summarize, what attitude and approach necessary to drive adoption in AI? Some careful thought, creative experimentation, and diligent work could make your business even smarter. People need AI attitude; creativity, diligence, and resilience to work smart. For example, Look at your business processes. Identify the ones where decisions are challenging. Ask yourself why the challenges. If the answer is that the historical data is too complex to derive knowledge from then this could be a good candidate for a machine learning. Well-documented decision-making processes can also sometimes be good candidates for machine learning using reinforcement learning.
You’ll need to identify which of these processes lend themselves best to modeling. Here you’ll need an educated review of your business processes and possibly some test modeling to determine if improvements can be made using AI. Not all problems can be solved this way and finding ones than can requires initiative, commitment, and expertise.
In a nutshell, make a list of your top processes you’d like to consider improving upon. Then engage some data scientists or AI professionals to help you review your options.
See the full RD Perspectives interview video soon. Check back here for the link!