Practical AI Examples in 2020

Author by Nathan Lasnoski

In my article on Top 10 Tech Trends for 2020 I started by talking about artificial intelligence as a mega theme where businesses are achieving real, measurable value from the AI front. If you look at any trend analysis on AI you could believe anything from “AI is still vaporware” to “AI has arrived and is replacing humans”. The reality is somewhere in between, but the impact brought to the business space is clear… it has measurable benefits it provides and stands to impact industry substantially this year (as it has started to already in previous years). This is understanding that as a technology AI has a LONG way to go, but that this high potential should not stand in the way of initial gains being made in practical application. A few of the areas we’re seeing customers already make practical impacts:

Customer Interaction

We all understand the difference between great customer interaction and poor interaction because we’ve all lived through it. If we have an excellent customer journey we are more likely to do business with a customer on a consistent basis. Where we’re seeing impact from AI is as a way to aid or augment the human element of serving the customer. If we can receive customer requests and then respond more quickly because of an accelerated Configure-Price-Quote (CPQ) or finding a part from a vast SKU database, we can improve our customer experience. We’re seeing huge opportunities for this impact in this area.




Intelligent Supply Chain

We’ve seen immediate and measurable impacts from the usage of AI in the manufacturing supply chain by optimizing where inventory is delivered, stored, handled, and how much is purchased in respect to demand from customers. The intelligent supply chain provides immediate opportunities to augment the intuition that industry experts bring. Even if a company is current staffing inventory management teams or has applied models from an ERP system we’ve seen clear opportunity to augment the existing processes with value delivered from AI/ML. The goal here is to free up capital instead of it sitting in purchased inventory. The secondary goal is to reduce warehouse shipping costs and optimize the workforce based on location within warehouses. We can also take advantage of when inventory is purchased against market trends.




Predictive Maintenance

In manufacturing or supply companies we’re seeing significant motions around predictive maintenance due to the significant impact of supply chains being impacted by outages. Most of these outages are preventable by gathering signal data that provides reasonable trending that replacing or maintenance to the device is necessary. We’ve had companies that have seen impact based on mitigating unexpected outages and instead preventing the supply chain pause. The financial impact to this is clear and the opportunity is not attainable through human intuition alone.

Customer Guidance & Scoring

A somewhat newer area is companies using data & AI to provide prescriptive guidance to customer purchases or operations. In companies that command a certain scale of customer scenarios the Product can provide expected performance based on comparison with other similar companies. For instance, let’s say that your customers are purchasing certain types of products from you and you have information from them surrounding their business operations. You can offer back to the customer a comparison with their industry performance based on that data and provide the opportunity to improve. Some example customers have called this the “company score”, “supply chain score”, “financial score”, “health score”, or “practice score”. Providing a service like this increases the stickiness of the customer relationship and often creates an up-charge.

These are four… more opportunities abound. There are many scenarios that are not presently great fits for AI/ML’s capabilities, but the above and many more are about bringing true impact immediately into the business’s products and operations.

Nathan Lasnoski

Author

Nathan Lasnoski

Chief Technology Officer