Finding the AI “Low Hanging Fruit” Inside Your Organization
I was talking with one of our large enterprise customers recently, and I was surprised when they told me that despite employing more than 100,000 employees and taking in billions of dollars of top-line revenue, they actually only have about 5 machine learning models in production.
This really surprised me because I know they have a fair number of data scientists and have been investing in this area for quite some time. It got me thinking about how hard this stuff actually is - if a company of this size and resources is struggling with machine learning and artificial intelligence, what about most of the companies out there who are much smaller?
The truth is that while most enterprises are beginning to explore data science and ML/AI more and more, the majority of these companies have a really hard time getting it into production, where it can impact the bottom line.
So, why is this? Well, there are many obstacles to overcome, including establishiung the right AI foundations such as:
Finding experienced talent
Architecting the right technology platform
Build scalable processes that truly work for your company
Determine how to best integrate ML into the fabric of your business operations (whether software development, retail, manufacturing or healthcare)
For an established company, launching a ML initiative usually means it has to drive cultural and organization changes, which doesn’t happen overnight. Patience is required.
That said, the new breed of companies that are disrupting entire industries, such as AirBnB, Uber, Instagram and Netflix were born into this. A big part of their disruptive success is the ability to tap into AI to fundamentally transform customer experience, operations, and growth.
This generally means that if you want to compete in this new era, you too will need to tap into AI and put it to work for your business. Regardless of the challenges I pointed out above, we at F33 are big believers in finding a set of initial high-impact use cases to prove the business value and viability, and set the stage for a broader transformation.
With that in mind, we put together the following 9 killer use cases for AI that are generally useful and are viable starting points to try, and get some quick wins:
1. Churn Prediction:
When you factor in the cost of acquiring each new customer, it makes sense to maximize the value you get from them over their lifetime. And part of this assessment will involve predicting the level of customer churn.
AWS Marketplace notes that you can “reduce customer churn by using machine learning (ML) models to predict the probability of a customer leaving the company.”
2. Inventory Optimization:
You have three potential challenges to address in your inventory. You may carry too many items, not know how much inventory you actually need, or maintain inventory in the wrong location.
Oracle explains that machine learning enables your inventory management processes to adapt and learn over time, in a “world where you can realize inventory reduction of up to 30%, while driving a revenue increase of 2%, without any changes to your existing replenishment or supply chain solutions.”
3. Document Processing:
Companies can use optical character recognition or OCR to automatically process enormous quantities of documents, which they can then analyze with artificial intelligence tools to derive useful data.
Patterns previously undetected now appear, helping guide your next business decisions. An easy way to dip your toes into machine learning like this is by working with Google Cloud’s Document AI system.
4. Customer Lead Prioritization:
The more efficiently your sales team addresses customer leads, the better for your bottom line. Forbes noted that “AI and machine learning technologies excel at pattern recognition, enabling sales teams to find the highest potential new prospects by matching data profiles with their most valuable customers.”
You use an artificial intelligence-fueled customer relationship management or CRM application to define characteristics of your most lucrative prospects, saving countless hours for the sales staff.
5. Demand Forecasting:
Forecasting demand more accurately makes for better warehousing logistics and sets you up for the highest customer satisfaction too. Google Cloud noted that in the past, companies worked with a business forecasting team (using enterprise resource planning software) or a science forecasting team (using a cloud AI platform.
But now, a new hybrid form of forecasting combines “the advanced modeling of the Science Forecaster and the deep domain knowledge of Business Forecaster.” Google Cloud Tools include solutions for predicting customer lifetime value and their propensity to purchase.
6. Product Recommendation:
If you had a better idea of what your customers want, recommendations you make will naturally drive up sales. Providing better recommendations is central to gaining customer trust as well as their loyalty.
To that end, search engine giant Google has been delivering content it recommends, via YouTube, Google Search and Google Ads. The company’s “Recommendations AI draws on that experience and expertise in machine learning to deliver personalized recommendations that suit each customer’s tastes and preferences across all your touchpoints.”
7. Employee Retention Forecasting:
With all of the time and effort you put into recruiting, onboarding and training employees, you’ll have a vested interest in doing a better job of predicting which ones will remain and which are more likely to jump ship.
As Business Science put it, “until now the mainstream approach has been to use logistic regression or survival curves to model employee attrition. However, with advancements in machine learning (ML), we can now get both better predictive performance and better explanations of what critical features are linked to employee attrition.”
8. Predictive Maintenance:
Maintaining your equipment on a timely basis is crucial for reducing the total cost of ownership. On first glance, predictive maintenance boils down to doing mathematical computations about the state of your machines’ conditions, so you schedule maintenance at the most effective time.
But “ML eliminates most of the guesswork and helps facility managers focus on other tasks,” as noted by Towards Data Science. Training the ML algorithms helps you spot anomalies (that indicate repairs are needed soon) through patterns picked up in the different data feeds your AI system is monitoring.
9. Production Line Quality Control:
With modern industrial factories, production typically runs at superhuman speeds, which means that ordinary people are useless for keeping on top of quality control. Machine learning comes to the rescue, as it is well suited for massive amounts of high speed image recognition.
“By adding smart cameras to software on the production line, manufacturers are seeing improved quality inspection at high speeds and low costs that human inspectors can’t match,” explained IEEE Spectrum. And adding machine learning to the mix helps with social distancing to protect people working on the line.
Getting Started With Artificial Intelligence in Your Enterprise
Becoming more familiar with your organization’s AI “low hanging fruit” is an important first step for you and your colleagues. This will provide your beginning roadmap for ways to get AI implemented and driving material and visible business impact. Implementing machine learning and AI will go more productively if you partner with experts, such as the team at F33. To learn more about our capabilities or to discuss your upcoming AI project, please connect with us today.
AWS Marketplace: Churn Prediction
Oracle: Three Inventory Challenges Solved with AI and Machine Learning
Google Cloud: Document AI
Forbes: 10 Ways Machine Learning Is Revolutionizing Sales
Google Cloud: Retailers find flexible demand forecasting models in BigQuery ML
Google Cloud: Recommendations AI
Business Science: HR Analytics: Using Machine Learning to Predict Employee Turnover
Toward Data Science: How to Implement Machine Learning For Predictive Maintenance
IEEE Spectrum: Deep Learning Has Reinvented Quality Control in Manufacturing—but It Hasn’t Gone Far Enough