One of the most important things to do in this transformative era of AI is to identify use cases and opportunities in your company. Conversely, one of the biggest mistakes you could make is ignoring AI altogether. No business will be safe from disruption, and the sooner you get started with AI adoption, the better.
In my previous blog post we covered why management teams need to understand AI. Now, we will examine ways to identify AI/ML opportunities in your company. It’s crucial to get started as soon as possible so you can develop a strong competitive advantage in the marketplace.
1) Enhance Your Current Software
One way to identify AI opportunities in your company is to examine your current software. It could be your website, mobile apps, IT help desk tools, CRM software, etc.
Think about how your current software could be enhanced with AI and machine learning capabilities. Since machine learning is using data to answer questions, what data could you collect/process within your existing infrastructure?
Let’s look at mobile applications.
For example, if your users request customer service through your mobile app, could you automate the interactions using conversational AI and backend services?
Or, in the case of healthcare, suppose your users use your mobile app to take a photo of an area of their body they feel needs medical attention. The photo is received by a medical professional, assessed, and further actions are taken. Could you enhance your app’s capabilities by having AI/ML make medical detections and assessments in real-time?
These examples don’t require from-scratch software development – they are simply functional extensions of current capabilities.
2) Streamline Operations
Continuing with the example of mobile apps, one of our clients in the retail merchandising space hired us to help streamline their operations using AI/ML.
Before we used AI, their merchandising representatives would visit stores (such as Walmart) and execute tasks involving placing specific merchandise on store shelves. Then, they would manually answer questions on the device, such as, “Is product X priced correctly?”
Our client would then audit their work by having them take photos of the shelves they set up. These photos would go to their backend system for review by humans. Due to their volume of work, they had millions of photos coming in every month. The human reviewers would get to review 10% per week, at most, leaving the company unable to properly verify the executions. When discrepancies were found between the merchandising representatives’ answers and the corresponding photos, actions were taken to correct the mistakes. By that time, sales were lost, customers were misled due to incorrect pricing, etc.
What if we could solve these problems in real-time with minimal human intervention?
We decided to implement an object detection algorithm into their mobile app to identify specific instances of products in real-time. This allowed them to audit their reps’ work as soon as they completed it. If the algorithm finds objects missing or out of place, it prompts the merchandising rep to take action. Regardless of their action, all this data is collected. Additionally, the questions that used to be answered manually are now automatically answered based on the AI’s detections, leading to time and cost savings. The image detection and processing happened on the devices, eliminating the need for internet connectivity.
This use case turned out to be just the beginning of their AI transformation (check out our case study for more information).
What operational inefficiencies can you optimize, leading to an increase in your company’s bottom line? AI/ML is often a great candidate for this type of use cases.
These are just a couple of ways to think about AI/ML adoption in your company. If you are starting out with AI, we recommend choosing a problem you are already solving (check out my blog post titled, “Getting Started With Enterprise AI” for more information).
If you need help to accelerate your company’s machine learning efforts, or if you need help getting started with enterprise AI adoption, send me a LinkedIn message or email me at email@example.com and I will be happy to help you.
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Client case studies and testimonials: https://gradientgroup.ai/enterprise-case-studies/
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