How to drive ROI with AI
Artificial intelligence, AI, machine learning, big data … Everyone is familiar with these terms nowadays. They have become ubiquitous in presentations and pitch decks. Every company claims they will solve any and every problem. But what is the reality? How can you tell the difference between vaporware and projects that make a difference? In short, how can you drive ROI with AI, whatever your business?
A quick AI primer
AI is a catch-all term for a number of technologies that have emerged over the past decade. Artificial intelligence itself isn’t a new idea. However, it is only recently that computers have become powerful enough to exhibit any form of actual intelligence. Most of the things we call AI fall into one of two categories:
- Narrow intelligence based on machine learning techniques. This includes applications such as anomaly detection, classification, or forecasting. These solutions are described as narrow intelligence because they are only able to do a single task. However, they will often do that task extremely well.
- General intelligence based on deep learning. These systems use deep neural networks that are modeled on the structures found in our brains. They are capable of a degree of self-learning. They are more capable than narrow intelligence, offering things like image recognition or voice recognition.
AI can be distinguished from classical computing because AI algorithms are created using a learning process. The computer learns to spot patterns within the input data and uses these to draw conclusions. Depending on the input data, you might use supervised learning, unsupervised learning, or reinforcement learning.
Four key commercial AI applications
Business AI applications typically fall into four categories. These are distinct from B2C applications like virtual assistants. The four categories are:
Forecasting. This is where you train a machine learning model to forecast future events based on historical data and current conditions. For instance, this might involve predicting stock levels, understanding future demand, or predicting loan defaults.
Anomaly detection. This involves looking at historical data and using this to identify anomalous results in current data. For instance, you can use this for fraud detection, or to identify impending machinery failure in a factory.
Knowledge discovery. There are two main forms of knowledge discovery. The first is focused on extracting useful knowledge from a set of textual data. For instance, analyzing a large number of emails to identify the relevant ones for use in a court case. Or looking through your knowledge base and identifying the correct answer to a user’s support query. The second is used to identify gaps in knowledge. For instance, examining a group of patents and discovering a patentable gap.
Classification. These systems are used to sort data into categories. This is particularly useful where you have a large amount of unstructured data. It is often used in conjunction with other AI applications, especially where you have limited knowledge of the input data.
All of these approaches can be achieved by creating models from scratch, or by refining and adapting existing models.
Driving ROI with AI
AI can solve all sorts of business problems. However, AI isn’t a magic wand. It can’t achieve miracles that will save an unsalvageable business. What it can do is deliver actionable insights that drive ROI and improve efficiency. The important thing is that AI is purely data-driven. Without data, you cannot create an AI solution. This is why we recommend every business follow our 7-step plan.
The 7 steps to a successful project
The following steps will help you to drive your ROI with AI.
- Scope the problem carefully. Identify what you want to achieve and talk to data scientists to establish if this is feasible.
- Identify all sources of data and make sure they are usable. As I said above, without data, you won’t have an AI project. Often the challenge is to identify all the data you have and work out how to use it.
- Create a working model and validate the performance. This is where you turn to your data scientists and ask them to create your model. Once they have a working model, they will check that it performs well enough.
- Embed the model within your existing system. An AI model is just an algorithm. The tricky part is working out how and where to embed it within your existing systems. You need to be aware of what input data it needs and how the output will be used.
- Run a pilot with your staff and evaluate the results. Now you are ready for real-life testing. You need to do this in as realistic a fashion as possible. So, involve your staff and test the performance against any existing approach.
- Make sure all your staff are on board and buy into the project. Often, senior management overlooks the human factor. Many people are nervous about AI, viewing it as a threat to their jobs. They also tend to distrust things they can’t understand.
- Constantly reevaluate and retrain your model. AI models rapidly become out of date. If you are using AI to forecast stock, then the AI model itself starts to impact the results. This means you need to constantly check and recheck the model and retrain it when necessary.
How NuGene helps you drive ROI with AI
Sonasoft NuGene will help you overcome many of the hardest parts of creating AI projects. As a result, it helps you to drive your ROI with AI. NuGene is an AI platform that is designed specifically to create usable AI models or bots. We like to call it our AI Bot Factory. NuGene is an autonomous platform that takes your raw data and learns from it. It draws inferences, which it tests for causality before creating and validating an AI model. Finally, it provides you with a bot to embed in your systems. This system constantly monitors the model and retrains it when needed. The result is a solution that outperforms your existing systems, often with improved efficiency.
NuGene solves most of your problems, however, there are still things you need to do to make your project a success. For instance, you still need to correctly scope your problem. You also need to identify all the possible data sources. It’s worth noting that NuGene understands a whole range of data, from structured databases and time-series to IoT sensors and audio-visual inputs. However, the biggest issue remains the need to get your staff on board. That is something that only you can solve. So, you need to work with your staff to get them enthusiastic and show them how AI will improve their work. If they buy into the project, I guarantee it will be more successful.
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