AI is talked about a lot these days. And this risks downplaying its potential as just another buzzword. AI can be described as a digital brain i.e. a piece of software that is “intelligent”. It has been with us since the 1950s but in the last few years its adoption exploded. And there are clear good reasons for that
a) we have more readily available data to train these digital brains
b) processing power is cheap and readily available
c) we have a lot of readily available products, services and algorithms that make the adoption simpler, faster and better than ever before
AI in business is used in many ways. We can classify the uses as follows
* Sales prediction (demand prediction) – how much am I going to sell this Black Friday, this Christmas. When will my sales start ramping up? i.e. by when do I need to have the items in stock? (for inventory optimisation)
* Predictive maintenance
* Predicting a price in the future (for price optimisation)
* Predicting delays in time-sensitive processes like logistics
* Customer churn prediction
* Credit scoring
2. Personalisation at scale based on customer profile and buying history (hyper-personalisation)
* Customer segmentation
* Recommendation systems – suggesting the product/service the customer would be most interested in
* Customisation of the product/service itself
* Marketing of the product/service – such as targeted emails
* Automation of manual processes (digitisation)
* RPA (robotic process automation) & intelligent OCR can user to automate manual task like dealing with inputting forms, invoices and HR documents. Very useful when you are suffering from an information overflow in your business.
* Driving (autonomous vehicles)
4. Anomaly detection
* Defect detection in product manufacturing
* Fraud detection
* Suspicious behaviour
5. Natural language processing (read text, hear speech and interpret it)
* Chatbots – that can answer the most common questions like “What are your opening hours?” “Do you have vegetarian options?” “Is there a parking area?”
* Sentiment analysis
* Virtual assistants
* Interpreting legal contract and handwritten documents
6. Image Recognition through Computer Vision
* Identifying the age, sex, and mood of customers – leading to customer profiling – from CCTV cameras.
Uses in the industry are wide and diverse. Here are 5 popular uses of AI in the industry :
1. Healthcare – identifying health issues from image scans
2. Building management and the analysis of energy usage
3. Insights from satellite images for weather forecasting and agriculture
4. Helping the HR team to automatically screen candidates
5. IOT platforms – where data gathered from intelligent sensors leads to decision making
If you read the above, it all makes sense but do not know where to start then you should take this journey of using AI gradually – step-by-step. Make sure you keep some basic rules in mind.
1. Do not focus on AI itself. Focus on the business challenge that you are facing and then see if AI is the best option to solve this challenge
2. Onboarding an AI-based solution does not necessarily mean that you have to have the expertise to do it in-house not that you have to hire external data scientists to do for you. There are a lot of off-the-shelve solutions where AI is already fully incorporated in their product/service. Even if you decide to build your own solution – there is no need to reinvent the wheel – nowadays we speak about “AI-as-a-service” as many AI models are readily available on the cloud, deployable with one click.
3. AI is all about data. It is very important that you have clear and readily available data. That is the first step before any AI-based solution.
4. Start small. Start where you already have a good enough dataset. For example your sales data / booking data is usually your low hanging fruit in terms of the benefits that you could gain by finding insights from that data.
Finally a small word of caution. Onboarding AI-based solutions does some with its own set of. The main three challenges being
1. the AI model can be biased. This bias is very dependent on the training dataset using to train the model. But yes, look out for bias.
2. Lack of explainability. it is very often the case that you cannot explain how and why the AI model gives out the output / decision it does.
3. There can be ethical aspect to consider when deploying an AI-based solution. For example is it ethical to have face recognition based systems for customer profiling.
We hope this blog post help you understand better the opportunities (and the challenges) of on-boarding AI solutions in your business. If you have any comments or questions feel free to send them over. We are here to help.
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