Artificial Intelligence (AI) is an increasing part of our everyday lives, powering our smartphones and the internet of things. But few people really understand what it is, how it works and more importantly, why it is so important to their business.
What is Artificial Intelligence or Machine Learning?
The Oxford English Dictionary defines artificial intelligence (AI) as the theory and development of computer systems able to perform tasks normally requiring human intelligence such as visual perception, speech recognition, decision-making, and translation between languages.
For many people in Business, the language used in data science can be confusing. It is far simpler to explain by simply saying, ‘powered by AI’.
However, when investing in large scale technology transformation projects it is important to know what element of the process is powered by AI and how the AI itself works.
Artificial Intelligence or Machine Learning are typically classified into two broad categories:
The computer is presented with example inputs and their desired outputs. “Teacher” or “Training set” data are used to establish a general rule that maps inputs to outputs.
No labels are given to the learning algorithm, allowing it to independently find structure in its input. Unsupervised learning can be an effective method of discovering hidden patterns in data.
Both Unsupervised and Supervised Learning can be used to establish baseline behavioural profiles for various entities which are then used to find meaningful insights & anomalies. Within the field of Business data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to the following tasks or processes:
- Inventory Management
- Invoice Payment – Invoice Fraud
- Supplier Relationship Management
- Sales Pipeline
- Marketing Analysis
- Customer Segmentation
- On-Time Delivery
- Operational KPIs
- Supplier on-Boarding
- Spend Analytics
‘Powered by AI’ is a common feature or term used to sell the benefits and merits of digital transformation solutions. The aim of these analytical models is to provide Business teams with reliable, repeatable decisions and learn from historical relationships and trends in the data.
Therefore, it is important to have a general understanding of how it works and what is powering the AI.
What is the real power behind AI?
The power behind AI is a series of structured learning algorithms or code used to analyse input data. Often open-source coding software like R or Python is used to develop the AI power that these software models use. Within these applications, there are several models or libraries that can be used to “power” the AI.
Below is a list of the most popular decision libraries that power AI:
- Decision tree learning
- Association rule learning
- Artificial neural networks
- Deep learning
- Inductive logic programming
- Support vector machines
- Bayesian networks
A criticism levelled at AI models is the term “Blackbox”, likening it to an aeroplane flight recorder. It is true that AI models are not always transparent. It can be difficult to understand how they derived the outcomes and exactly how the calculation or prediction was made.
AI Model Construction & Accuracy
A model can have more than 95% accuracy and be optimised using several iterations called epoch(s), to produce stable functioning. This does not mean the output from the model is accurate, simply that the model itself is stable and performs as it should.
Data Training & Validation Time
Completion of the model can take several hours to finalise. But it can take days or even months to achieve accurate outcomes and run the risk of the model over-fitting. Additionally, it may require an expert subject matter resource to be set aside upfront to validate the outcomes.
Accuracy is critical and, in many cases, dependent upon the training data set itself. The training set needs to be considered carefully to make sure there is no data bias. Bias occurs when one or more groups feature more often than others and distorts the outcomes.
However, repetitive tasks that are predefined and use a flow process called robotic process automation that replicates manual tasks makes uploads faster, the process slicker and produces very accurate results.
Understanding the benefits of AI
For most Business professionals these models seem complex. Models that initially require highly skilled data engineering and data science professionals to write the code is costly to produce. For many, AI echoes the same sentiment which drove big data a decade ago, with its varied success use cases and business benefits.
With the proliferation of marketing hype and veiled technology “Blackbox” solutions, it is more important than ever to fact-check and get clear answers about how the AI is powered.
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The aim of business digital transformation is to perform tasks and process more efficiently using technology to deliver benefits seamlessly, without impacting its current operations.
Like many sourcing decisions, it is important to understand the benefits of AI, the business case for it and the return on investment.