Despite Artificial Intelligence and Machine Learning gaining rapid grounds, they continue to baffle people with terms that are not-so-familiar yet
AI (Artificial Intelligence) is everywhere, or it will be soon. AI/ML (Machine Learning) ranks as the top emerging technology workload in Red Hat’s 2022 Global Tech Outlook, with 53% of IT leaders reporting plans to use it during the next 12 months, a three-point increase from the previous year. Meanwhile, 56% of respondents in McKinsey’s 2021 State of AI report said they’ve already adopted AI in at least one business function, up from 50% in 2020.
Nevertheless, despite AI & ML becoming ubiquitous it is continuing to baffle people with the plethora of terms like supervised vs. unsupervised learning, neural networks, deep learning, and explainable AI floating around. What is an AI product? This is the question that needs to be explained as more and more companies use AI, and it permeates our daily lives through devices like Alexa or even the smartphone we use every day.
What separates AI from automation?
Questions give rise to other questions. What separates AI from other forms of automation? What qualifies a tool or service as a full-fledged AI product – rather than something that simply leverages AI or other automation? And while we’re at it: Why does it matter? Because every company worth its salt now claims to have adopted AI or using it in some form or the other, especially after the pandemic.
Suddenly. AI has become a buzzword that must be included in every corporate communication or marketing sales pitch. One is reminded of the early days of the software industry when the mere mention of the word could increase market capitalization. An extreme instance was a company manufacturing innerwear, which had used the term ‘soft-wear’ in its branding that ludicrously led the capital markets in India to categorize it as a technology company!
An AI Product performs like a human would
As a high-level term, an “AI product” clearly implies AI as the central technology in an application or service – rather than something that is merely “AI-like” or an aspirational claim about what something might be able to do at some undefined point in the future.
There are two good rules of thumb that can guide your own definitions and analysis:
- Can it perform work like a human would?
- Can you develop and customize it to support your specific requirements (turnkey, one-size-fits-most solution?)
RPA is not AI
Simply put, if a product can perform a task that normally a human would – like troubleshooting a customer or employee issue – or it can initiate an automated process from a simple question, that’s an AI product. The distinction explains why, for example, robotic process automation (RPA) is not an AI product. RPA is good at rules-based, if-then tasks but it can’t think for itself or adapt to changing conditions. That doesn’t mean RPA isn’t valuable – it just means it’s not AI. RPA is good at rules-based, if-then tasks but it can’t think for itself or adapt to changing conditions. That doesn’t mean RPA isn’t valuable – it just means it’s not AI.
The AI process is usually learned over time using ML algorithms that are constantly being tweaked to make those human-like interactions possible.
AI is the decision-maker in an AI product
The key differentiation between something that is truly an AI product versus something that simply leverages AI/ML comes down to the degree of customization supported in the platform, right down to the engine itself. Tools that fall into the latter category often use off-the-shelf libraries to embed some AI capabilities in a broader solution. This is perfectly fine in scenarios with AI/ML just “a cog in the wheel” and the actual focus is something else. An AI product, on the other hand, features AI as the centre piece and the main decision-maker. These products often have a large degree of customization options so that the AI can be fine-tuned for the specific use cases where it is being deployed.
Difference between an AI/ML product and an AI/ML model
AI in the enterprise today commonly means that an organization is running machine learning models in production. A model – or even dozens or hundreds of them, in the case of more advanced teams – is not a product, per se. But what you do with the outputs of that model can become an AI product.AI and ML can analyze existing data or generate new data. What’s done with that analysis and how it fits into the ecosystems customers live in is a question solved by-products, not models.
A service that looks at existing weather data and makes a prediction about the average rainfall is certainly an ML model – likely a regression or a neural network. An AI product would include the dashboards, workflows, likely integrations, and necessary user controls.
AI is not “just” automation
A related concern here is that AI sometimes gets used interchangeably with automation. AI is a form of automation and an automation enabler, but there are many other types of IT automation that aren’t AI – so the terms shouldn’t be used as synonyms. From a technological point of view, it is easy to differentiate an AI from an automation-based product. Automation-based software is only able to respond to outcomes it has been programmed for. Automation follows pre-programmed rules and runs with little to no human interaction. Once the specific patterns – usually for repetitive tasks – have been identified and arranged in that piece of software, the need for human contact after is minimal.
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