Intelligence vs. automation: The scope of analytics in successful AI deployment


There is a lot of excitement about artificial intelligence (AI), and also a lot of fear. Let’s set aside the potential for robots to take over the world for the moment and focus on more realistic fears. There is a growing acceptance that AI will change how we work. There is also agreement that it is likely to result in a number of jobs disappearing – or rather, being replaced by AI systems – and others appearing.

This has led thoughtful commentators to consider questions of the ethics around AI and note that it is unwise to separate the two. Some have suggested frameworks for the ethical development of AI. Underpinning ethical discussion, however, is a question of what, exactly, AI will be used for. It is hard to develop an ethics framework blind. In this blog, I want to unpick this issue a little and start to think about where and how AI is used, and how this will affect the value that organisations obtain from AI.

Defining intelligence

AI has been defined as the ability of a system to interpret data, learn from it, and then use what it has learnt to adapt and therefore achieve particular tasks. There are therefore three elements to AI. First, the system has to correctly interpret data and draw the right conclusions. Second, it must be able to learn from its interpretation. Third, it must then be able to use what it has learnt to achieve a task. Simply being able to learn or, indeed, to interpret data or perform a task is not enough to make a system AI-based.

As consumers, most of our contact with AI is with systems like Alexa and Siri. These are very definitely "intelligent," in that they take in what we say, interpret it, learn from experience and perform tasks correctly as a result. However, in industry and business, there is general acceptance that much of the real value from AI will come from automation. In other words, AI will be used to mimic or replace human actions. This is now becoming known as "intelligent automation."

Where, though, does intelligent start and automation stop? There are plenty of tasks that can be automated simply and easily, without any need for an intelligent system. A lot of the time, the ability to automate tasks is overshadowing the need for intelligence to drive the automation. The end result is typically very well-integrated systems, which often have decision-making capabilities. However, the quality of those decisions – good or bad – is often ignored.

Good AI algorithms can suggest extremely good options for decisions. Ignoring this limits the value that organisations can get out of their investments in AI. Equally, failing to consider whether the quality of the decision is good enough can lead to poor decisions being made. This undermines trust in the algorithm. This results in less use for decisions, again reducing the value. But how can you assess and ensure the quality of the decisions made or recommended by the algorithm?

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Balancing automation and intelligence

An ideal AI deployment should have a balance between automation and intelligence. If you lean too much towards the automation side and rely on simple rules-based automation, all you will basically be able to do is collect all the low-hanging fruit. You will, therefore, miss out on the potential to use the AI system to support more sophisticated decision making. Go too far in the other direction, and you get intelligence without automation or systems like Alexa and Siri. Useful for consumers, but not so helpful for business.

In business, I think analytics needs to be at the heart of an AI system. The true measure of a successful AI deployment lies in being able to mimic both human action and human decision making.

An AI deployment has a huge range of components; it would not be unreasonable to describe as an ecosystem. This ecosystem might contain audio-visual interpretation functions, multisystem and/or multichannel integration, and human-computer… Click To Tweet

An AI deployment has a huge range of components; it would not be unreasonable to describe as an ecosystem. This ecosystem might contain audio-visual interpretation functions, multisystem and/or multichannel integration, and human-computer interface components. However, none of those would mean anything, even in combination, without the analytical brain at the centre. Without that, the rest of the ecosystem is simply a lifeless body. It needs the analytics component to provide direction and interpretation of the world around it.


About Author

Yigit Karabag

Regional Director, Customer Advisory EMEA Emerging & Asia Pacific

Yigit is the Regional Director of Customer Advisory EMEA Emerging & Asia Pacific. He has over 15 years of domain experience in enterprise-level information management solutions. He has been involved in large-scale projects dealing with structured and unstructured data across the banking, telco and public sectors. Yigit is also a board member for various data management organizations and a regular speaker at data management & data governance events. He holds a degree in Computer technology and Programming from Bilkent University in Ankara, Turkey. Aside from being a fan of sci-fi authors such as Isaac Asimov, Philip K. Dick, he is also a musician and an avid model train builder.

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