Augmented Intelligence for Industrial Innovation

DataStories,posted on 24th July 2019

How can we address today’s Industry needs with AI? What exactly are Artificial and Augmented Intelligence and what’s their impact on productivity or the end-to-end-product?

We’ll break down these questions in this article.

Industry 4.0Augmented IntelligenceArtificial Intelligence

Augmented Intelligence Innovation

To stay on top of the competition, industry leaders have to continuously invest in innovation, modernization and digitalisation. But how exactly do we speed up the innovation process in the industry? And where does AI fit in all of this?

To answer these questions, let’s start off with defining what Innovation exactly is.

Innovation is a process and product of Invention and Commercialization. Without commercial success we cannot just put a value on an invention. That means that every innovation project needs to be outcome-led. The fastest way to do innovation is to go data-driven.

Data-driven innovation is a process of using facts (data) to create business value. This makes the business value, the bottom line and the business question very important.

Video: DataStories

Million dollar questions for today's modern Industry

Today, we should ask ourselves two questions:

  • How should we address the growing complexity of product design, manufacturing, and the value chain?
  • How can we stay on top of the competition?

We are making products which are becoming more and more complex. They have various competing performance targets and they will be used in rapidly changing environments.

A certain product, for example a composite pipe, can be used by different customers, for different purposes within different processes and then in different geographies with different climates. As you can imagine this massively increases the complexity of the challenge to guarantee flawless and reliable exploitation and life-time.

We will need to use raw materials and components more effectively to drive down costs to stay competitive. We also need to make sure that we increase the lifespan of our products because we may want to switch the business model from just selling them, to offering them as a service. Eventually we need to ensure that at the end of their life we can upcycle these products more effectively.

We have to do all of this in an environment where hundreds and thousands of factors such as oil and raw material pricing, climate change, socio-economic indicators, the state of production facilities,... can all potentially impact the outcomes.

Emerging Technologies

Emerging technologies like Artificial Intelligence, Data Science and Machine Learning are critical enabling factors that can help us understand and manage this complexity.

They aid us in figuring out:

  • what kind of products we should be making
  • how we should make them
  • who we should be selling them to

And ultimately

  • how we can close the loop for a circular economy

How do we act on this given that the number of actions we can make is simply astronomical, while we have limited time, budgets and very little room to make mistakes?

The single most important benefit of AI and predictive modeling is the possibility to create forward looking insights and quantify the consequences of our actions or in-actions (before we made them).

Augmented Intelligence for Innovation

To further speak about Artificial Intelligence (AI), we need to agree on the definitions.

The 'I' in AI stands for 'Intelligence'. We define Intelligence very simply.

Intelligence is a capability to predict.

If we can predict the future, no matter how near of far, we are intelligent.

If a computer program or a program deployed on a device or a robot can predict the future, it becomes Artificial Intelligence.

If we can describe a process, we can write a computer program - an AI - to automate it and give us forward-looking insights. Artificial Intelligence are very powerful automation tools. Think of them as hammers and chisels - bringing value in many industrial applicatons.

For example deep learning, video-, audio- and image recognition systems are poster children of successful AI applications.

A caveat with automation tools is that we can only automate what we can truly describe. If there is an uncertainty in why a process goes the way it goes, and you can't describe it fully, then you cannot write an AI program to automate it.

Human- & Augmented Intelligence

Innovation is all about figuring things out. It pushes us to understand cause and effect relationships, to explain edge cases and to test and validate various hypotheses. All of these things cannot be fully described because we don't understand them completely yet. This means that Artificial Intelligence alone is not going to cut it. It's Human Intelligence that needs to be at the centre of Innovation.

It is a human who has to drive Innovation. A human needs to communicate innovation to stakeholders and needs to be making it happen. That's why we now talk about a new term 'Augmented Intelligence'.

We achieve Augmented Intelligence when we enable a business expert who owns the context about a problem with very powerful automation tools. This expert owns the business question at all times and is responsible to evaluate and validate the answers to this question, provided by AI.

In other words, we augment the human intelligence of this expert with very powerful computational modelling- and AI-tools. This combination, Augmented Intelligence, is the symbiosis of a man and AI. And this is the future.

We at DataStories believe this is the path to efficient and systematic innovation and it is here to stay.

Talk to us about how you can turn your data into a system to deliver success

Our core expertise is in business-driven applications of predictive analytics and data science to solve complex business challenges which directly impact the bottom line.