What are the use cases for AI Augmented Workplace?

Most of the client engagements start with the question

"What is Artificial Intelligence (AI), how can it work for me?"

"Tell me what is possible and I will tell you if I need it." 

That is the first reason why most of our client engagements start with a three hour workshop on AI where we first talk theory. Then show best practices in AI application implemented so far (by us or our partners or community members) and then we use an adapted design thinking methodology to brainstorm possible use cases for a given client. 

The second reason is that this investment from both parties is a very important step on the way to AI adoption for any organization as any other change management process.

The framework that we developed here is simple - educate the client, show what is out there, brainstorm client-related use cases, pilot the quick wins. Normally it takes three to six month to see, touch and evaluate the first viable results. 

Let me reveal some use cases that we have come across in the past years. I will describe each of them in detail in the next articles, I want to show the already existing broadness of application of AI in corporate environment. AI potentialities in business are exponential, AI has applications in broad economic sectors such as Finance, Health, Law, Education, Tourism, Journalism and so on (Brynjolfsson & McAfee, 2014).

But first, let me put the boundaries on what we mean when we say AI systems. There are two types of AI, the ‘weak’ one and the ‘strong’ one (Susskind & Susskind, 2015, p. 272). The weak one is present in the everyday life of people and it includes Expert Systems (ES), Machine Learning (ML), Natural Language Processing (NLP), Machine Vision and Speech recognition (Dejoux & Léon, 2018, p. 190) - see the image.

ES is “a computer system designed to simulate the problem-solving behavior of a human who is expert in a narrow domain”.

ML is “the ability of a computer to automatically refine its methods and improve its results as it gets more data” (Brynjolfsson & McAfee, 2014, p. 91).

NLP is defined as “the process through which machines can understand and analyze language as used by humans” (Jarrahi, 2018, p. 2).

Speech recognition technique is based by definition on NLP techniques. Machine vision is “algorithmic inspection and analysis of image” (Jarrahi, 2018, p. 2). 

The weak AI embodies a lot of potential for the future of work as AI can support humans in their tasks and replace humans in routine tasks (Jarrahi, 2018, p. 2; Dejoux & Léon, 2018, p. 191).

And this is what we aim for. During the client enagagements we have developed fuctionaing systems or MVPs int he following areas:

• Internal employee support assistant
• Computer vision as an engagement mechanism
• Personal assistant for defined needs of an employee, department or function
• Onboarding process in the corporate environment
• Learning and training assistant
• Networking between employees and/or a powerful engagement tool during the conferences

Start with a pilot, make sure that employees love it and want more, rollout and evaluate the next MVP.