We’ve been using machine learning and AI for years; we’ve just never realised it. With that in mind, is adopting modern AI into a business such a big step now? In this first of a four-part series, I look at AI in the workspace and its potential for disruption and innovation. 

Table Of Contents

• My first encounters with early AI 
• What is ‘AI’, really? 
• Is the AI hype causing misconceptions? 
• Machine learning. Human innovation 
• Summary
• FAQ

My first encounters with early ‘AI’ 

At school, English wasn’t my strongest subject. It may have been my native tongue, but I couldn’t spell for toffee, and my dyslexic twin brother found it even harder.  

When I started working in an office, I focused on writing code and scripts, as these relied less on spelling and punctuation and played to my strengths. This changed when spellcheckers started appearing in applications such as Microsoft Word, which was first seen in MS WinWord back in 2003. Autocorrect and Dictionary appeared in 2007.   

Spellchecker is one of the first AI or ML features we all used; maybe without even realising. Before we go any further, I want you to check this against your own perspective. Did you consider AI or ML as technology that has only just hit the public domain? 

What is ‘AI’, really? 

Let’s pause, rewind and look at what ‘AI’ means. We know that it stands for “Artificial Intelligence”, but I would argue that this is a misnomer. 

We all have our own perspective, but the guide I use is that AI or ML is a machine being; able to mimic or copy a human behaviour or action. Using this definition, we can see that a spellchecker program is absolutely an AI tool, and also a perfect example of where we have taken for granted machine input into our daily life.  

Whenever I talk about the concept of AI or ML, I try to highlight that the virtual assistant paperclip ‘Clippy’ was launched by Microsoft in 1996 and retired in the mid-2000s (mostly because people found it quite annoying). But the point that I want to remind us of is that AI/ML has been in our daily lives for almost 30 years. The recent surge in excitement about AI is still focused on the same concepts we have been using since the mid-90s. 

If we have been using AI and ML capabilities for the last 30 years, what has fundamentally changed to make us more wary of utilising AI and questioning its risk and reward? Did we have the same concerns about software features like spellchecker? Or Clippy?? 

Is the AI hype causing misconceptions? 

Having wandered down memory lane and remembered fondly (or not) Clippy and Spellchecker coming to our desktops, we need to look at what the situation is now. The evolution of processors and cloud technologies has put Clippy on steroids and given it access to an unfathomable amount of information. Should this not be seen as positive and progressive? I believe it should, but, as I will cover in the other parts of this series, we must consider the challenges we will face along the way and prepare in advance of scaled consumption. 

Machine learning. Human innovation 

The current AI and ML that is publicly available is, without a doubt, a disruptor of our daily lives, but it is also gives us the power to support our innovative side. With correct and responsible use of AI and ML we should be able to remove the mundane, simple, and repetitive tasks from a lot of our daily lives and instead focus on the human elements, like emotion and creativity. 

“Facts rule the world, emotions run the world”, my father taught me, and we must keep this in perspective as we look at the utilisation of AI and ML in our daily lives. Any machine can elicit an emotional response, but it cannot experience one. The human experiences of reasoning, feeling and truly understanding can never be replaced by a machine. 

We must use the disruptive power of AI and ML to allow us room to create and innovate. 

Summary 

This is the first in a 4-part blog series written by Tim Russell, the Chief Technologist for Modern Workspace at CDW UK. The focus in this article has primarily been the end user experience and journey. 

However, AI comes in many forms and use cases; from the likes of Microsoft CoPilot, which focuses on productivity enhancements, to fully bespoke Large Language Models (LLM) designed to analyse data - either from an exception or a classification level.

AI is an overly generic term, but hopefully this article will have given you some food for thought about how you interpret the term and apply it to your business. The remaining articles in this series will help prepare you by providing insight, opinions and maybe some more food for thought around how, when, and most importantly why you will embark on your AI journey. 

There is also a journey for the enablement of your platform and the creation of the capability that users will require; my colleague Rob Sims is providing supporting articles and useful information around the platform and data requirements and the journey to enable your users that can be found here. 

Other blogs in this series: 

• AI in the Workspace - Part 1: AI – Disruptor or Innovator? 
• AI in the Workspace - Part 2: AI – A Business Dilemma 
• AI in the Workspace - Part 3: Adoption challenges of AI 
• AI in the Workspace - Part 4: Measuring AI’s Value and Impact 

Subscribe to updates at https://news.uk.cdw.com/author/office-of-the-cto for this and other content from the Office of the CTO at CDW UK. 

FAQ: 

What more can I do to enhance AI? 

Assuming you have a clear vision and purpose for AI within your organisation, there are many solutions you can consider to provide more relevant data to your AI solution. Imagine that the purpose of the AI you have selected was to improve employee experience in your workplace. This could be through IoT devices that measure the environment, or it could be linked to sound measurement information provided by unified communication platforms to show background noises impacting employees. In short, the provision of relevant, real-time data will help improve the quality of the analytical work that AI will be preforming. 

How can I prepare for AI?

There are many steps on the journey to being ready to safely introduce AI to your environment. CDW offers readiness assessments, but outside of this you need to be very clear on the expected gains; is it time, data, productivity or a combination? Having this clarity of purpose along with support from the organsiational leaders will help you start out on the right foot. 

What are the benefits and challenges of AI in the workplace? 

There is a lot to be considered when creating the pro/con table for AI. Fortunately, I will be touching on this in later articles in this series. 

Help! Our users are already using internet-based AI solutions and I’m worried we’re leaking data. 

This is a very real concern, and one that I believe all businesses will face at one time or another. There are several steps you can take and CDW can help you with this scenario. Initially, user education and blocking of these specific internet services will help you create room to audit and understand what, if anything, has been shared and why.  

The ‘why’ will help you to understand why users require access to AI, and to develop a business case to either create policies or platforms to enable the use of AI in your environment. It is not a case of ‘if’ but ‘when’ your environment is ready for AI.