What is AI? A Non-Technical Breakdown for Leaders

Artificial intelligence isn’t a far-flung idea. It’s a disruptive technology that business leaders need to understand. Thankfully the starting point for tackling AI isn’t coding, or fighting bloody battles with unrelenting robots. It’s about getting the fundamentals right, and that means first developing a laser focus on business challenges and strategy, and only then thinking about AI. 

The robots aren’t coming…they’re with us already!

People often talk about AI as if it’s something that’s going to happen in the far future. Perhaps because our mental image of AI is shaped by sci-fi portrayals of rogue robots and recalcitrant computers that refuse to open the pod bay doors.

That’s the fiction. The reality is that artificially intelligent systems shape our lives and the information we process every day.

How do we define AI?

Any computer system that exhibits some form of intellectual quality that we’d normally associate with natural cognition – learning, understanding language, problem solving, creativity – is an example of artificial intelligence (AI).

In business the techniques most commonly associated with AI include:

• Machine Learning (ML) – using data to help a computer improve how it treats new data.
• Natural Language Processing  (NLP) – helping a computer process and interact with words from everyday language.
• Automated Systems – physical and virtual systems that react in predictable and useful ways to data, to take away the burden, speed up, or improve the quality of specific tasks, such as driving.

How is AI shaping our lives?

We encounter artificial intelligence in a variety of everyday settings. Some are more obviously artificial routines. Ask Alexa or Siri to “open the pod bay doors,” and you’ll get a set of wry responses which, though scripted, do rely on natural language processing to interpret your voice and automatically select an appropriate response, based on what it has learnt.

Many of our interactions with AI are more subtle but perhaps more powerful. Powerful because of their ability to shape our lives and influence our decisions. Powerful because they make our interactions with machines feel more intuitive, more natural, and more intelligent.

Navigation apps learn our routine so they can help us decide the best time to leave for work. AI systems personalise our social media content based on browsing habits. Smart cars help us stay in lane and avoid trouble.

The utility of AI in these circumstances is in its ability to process data, and either assist in making timely decisions (“leave for work now to avoid delays”) or actually make decisions for us (“you’ve missed it, but I, the car, am applying the brakes to avoid ramming that car in front’’).

And, in our data rich, time poor world, the ability to make reliable decisions, informed by large amounts of data isn’t just the preserve of consumer apps and products.

Whether it’s drug discovery, or testing pricing strategies, AI can improve both the speed and quality of decision-making. Just one reason it’s perceived as a potential source of value creation for businesses, and therefore competitive advantage.

How do you get started with AI in a business?

Tech companies big and small make a convincing case for AI in business.

There’s undoubted value to be gained from automating tasks or finding new insights in data. But that doesn’t necessarily mean everyone needs to dive straight into AI. In fact, diving in might sound exciting but it can create more problems than solutions. Especially if you’ve got challenges with data or don’t think carefully about the human side of your business.

It’s not just about changing up to new tech. People and processes need to change too.

Through our work with companies big and small, we’ve encountered a set of strategic steps that add value when followed because they address both the tech and the human side of your business.

These steps include:

1. Identifying your current level of data maturity through the lens of decision-making, then creating a plan that will evolve your data practices in ways that enable you to take advantage of AI.
2. Improving your organisation’s ability to reframe business challenges as data questions.
3. Considering how AI should reflect both your mission and your values
4. Making sure your plans for AI  address ways of working, improving skills, and adapting processes.

The next blog in this series explores each of these steps in more detail.