Unleashing the Power of AI: A Beginners guide for Business Leaders.
You know that Artificial Intelligence is the future. You know that you want to introduce it into your organisation. But you’re not completely sure where to start. There are a lot of terms swirling around, and there seem to be a variety of different types and applications of AI. What you need is a beginner’s guide to help you navigate all the considerations.
Scope
This article is a useful guide to the basics of AI for anyone just starting out on their AI journey. We’ll discuss the terminologies most commonly thrown about and the nuances that exist between them. We’ll also look at the ways AI can impact a business, from revenue generation to healthcare innovation. We’ll also explore some of the key skills a business leader may want to cultivate to create an AI ready organisation.
Artificial Intelligence is conversation currently invading every board room. Budget meetings and strategy sessions are having their agendas dominated by AI. People at every level, from business leaders to front line team members are thinking about how AI will impact them. And for those who aren’t talking about it? Well, there’s a pretty good chance you’re missing a trick.
But part of the problem with the AI conversation is that it’s plagued by hype, half-truths, and myths. Push past that, and you’ll find a technical revolution in the waiting, ready to reshape industries, inspire new forms of business, and explode customer expectations right across the globe. If we can deal with elements of fear and the untruths following AI around, there are more opportunities than we could ever imagine.
AI has already landed in our lives in a way that is more pervasive than you possibly realise. From the last thing you listened to on Spotify, to your conversation with Alexa, to your neatly filtered email inbox, to the sandwich you just bought for your lunch, AI has already impacted your life. It analyses your music preferences to add new songs to your playlist. It recognises and responds to your voice. It identifies language that is and isn’t associated with spam. It tracks stock levels to ensure the right things are on the shelves at the right time.
But what does all this mean to you? What impact is it having on you and your bottom line? Let’s get into it – the definitions, the jargon, the applications, the implications, and the key skills.
Understanding AI
Artificial Intelligence, or AI, refers to the simulation of human intelligence in machines that are programmed to think, learn and perform tasks. Things like problem solving, decision making, understanding, analysing and translating language. It’s different from traditional computer programmes because AI isn’t static. It adapts and improves its performance over time, without needing to be explicitly programmed for every task. It learns. It becomes more accurate with practice. Which is why we refer to it as “intelligence”.
Types of AI
There are a myriad of ways we can talk about AI and group the different aspects of it, but AI can broadly be organised into three main types:
1. Narrow AI (Weak AI):
Which is designed for specific tasks and operates under a limited, pre-defined set of circumstances. AI virtual assistants like Siri and Alexa, chatbots, and facial recognition systems are examples of Narrow AI (sometimes known as Weak AI).
2. General AI (Strong AI):
Which possesses human-like cognitive abilities and can understand, learn, and adapt across a wide range of tasks, even if it’s never encountered them before. AGI hasn’t been fully realised yet, and it remains a goal for the future, but there are growing capabilities within narrow AI that look promising.
3. Artificial Superintelligence:
A hypothetical form of AI that would surpass human intelligence in all aspects, potentially leading to unprecedented advances or challenges. There’s considerable dispute over the possibility of this ever coming to fruition, and, as yet, is mostly a theory explored in science fiction.
Explaining AI Terms and Jargon
There is so much jargon swirling around the topic of AI that it’s easy to get confused. Here are some of the main terms you might hear in relation to AI:
1. Generative AI.
Generative AI refers to the subset of AI that generates content – and that could be text, images or even music. It uses deep learning techniques (like Generative Adversarial Networks or GANs), to create content that can be indistinguishable from human generated content. This is often based on predictive learning. Generative AI can be useful to businesses for content creation, like video production, blog planning or product descriptions
2. Natural Language Processing.
NLP is the branch of AI that focuses on computers having the ability to understand text and spoken words the way human can. It can understand text or voice data, determining the meaning and context. It’s at work in speech recognition and sentiment analysis
3. Large Language Models.
Generative AI refers to the subset of AI that generates content – and that could be text, images or even music. It uses deep learning techniques (like Generative Adversarial Networks or GANs), to create content that can be indistinguishable from human generated content. This is often based on predictive learning. Generative AI can be useful to businesses for content creation, like video production, blog planning or product descriptio
4. Knowledge Graphs.
Knowedge graphs organise structured data to represent relationships between different entities. They connect different bits of information together and understand how they relate to one another. Knowledge graphs are often used for things like data discovery, upsell or cross-selling opportunities, recommendations (like your Netflix “you might like” section), or improving search engine results
5. Computer Vision.
Computer vision is AI that can interpret and understand visual information such as images and videos. It’s computer vision that’s behind image analysis in healthcare, self-driving cars, number plate recognition in carparks and visual search recommendations.
6. Reinforced Learning.
Reinforcement learning is where AI learns to make decisions by interacting with its environment. It receives feedback in the form of rewards and penalties, so it learns the optimal strategies. It’s often used in game playing.
7. Neural Networks.
These are the building blocks of many AI systems. They are models inspired by the human brain’s structure and function. They are what deep learning is built on, and they’re integral to tasks like image and speech recognition.
8. Sentiment Analysis.
Sentiment Analysis is all about using AI to determine the emotional tone and sentiment behind text. It’s often used to understand customer opinion, to parse reviews and to make data driven marketing and product decisions.
9. Predictive Analytics .
Predictive analytics uses AI within statistical algorithms to forecast future events and trends based historical data. It helps businesses make informed decisions related to inventory management, sales forecasting, and risk assessment.
AI Applications for Businesses
Now we’ve given you an overview of the most common types of AI, we’ll look the ways AI can impact businesses. There’s far more to its ability that just automating routine tasks. Used properly, AI is a strategic asset with far reaching benefits for almost every area of your business.
1. Revenue Growth
There are new revenue opportunities to be found through AI’s ability to analyse massive amounts of customer data and current market trends. Personalising marketing strategies and jumping on trends (or even predicting trends), enhances customer engagement and drives sales.
2. Cost and Production Efficiency
There are new revenue opportunities to be found through AI’s ability to analyse massive amounts of customer data and current market trends. Personalising marketing strategies and jumping on trends (or even predicting trends), enhances customer engagement and drives sales.
3. Improving Customer Satisfaction
There are new revenue opportunities to be found through AI’s ability to analyse massive amounts of customer data and current market trends. Personalising marketing strategies and jumping on trends (or even predicting trends), enhances customer engagement and drives sales.
4. Connecting Disparate Systems and Knowledge
There are new revenue opportunities to be found through AI’s ability to analyse massive amounts of customer data and current market trends. Personalising marketing strategies and jumping on trends (or even predicting trends), enhances customer engagement and drives sales.
5. Computer vision in retail
There are new revenue opportunities to be found through AI’s ability to analyse massive amounts of customer data and current market trends. Personalising marketing strategies and jumping on trends (or even predicting trends), enhances customer engagement and drives sales.
6. Fraud detection and prevention
There are new revenue opportunities to be found through AI’s ability to analyse massive amounts of customer data and current market trends. Personalising marketing strategies and jumping on trends (or even predicting trends), enhances customer engagement and drives sales.
7. Healthcare
There are new revenue opportunities to be found through AI’s ability to analyse massive amounts of customer data and current market trends. Personalising marketing strategies and jumping on trends (or even predicting trends), enhances customer engagement and drives sales.
Key Skills for Implementing AI
Implementing AI into a business requires high performing team members with the key skills that support a digital and AI driven organisation. There are particular skills that are highly advantageous to develop and cultivate within your organisation.
1. Data Science
Data scientists are crucial to the growth and development of AI – they’re the people who create the algorithms designed to identify and learn patterns in the data that is fed to them. They know how to use AI to collect, clean and analyse data, giving them strategic, actionable insights.
Added to this, skills in Machine Learning (ML) and Natural Language Processing (NLP) will mean you can develop the algorithms you need to learn from data, make predictions and build language-related AI tools such as chatbots and sentiment analysers.
2. Data Infrastructure
Having proper Information Architecture means being able to access and democratise data to leverage it to its full ability. This means creating an infrastructure that can draw in data from many different sources and aggregate them. Recruiting someone with an understanding of data fabric and data mesh architectures as well as familiarity of big data and its storage options will be a huge asset to your team.
3. AI Ethics and Regulation
Many have commented that AI is moving faster than regulation is capable of keeping up with, so having experts to hand who are able to stay abreast of rapidly evolving situations, and navigate regulations like GDPR, as well as those that are still being written and decided upon will help to make sure you don’t get caught out and are well prepared with responsible AI usage.
Why AI is a hot topic
AI has undeniably become a hot topic in 2023, gaining widespread attention and creating a buzz across various industries. Why? Well, between rapid technological advancements, the pandemic, and the shift in consumer expectation, it has been the perfect storm for AI to develop.
Firstly, the rapid advancements in AI technology have played a significant role in raising its prominence. Over the past few years, there have been considerable breakthroughs in the field of AI, including natural language processing, computer vision, and machine learning algorithms. These advancements have made AI more accessible, reliable, and efficient, leading to its integration into various aspects of our daily lives. From virtual assistants and autonomous vehicles to personalised recommendations and medical diagnosis, AI has shown its potential to impact industries and improve human experiences.
Another factor contributing to the sudden surge in AI development is the global pandemic. The COVID crisis highlighted the urgent need for advanced technologies to address healthcare challenges, economic disruptions, and social distancing measures. AI proved its value throughout the pandemic, analysing vast amounts of data to track the virus’s spread, predicting infection rates, and even assisting in the development of vaccines. The pandemic also accelerated the adoption and acceptance of AI solutions, making it a crucial tool for mitigating future crises and improving preparedness, as well as helping organisations to continue to operate remotely.
Moreover, the overflow of consumer expectation has made AI a necessity for business. The expectation overflow is the term used to describe the shift that has taken place in customers’ value judgements, or the hoops you need to jump through to please your customers. Advancements in technology have enabled larger players to offer a greater level of service and/or experience to their customers. As customers become used to this, they no longer place value in it. Instead, they come to expect it as normal practice. Think, for example, of Amazon Prime’s next day delivery. Rather than this being seen as a valuable addition by consumers a positive value judgement, the lack of it is seen as a shortcoming – a negative value judgement.
Overall, the combination of technological advancements, the impact of the pandemic, and the expectation overflow has catapulted AI to the forefront of public consciousness in 2023. As AI continues to evolve and integrate into our lives, it will be crucial to ensure responsible development, address ethical concerns, and leverage this transformative technology to address the challenges we face and create a better future.