The role of AI
in decision-making:
a business
leader's guide

Artificial intelligence (AI) once seemed like the stuff of science fiction. While we’re far from an age where digital helpers cater to our every whim, the advances made to this progressive form of technology have been staggering in the past decade alone. 

We tend to associate this brand of tech with automation and assistance for daily tasks, but this is far from the extent of its capabilities. AI has the potential to transform businesses and help industry leaders make important decisions which allow their company to grow.

But knowing how to utilize this revolutionary technology to its fullest may be unclear for some. With the robotization of some working tasks still a very new concept, it can be tough to understand where, when, and how to turn to AI for key business decisions. 

In this guide, we’ll aim to provide clarity on how AI can best be used in the decision-making process. We’ll explore how it continues to adapt and evolve, how to integrate it into your business strategy, the risks involved, and the best way to grow your business.

Chapter 1

The evolution and use of modern AI in business

The history of AI

AI as we know it today didn’t spring up overnight. For the best part of nearly a century, innovators have been experimenting with the concept of automated digital support systems. Here’s a brief rundown of how this futuristic form of technology has suddenly become a reality. 


  • 1948

    Claude Shannon publishes a book on “n-grams”, discussing what the likelihood of the next automated letter in a sentence might be.

  • 1950

    Alan Turing publishes a paper on “Computer Machinery and Intelligence”, which tests whether machines can use logic in the same way as a human.

  • 1952

    A.L. Hodgkin and A.F. Huxley show how the brains use neurons to form an electrical network, which serves as the inspiration for future AI.

  • 1956

    The Dartmouth Summer Research Project on Artificial Intelligence brings together 100 researchers from various fields to begin theorizing about how to create AI.

  • 1956

    Arthur Samuel built the IBM 701 Electronic Data Processing Machine, which utilized an optimization process to search for trees.


  • 1961

    Marvin Minsky publishes the paper Steps Toward Artificial Intelligence, which discusses the idea of a society of machines all working and talking together. 

  • 1964

    The US National Research Council (NRC) establishes the Automatic Language Processing Advisory Committee (ALPAC). This is the first example of natural language processing (NLP) used by AI. 

  • 1966

    The first ever chatbot is created. ELIZA is able to use a simple algorithm to create text responses to questions. 

  • 1966

    Research is halted for years on NLP research, after the Automatic Language Processing Advisory Committee (ALPAC) releases a report showing skepticism towards the practice. 


  • 1980

    At the beginning of, and then throughout the entirety of the decade, NLP research begins to recover. IBM develops several statistical models which use machines to make probability-based decisions. 

  • 1982

    John Hopfield develops the “Hopfield Network”, which is a recurrent neural network capable of remembering sequences and patterns. 

  • 1997

    A computer program named Deep Blue is able to defeat reigning world chess champion Garry Kasparov. 

  • 1997

    Sepp Hochreiter and Jürgen Schmidhuber introduce the idea of long short-term memory (LSTM). These networks allow computer programs to identify patterns and solve common problems. 


  • 2003

    Yoshua Benigo and his team create the first ever feed-forward neural network, which allows AI to predict the next word in a given sequence. 

  • 2005

    Honda creates the ASIMO robot, which is able to walk like a human, and deliver trays at restaurants. 

  • 2009

    Google builds its first ever autonomous car. 

  • 2011

    Siri is released by Apple. This digital voice assistant is the first time that the masses have been privy to using commands to control AI and NLP assistants. 

2020s and beyond

  • 2020

    Microsoft introduced its Turing Natural Language Generation (T-NLG), which at the time was lauded as the "largest language model ever published at 17 billion parameters.”

  • 2022

    ChatGPT is released to mixed acclaim. This AI system is able to write large amounts of text in a short period of time. Its release serves as one of the biggest talking points for the use of AI in modern society. 

  • 2023

    In response to ChatGPT, Google releases its own chatbot named Google Bard. It uses the LaMDA family of large language models.

So, where are we today? 

While AI adoption is growing, many AI projects still fail. Research by Gartner suggests that only 54% of AI projects reach the production stage, and according to McKinsey, only 15% of Machine Learning projects are successful. 

There are various reasons for these failures. However, organizations can improve their chances of success by focusing on key factors like data access, domain knowledge, confidence, and faster time to value. By addressing these fundamentals, organizations can significantly increase their success rates when using AI in their decision-making processes.

A statistical look at the current state of AI use in business 

Despite being relatively new on the scene, AI is already playing a vital role in a lot of existing companies. In a recent survey, as many as 35% of companies reported using AI in some form, while a further 42% are exploring their options for implementing it into future strategies. 

Perhaps more poignantly, 91.5% of leading businesses are investing in AI on an ongoing basis. This sudden uptake in digital adoption has unsurprisingly resulted in something of a boom for the market as a whole. Forbes reports that by 2027, the AI market is expected to be worth $407 billion. That represents a significant increase on the 2022 figure of $86.9 billion. 

Encouragingly for businesses, consumer trust and wider perception of the technology appears to be moving in the right direction. Nearly two-thirds (65%) of those asked said they were still likely to trust a company that leaned on AI as part of their day-to-day operations. The full figures showed: 

  • 33%

    Very likely (to trust a business that uses AI) 

  • 32%

    Somewhat likely 

  • 21%

    Neither likely or unlikely

  • 7%

    Somewhat unlikely 

  • 7%

    Very unlikely

A report by O’Reilly highlights which sectors are utilizing AI technology most. They found that, perhaps to no great shock, the technology sector was where the use of digital assistance was most prevalent. The full figures showed:

  • 17%


  • 15%

    Financial services

  • 9%


  • 8%


  • 6%

    Government/Public sector

  • 5%


  • 4%


  • 4%


  • 3%


  • 3%


  • 3%


  • 23%


But what exactly are people intending to use AI to do? Forbes found that answering and sending messages to friends and colleagues was chief amongst digital assistance usage. The full numbers highlighted:

  • 45%

    Respond via text and email

  • 43%

    Answer financial questions

  • 38%

    Plan a travel itinerary

  • 31%

    Write an email

  • 30%

    Prepare for a job interview

  • 25%

    Write something on social media

  • 19%

    Summarize complex copy

Perhaps most tellingly of all, as many as 9 out of 10 leading businesses have investments in AI, even if less than 15% of those are currently employing them on a daily basis in their work.

How different sectors use AI to enhance performance 

AI can be a powerful tool for a wide array of industries. Almost every sector could benefit from the use of automated assistance in some way, as they look to streamline processes, negate potential errors, and reduce workloads. Here are some examples of where it comes in handy: 

Customer service

It’s not always necessary to hire agents to speak directly with customers. Sometimes their queries can be answered using a series of prompts, with a digital assistance able to instantly recognize the information they’re seeking. These “chatbots” can streamline the process, and even save customers time.

Business intelligence

Processing and understanding data isn’t always the simplest of tasks – at least not for a human brain. AI makes it possible to analyze business data in an instant, and provide a tangible and measured approach to strategy.  

Marketing and Advertising

Predictive analytics used by artificial intelligence make it possible for businesses to target users with direct advertising online. This goes beyond just showing them products they might enjoy. AI is able to calculate times of the day, or even certain websites, where a consumer is more likely to follow through on an ad and complete a purchase.  

Natural language processing

It’s becoming increasingly common for AI to write vast swathes of copy or content, as well as streamlining the process of writing detailed business reports. While these still need to be proofread by a human, it more than halves the time required for certain writing tasks.

Famous brands that use AI 

A number of household names have turned to AI to help support them in the day-to-day running of their business. Their use of automated technology varies from minor to significant. Famous brands which are utilizing this type of tech include: 


In Germany, automatic delivery robots bring pizzas directly to your door. These robots can travel at speeds up to 10 mph, and are used in place of regular methods in the case of short distance deliveries.


This social media platform uses AI to recognize and deal with any threats of bullying which are made on the website. They’re able to identify potential threats and harassment using trigger words, and can even delete posts or ban users if necessary. 

X, formerly known as Twitter

Hate speech, fake accounts, and any kind of illegal content are quickly picked up on my automated bots that run in the background of Twitter. They’re able to flag any content or accounts which might be harmful, and have them sent for removal review.


As one of the most counterfeited brands in the world, it stands to reason that Burberry would want to take action against fake products. They do this by using AI to find images online, and determine if a product is authentic or not. The technology they use is said to have a whopping 98% accuracy rate. 


Types of AI

AI comes in many forms. And while the general process of automated technology carrying out a series of tasks remains consistent, how and why this happens will vary. Here are some examples of different types of AI which you might come across.

Deep Learning

An evolution of machine learning, this more thorough approach sees AI programmed in such a way that they’re able to identify images, sounds, and text without the need for human input. While with machine learning you may have to physically describe an image to AI, with deep learning they will be able to process and understand it themselves. 

Natural Language Processing (NLP)

If you’ve ever spoken to Siri, Alexa, or any other virtual assistant, you will have interacted with NLP. This technology is able to comprehend, manipulate, and generate human language in a way that allows it to have its very own “voice”. NLP can understand questions you give it, then respond accordingly. It can also be used in text form, such as a chatbot on a website. 

Computer vision

This futuristic form of tech allows computers to interpret and analyze the human world through the classification of images and objects. In doing so, it allows an AI to see the world through the eyes of a living person. This kind of technology is most commonly associated with driverless cars, where the vehicle needs to be able to process the world around it as a normal driver would. 

Machine Learning

This AI approach sees a series of data and algorithms run to formulate a picture of how a human would approach a situation or task. Over time, the program is able to adapt and even learn more about the human thinking process, which helps it to improve its overall accuracy. 

Generative AI

A popular online fad in 2023, generative AI is the name given to technology which is able to create images, text, or other media independently. A user simply needs to input what they want created, with the AI able to draw on their input training to produce something that has similar characteristics. 

Speech recognition

One of the oldest forms of AI, this tech is able to understand and interpret what you’re saying out loud, then convert it into text or audio format. This kind of technology is often confused with voice recognition – which instead of transcribing what you’re saying, will instead only be able to recognise the voice of the user. 

Robotic Process Automation (RPA)

RPA technology is a software which makes it easier to build, deploy, and manage robots that emulate human interactions. The robotic helpers are able to carry out a number of tasks virtually, at speeds which humans would be incapable of replicating. 

AI models and techniques

Just as there are a multitude of ways that AIs exist, so too are there a host of methods through which they learn, adapt, and evolve. The model or technique that’s used in these instances will have a big impact on the productivity and output of a robotic assistant. 

Supervised learning

This hands-on approach requires a human element, as it sees data manually inputted into an AI software system. As data is fed into the model, the output is cross-checked with existing information to assess if the virtual assistant is processing and learning correctly. 

Unsupervised learning

In stark contrast, this type of learning technique allows AI to evolve and process things on their own steam. While this gives more time back to a business to focus on their own tasks, it does run the risk of AI going down a rabbit hole which might not have been the initial primary focus. 

Semi-supervised learning

Semi-supervised learning works by using small portions of labeled data, alongside a larger subset of unlabelled data. This allows an AI to largely learn on their own, but with the hands-off guidance from a human inputter. Some see this approach as the perfect middle-ground between supervised and unsupervised learning. 

Transfer learning

In these instances, pre-trained models are used again and implemented into a new AI learning strategy. It draws on knowledge gained from a previous task, then applies it to a similar, but slightly different, job to try and find a solution. For example, something that was learned in task A could be handy to know when carrying out task B.

Chapter 2

AI decision-making in business

Now that we’ve explored more about the history of AI, as well as how it’s being adopted across a number of industries and top brands, let’s explore what the technology can do to benefit you.

10 ways AI can help leaders make better decisions

Making tough calls is arguably one of the most important aspects of running a business. They can make the difference between a quarter in the black, or a period of financial uncertainty. And while you’ll never want to fully automate these kinds of decisions, having AI to help support the process can be hugely impactful. 

For those hesitant to turn to digital assistance, it’s important to understand exactly how it can help. Here are ten ways that AI will make decision-making easier for your business:

Complex or uncertain scenarios

Sometimes a decision needs to be made when all the information isn’t immediately to hand. The gray areas which leaders have to deal with can be stressful, and cause them to make a judgment call that is ill-informed. With AI, these gaps in the data can be finitely assessed and simulated, providing clarity on what the best course of action might be. 

Help keep you objective

It’s only natural that an element of bias may creep into human decisions – particularly if your business is something close to your heart. By removing this emotional element, a computer will be able to determine the best course of action based solely off of the data at hand. 

Quick and efficient data collection

Imagine a rolodex of information is sitting in front of you, with hundreds of files that need to be read through. What might take you hours, or even days, can be done by a computer in a matter of seconds. This means you’ll be able to quickly identify the data that is or isn’t relevant to a business decision.  

Save time on repetitive tasks

By reducing the time taken to carry out menial tasks, you’ll have more time freed up to focus on other areas of your business – keeping your mind fresh and your focus concentrated on the decisions which are more in your remit. 

Keeping up with complex data

With so much data to hand in the busy world of modern business, it can be tough to know what’s useful and what isn’t. AI is able to organize and filter information, and catalog it according to its relevance. 

Communication is enhanced

Communication is key in business. By using complex tools which help to enhance your communication abilities, you’ll be better able to target consumers, while also improving how you interact with your workforce.  

Identify the root of a problem

Real-time insights and feedback allow business leaders to get to the meat of an issue, and begin to resolve any problems they might have at any point in their supply chain or company setup. By identifying these issues quickly, leaders will find it much easier to overcome them and see their business flourish. 

Human-error in decision-making is reduced

While the final say still lands at the hands of a human, the clinical nature of AI means that traditional human error is vastly reduced throughout the entirety of the decision-making process. Just be sure not to rely 100% on AI, as misinformation during data entry can still occur.

Keeping up with the latest trends

It’s easy to lose track of what the latest innovations in a sector are when you’re so focused on your own business. AI can take care of that for you; running tabs in the background to see what possible new avenues your company could explore. 

Identify both opportunities and threats

In order to make the right call in any given situation, you’ll want to know both what opportunities you’re presented with, as well as any potential hidden threats. AI can assess both customer and competitor habits and information, providing you with a series of data which might not have been immediately obvious. From there, it’s a lot easier to know what is or isn’t the right step to take. 

The risks of using AI as a business leader

As you might expect, tapping into this explorative form of technology isn’t without risks. And while these can be easily overcome, it’s important to understand exactly what they might look like for you and your business. Here are some examples of risks:

Unintentional biases

If the data being used by AI has been inputted by a human, or the data itself contains systematic or historic impartiality, any calculations made as a result will contain some form of unintentional bias. This phenomenon has even been addressed by the U.S Commission of Civil Rights

Unexplainable results can damage trust

One area of debate with the recent introduction of ChatGPT has been the AI’s return of results which are factually incorrect. While these results tend to be anomalies, they nonetheless break the trust that some employees and customers might have with automated assistants.

Unethical behavior is possible

The lack of human element has the inadvertent effect of AI making ethically questionable suggestions. A good example came from Science, Technology, Engineering and Math (STEM) career ads. The ads were intended to be gender neutral, but were disproportionately displayed by AI to male applicants, owing to the cost of advertising to females being higher.

Liability issues are still undetermined

When products fail to perform, or employees make critical errors, it’s easy to determine where fault lies. Legal liability issues are still yet to be fully hammered out when it comes to AI, which leaves some companies hesitant to use robotic assistance.

How to use AI to its fullest potential 

Utilizing AI isn’t as easy as throwing a command into a search bar. When it comes to successfully using it to your advantage, there are some approaches which are bound to bear more fruit than others. Keep these helpful tips in mind when using the technology: 

Have your data prepared

Data is the bread and butter of what makes AI tick. In order to get the most out of an automated assistant, you’ll want to have it prepared in a way that’s easily digestible. Lay out the exact facts and figures which you want to assess, any specific customer profiles, and make sure that AI has the full context needed to process decisions effectively. 

Don’t underestimate your AI tool

AI goes beyond automated processes which can be carried out the same way, but quicker than a human operator would. The system is able to merge a combination of techniques to provide all manner of feedback and solutions. An effective use of the system might be to run tests on a change in how you operate as a business, as well as predicting upcoming customer trends and changes in consumer behaviors.

Identify the “right” problem

AI is more than a yes/no form of technology. It can be used to provide detailed, amplified feedback on specific issues. It’s important to identify what issues your business is facing, and input data which is going to provide relevant, useful feedback. 

Using AI to grow your business 

The ultimate goal of any business is to grow. And using AI at the core of what you’re doing is proving to be a viable way of achieving that end. There’s a myriad of ways that a business can leverage AI to grow at an efficient, manageable pace. 

Automate certain processes

Reducing the time and effort needed to carry out some processes helps to dedicate more time back to the business itself. This time can be invested in supporting other areas of your company, or even helping to pioneer something totally new. All of this contributes to the continued growth of any enterprise. 

Create a navigable knowledge base

AI has the power to help store, organize, and catalog information which can be useful on a day-to-day basis for your staff to learn from. These don’t have to be exclusively educational resources either. Sometimes official documentation or forms for customers can also be stored in these easy-to-find locales. 

Predict forecast demand

There’s nothing more frustrating in business than missing out on a golden opportunity to cash in. AI can preemptively predict when there’s going to be an influx in demand for a specific service or product. It may also be able to calculate when and how much to change the price of a product to. 

Boost cyber-defense

Cyber crime has the ability to derail any business in a matter of minutes. AI helps to identify and protect against those potential threats, while providing genuine insight into how a criminal might try to strike in the future. As such, your business never stalls or takes a step back as a result of an attack. 

Quickly detect anomalies

Sometimes unexplained or one-off events can accidentally steer leaders in the wrong direction when it comes to business calls. These anomalies are easier for AI to quickly identify as potentially misleading snippets of data. They can be quickly removed from a wider business plan, helping you to focus on the strategies which really matter. 

Chapter 3

Useful links

We’ve discussed a lot in this guide, but there might still be more you want to discover about using AI in the decision-making process of your business. Browse these handy secondary sources to learn more about this ground-breaking, and exciting, new breed of technology: