A Comprehensive Guide to AI in Business

The Difference Between Traditional AI and Generative AI

If Chat GPT rather than Watson springs to mind when thinking about AI, that may be because of the buzz currently generated by generative AI (no pun intended). Within the realm of AI, generative is basically the next generation, but traditional AI is still important to know and harness in your business. 

Traditional AI is also known as classic AI or conventional AI, it focuses on performing tasks according to specific rules or programming.  It takes data, analyses it, and then makes decisions or predictions. Systems like chatbots, SEO, fraud detection, and even voice assistants would fall under this category. 

Generative AI, on the other hand, focuses specifically on the creation, or generation, of new content. Generative AI systems can produce text, images, music, and even code. Chat GPT, Google Bard, Bing Chat - these systems are often trained using extensive datasets of existing content, where they learn patterns and rules that are subsequently used to create new (for all intents and purposes) content. 

So, in essence, the fundamental distinction between traditional AI and generative AI lies in their respective purposes. While conventional AI systems are mainly designed to understand and analyse information, generative AI systems are engineered to generate new information. 

Recognizing the difference between these two branches of AI provides a clearer understanding of their distinct roles and capabilities. While traditional AI offers powerful insights and predictions derived from data analysis, generative AI provides a creative edge by generating content based on learned patterns. Together, they present huge possibilities, fueling innovation and enabling businesses to leverage the power of intelligent technology for both understanding and creation.

How AI Works

Ok, we now know that, at its core, AI aims to develop systems that can simulate human intelligence and perform tasks that typically require human cognitive abilities, let’s look at how AI actually works.  

In straightforward terms, AI operates by merging extensive datasets with intuitive processing algorithms. AI then refines these algorithms through the acquisition of behavioural patterns found within the dataset.

Picture AI as a comprehensive toolkit, capable of solving problems and suggesting potential outcomes. 
It's crucial to grasp that AI is far from a single algorithm—it's an intricate machine-learning ecosystem that wields the power to tackle challenges and offer informed insights. Let’s take a look at each of the steps that make AI tools like chatbots, data analysis, and content generation possible. 

The Four Concepts Of AI

AI isn't just a single entity; it's a mosaic of capabilities that reflect human thinking. Not every tool or platform will work for your business, but there are four main concepts to remember when working with AI in any setting: Reactive Machines, Limited Memory, Theory of Mind, and Self-Awareness. These concepts peel back the layers, showing us how AI's journey unfolds.

As we continue our journey through the landscapes of AI, these concepts beckon us to explore how the boundaries of intelligence are constantly being pushed, creating a future where machines evolve into something far beyond mere tools.

How Does AI Benefit Businesses?

AI has already made significant changes to the business landscape, and it’s certainly showing no signs of slowing down. As AI evolves, it will undoubtedly drive innovation, impacting the way in which businesses operate and how they can deliver value to their customers. Here are just a few of the ways AI is already changing the business world: 

Improved Decision Making 
AI technologies, such as machine learning and data analytics, help businesses process vast amounts of data to gain valuable insights. This enables data-driven decision-making across various aspects of the business, from sales and marketing to supply chain management. So what previously might have taken a full day of meetings to analyse, discuss and make decisions, could now be resolved in minutes using AI. 

Personalisation and Customer Experience
AI will now be able to predict what customers need, even before they do. This can be done by analysing all customer data and recommending products or services tailored to individual preferences, taking out any of the guesswork that was previously done by humans. So for example, before AI, someone would have manually set up email workflows recommending different products or services they think would complement that specific customer segment best, based on previous transactions and customer behaviour. AI can now remove the human guesswork involved in this and analyse all of the data to recommend the products that have the highest chance of being purchased, based on actual data. This can help lead to increased revenue, enhanced customer experiences and increased customer loyalty. 

Process Automation
AI-powered automation has been revolutionising the way businesses operate. Tasks that were once performed manually can now be automated, leading to increased efficiency, reduced errors, and cost savings. For example, AI can be used in the recruitment process to narrow down candidates. The AI can perform data analysis on all job applications and can eliminate unsuitable candidates, leaving the ones that are most qualified for the job. This can massively increase productivity as previously this may have taken someone hours to manually analyse. 

Chatbots and Customer Support
AI-driven chatbots have become commonplace in customer support, providing immediate responses to customer queries and improving response times, even outside regular business hours. The chatbots can use conversational AI to simulate a discussion with a customer and use natural language, making it less obvious that it’s a bot. Chatbots can save businesses as much as 30% on their customer support costs.

Source: Venture Beat

Supply Chain Optimisation
Managing a supply chain is complex, often difficult work, involving coordinating multiple parties and considering factors such as availability of materials, labour, price fluctuations and much more. AI has transformed this process as it can manage large amounts of supply chain data, analyse and identify trends and predictions about future concerns. 

AI enables businesses to better manage their supply chains by predicting demand, optimising inventory levels, and streamlining logistics and transportation. Forecasting capacity constraints and customer demand is essential to successfully managing a supply chain, and AI can be trained to do both tasks with efficiency. 

Predictive Maintenance
It can be difficult to detect potential equipment malfunctions with the human eye. In industries like manufacturing and aviation, AI can provide a helping hand by predicting equipment failures before they even occur. Businesses can use these insights to plan ahead for repairs to ensure equipment is kept running smoothly, resulting in less downtime and increased operational efficiency. 

Fraud Detection and Security
AI-based algorithms can identify suspicious activities and patterns, helping businesses detect and prevent fraudulent transactions and improving overall cybersecurity. For example, machine learning can use historical data labelled as fraudulent or non-fraudulent and use anomaly detection algorithms to identify transactions that are significantly different from the norm based on given features.

Job Losses and New Business Models and Industries
Without a doubt, some jobs may be replaced entirely by AI or dramatically impacted. Office administrative support, legal, architecture and engineering, marketing, sales and financial operations are just some of the sectors that could be at high risk, but there is still large potential for it to create entirely new business models and industries such as autonomous vehicles, personalised medicine, and AI-powered financial advisory services.

It's essential to note that the widespread adoption of AI also raises ethical and social implications, including concerns about job displacement, privacy issues, biases in algorithms, and the responsible use of AI technology.

AI for Customer Service

Customer service can use AI in various ways to enhance efficiency, improve customer experience, and streamline processes, which helps lead to increased customer loyalty and retention. It’s vital however that AI enhances customer service and doesn’t replace the human aspect of it altogether. 

Here are some examples of how AI can be used in customer service: 

AI for Sales

Sales teams can leverage AI in several ways to boost productivity, improve sales processes, and increase revenue. Here are some ways sales teams can utilise AI:

By incorporating AI into their sales processes, sales teams can improve efficiency, accuracy, and customer engagement. It enables them to focus on high-value tasks, make data-driven decisions, and build stronger relationships with customers, ultimately driving revenue growth and achieving better results overall. The human side is still an integral part to sales however so be mindful that to build long-lasting relationships with your customers, not every aspect of the sales process can be driven by AI. 

AI for Marketers

Marketers can harness the power of AI to enhance their strategies, improve targeting, personalise messaging, and optimise campaigns. Here are some ways marketers can utilise AI in their efforts:

By embracing AI technologies, marketers can streamline processes, improve efficiency, and deliver more personalised experiences to customers. AI-driven insights can guide strategic decisions, increase marketing ROI, and ultimately lead to stronger customer engagement and loyalty. It's crucial, however, to ensure that AI is used ethically and responsibly, respecting user privacy and maintaining transparency in its applications.

Source: Influencer Marketing Hub

Further reading: AI is Not Killing Marketing (Yet)

AI for Product Development

Product development teams can leverage AI in various ways to enhance their processes, improve efficiency, and create more innovative and customer-centric products. Here are some ways product development teams can use AI:

AI can also help tailor products to individual customer preferences by analysing data and generating personalised recommendations. This enhances the customer experience and can lead to higher customer satisfaction.

Incorporating AI into product development can lead to faster innovation, improved decision-making, reduced costs, and increased customer satisfaction. However, it's essential to carefully plan and implement AI solutions to ensure they align with the team's goals and the target market's needs.

The Security and Privacy Risks of AI

The integration of AI into various aspects of modern life brings transformative benefits but also introduces substantial privacy and security risks. As AI systems rely heavily on data, often personal or sensitive in nature, the collection, storage, and processing of vast amounts of personal information raise concerns about data breaches and unauthorised access. Businesses must prioritise data privacy and protection, adhering to relevant regulations such as GDPR (General Data Protection Regulation). Ensuring transparency in data usage and obtaining informed consent from users is essential to maintain trust and ethical standards. And it’s important to remember, that everything you provide to AI will be stored and used for future training. Personal data like names, addresses and contact details can be inadvertently and easily collected during normal day-to-day interactions with AI This data is all saved within the AI meaning there is a risk of unintentionally generating sensitive information that violates privacy regulations, putting individuals at risk.

Research by Cyberehaven found that: 

• 11% of data that employees paste into Chat GPT is confidential
• 4% of employees have pasted sensitive data into it at least once

Source: Cyberhaven

So with such personal information being shared on sites like Chat GPT and Bard, there are serious security risks to consider. Say, for example, a company has an AI-driven chatbot to handle customer support inquiries. That chatbot stores everything it’s given, which will likely include customer information such as names, addresses, card details and purchase history to be able to provide personalised assistance. Depending on the security measures in place, this data can very easily be hacked, raising concerns of privacy and potential identity theft. AI systems need strong encryption, access controls and regular security checks to ensure that confidential information is not accessed and customer privacy is maintained. 

AI tools themselves need to have strong security measures in place to mitigate the risk of model poisoning. This is when malicious data or code is manipulated into the training data to compromise the AI models’ functionality, causing it to produce malicious results and outcomes that would be harmful when deployed. These attackers can also initiate more subtle forms of manipulation, known as bias injection. These attacks can be especially harmful in industries such as healthcare, automotive and transportation. 

Another security concern is the ever-growing number of security measures that are controlled by voice and facial recognition. AI presents an opportunity for hackers to create deep fakes and audio prompts to be able to trick security systems into verifying identities and accessing personal and company information. Using just four minutes of audio, an Australian journalist at The Guardian, was able to generate a clone of their own voice to gain access to Centrelink accounts, which holds sensitive material such as financial information and important personal documents. 

The Ethical Implications of AI

Artificial Intelligence (AI) is transforming the way businesses operate, however, this technological advancement also brings with it a host of ethical considerations that cannot be overlooked. Alongside the issues discussed above, it’s important for businesses to consider the following: 

Automated Decision-Making and Accountability: AI systems often make autonomous decisions, raising questions about accountability. When things go wrong, determining who is responsible can be challenging. This ethical grey area requires businesses to establish clear protocols for attributing responsibility and ensure that human oversight is maintained, especially in critical decisions. Businesses should maintain transparency, making it possible to explain AI's actions and allowing for recourse when things go wrong.

Job Displacement: AI automation may lead to job displacement in some industries. Businesses must ethically address the impact on employees by investing in reskilling and upskilling programs and adopt some strategies to mitigate the impact of job displacement including: 

• Reskilling and upskilling: Businesses can provide training programs and resources to help employees gain new skills that are in demand in the evolving job market. Training programs can be tailored to the specific needs and interests of employees, taking into account their current skills and future career goals. 
• Create new roles: This might not always be possible but try to identify areas within the organisation where AI and automation can create new opportunities and roles, and actively develop these positions.
• Collaborate with educational institutions: Partner with local colleges and universities to develop training programs that align with the skills needed in the job market. Consider offering financial incentives to employees pursuing further education or training. Encourage existing employees to participate in pursuing further education and allow flexibility within their role to facilitate their new learning. 

Environmental Impact: The environmental footprint of AI, particularly in terms of energy consumption, is a growing concern. AI, especially deep learning models, requires extensive computational power, leading to high energy consumption. Training large AI models can be as energy-intensive as running several households for months. Businesses must explore sustainable AI solutions and adopt eco-friendly practices, such as optimising algorithms for efficiency and using renewable energy sources for AI infrastructure. 

The pace of AI hardware development and innovation can also result in the rapid obsolescence of equipment, leading to the generation of electronic waste (e-waste). This type of waste poses environmental challenges when not properly disposed of or recycled, as it can contain hazardous materials. 

Further reading: AI Ethics: What You Need to Know