What Is Artificial Intelligence (AI)?

The history of artificial intelligence primarily began in the 1940s with the British mathematician Alan Turing[1], who developed the first modern computer. He began testing the theory that machines could be programmed to think like humans and mimic their behaviour.

‘Smart’ machines now display core human abilities such as learning, reasoning, and problem-solving. They can analyse datasets, recognise patterns, and make predictions based on the data.

Some of the key capabilities of AI systems include:

• Learning: Machines take datasets and identify patterns and commonalities in them. Much like the way humans learn from experience, the more often a machine is exposed to the data, the more ingrained the outcome or action becomes.
• Reasoning: Logical sequences and rules built into the machine allow it to arrive at data-led decisions or predictions.
• Problem-solving: AI can use data analytics and pattern recognition to find solutions for pre-defined goals.
• Perception: With access to billions of real-life examples, AI machines can now react to and interpret visual and auditory information. They can recognise and manipulate objects, as well as understand and respond to speech.
• Automation: Once an AI machine understands the sequence of events for a task, it can automate these tasks with increased speed and accuracy.

Perhaps the most important development to keep in mind when thinking about AI is that it isn’t just defined by its ability to follow instructions and act upon them. It has moved beyond this point. Now, AI has the ability to learn and adapt its behaviour, making it more nuanced.

AI runs on an algorithm (a step-by-step set of instructions) that gives it the ability to interpret huge sets of data in order to solve a specific, pre-defined problem or perform tasks. AI will analyse the data and determine what is relevant to the algorithm and what isn’t.

There are three major components of AI learning, each designed to complete different types of tasks:

• Machine learning: This is the foundation of all AI. It’s the process through which machines can analyse data to interpret patterns and achieve outcomes. It’s based on reinforcement learning.
• Deep learning: This is the next step up from machine learning. Over time, and with repetition, the machine will understand deep learning and which patterns lead to the desired outcome. It will essentially develop a neural network, meaning that it won’t need anyone to explicitly or manually program it to achieve the results.
• Natural Language Processing (NLP): This is the process that allows AI to communicate with us. Through extensive exposure to text and speech data, AI can use computational science to interpret what it’s being instructed to do and respond with human language.

While the development of artificial intelligence is becoming increasingly nuanced, the fundamental process for AI learning remains the same regardless of what it’s being used for.

AI relies on data to act as its fuel. It can take the form of numbers, images, text or sound, depending on the intended outcome. A machine will be fed millions of pieces of data, and then it will process and organise the data.

Next, the machine needs to decide which pieces of data are relevant to the outcome. To do this, it will pass all data through pre-programmed algorithms to see which datasets meet each requirement.

For future instances and fresh datasets, the machine will then have the necessary information to remember that when this specific data passes through the algorithm, the intended outcome is achieved.

The ‘training’ of AI involves tweaking parameters and outcome criteria until the machine is in a position to solve more complex problems. Programmers will continue to feed it data until it can use it to achieve several outcomes.

The terms ‘artificial intelligence’ and ‘machine learning’ are often used interchangeably, but it’s important to know that they’re distinct terms with differing outcomes.

It’s perhaps easier to think of AI as the concept and machine learning algorithms as the mechanism:

• Artificial intelligence is the overarching concept of programmers using tools to allow machines to emulate human thinking. Machines can replicate skills such as reasoning and logic (abilities that are innately human) in order to solve problems or make predictions.
• Machine learning is how a computer learns how to do something new without a programmer needing to provide additional instructions. It’s the method for developing artificial intelligence. The goal of machine learning is simply to program computers to eventually provide automated outcomes. It doesn’t produce higher-level thinking such as innovation or emotional intelligence.

AI is so prevalent these days, it’s often hard to remember that we’re only beginning to understand what AI is and what it’s capable of. However, that hasn’t stopped people from making claims about what AI will be like in the future.

Given what we understand about it today, it’s possible to imagine that we might see the following three types all working interchangeably.

Narrow AI is a term used to contrast what AI can do today with what it could potentially do in the future. Even though the name suggests its capabilities are limited, narrow AI is incredibly powerful and versatile. It’s used for a vast range of situations and software, such as virtual assistants like Siri and Alexa, facial recognition, text generation, generative AI art, and automation of manual input tasks. However, most AI applications are currently only designed for a single purpose.

So far, we haven’t reached this stage yet, though many data scientists and AI programmers agree that it’s only a matter of time. It’s the idea that AI, led by computer science, will be able to emulate human-level intelligence capabilities and nuanced thinking. Skills such as critical thinking, verbal reasoning, and logic are likely to become second nature to computers when we reach this stage.

Currently, this is purely theoretical, but many AI developers believe that, technically, there’s no limit to what AI could potentially achieve. So we could be looking at a future when self-aware AI surpasses human intelligence – and our limited memory capacity – in every way. Things such as lightspeed travel or teleportation are considered beyond the scope of human ingenuity to uncover, but perhaps with AI superintelligence, they might become a reality one day.

Let’s forget about time travel and fully-fledged cloning for one second and focus on how AI is being used today. Because in many ways, it’s no less extraordinary.

Generative AI refers to the process of creating text, image, video, or audio content. Through data processing and algorithmic training, generative AI software can replicate authentic-sounding media for individuals and businesses.

It’s a very prevalent type of narrow AI, and there are many mainstream examples of it in use today. ChatGPT is perhaps the most obvious, where users can enter prompts or requests and receive in-depth pieces of written content or images in return.

It’s not hard to understand why generative AI is so popular and widely used. It is a much quicker way to generate content, and it can help boost creativity, empowering individuals to create the projects, stories or artwork that they’ve always wanted to see but never believed they had the ability to create.

There is contention, however, about whether this can be considered ‘new’ or ‘original’ content because it’s generated from information available in previously produced and copyrighted content . As AI continues to evolve, it is essential that ethical and responsible guidelines evolve with it in order to protect intellectual property and creative artists, to guard against bias and misinformation, and to ensure AI is empowering employees rather than replacing them.

So: where are we likely to go from here? We’re not quite ready for super-intelligent computers, but we are getting to the point where businesses and individuals can start using the various machine learning platforms, applications of AI, and software interconnectedly.

This is what is known as agentic AI. While machine learning applications can make processes more efficient, they’re designed to solve one problem at a time, which limits their usefulness in terms of scope and expansion. AI agents, on the other hand, take an overarching problem or situation without human intervention and use a variety of machine learning programs at the same time to arrive at the solution.

For example, an AI agent will eventually be able to plan every aspect of a holiday break. They’ll use AI to automatically book hotel reservations, scan for suitable flights, generate lists on what to see and where to go, find optimal travel routes, and so on. The vision behind agentic AI is a vast set of AI programs linked and working as one to make people’s lives as easy as possible.

Unlocking AI’s True Business Potential

There’s no doubt that AI is acting as a major disruptor in terms of business and personal use, and there’s no denying how useful it is.

Here are some examples of where AI can help businesses:

Put simply, AI helps businesses get things done faster. It can streamline processes in several areas, such as populating data forms or fields within a database, developing research summaries, managing manufacturing and logistics processes, providing customer assistance or generating creative content.

When an AI machine has learned what it needs to do with the data it’s provided, it’s less likely to make errors. The entire process simply follows a set of instructions.

AI enables businesses to make data-driven decisions by analysing large volumes of data and identifying patterns and trends that may not be immediately apparent to humans. AI can do this work very quickly, speeding up the overall decision-making process. It helps take the guesswork out of situations and makes a decision far more clinical.

AI differs from humans in another important way – it doesn’t need to sleep or take breaks, so it’s always available. If you feel a burst of creativity in the middle of the night, you can start AI up to help with some image work. If you contact a business after hours, their chatbot or AI assistant is on hand to help you out.

Businesses can save money in the long term by using AI to automate tasks that previously required manual input. The more efficient a business is, the better the customer retention will be, and the more potential revenue will be available. For individuals, agentic AI could handle all of their creative and personal needs with one service for one payment.

Whether it’s helping teams work more efficiently or creating better customer experiences, AI is becoming an essential part of how businesses grow and thrive. Here's where it's making the biggest difference.

AI can help sales teams streamline processes, enhance productivity and drive sales growth. AI can automate repetitive tasks, analyse customer data to gain insights into customer behaviour, improve buying experiences and boost sales. AI can also assist sales teams with predictive analytics, identifying potential leads and optimising sales strategies.

AI has significantly transformed the customer service landscape by enabling businesses to provide faster, personalised support. AI-powered virtual assistants can handle customer queries, troubleshoot common issues and offer relevant product recommendations. With AI, businesses can deliver 24/7 customer service, ensuring satisfaction and loyalty. Furthermore, AI can analyse customer feedback and sentiment to identify improvement areas and enhance overall service quality.

AI has also revolutionised marketing by enabling businesses to deliver targeted, personalised campaigns. By analysing customer data and predicting customer preferences, AI allows marketers to create tailored content and offers. AI-powered tools can automate email marketing, social media management and ad targeting, optimising marketing efforts and driving higher conversion rates. It can also provide valuable insights into marketing performance, helping businesses refine their strategies and achieve better results.

Establishing trust in artificial intelligence is crucial for its successful and ethical implementation. You must have confidence in the reliability, accuracy and security of AI expert systems to fully embrace their potential. Building trust in AI involves addressing several key concerns.

Ensuring the reliability and accuracy of AI systems is critical for building trust. That’s why we advocate for rigorous testing, validation and ongoing monitoring of AI algorithms. It also means implementing strong data management practices to safeguard the quality, relevance and integrity of the data used to train and operate AI models.

If the initial data is incomplete or inaccurate, your model will never be able to function as efficiently as it should.

It’s important that users understand how AI systems make decisions and why. When AI systems provide clear explanations for their decisions, users can be more confident that they’re making the right choices. This can be accomplished with user-friendly interfaces that explain how AI algorithms arrive at their conclusions, as well as documentation that provides more detail.

Security is a fundamental component of building trust in AI . By protecting user data and ensuring the privacy of personal information, businesses can build trust with their customers and stakeholders. Robust security measures must be implemented to safeguard data from unauthorised access, use or disclosure.

This involves employing encryption techniques, access controls, and regular security audits to mitigate potential vulnerabilities.

AI systems must be deployed and developed in an ethical manner, considering the potential impact on individuals and society. This involves addressing concerns such as bias, fairness, accountability and potential job displacement. Ethical guidelines and principles should be established to ensure that an AI system is used for socially beneficial purposes and does not discriminate or cause harm.

AI systems are great at automating tasks and decision-making, but human oversight and control are essential to ensure responsible AI use that is aligned with human values and intentions. It’s essential to have robust governance structures in place so AI systems remain subject to human review and intervention when needed.

Establishing trust in AI is a challenge that requires a comprehensive approach. This approach should address technical, communicative, ethical and human-centred dimensions. By working to address all of these areas together, organisations can cultivate a climate of trust and confidence in AI systems. This will help pave the way for their broad implementation and use.

Salesforce is promoting trusted AI with the Einstein Trust Layer. It is a secure AI architecture built into the Salesforce platform. It is a set of agreements, security technology and data, and privacy controls used to keep your company safe while you explore generative AI solutions.

Customer Relationship Management (CRM) systems play a crucial role in managing customer interactions and data. Integrating AI with CRMs can further enhance their capability and provide businesses with a 360-degree view of their customers.

Integrating the two infrastructures can help you to understand your customers better. They can analyse customer data and segment customers based on their preferences, behaviours and demographics. This enables you to tailor your marketing and sales strategies to specific customer segments, resulting in more personalised and effective campaigns.

CRMs and AI can help to identify possible leads and qualify them based on their interactions with the business. These interactions include things such as engagement with content, click-through rates (CTRs) on internal links and time spent on your website. This allows sales teams to focus their efforts on the most promising leads, improving lead conversion rates and efficiency.

Through the use of AI in CRMs, chatbots and virtual assistants are able to offer immediate help to customers, addressing their questions and queries and resolving problems effectively. This leads to increased satisfaction and trust between the business and its customers and alleviates the workload of human customer service agents.

AI can also use the customer data from your CRM tools to give you a complete context of your customers to help you connect with them in new ways. It allows you to predict future trends and patterns by analysing historical data and customer behaviour. This enables you to anticipate customer needs and address potential issues before they become problems.

Implementing AI into your business may seem daunting, but there are several resources available to help you get started. Here are a few tips:

• Assess your needs and plan your approach: Identify the specific areas in your business where AI can add value. Consider tasks that are repetitive, data-intensive or require human decision-making.
• Choose the right tools: Research and select the tools that best align with your business needs and technical expertise.
• Connect your customer data: A data platform lets you integrate large amounts of data from any source, standardise formats and ensure consistency and integrity. You can’t expect all of your data to live in one place, so you’ll need to create sharing mechanisms, such as data lakes or warehouses, to allow different departments to access and use the data.
• Invest in employee training: Workplaces around the world are using AI. In fact, one study reported 70% of workplaces surveyed use it in some capacity. However, making the most of AI is a skill, and just like any other skill, it needs to be nurtured and improved. So make sure you provide training and support to your employees to ensure they’re comfortable using it.

If you require further in-depth guidance, Trailhead is a free learning platform where you can upskill in many of today’s most in-demand skills. Here are a few trails to get you started:

• Artificial Intelligence for Business
• Gain the AI Skills You Need to Succeed
• Prepare Your Data for AI
• Responsible Creation of Artificial Intelligence

AI has the power to revolutionise the way businesses operate, providing significant benefits in terms of efficiency, customer experience, decision-making, innovation and cost reduction. By integrating AI with CRM systems, businesses can further enhance their customer management capabilities and deliver personalised experiences.

Embracing AI technologies can unlock opportunities for growth and success in a rapidly evolving business landscape.