What is AI?

When artificial intelligence (AI) is mentioned, sci-fi epics like Blade Runner or the Terminator often spring to mind. But outside the realms of pop culture, the technology is having a profound impact on every aspect of our personal and professional lives. But what is AI and how does it work?

At the heart of any AI system lies a highly complex amalgamation of algorithms, data, and human ingenuity. The fathers of AI are generally held to be Marvin Minsky, who founded MIT’s AI laboratory, and Alan Turing, whose work on the subject in the 1950s laid the foundations for the explosion of AI we’ve witnessed in recent years.

Indeed, AI has woven its way into virtually every fabric of business. From banks using AI to identify suspicious activity to automated code generation and cybersecurity operations, it’s everywhere we turn.

It can all sound very worrying that machine intelligence is now a mainstay in many enterprises, but understanding the benefits of AI, along with the potential risks, is critical for safe and responsible adoption.

How does AI work?

AI is an umbrella term that includes a range of different technologies, approaches, and architectures. Machine learning (ML) is a subset of AI that forms the basis for many AI systems in use today.

ML is the practice of feeding large amounts of data into algorithms, which over time result in a system that can make predictions and decisions without specific human instruction. For example, streaming platforms and online marketplaces have long used ML to learn consumer preferences and make accurate show or product recommendations.

Modern AI systems often work by running data through a complex ML structure called an artificial neural network (ANN), built to mimic the flow of neurons through a human brain.

Deep learning, a subset of ML used for AI systems that need to process large amounts of unstructured data, and ANNs are connected in a structure similar to a human brain. The "deep" aspect of deep learning is reflected in the multiple layers of ANNs within the structure, with layer processing data independently before passing it to the next layer for further analysis.

In this way, deep learning models can be trained to recognize patterns in vast swathes of unstructured data. This is important when it comes to processing data in near real-time, as in a text-to-speech system or natural language processing (NLP).

Today, we’re all too familiar with generative AI following the advent of ChatGPT in late 2022. Generative AI relies on deep learning architectures to produce outputs based on user queries and interactions.

Most of the popular generative AI models currently operate using an architecture known as a ‘transformer’, which helps provide responses based on contextual processing of user inputs.

Notably, with the arrival of multi-modal systems, which are capable of processing text, audio, images, and video, these rely on dedicated vision transformers or hybrid transformers.

Since 2022, AI has evolved rapidly, with major providers such as Google, OpenAI, and Anthropic rolling out increasingly powerful models. Similarly, the industry at large has also turned its focus to agentic AI.

This refers to autonomous – or semi-autonomous – AI systems capable of operating independently on behalf of a human worker, as opposed to chatbot-based responses which deliver basic query responses.

Agents represent the next evolutionary step in AI, offering businesses – and consumers – the ability to offload tasks to these autonomous bots. Professionals in a host of industries, spanning software development to cybersecurity and IT operations are now using agents in their daily workflows.

There’s also a blossoming consumer market with agents. Companies such as Meta have launched personal AI agents aimed at helping people keep track of their daily lives and keep them posted on upcoming events such as birthdays.

Notably, research shows the agentic AI market is expected to surge within the next five years. Analysis from PwC projects the economic contribution of AI agents to reach as high as $4.4 trillion by 2030.

Benefits of AI for business

Like any technology, AI has pros and cons. First and foremost, the technology allows for the automation of menial tasks so that employees can focus on more important and enriching activities.

A slew of studies in recent years have highlighted the productivity benefits of AI tools. Glean’s 2026 AI Work Index, for example, found 75% of workers believe AI makes them more productive, saving around 11 hours each week through automation of tasks.

Aside from automating tasks that humans would normally do, AI can be used to perform jobs at a speed or precision that humans could never achieve. For example, AI can carry out big data analytics on a firm’s unstructured data, to identify trends or flag anomalous results.

In short, AI is a vital technology when it comes to noticing the unnoticeable. It excels at drawing connections between data points. This is why AI cybersecurity has great potential to help businesses defend themselves, with AI systems capable of proactively identifying threats based on anomalous activity, and thereafter recommending courses of action for practitioners.

Cybersecurity powered by AI will also be necessary to counter the new wave of AI threats businesses face. This includes the use of AI for deepfakes and social engineering campaigns.

Risks of AI for business 

As with any technology, AI also has its drawbacks. From an employee perspective, AI-linked job cuts have been a recurring talking point over the last four years, particularly as systems become more capable at handling basic tasks.

While the prospect of mass job losses due to AI were a key concern during the early days of the generative AI boom, more recent research casts doubt on claims of a looming ‘jobs apocalypse’.

Research from Boston Consulting Group (BCG) in April 2026, for example, found that between 50% and 55% of jobs in the United States will be “reshaped by AI” by 2029. That’s not to suggest these jobs will be rendered obsolete, however. In fact, the technology will drastically change how workers operate on a daily basis.

Those figures align closely with similar research from Gartner, which predicts that millions of workers globally will be forced to upskill and reskill to compensate for AI, with roles also evolving.

AI also comes with the risk of making incorrect decisions or producing outputs that do not align with facts. Generative AI models are prone to ‘hallucinations’, a term used to describe confidently incorrect responses to user inputs.

These can be damaging to a company’s reputation if sent directly to customers or the wider public and can mislead decision-making if not identified at an internal level. The topic of AI hallucinations sparked fierce debate over the long-term potential of the technology across 2023 and 2024.

On a broader level, all AI systems are capable of inherent bias. For example, some hiring algorithms are biased against applicants from underrepresented groups, which can result in decisions that are ableist, sexist, or otherwise discriminatory.

Many developers have committed themselves to ethical and responsible AI development, with a goal to remove as much bias as possible and to ensure models are accountable.

Legislators have been targeting the potential risks of AI for some time, but laws are only just coming into place to regulate the technology. For example, the European Union (EU) has introduced rules designed to limit deployment of AI systems that are deemed to be high risk.

For these reasons, some businesses are choosing not to invest in generative AI for now with leaders in controlled industries particularly cautious over the potential for unwanted outputs.

Agentic security risks

With the arrival of agents on the technology market, cybersecurity experts globally have issued repeated warnings about the risks associated with these autonomous bots.

First and foremost, agents require deep access to mission-critical data to perform the tasks assigned to them by users. This presents significant risks with regard to identity security and governance.

Figures from the 2026 SANS Identity Threats & Defences Survey show that the number of non-human identities (NHIs) is creating huge challenges for security teams, with enterprises unable to keep track of the various agents that now work quietly behind the scenes.

Indeed, separate research from the Cloud Security Alliance (CSA) in March 2026 found that more than two-thirds (68%) of workers can’t accurately identify agent activity compared to human activity.

These internal agent-related risks are also mirrored elsewhere worldwide, with a host of incidents across 2026 placing agentic security firmly in the spotlight.

Within the space of a few weeks, OpenAI, Anthropic, and Meta all admitted that AI agents escaped sandbox test environments and even breached third-party organizations.

OpenAI, for example, revealed its agents escaped containment and breached AI model library provider Hugging Face.

Examples of AI in use today

Digital assistants are often how most people are exposed to an AI for the first time. The use of chatbots for customer services has expanded massively as these AI tools have improved, with generative AI having improved the detail of chatbot responses.

AI has also become a common feature in a range of popular applications and software suites. Take Microsoft 365 or Google Workspace, for example. Both companies have embedded AI features within these collaboration suites, with Google Gemini now a mainstay within Gmail, Google Docs, and other applications.

Microsoft, meanwhile, has integrated LLMs such from OpenAI and Anthropic within its productivity suite.

As mentioned above, AI can be fantastic for identifying anomalous, erroneous, or suspicious data across an organization’s estate. This has numerous applications, with AI systems able to chip away at laborious tasks night and day – with the right training – more accurately than human workers.

AI is already empowering security teams to turn the tide against hackers with enhanced protections against network or identity-based attacks, while generative AI tools such as Copilot for Security provide cybersecurity teams with summaries of attacks and suggestions for responses.

Leaders can also use AI to improve their supply chain through targeted analysis of inefficiencies and projections for how changes could improve overall operations.

The same process can be applied to business models to help leaders make data-driven decisions, or even for detailed weather forecasting based on climate data. On a larger scale, it is already being used by scientists to feed into decisions in the fight against climate change.

Another practical application of AI is in healthcare. The technology is being employed to analyze medical data, and diagnose diseases. The power of AI is the ability of these systems to sift through vast amounts of data about a patient and their condition, which would be impossible for a human clinician.

Already, preventative care for breast cancer is being revolutionized by AI. For example, Imperial College London is testing an AI system that can detect 13% more cancers than a human clinician alone.

In the near future, improvements in AI computer vision could allow devices to better guide blind or people with vision impairment. The same technology is already being used on a more basic level for autonomous robots on manufacturing floors, as part of the move toward smart manufacturing.

Applied AI vs AGI

Applied AI, also known as weak AI, refers to AI systems designed for a specific purpose. These systems excel at making recommendations based on historical data and processing vast amounts of information to provide more precise predictions or suggestions. This can include weather forecasting, for example.

Artificial general intelligence (AGI), meanwhile, is a term for a theoretical AI system that could equal or surpass the intellectual capabilities of a human. Such an AI would be self-aware and far closer to the kind of AI one sees in sci-fi movies.

If realized, AGI could pose a serious risk to humanity. When generative AI development picked up in late 2022, some called for a six-month pause in AI development so that innovation did not dangerously outpace legislation.

Some companies, such as OpenAI or Google DeepMind, are expressly seeking to achieve AGI. But at present, such models can only be found in academic papers and the realm of science fiction.

The future of AI

We have witnessed an unprecedented expansion of AI tools over the last four years. ChatGPT marked a step change in how AI is viewed, and crucially, used, by millions of people worldwide.

Across all sectors, AI’s transformative capabilities have reshaped the way we work, communicate, and interact with technology – and this transformation will continue as agent integration continues at pace.

While the technology delivers huge benefits, it also introduces significant risks and challenges. Concerns surrounding data privacy, bias, job displacement, and cybersecurity threats loom large, underscoring the importance of ethical and responsible AI development and deployment.

The spectre of super intelligent AI capable of outpacing human intelligence and autonomy also raises existential questions about the future of humanity.

As we navigate this rapidly evolving technological landscape, it is imperative to strike a balance between innovation and regulation, harnessing the power of AI while mitigating its potential harms.

Collaborative efforts across governments, industries, and academia are essential to establish frameworks that promote transparency, accountability, and inclusivity in AI development and deployment.

Looking ahead, the future of AI holds immense promise, yet it is also fraught with uncertainties. Continued research and investment are crucial to unlock AI's full potential while addressing its ethical, social, and economic implications.

By fostering a culture of responsible innovation and human-centric design, we can steer the future of AI toward maximum benefits and minimum risk.