Generative AI: What is it and how is it being used?

Generative AI is everywhere. You’ve likely seen AI-generated images, chatbots writing emails, or search results that give more dynamic answers to layered questions.

However, while generative AI is taking off, trust is falling. Only 42% of customers say they trust businesses to use AI ethically, down from 58% just last year. Part of this shift might be due to increased awareness of the ways AI can be harmful if used unethically.

In this article, we’ll look at what generative AI is, how it’s being used right now, and what customers expect from the companies using it. Sprinkled in will be insights from our State of the AI Connected Customer report, based on responses from more than 16,000 consumers and business buyers worldwide.

Generative AI is a type of artificial intelligence that produces content (such as text, images, or music) by learning patterns from existing, available data. Generative AI models then produce content that mirrors the data it was trained on.

It’s important to understand the recent advances in generative AI capabilities since a wide variety of applications now rely on them. These days, you encounter generative AI whenever you interact with a virtual assistant or use a search engine.

Some of the most common platforms that use generative AI are:

• ChatGPT
• Google Gemini
• Claude
• Midjourney
• Canva
• Notion

McKinsey research also indicates that generative AI could add between $2.6 trillion and $4.4 trillion in value to the global economy every year, across a wide range of industries. Past the hype of AI, this huge shift points to a fundamental change in how work gets done, value is created, and customer expectations.

Generative AI learns from huge amounts of existing content, then uses that knowledge to produce a mash-up of the information it’s been fed.

Here’s how most generative AI tools work under the hood:

1. Trained on data: The AI is fed large amounts of content (like text, images, or code) to learn from.
2. Learns patterns: It analyses how the content is structured, like sentence flow, image composition, or coding logic.
3. Improves with repetition: The model runs through millions of examples to fine-tune how it responds.
4. Generates content: You give it an input, and it creates something similar to what it was trained on.
5. Refines with feedback: Developers and users help improve results over time by flagging errors or giving corrections.

Several key concepts and technologies enable generative AI tools to function:

These are the foundational elements of generative AI. Neural networks simulate the way the human brain processes information, allowing machines to recognise patterns and relationships within data.

Deep learning involves multiple layers of neural networks that can learn hierarchical representations. This enables the generation of complex and nuanced content across various data types like text, images, and audio.

Transformers are advanced models that help to understand context in human language. This is called natural language processing.

Many first-time users of tools like ChatGPT are impressed by the advances in transformers, which enable these AI tools to generate coherent and contextually relevant text as responses to human queries at speed. (Note: the GPT in ChatGPT stands for generative pre-trained transformer.)

VAEs study a bunch of paintings and then create new artworks in the same style. They learn the essence of the data (what makes a Van Gogh recognisable as a Van Gogh, for example) and can generate new items that resemble the originals. The output will be similar but not identical to the original dataset.

Please note: Always ensure that you don't train a model like this on copyrighted art without permission. Artists deserve credit, consent, and compensation, and using their work to generate knockoffs without these things crosses an ethical line.

Imagine two players in a game: a generator and a discriminator.

• The generator creates fake images and new data points based on a dataset
• The discriminator tries to detect real or fake images by comparing them to authentic data

Over time, the creator gets better at making realistic images to ‘fool’ the detector. Through this adversarial training process, GANs work to produce high-quality images and videos: AI-generated content that can potentially pass as genuine.

These models start with random noise and gradually turn it into meaningful data, like shaping a lump of clay into a sculpture step by step. They refine the data in stages until it becomes a clear and coherent output. Diffusion models have shown exceptional performance in generating high-resolution images and are gaining attention as a viable alternative to GANs.

Think of training a dog by giving it treats for good behaviour. Similarly, AI models learn the best actions to take by receiving rewards or penalties, helping them make better decisions over time. In this way, AI experts and engineers can shape the desired performance of AI models, fine-tuning them to achieve ever more specific goals.

For example, AI engineers can use reinforcement learning to reward models for adhering to ethical guidelines when training them.

Quantum computers are super-powerful machines that can process complex calculations faster than regular computers.

While still in the developmental stages, quantum computing has the potential to greatly enhance the capabilities of generative AI by handling larger datasets and more complex models more efficiently.

This could lead to significant advancements in processing power, pushing the boundaries of what generative AI can achieve while lowering environmental costs.

Generative AI models can perform tasks and create content across various domains by leveraging these technologies and concepts. The alignment of AI algorithm advancements and greater computational power pushes the boundaries of what machines can create. It’s this relentless progress within the field that is making generative AI such a potentially transformative force.

On the flip side of that same coin, the relentless progress and transformative power of generative AI are contributing to falling trust in AI .

• Consumers are concerned about data breaches and how their information is shared and stored, with 64% of customers believing that companies are reckless with customer data .
• Employees are worried about losing their jobs and livelihoods to AI, a concern that has the potential to rock global economies.
• Generative AI models have already been trained on copyrighted materials without permission or compensation, and artists such as writers, photographers, musicians, and designers are justifiably demanding laws that protect their work, likeness, and intellectual property.

Right now, there are no global guardrails in place. It is up to individuals and businesses to use AI responsibly and ethically. Doing so will help ensure generative AI’s remarkable abilities benefit humanity, rather than causing harm.

Sometimes, it’s best to show rather than tell. Explaining the technologies and concepts above is useful, but for many of us, it’s still abstract until we see it in practice. Here’s a snapshot of how different industries and sectors leverage generative AI.

In healthcare, generative AI contributes to diagnostics, treatment planning, and patient care advancements.

• Medical imaging: Generative models improve the quality of medical images, such as MRI scans, which enhance resolution and reduce noise. This improvement helps doctors make more accurate diagnoses.
• Drug discovery: AI models can generate potential molecular structures for new drugs , accelerating the initial discovery process. This is due to their ability to analyse vast amounts of data to propose new compounds that may be effective against specific diseases.

Improve efficiencies: AI can help relieve the burden on healthcare professionals by assisting with some administrative tasks. In fact, 87% of healthcare leaders believe AI can help reduce burnout issues. However, while 40% of customers are comfortable with AI scheduling appointments, only 26% are comfortable with AI giving medical advice, showing there’s still a strong preference for human expertise in sensitive scenarios.

Better imaging and AI-driven, personalised treatment plans lead to better healthcare overall. Doctors recognise AI as a tool they can leverage to boost their service while still being a people-centred profession.

Generative AI also reduces the time and resources required to develop treatments and manage patient care. These efficiencies translate into significant long-term cost savings for healthcare providers and patients.

Yet, even in healthcare, there are drawbacks. First, there is still a question of reliability. We must thoroughly validate AI models to ensure they provide accurate and safe recommendations. Even though humans are fallible, many of us would still prefer to rely on human expertise over an algorithm when it comes to health outcomes.

There’s also a data privacy issue. Handling sensitive patient data requires strict compliance with privacy regulations.

Customers across the ANZ region are increasingly expecting quick, efficient, personalised customer service interactions.

• AI-powered chatbots like ChatGPT can handle customer questions and offer personalised recommendations. These can feel like ‘real’ conversations, thanks to the latest advancements.
• AI can generate email replies based on customer queries, making communication more efficient for all stakeholders.

AI agents can simultaneously provide 24/7 support to many customers. When done well, this improves customer satisfaction. Since many companies have access to customer data, AI can offer tailored solutions. Even though you’re talking to a ‘robot,’ the interaction feels more real thanks to the hyper-personalisation made possible through access to data.

Formula 1’s use of Agentforce provides a powerful example of what this can look like in practice. F1 fans can now solve common issues like login problems on their own, and service reps get AI support drafting quick, on-brand replies. With those productivity gains and connected customer data present during every interaction, the F1 support team has reduced their response times by 80%.

However, public sentiment around AI agents is still mixed. Only 32% of customers would choose an AI agent over a person for faster service, and only 26% would share personal information with an AI agent. About a third remain undecided, highlighting the uncertainty customers feel towards AI-based support.

Companies in the finance sector are in a race to develop tools that use generative AI to predict market trends. They’re also using it to improve access to personalised advice.

• AI models generate trading strategies by analysing historical data, aiming to optimise investment portfolios.
• Generative models simulate various financial scenarios to help institutions assess potential risks (and prepare accordingly).
• AI chatbots can offer financial advice to customers, boosting access to financial advice.

AI can provide insights that lead to better financial strategies, both for corporations and individual investors and savers. With incredible computing power, AI models can complete calculations and simulations more efficiently and accurately than humans can imagine.

However, public trust remains a barrier. Only 30% of customers feel comfortable with AI providing financial advice, and only 17% are comfortable letting AI make financial decisions on their behalf.

On top of this, markets can be unpredictable. Over-reliance on AI models may be risky due to unforeseen events. For instance, how can these models predict another pandemic that results in a black swan event? Another drawback is that these financial AI applications are currently experiencing unclear regulatory issues.

For more information, read our white paper: A Guide to Generative AI in Financial Services . It explores the significant ways AI activates growth, impacts risk management, and improves customer satisfaction in the financial sector.

We’ve referred to specific limitations within each sector above, but there are broader challenges to AI that we need to address as generative AI continues to evolve.

Generative AI creates new content based on its training data. As such, the new content reflects the training data. So what if the data is flawed in some way? Logic follows that the output will also be flawed.

The most prevalent issue related to this is bias. Biased training data can lead to unfair or discriminatory outputs. Consider the repercussions if biased data is used in AI tools related to hiring or lending.

Of course, AI professionals see the solution in better AI. Implementing responsible AI practices, such as bias detection algorithms and diverse training datasets, can mitigate this issue.

Training and running advanced AI models require significant resources. ChatGPT costs more than $100,000 a day to run alone.

However, developing more efficient algorithms, utilising cloud computing resources, and potentially accelerating investment in quantum computing research may reduce some of these demands.

The creation of deep fakes and the alarming rise of disinformation – the spread of deliberately malicious content. Generative AI has lowered the barriers of entry for those looking to sow discontent, and this poses risks to our society.

But sometimes, AI can be harmful even without malicious intent. For example, what would happen if a security camera used facial expressions to identify a scenario as ‘safe’, but in this case, it was not? Who will be responsible for the outcome?

Determining responsibility for these AI actions is complex. When AI systems make mistakes, it's unclear who is accountable — the AI developers, users, or the AI itself.

On top of this, only 42% of customers now trust businesses to use AI ethically , down from 58% in 2023. That trust is slipping, and generative AI’s misuse is partly to blame.

Governments are starting to rein in the AI wild west. It’s often the case that technological advances outpace government reactions. Regulation is needed worldwide to establish ethical guidelines and to curb some of the more harmful uses of AI.

We see this in Australia, where new laws have been passed to combat deepfakes . Legislators are working toward a broad, all-encompassing Australia AI Act to protect users and prosecute bad actors.

So, what would it take to restore trust? According to our recent report , 42% of customers say they would trust AI more if companies provided transparency into how it’s used, 35% if there was human validation of outputs, and 32% if they had greater control over how AI is used. Built-in protections, government oversight, and third-party ethics reviews also rank high.

Using large amounts of data raises privacy concerns; collecting and processing personal data can infringe on individual privacy rights. Some of the best use cases for generative AI fundamentally rely on data collection, yet a staggering 64% of customers believe companies are reckless with their data.

One solution is for companies to comply fully with European privacy laws, such as GDPR.

Further, to gain public trust, companies should ensure transparency regarding data management practices. In some cases, customers can opt in or out of data collection, understanding that they know how their data can be used by opting in.

Australians are understandably concerned about losing their jobs to AII. This worry isn’t unfounded. A recent report suggests that 1.3 million Australian workers might lose their current jobs to automation through AI between now and 2030.

On the other hand, there will be other opportunities, such as a growing need for AI tech workers, with more than 200,000 needed by 2030 , but this largely depends on businesses’ state of AI readiness, which isn’t in great shape.

This is a complex issue, with the potential for global economic repercussions. There’s no easy solution to the risk of job displacement due to AI. The calls for reskilling programs (steering workers to positions that require human skills or interaction) are already outdated.

The balance lies somewhere within government oversight and protections, and businesses prioritising the use of AI as a tool for human employees, rather than replacement. This, in turn, involves training employees on the use of AI systems and having a clear vision and strategy for the use of AI within the business.

Soul Machines , based in New Zealand, creates digital humans powered by generative AI. These virtual assistants can interact with customers in a natural and engaging way. Each avatar has a specialism, so customers choose a digital human based on their specific needs.

In their own words: “Soul Machines’ AI Assistants provide personalised one-on-one, judgement-free coaching and support anytime, anywhere, for free.” So, you can check with Robin to discuss financial advice, practise your Spanish with Elena, or prepare for a job interview with Kai.

By signing up, customers benefit from access to a wide range of experts. Sitting down regularly with a financial advisor can be a significant outlay. The AI avatars essentially act as a middle ground between sitting face-to-face with a career advisor and doing the research yourself.

Trained on vast datasets, these avatars convey the necessary information via personalised conversations, improving customer satisfaction.

From the business's perspective, they can provide unlimited service providers (AI avatars) without the limitations of human staffing. The only limits relate to the technical complexity of creating the avatars and any computational resources required.

A second limit or concern would be ensuring that safeguards are in place to respect user privacy and that the AI behaves appropriately.

Nevertheless, this reflects an innovative way in which a company is leveraging generative AI to offer customers a new, in-demand service, at least for those who aren’t put off by the uncanny valley aspect of conversing with a humanoid form.

Generative AI is becoming increasingly accessible, with various AI platforms and tools available for individuals and businesses. Here are some popular options to explore:

• ChatGPT by OpenAI: An AI language model that can generate human-like text. It can help draft emails, write code, do maths or even compose AI content like poetry. It's a prime example of how transformers enable natural language processing.
• DALL·E 3 image generator: Also from OpenAI, this tool generates images from textual descriptions. It's now integrated into ChatGPT. Artists and designers use DALL·E 3 to bring creative concepts to life quickly and easily.
• Google Gemini: A conversational Google AI service that assists with research, drafting content, and answering questions across a wide variety of topics.
• Salesforce Einstein AI: Integrated within Salesforce's CRM platform, Einstein uses AI and machine learning to deliver predictions and recommendations. Businesses can leverage Einstein AI to enhance customer experiences, automate tasks, and make data-driven decisions.

If you don’t feel like diving straight into these generative AI solutions without deepening your understanding, plenty of resources are out there to help.

• Consider AI and machine learning courses on Coursera .
• Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig is a great book offering a comprehensive guide to AI principles and practices.
• Salesforce’s free Trailhead course allows you to build AI skills at your own pace.
• If you’re super keen, dive into the latest research papers on transformer models and GANs.

AI Stack Exchange and GitHub are two online communities where you can browse forums and access repositories related to AI.

The potential of generative AI is vast and continually expanding. Here are some of our predictions for where the technology may lead:

• There will be even more personalisation in areas like education, healthcare, and marketing. AI systems can generate content and recommendations that meet specific needs based on more data analysis.
• AI models are expected to become even better at understanding and generating realistic human language. This will make interactions with virtual assistants and chatbots more natural.
• Combining generative AI with virtual reality (VR), augmented reality (AR), and the Internet of Things (IoT) could create immersive and interactive experiences. For instance, AI-generated environments in VR could adapt in real-time to user inputs.
• Policymakers and industry leaders will need to collaborate to establish guidelines and regulations as generative AI becomes more prevalent.
• Companies will rise to meet the challenge of computational and environmental costs, developing more sustainable practices.

All of this is happening against a backdrop of rising public expectations. Sixty-one per cent of customers believe that AI advancements make it more important for companies to be trustworthy. That trust will shape how widely and successfully generative AI is integrated into our lives.

If you’re ready to start planning your next steps, watch our video below on how to build your own AI strategy.

How to Build Your AI Strategy

AI has long been integral to the Salesforce Platform. For example, Einstein AI technologies deliver more than 200 billion daily predictions across Einstein 1, helping businesses close deals faster, provide AI-powered human-like conversations for frequently asked questions, and better understand customer behaviour.

Now, with Einstein GPT, you can create personalised content at scale, from sales emails and service replies to marketing copy and auto-generated code, all powered by trusted data from Data Cloud and tailored to your business.

Watch the Einstein AI demo to see this and more.

For more insights like the ones featured in this article, explore the full State of the Connected Customer report. Inside, you’ll find more data-backed perspectives on AI, customer expectations, and the future of business from over 16,000 global respondents.