Generative AI exploded into the mainstream in November 2022 with the launch of OpenAI’s conversational AI ChatGPT – but what the heck is it?
If the chatter around AI image generators, chatbots, and text classifiers or detectors has you scratching your head, you’re not alone.
Based on the headlines spinning by in search and social, it may seem that every business and individual is already deep in the generative AI game.
However, nearly 60% of US executives told KPMG recently they’re still a year or two away from implementing their first generative AI solution, and less than 50% believe they have the necessary technology, talent, and governance to implement generative AI successfully.
It’s still an emerging industry with plenty of first-mover and early-adopter advantage to spare.
So what exactly is generative AI, and what do the non-technical businesspeople among us need to know about it? You don’t have to be a developer or software engineer to benefit from beefing up your knowledge; in fact, having a working understanding of how generative AI works and where it can be used in business will only benefit professionals of all kinds as the space matures and new platforms and tools emerge.
In this post, you’ll learn what generative AI is, how it works at a high level, what it can do for your business, risks and limitations, and questions to consider while evaluating your company’s options for implementing generative AI in different ways.
What is Generative AI?
Generative AI is a subset of artificial intelligence (AI) that involves creating or generating new data or content, such as images, audio, video, or text, similar to what humans might produce. This is often done using machine learning algorithms trained on large datasets, making content generation scalable, faster, and more efficient than having humans perform the same tasks.
How is Generative AI Used in Business?
Companies use generative AI in various ways to scale processes, improve efficiency, cut costs, save time, and capitalize on emerging opportunities. However, as with any new technology, it’s essential that any new tools or platforms you introduce to existing workflows serve your company’s underlying strategy.
There’s been a knee-jerk reaction in some circles to use generative AI tools to replace human writers, editors, and other content creators. It’s an incredibly risky move for several reasons. For starters, Google is working hard on its AI content detection capabilities, and we have no idea yet how machine-generated content might be treated by the world’s largest search engine in the future.
Second, generative AI algorithms have no personal experience or unique perspective to share. They are devoid of empathy, conscience, or critical thinking skills. Human editors are still very much necessary to inject quality controls, emotional intelligence, and creativity throughout the content creation process. We’ll look further into the risks of AI content in the next section.
Still, companies use generative AI in many ways to enhance business operations. Here are a few examples:
- Content creation including images, videos, or text that can be used for marketing, advertising, or other purposes.
- Product design: to create variations optimized for specific criteria, such as comfort or aesthetics.
- Personalization such as making recommendations for individual customers based on their preferences and past behavior.
- Creative applications like creating new forms of art or music, or assisting human artists in the creative process.
- Simulating and optimizing complex systems, such as supply chains or logistics networks, improves efficiency and reduces costs.
- Virtual assistants and chatbots that can interact with customers and provide personalized recommendations, support, or information to improve customer support and engagement.
- Data augmentation; for example, generating synthetic data that can be used to augment existing datasets, improving the accuracy and robustness of machine learning models.
- Game development including creating new game levels, characters, or environments.
- Fraud detection, such as creating synthetic data mimicking fraudulent activity, which can be used to train machine learning models to better detect and prevent fraud in industries like finance and insurance.
- Scientific research, including creating new hypotheses, simulations, or models in scientific research.
- Language translation with improved accuracy and efficiency.
- Quality control and detecting defects or anomalies in healthcare, manufacturing, and other products.
- Music composition including generating new melodies or harmonies that can be used as the basis for new musical pieces for advertising and marketing materials.
Recommended reading: A Complete Guide to Wordtune, Your Personal AI Writing Assistant
How Does Generative AI Work?
Generative AI works by using machine learning algorithms to learn patterns and structures from large datasets of existing data, then using this knowledge to generate new data similar to the original data.
As a marketer, content creator, executive, or another non-technical professional, you don’t need to understand the intricacies of the technology powering the tools you might use in your business. However, a working knowledge of what’s happening “under the hood” will help you make better tool selections, understand the risks inherent to using generative AI for content creation, and use these tools and capabilities more effectively.
GANs
Generative adversarial networks (GANs) involve two neural networks trained together. One network generates new data, while the other network evaluates how realistic the generated data is. Through repeated training, the two networks learn to improve the quality of the generated data until it becomes indistinguishable from human-created content.
Variational Autoencoders (VAEs)
VAEs are a type of neural network architecture that learns to encode data in a low-dimensional space, allowing it to generate new data similar to the input data. VAEs are often used for image and video generation.
Autoregressive Models
Autoregressive models are a family of models that generate sequences of data one element at a time, where each element is conditioned on the previous elements. This approach is commonly used in natural language processing (NLP) tasks, such as language translation or text generation.
Transformer Models
Transformer models are a type of neural network architecture that uses self-attention mechanisms to generate new data. These models have been highly successful in natural language processing tasks, such as language translation and text summarization.
Reinforcement Learning
Reinforcement learning involves training a neural network to generate new data by interacting with an environment and receiving rewards or penalties based on its actions. This approach has been used to train AI agents to generate novel game strategies or to create new artwork.
Evolutionary Algorithms
Evolutionary algorithms involve generating new data through a process of mutation and selection, similar to the way that biological evolution works. These algorithms have been used to generate new images, music, and other types of creative content.
Generative AI Risks & Limitations Business Users Need to Know
The success of your company’s AI integrations depends entirely on how it’s used. The popularity of generative AI and buzz about it in the media might leave you feeling you’re the only one left on the planet who hasn’t implemented it yet in some way. However, this is the time to experiment, test different tools and platforms, and see how generative AI will impact your team, existing technology stack, customers, and processes.
Here are some risks and limitations to keep in mind as you weigh your generative AI options.
Data Quality and Bias
Generative AI relies on training data to generate new content, and the quality and bias of that data can affect the quality and fairness of the generated content. It’s vital for companies to ensure that their training data is representative and unbiased.
Overfitting
Generative AI models can sometimes overfit to the training data, which can result in the model being too specific and not generalizing well to new data. This can lead to poor performance and generate content that is too similar to the training data.
Computing Resources
Generative AI models can be computationally expensive and require significant computing resources to train and generate content. This can make it difficult or expensive for smaller companies to implement generative AI.
Intellectual Property, Copyright & Ethics
Generative AI can create content similar to existing works, raising questions about intellectual property rights. Companies should be aware of potential copyright issues when using generative AI to generate content.
Other Legal & Ethical Concerns
Generative AI can create content that is misleading, offensive, or harmful. Companies should be aware of potential legal and ethical concerns and ensure that their use of generative AI is consistent with applicable laws and ethical guidelines. Having a policy is the bare minimum; communicating that clearly to all relevant team members and providing the training and support necessary to execute are key.
Lack of Control
Generative AI can sometimes generate unexpected or uncontrollable content, making it difficult for companies to manage their brand and reputation. It’s important for companies to monitor and control the content generated by generative AI to ensure it aligns with their values and objectives. Having strong editorial processes and workflows in place is foundational to the success of your generative AI program.
Limited Applications
Generative AI is well-suited to certain tasks, such as image or text generation, but may be less effective for other tasks. Companies should consider whether generative AI is the best solution for their use case.
Technical Expertise
Generative AI requires specialized technical expertise to develop and implement. Companies may need to invest in training or hiring specialized talent to use generative AI effectively. Even when employees are using third-party platforms and out-of-the-box solutions, they need to understand why they’re using it and how to elicit the best possible output.
Questions to Ask About Your Generative AI Options
YES! Play with every AI tool out there. But no, don’t throw your team on it this week without a solid strategy, proper training, and a clear business case for adding a new tool to your stack.
As you assess your options, ask about each one:
- What is the business problem or opportunity we’re trying to address with generative AI? Are there other solutions that may be more effective or efficient?
- What is the potential return on investment of implementing generative AI in our business? How long will it take to realize the benefits?
- Do we already have a tool that can do this task?
- Does this AI tool’s output add real, measurable value for our team and/or customers?
- Do we have the talent in-house to use this effectively, or can we outsource it?
- What are the potential ethical and social implications of using generative AI in our business? For example, how will we ensure that the generated content does not perpetuate harmful stereotypes or biases?
- How well will this fit into our existing workflows, and are we accounting for any additional reviews and approvals?
- Who is ultimately responsible for ensuring all generative AI content meets our brand’s values and standards for messaging, visuals, fonts, placement, etc.?
- What changes will be necessary to fully realize the benefits of the model, and what costs are associated with those changes?
- When will we use AI content – and in what cases should it be avoided?
- Do we need to identify AI content as such to our customers or audience?
- How will we address any concerns or questions they may have about the technology?
- How will we measure the success of our generative AI implementation? What key performance indicators will we use to evaluate the model’s effectiveness?
- How difficult is it to roll back our process and remove this tool if it doesn’t work out?
By considering these additional questions, businesses can better understand the broader implications of introducing generative AI to their operations and develop a more comprehensive strategy for implementation.
Learn more:
