Generative AI for business
What is generative AI?
Generative AI is artificial intelligence capable of generating new and original content, including images, text, music, code and even dynamically created video game elements.
Rooted in machine learning, this subset of AI examines patterns such as style, structure and aesthetics from vast amounts of existing data. When prompted, it generates new material that is based on the features of the training data but contains new, original elements.
This ability to learn and replicate patterns from existing data is what makes generative AI a valuable tool promising to drive creativity, efficiency and competitiveness in business.
Potential uses and applications of generative AI
Generative AI has applications in a wide range of fields and business operations.
Generative AI can create engaging, personalised and original content at scale. The most widely recognised tools in this field include ChatGPT, Bard and Copy.ai, although many more solutions exist. Examples of text generation outputs include news articles, social media posts, product descriptions, advertisements, code snippets, and even creative writing and poetry.
In addition to text generation, generative AI can create original artwork and offer new visual and auditory experiences through generated digital images, paintings, music compositions and more. Some more commonly known art generator tools include DALL-E, Craiyon and ArtBreeder. Examples of AI music generators include Aiva, Soundful and Riffusion.
Video game developers are using generative AI to drive greater personalisation of gameplay. The technology allows players to contribute to the creation process and generate their game levels, characters, and realistic narratives, leading to a more immersive gaming experience. Examples of accessible generative AI tools for this include Unity ML-Agents, Houdini and Charisma AI.
Product or service personalisation
Generative AI can tailor experiences for individual customers. For example, push product recommendations in e-commerce and offer personalised messages or user interfaces that resonate with each customer’s preferences. Examples of such tools include Adcreative.ai, Maverick, Lumalabs.ai and many others.
Innovation and product design
Businesses can leverage generative AI to facilitate design processes, create prototypes and simulations, and quickly generate and test product concepts. Some tools used for this include Stable Diffusion, Midjourney and Vizcom. By analysing market trends and preferences, AI technology can suggest features and designs that align with demand, leading to products that are more likely to succeed in the market.
Businesses can use generative AI tools, such as Augmentor and DataRobot, to synthesise new data points similar to the original data, to improve the accuracy of data-based decision making in business. Practical applications include object detection, text classification, sentiment analysis, anomaly detection and more.
Benefits of using generative AI in business
From content generation to data augmentation, AI has the potential to improve efficiency across your business. Integration can also lead to:
- cost savings – through automation of a wide range of tasks
- faster innovation – leading to a shorter time to market and improved revenue streams
- increased productivity – including streamlining of creative processes and operations
- enhanced customer engagement – leading to improved brand loyalty and growth
- better data-driven insights – improving decision-making across the business
Generative AI can also offer businesses a substantial competitive advantage, especially for early adopters leveraging technology to position themselves at the forefront of their industries.
Concerns around generative AI in business
As with any novel technology, generative AI can pose certain business risks. You should consider them carefully to ensure responsible implementation and usage.
Reliability and authenticity
AI tools frequently interrogate data from unknown sources to create text and images. This data could be unverified, used without consent, or originate from an inadequately governed source. As a result, the media generated by AI could be unreliable, misleading or deceptive. It could also lead to reputational and financial risks should the images or text be based on someone else’s intellectual property.
As generative AI learns from existing data and creates content based on human text prompts, there is a potential for significant bias. If not addressed, this bias could intentionally or unintentionally result in the technology generating unfair or discriminatory content.
There is no guarantee that the output generated through AI will meet the desired quality standard of your business. How well the system works for you largely depends on the source of data it uses. For example, an AI tool mining the internet for data using poor quality or irrelevant information may not generate an output aligning with your business values and brand identity.
The future of work is changing and the potential for generative AI to automate tasks may raise worries about job displacement. If you’re considering integrating generative AI into your systems, you could think about using the technology not solely to automate tasks, but to empower employees to do more than they could before, and make them more productive. Alternatively, you could realign personnel toward tasks requiring critical thinking and empathy. This approach will not only minimise the negative impacts, but it can also help prepare your business for future growth.
Privacy and data security
Generative AI relies on vast datasets, including people’s personal information. This method raises significant privacy concerns. Businesses must take steps to safeguard sensitive information and ensure compliance with data protection regulations before rushing to adopt novel AI.
To help with this, the Information Commissioner’s Office (ICO) has set out eight questions for businesses that are developing or using generative AI that processes personal data. Their guidance on AI and data protection and accompanying risk toolkit further offer a roadmap to data protection compliance for developers and users of such technologies.
Applications of generative AI are diverse and promising, but require stringent safeguards to ensure that innovation and ingenuity in your business are driven safely and responsibly.