How to make generative AI actionable for your business

In this blog, we go beyond the hype and explore real and actionable use cases for effectively harnessing the power of Generative AI.
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In November 2022, ChatGPT re-defined momentum for Generative AI, moving at lightspeed to one million users in only five days. The unrivalled uptake is reflective of the enthusiasm and curiosity we hold for Generative AI, but with new waves of technology also comes hype, and the challenge becomes finding value amidst the noise.

What is generative AI really?

Generative AI models use neural networks to identify the patterns and structures within existing data to generate new and original content, such as digital images, video, audio, text, or code. The big differentiator today is that we now have the computing power to ingest and interpret huge amounts of images and text, making systems that are therefore 'smarter' and more human, which helps adoption. It means that generative AI is perfectly positioned to help with content generation, productivity and operational efficiency, customer experience, and even product design.

Generative AI content generation for the win

The news is awash with stories of students getting caught using generative AI to generate everything from essays to photos to artwork. It’s raising questions around the validity and value of AI-generated content, as was demonstrated recently when a German artist was denied an award from an international photography competition after revealing that his submission was co-generated by AI.

However, from a business perspective, there is significant value generated when generative AI automates the creative process of content generation across day-to-day business. It can summarise or generate written content, or convert text into audio. It can generate images or videos, and it can even generate code.

Brands are using generative AI as a shortcut in the content creation process, ultimately saving significant production time and cost. A great example of a tool that is making a demonstrable difference is Jasper. Customers can describe in natural language what they want Jasper to write, and then it can use this input to produce any type of textual content.

It’s important to note that Jasper has been trained with data representing 10% of the web and fine-tuned for customer specificity, which is what will ultimately help companies win with contextual, relevant content.

How generative AI can solve the productivity predicament

Imagine an assistant that augments your workflow to search archives, generate emails, design interfaces, build presentations, summarise meetings, write emails, generate training videos, and more.

Generative AI is already helping many companies to increase productivity and improve results by leaps and bounds. Consider tools like Beauitful.AI: it’s able to create beautiful slides by listening to just a few words of user input. Fireflies is another productivity booster tool: companies use it to take online meetings to the next level by chatting with their meeting transcript to product meeting summaries and post-meeting emails and content.

From an operational efficiency perspective, we’re set to see integration between 'off the shelf' tools such as ChatGPT with proprietary data sets and knowledge bases shortcut processes and slash costs. This means companies will have equivalent teams of AI workers, changing the fundamental economics of certain industries, and giving them the ability capitalise faster on the unprecedented operational efficiency of generative AI.

Beyond Chatbots: How Generative AI can refine the Customer Experience

Chatbots are the best-known form of generative AI, and ChatGPT has made headlines for its ability to generate human-like, highly contextual responses. As a Large Language Model (LLM) ChatGPT is trained on a massive amount of input data to identify patterns, and in the future we’re set to see it become increasingly more specific in its responses, analysing individual customer’s data and combining it with business-specific domain knowledge domain to answer questions in a highly personalised way.

However, this isn’t the only way companies will use generative AI to create more personalised experiences for their customers.

Tastewise is one great example among many that highlights the importance of identifying and analysing priority customer data to produce a next-level customer experience. It is a powerful and innovative tool to transform the way companies approach product innovation and development. Real-time trend identification combined with Tastewise’s customer data enables food and beverage companies to identify emerging trends in real-time, and quickly adjust their offerings and marketing strategies to better meet customer needs. This not only helps businesses stay ahead of the competition but also allows them to create more personalised, inclusive experiences for their customers.

How generative AI is re-defining product design

Unlike traditional product design, where the process begins with a model based on an engineer’s knowledge, generative AI-based design begins with design parameters and uses AI to generate the model.

It allows product designers to display and compare design options in a way that enables engineers to quickly and efficiently find the ones that best meet a project’s parameters and needs. It also allows them to test new complex design iterations quickly, efficiently, and at scale so that they can drastically shorten research and development timelines for new products. They can also use generative AI to create complex designs like organic features and internal lattices to leverage the unique design freedom offered by additive manufacturing technologies.

By modifying the design parameters in an increasingly refined feedback loop, product designers can find highly optimised and customised design solutions to a wide range of engineering challenges, such as making product components lighter, stronger, and more cost-effective.

Making generative AI actionable

Savvy businesses can see beyond the hype, and they are taking a structured approach to identifying generative AI projects, validating them conceptually through experimentation.

Along with the hype, it’s also understandable that there is a bit of panic. Perhaps more than any other emerging technology, there are ethical questions, potential risks, and potential business model pitfalls surrounding generative AI. However, this shouldn’t stop organisations from getting started on experimenting. We have a unique window of time to begin playing with these tools to learn how to use them to our advantage.

Remember, innovation with generative AI is impossible without humans - it will only ever be as good as the data fed into it, and its human-led application. Generative AI itself is not creative, and it will also require people who can work with it to identify the needs surrounding it, customise solutions for its offerings, and navigate the complexities of the new efficiencies it introduces.

Beyond this, it’s also important to remember that while generative AI models have shown impressive results to excel in specific domains for which they have been trained, there is still further scope in what is known as Artificial General Intelligence (AGI.) This is a type of AI that can apply reasoning, problem-solving, and even social intelligence to a broad range of tasks and domains, just like humans. While AGI is still a hypothetical concept, its vast potential to increase the scope and capabilities of AI in business shows just how much more we can do.

With the right human-led direction, businesses have an exciting time ahead as they navigate generative AI and AGI to improve the customer experience and to generate, iterate, and get new ideas to market faster than ever before.

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