Generative AI and Innovation | Uses & Applications

Chamsa Arfaoui

Marketing Manager

Photo by Andrew Neel on Unsplash

How will generative AI shape innovation?

Unless you’ve been marooned on a desert island for the past 12 months, you will have been subject to the enormous hype around generative AI. The headlines have been mixed, oscillating between ‘the end of the world is nigh’ to proclaiming it will fix all of the world’s ills. Either way, they all agree that this new breed of AI technology will change the world. 

I see the future use of these technologies as being overwhelmingly positive—offering a host of advancements for the world, society and business. But we are still in the very early stages of applying generative AI to real-world use cases. Gartner claims that most companies using generative AI have less than 1% market adoption (with exceptions; see below.)

Before we get into the how, let’s cover the what. In simple terms, generative AI uses existing data to generate responses (text, visual, audio, etc.) to a prompt. A popular example is ChatGPT, a conversational text tool with 100 million monthly users just two months after it launched—making it the fastest-growing consumer application in history.

Photo by Levart_Photographer on Unsplash

Let’s address the elephant in the room; can business leaders use such technologies to drive innovation? And if so, what practical considerations must they be aware of before diving in? 

Can generative AI help you innovate? 

The short answer is yes. Here are just a few examples of how it will change the way we innovate and what we create in the process. 

Ideation

With the right inputs and prompts, generative AI can be a creative sounding board to develop novel ideas and stimulate different avenues of inquiry— it might even find innovations. Suggested use cases for the technology include developing new pharmaceutical drugs, for example. 

Generative AI will also be the basis for additional innovations once teams consider how to use the technology to disrupt their own markets. For example, think about TV networks where shows are created on demand, construction businesses that present architectural plans in minutes, education companies that offer lessons based on a learner’s ability and behaviour at that moment or healthcare companies triaging patients with Generative AI “doctors” monitoring their symptoms.

Prototyping 

Considering the need for speed in innovation, anything that helps you validate your ideas faster will help you get ahead. Generative AI could help by developing models and prototypes with simple text prompts. Another area of considerable focus is using generative AI for writing code for software – again potentially allowing your business to develop basic working prototypes without a whole software team working on it. 

Project management 

When you analyse the time spent on different business activities surrounding innovation (and most other business functions), much of it is spent on management—communicating with team members, updating documents, tracking progress, adjusting plans based on feedback, etc. All of which are prime opportunities for generative AI to assist with. Taking away much of the busy work frees your team to focus on creative/critical thinking or problem-solving. 

Decision making

Many of the generative AI tools are modelled on incomprehensible volumes of data. While this doesn’t make them automatically good at decision-making (especially when decisions need to factor in the squishy unfathomable nature of humans), they can be beneficial for analysing complex data sets to provide recommendations. 

But for now, generative models and their decisions should generally be augmented with human thinking. I recently heard about the launch of ‘AIsthetic Apparel”, a company built and run by GPT4. While two months in, it (GPT4) and its ‘human co-founder’ João ferrão dos Santos have raised $100k, some of its decisions haven’t been great. Decisions such as proposing a fee of 170,000 euros for a 30-day influencer deal to a prospective sponsor (which was quickly declined.) 

Again, the application here for innovation is helping to make faster decisions (e.g., moving forward with a winning experiment) and better decisions (assisting people in easily interrogating complex data sets.)

Practical considerations when using generative AI for Innovation

Due to generative AI’s vast applications and possibilities, I’ve provided a general list of questions to ask when using such technologies. 

What data was used to model the generative AI? 

How does the technology arrive at its decisions? How can you ensure the responses don’t infringe on Intellectual Property rights? Often these questions are too hard to answer because algorithms are ‘under the hood’ and data sets aren’t always publicly available or traceable. There is the emergence of Explainable AI (XAI) where (as the name suggests) anyone can comprehend how the generative AI arrived at its decision or output as a way of mitigating such issues. 

Is the generative AI biased or discriminatory?

How might that impact the output and how you use it within your business? 

Is the data you input into a generative AI secure? 

What are the implications of inputting sensitive data? Are your inputs more likely to be reshared because the model uses them to learn from? 

What liabilities are you open to if you use generative AI? 

Generative AI can hallucinate when there’s insufficient information to provide an answer. Like human hallucinations, the AI believes the answer is true and presents it as such, making it a minefield for human counterparts to know what is factual. Therefore you need to ensure your team is aware of the limitations.

Final thoughts

Generative AI can assist innovation teams in moving faster and making more informed (better) decisions. It can also be a creative sounding board and reduce time-consuming project management work so teams can focus on the highest-value tasks. I’m excited to see how this area of technology develops over the next year and is applied to innovation practices. 

Finally, It wouldn’t be a post about generative AI if I didn’t allow some of it to be written by Chat GPT, so here are its final thoughts on the matter; 

“Generative AI holds immense potential to shape innovation positively. It offers opportunities for creative problem-solving, prototyping, streamlined program management, and data-based decision-making. It can revolutionize industries like pharmaceuticals, personalized content creation, architecture, education, and healthcare. 

However, it should augment human thinking rather than replace it entirely. Considerations for data sources, biases, security, liabilities, and user awareness are crucial. Generative AI represents a transformative force that empowers businesses to unlock new frontiers and drive positive change.”