Let us go back a bit in history to the first Industrial Revolution to frame the context for this question better. The revolution occurred in late 18^th century England and was responsible for the rapid urbanization & industrialization of the west.

For the first time, machines were able to provide labour much more efficiently. As a result, incomes more than doubled, and England became the Industrial powerhouse of the world. By the early 1900s, more than 70% of the world’s textiles were manufactured within 40 kilometres of Manchester. The global south which missed this opportunity is playing catch up even today.

I guess we have a similar opportunity today. Generative AI is transforming our world in ways that were not thought possible just a few years ago. AI is no longer just a tool for business.  It is going to be the very engine on which businesses will deliver value to their customers. The one thing that is different from the Industrial revolution is that this technology is more democratized. It is available for anyone willing to use it and makes the situation both exciting and terrifying for the enterprises.

Let us analyse some reasons why organizations today might adopt generative AI, including some potential risks.

Automation: Generative AI can automate a wide range of tasks, from data analysis and pattern recognition to content generation and customer support. Automating these tasks empowers human employees to focus on a higher level of tasks that require human creativity and ingenuity.

Hyper-Personalization: Generative AI can analyse vast amounts of data to deliver personalized experiences to customers and increases customer satisfaction and loyalty. By leveraging AI-generated content and recommendations, you can create targeted campaigns and customized user experiences that cater to individual preferences and needs.

Innovation & Differentiation: It is all not just an efficiency game with generative AI. It can also power innovation and differentiation in ways that can leapfrog your business ahead. Some of the use cases that can be explored are as below:

1. Drug Design: A 2010 study showed that the average cost of taking a drug from discovery to market was $1.8 billion, of which the drug discovery costs represented a third, and the discovery process took three to six years. Generative AI is being to design drugs within months at a fraction of the cost.
2. Material Science: Generative AI is impacting the automotive, aerospace, defence, medical, electronics, and energy industries by composing entirely new materials targeting specific physical properties. The inverse design process defines the required properties and discovers materials likely to have them, rather than relying on serendipity to find a material that possesses them. The result is to find, for example, more conducive materials or greater magnetic attraction than those currently used.
3. Chip Design: Generative AI can use Reinforcement Learning (RL) to optimize component placement in semiconductor chip design (floor planning), reducing product development life cycle time from weeks to hours.
4. Synthetic Data: Generative AI can be used to create synthetic data, which is a class of artificial data that is generated rather than observed from real data. This helps protect the privacy and is turning out to be invaluable, especially in training healthcare models.

Of course, like any new technology, it has its set of problems; hallucinations – the unexpected falsehoods generated by the model, ownership, and security issues that weigh heavily on enterprises.

The technology is rapidly and exponentially improving, so we can expect major leaps beyond the current set of capabilities over the coming weeks and months. In fact, the hallucinating tendency of GPT-4 is significantly lower than GPT-3.

Security is a concern especially when using the API capabilities of these tools. However, it is no reason to delay the usage. Rather, be prudent about the type of data that is being used.

It will be a good idea to implement guidance on the responsible use of generative AI through a curated list of approved vendors and services, prioritizing those that strive to provide transparency on training data sets and appropriate model usage, and/or offer their models in open source.

From an organizational perspective, the initial focus will be efficiency with accuracy. It is about doing tasks faster. Over time, the generative AI models will constantly learn and get better and they will go about doing things better. Ultimately it will be able to not only do things faster and better but also re-invent business models to take advantage of newer opportunities.

Nothing is more powerful than an idea whose time has come – Victor Hugo. Generative AI is the defining technology of our times and will change the world in ways that we cannot yet imagine.  GPT-4 and Dall-E are just the beginning, the capabilities of these tools and others like it will grow exponentially.

Bottomline: Confronted with this reality, businesses should embark on this journey with Generative AI immediately in a prudent and responsible manner. This may be the single most important decision for business(es) to sustain leadership positions within their domains over the coming decades.