Generative.AI

AI is a broad term that refers to any machine that can learn and perform tasks that would typically require human intelligence. 

Unlike traditional discriminative AI techniques, which improve decision-making by analyzing data, Generative AI can create new content, such as text, images, audio, and video, therefore augmenting human capabilities. 

It does this by learning patterns from existing data, then using this knowledge to generate new and unique outputs.

Generative AI produces highly realistic and complex content that mimics human-like creativity, making it a valuable tool for many industries. 

Generative AI is generally prompted by text or speech, using natural language, before it can create new outputs and prompt windows to appear in a lot of applications (search engine, office tools, creativity tools…). 

Click through the below images to discover a brief history of AI leading to Generative AI.

Increased Productivity 

Generative AI can automate many tasks that are currently done by humans, freeing up time for people to focus on more creative and strategic work. 

Improved Quality 

Generative AI can produce high-quality content that is more consistent and accurate than human-generated content. 

Innovation & Creativity 

Generative AI creates new possibilities for innovation. For example, it can be used to create new products and services, improve existing ones, and automate tasks. 

Text Generation 

Create new texts for blog posts, news articles, IP and even creative fiction. This could save a lot of time in compiling memos and presentations. 

Image Generation 

Create new images with only a few words of text input. This could be highly efficient in adding illustrations, icons and other visual aids to documents, digital sites and presentations. 

Video Generation 

Using text-to-video, this process allows the generation of videos with realistic AI avatars and voice-overs in minutes, including realistic characters, objects and backgrounds. 

Audio Generation 

Using a process of deep learning, generative AI will create new audio content such as music, speech (e.g. voice-overs) or any other type of audio content (e.g. audio effects). 

3D Generation 

Creating 3D assets is extremely time-consuming. Generative AI can speed up the process by creating realistic 3D models, shapes and textures, based on learned data patterns. 

Coding 

Users have shown preference in using ChatGPT and Bard as a coding starting point to generate a block of code based on a prompt, to assist with code solutioning, and to improve or update a current code block. 

Generative AI technologies are based on “foundation” models, designed to capture the statistical patterns and semantic relationships present in a massive amount of data (the GPT-3 LLM used to build ChatGPT foundation model was trained on more than 500 billion words). 

Once a foundation model is trained, it can be fine-tuned for specific applications or tasks. 

When using Generative AI tools like ChatGPT, Bing, Bard, Midjouney, users asks questions through a prompt. The foundation model is used to analyze and understand the input. The model retrieves relevant information from its knowledge base, and generates a response based on the understanding of the context and the relevant information retrieved. 

The current market landscape of generative AI is rapidly growing, with new players, applications and tools emerging all the time. 

The market is expected to reach $100 billion by 2025.

Here are some of the key drivers we see supporting the growth of the market: 

• Increasing availability of data and computing power, including the emergence of quantum computing; 

• Growing demand for individualized customer experience journeys; 

• A further need to automate tasks and processes; 

• The increasing focus on rapid growth, innovation and creative prowess. 

The golden rules for a successful implementation include Acculturation, Experimentation, and Adoption:  

• Acculturation is educating the organization about these technologies. It should be accompanied by a long-term change management phase. These technologies will replace certain jobs but will significantly enhance others, such as creative and engineering professions, which previous technologies could not boost in the same way. 

• Experimentation is mandatory in evaluating the potential of these technologies within a company’s ecosystem and prioritizing the initial use cases. While some companies have halted all activities due to data security fears, tools are evolving fast, notably driven by their integration into cloud hyperscaler platforms, to ensure safe experimentation. 

• Adoption involves implementing basic and high-value use cases on a larger scale, accompanied by change management and transformation. It requires setting up an agile operating model for an efficient delivery, capable of adapting to the rapid evolution of AI technologies. Business disruption, bringing about competitive advantage, will include leveraging the broad range of tasks generative AI can address, where preceding AI technologies were focused on specific use cases. If these new technologies create numerous opportunities for value creation, their sustainable adoption will necessitate evaluating and mitigating the associated risks. 

The progressive, far-reaching, and inevitable, spread of Generative AI technologies will have a profound impact on the way companies are organized. 

Whatever the degree of adoption within the company, these technologies will appear intrinsically, since they will be natively embedded in collaboration tools, office automation tools, search engines, specialized software, etc., resulting in the automation of tasks that are today carried out by employees, as well as an “augmentation” of other activities. 

Companies that deploy these technologies more extensively will be impacted in a more disruptive way, to create more value and competitive advantages. 

As long as they define, prioritize and assess the use cases they want to deploy, companies will update their strategic workforce planning to take into account new jobs directly linked to AI (ML engineers, prompts engineers,etc.), but will also take into account the impacts on the rest of the teams to anticipate the disappearance of certain jobs, the need for training or recruitment of others. 

To date, there has been little feedback on how companies have organized themselves in the face of the widespread deployment of Generative AI and disruptive technologies. However, we can assume that: 

• agile methodologies will remain the most appropriate in terms of software development, given the speed of evolution of Generative AI technologies, which render the performance of previous generations obsolete in a matter of weeks;

• as Generative AI models are much broader than traditional AI models, the "one use case / one model" approach that prevailed with discriminative AI could create a risk of "siloing", limiting the scope and disruption of Generative AI. What kind of organization should be put into place to avoid this pitfall? Do we need to involve more businesses/departments when designing a use case, or do we need to go as far as centralizing foundation models' build and run in a single dedicated team, serving the company's various businesses and tools? 

Potential risks 

To avoid repeating the errors of the past, there is a growing awareness surrounding the risks attached to generative AI. Here are some of the most common risks: 

• Data leaks: proprietary data can leak from the company if they are used to train generative AI models.

• Misinformation: as generative AI draws its data from existing sources, we need to curb the propagation of fake news and other forms of misinformation.

• Bias: generative AI can perpetuate the biases of the data they are trained on.

• Deepfakes: the combination of universal translation and generative AI in video and audio, poses a serious threat to misuse in generating deepfakes.

• Job displacement: the automation of many jobs could lead to displacement in several industries.

• AI Hallucinations: unreliability of information.

• Usage of generative AI for next generation cyberattacks.

• “Shadow” AI: generative AI could be banned of certain companies / departments, but employees may use external tools, available in one-click, creating a risk of data leaks but also a risk to the decision-making process. 

Open questions 

• Liability: if 3rd parties utilize generative AI services, who is ultimately liable when those services are being misused to infringe upon consumer privacy? 

• IP ownership: who ultimately owns the IP linked to creative output where generative AI was a part of? 

• Licensing: should there be a licensing system similar as that to banking to indicate those who can deliver generative AI services and need to abide to rigorous standards and norms? In addition, should this be organized on a global scale? 

• Algorithm discrimination: how to prevent biases to creep into the AI learnings? 

Internal Compliance 

Several large companies are banning or restricting employees from using generative AI tools like ChatGPT. 

Beyond aligning to applicable laws & regulations, consider the following: 

• Issuing guidance that requires employees to review any output generated by generative AI for accuracy, appropriateness and usefulness before accepting it. 

• Prohibiting the use of generative AI for sensitive or confidential data or using a private version of the tool that does not share data with external parties. 

Working with subject matter experts, auditors and technologists to ensure the reliability and quality of the data and output generated by generative AI. 

Regulatory framework 

Generative AI activities has a patchworked regulatory framework : although no specific legal text rules it’s use, existing general principles of multiple regulations remain applicable to the use of generative AI.