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Generative AI

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Generative AI, also known as Generative Artificial Intelligence, is an innovative subset of 
artificial intelligence that focuses on creating new and original content. Unlike traditional AI approaches that rely on existing data, generative AI models generate novel outputs that resemble human-created content.

By utilizing advanced algorithms and deep learning techniques, generative AI models can analyze vast amounts of data and capture underlying patterns and structures. This understanding enables them to generate new samples that exhibit similar characteristics and distributions as the input data.

Deep Learning Resurgence in 2010s with introduction to Generative Adversarial Networks (GANs) and development of transformer models, such as the Bidirectional Encoder Representations from Transformers (BERT) have given a significant fillip to generative models. From rule-based systems to deep learning models, Gen-AI has come a long way with copious amounts of data, enhanced computational power and advancements in neural networks.

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Examples of applications with generative models

 

State of Generative AI

The global generative AI market witnessed a significant growth trajectory, with a market size of USD 7.9 billion in 2021. It is projected to reach USD 110.8 billion by 2030, exhibiting a robust CAGR of 34.3% from 2022 to 2030. North America dominated the generative AI market, holding a market share of over 40% in 2021. The Asia-Pacific region is expected to experience substantial growth, with an estimated CAGR of approximately 36% from 2022 to 2030. Notably, China attracted considerable private funding, amounting to USD 17.21 billion in 2021. In terms of components, the software sub-segment dominated the market, accounting for over 65% of the total market share in 2021. The media and entertainment sector proved to be a lucrative end-user, generating a revenue of US$ 3.3 billion in 2021.

Panel I: Generative AI Market Size

Generative AI Market Size

 

How Companies are utilizing AI Applications

Companies are building language models into their products and customizing them to their unique context and offerings. These language models need to become more trustworthy (output quality, data privacy, security) for full-fledged adoption. The new stack for these applications centers on language model APIs, retrieval and orchestration, but open-source usage is increasing too. Today the stack for Large Language Model (LLM) APIs can feel separate from the custom model training stack, but these are blending together over time. Language model applications will become increasingly multi- modal. Some examples of LLMs are GPT-4, ChatGPT, GPT-3 by OpenAI, Bloom by BigScience, LaMDA by Google, MT-NLG by NVIDIA/Microsoft and LLaMA by MetaAI.

Industry/Sector Use Cases

McKinsey's Global Survey on AI (1,684 participants in organizations adopting AI in at least 1 business function; reference period 11-21 April 2023) titled “The state of AI in 2023: Generative AI's breakout year” found that organizations are increasingly adopting Gen-AI. Sales and marketing, product and service development, and service operations, came out to be the most reported business functions where Gen-AI tools are being deployed.

Some sector, industry specific use cases of GenAI

 

GenAI – Deployment after a thoughtful consideration

Feasibility analysis of implementing GenAI in organisations should consider integration and scalability issues with a cost-benefit analysis of potential benefits against incurred cost (say enhanced customer acquisition outweighing computational infrastructure), availability and quality of data with computational resources and lastly, the expertise and skill set in deploying efficacious generative model engendered business solutions. Considering the above factors, organizations can look to implement the below mentioned variations of Generative AI models (in increasing order of complexity and cost implications) –

Deployment after a thoughtful consideration

 

Governance issues in Gen-AI with respect to, inter-alia, regulatory compliance, bias, ethics, data privacy and explanability are other considerations for full-blown adoption of these tools.

Panel VI: Governance Issues - Gen AI

Governance Issues - Gen AI

 

Governance Issues - Gen AI

 

Our point of view – actions for management consulting firms in particular and businesses in general

The note above summarizes briefly the ethos of GenAI in transforming the business landscape. This transformation however is fraught with risks and requires a careful deliberation into the cost-benefit framework of implementing GenAI . In particular, focusing on business consulting services, GenAI. In particular, focusing on business consulting services, GenAI has the potential to offer deep data driven analysis and insights to improve decision making, generating AI-created simulations for scenario planning, publishing automated reports and BI dashboards, drafting reports and streamlining communications using NLP, and client risk assessment. Nonetheless, the human touch of empathy, creative thinking, emotional quotient, trust and ethical awareness are focus areas that the organizations should be mindful of in implementing GenAI solutions.

Protiviti, a global consulting firm, boasts of an adept data science team that has delivered multifaceted projects across different sectors and business and government clients, leveraging state-of-the-art machine learning models.

Panel VIII: Why Protiviti:

Leveraging Our Experience on Data Science

Protiviti India Member Private Limited is the India Member Firm of the global network of Protiviti firms, a group of independent consulting firms helping companies solve problems in finance, technology, operations, governance, risk and internal audit.  Protiviti Member Firms are separate and independent legal entities, are not agents of other firms in the Protiviti network, and have no authority to obligate or bind other firms in the Protiviti network.

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