By Shardul Chauhaan, Business Head, India and APAC, LatentView Analytics
Artificial intelligence (AI) is a fast-paced and rapidly evolving field. But all innovations and
technologies have a hype cycle — some pop and fizzle, others are radically disruptive, and a
few generate awe and fear due to their enormous potential. Generative AI, or GenAI, is a
marvel that can be compared with the discovery of electricity or any other groundbreaking
innovation.
GenAI is a subset of AI that can generate content — text, images, music, etc. — similar to
the existing data it has been trained on. Often, people think of traditional machine learning
(ML) when they imagine AI. Traditional ML is adept at solving specific problems by using
explicitly programmed pre-defined rules.
GenAI, however, goes a step further and leverages deep learning to understand the patterns
and relationships from large datasets and uses that knowledge to generate original content,
images, codes, and human-like text. The promise and potential of GenAI far exceeds that of
what has been achieved by its AI predecessors.
What Is Powering GenAI?
The exceptional performance of GenAI is attributed to massive amounts of data, neural
networks, enormous computational power, and open source collaboration in the field. Data
is procured from various resources and combines structured and unstructured information.
Intricate neural network algorithms operate on the data to mirror the human-like capability
that can capture the nuances of large datasets. GAN, RNN, VAE, and Transformers are
frequently used neural networks. Many of these models have long been the foundation of
computer vision and natural language processing (NLP).
The training of such generative models demands significant computational power. Graphics
processing units (GPUs) and specialized hardware accelerators like Tensor Processing Units
(TPUs) are often used to produce more coherent and contextually relevant outputs. A prime
example of a GenAI model is ChatGPT, a creation of OpenAI, which pioneered the
Generative Pre-training (GPT) method, laying the foundation for ChatGPT.
Why, When, and Where: GenAI Use Cases
From content creation, virtual reality, product and industrial design, data augmentation, and personalized recommendations to healthcare, chatbots, and virtual assistants, the use cases of GenAI span multiple industries and sectors. The ability of GenAI to reduce workloads by automating mundane tasks means that businesses are keen on implementing this technology to streamline their processes.
With its enhanced data processing capabilities, GenAI can efficiently analyze vast datasets. It can identify patterns and anomalies in data and improve accuracy. In the e-commerce
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sector, GenAI can sift through large quantities of customer data and derive valuable insights
into their purchasing behavior. Such insights help e-commerce firms provide tailored
customer recommendations and enhance the customer experience.
GenAI is a win-win for businesses and consumers as it can solve various problems across
domains and industries. GenAI benefits businesses by unraveling new business models and
driving innovation. For instance, a marketing company can use GenAI to craft engaging
content and personalize customer interactions, while a healthcare company benefits from
GenAI’s data analytics capabilities for medical research.
GenAI is helpful in the legal profession for drafting contracts and performing research. In the BFSI sector, GenAI reduces time spent on trivial tasks and allows employees to focus on
value-added ones. GenAI is proving useful in many applications, be it real-time or offline
tasks. Because of GenAI, early adopters are already witnessing a significant boost in
productivity and profitability.
How to Manage Risks Associated with GenAI?
Though these are exciting times, GenAI also has its share of disadvantages. One of the
popular GenAI models, ChatGPT, is well-known for providing answers on broad topics.
However, these responses often tend to be inaccurate and entirely fabricated. Similarly,
biases in existing datasets can result in systematic prejudice, thereby exposing businesses to
legal and reputational risks.
Content attribution, plagiarism, and copyrights have become contentious issues as the data
used for training has been created by someone else, thereby violating copyright laws. For
example, Getty Images, the visual media company, is suing the creators of Stable Diffusion,
an AI art tool, for scrapping its content. As GenAI gains wider acceptance, complaints and
counter-complaints will be far too familiar.
Data security risks exist where confidential information can be leaked to the external world.
It can lead to a range of lawsuits or loss of proprietary information. The leak of sensitive
data on ChatGPT by employees of electronics giant Samsung is an apt example of
information loss in the absence of proper checks and balances. As a result, the company
temporarily restricted the use of GenAI tools on company-owned devices.
There is a growing fear and anxiety around job losses and AI taking over tasks from human
employees. GenAI will augment business processes in certain areas, disrupt others, and
pave the way for innovation. The misuse of GenAI in many other forms is bound to happen,
but the way forward is to build a better regulatory framework.
The Gateway to AI Democratisation
The rise of GenAI is primarily accredited to its ability to enable AI democratization.
Democratizing and adopting AI has been at the top of the minds of CDOs, CIOs, and top
executives when they embark on the digital transformation journey. However, they face
hurdles such as siloed data, organisational culture, lack of necessary tools and workforce,
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GenAI enables this process because it is accessible and doesn’t require specialized analytical or technical skills. This ensures that the technology is available to anyone, irrespective of educational background.
The intuitive and user-friendly interface in GenAI allows non-technical users to interact with complex data and obtain valuable insights in their preferred ways, thus empowering a wide range of individuals within an organization to augment their capability and skill sets.
GenAI essentially breaks down the invisible barrier between individuals from technical and non-technical backgrounds and empowers the organization and people.
Scaling up the AI Value Chain
While GenAI is making waves all around, the implication would be that all businesses are
eager to embrace and implement it. However, this is not the case, as a survey reveals that
only 30% of companies are ready to leverage the capabilities of GenAI in their workplace.
While businesses are under pressure to implement GenAI, many worry about the adverse
effects of GenAI as well.
Businesses that intend to use GenAI need to scale up the AI value chain. Because of
extensive storage and computational requirements, investing in the proper IT infrastructure
is necessary. Building and deploying GenAI models requires skilled engineers and AI
specialists who must be trained in this new technology and thoroughly understand data
science. The investments are high and should be done in small yet consistent steps.
Businesses also need to identify the business problems to solve and scope out the AI
initiatives. Such initiatives should boost at least one aspect of a business — either revenue,
profitability, or customer experience — and have a significant business impact.
“Nothing succeeds like success.” Once the success stories and benefits of implementing
GenAI is disseminated by one function in an organization, it will have a domino effect on
the remaining functions. It will quicken the pace and likelihood of adoption. Consequently, it will also tone down the fear and insecurity among employees about GenAI.
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GenAI will herald a new era of innovation, and businesses and customers will gain
immensely by leveraging its transformative potential. However, companies should also
consider the ethical and responsible deployment of these technologies.
Fairness, transparency, accountability, and privacy should be integral to the adoption
process to mitigate potential risks and challenges. Continuous evaluation, adaptation, and
improvement will help determine the right balance.
While GenAI has benefits and risks, it cannot be a reason to hold back on your deployment
plans. An adage states that one who hesitates is lost. A few businesses have boldly decided
to advance with GenAI and created an elaborate charter. Their AI capabilities, business
growth, and returns will grow exponentially. They will find that the goalposts are much
closer than they had envisaged. Others will be wise to follow or else perish.
