A human feedback-based reinforcement approach is vital to ensure that outputs remain relevant and ethical without compromising accuracy
Generative AI, commonly known as GenAI, has rapidly evolved from basic context classification to cutting-edge generative capabilities in natural language conversations. When applied to text data, these models establish contextual relationships among words across documents, enabling them to understand and respond to user queries effectively. Furthermore, GenAI can simulate alternative scenarios and conduct “what-if” analyses, facilitating informed decision-making for businesses. To enhance GenAI outputs, specialized inputs, known as prompts, are used to emulate human conversations, effectively making English a new programming language.
GenAI has expanded its capabilities to various media formats, including images, audio, and video. This versatility has broadened its applications to designing and producing new components, materials, and even pharmaceuticals. GenAI has gained popularity among individuals, leading to the development of automated workflows. Gartner envisions GenAI as a General-Purpose Technology (GPT), indicating its potential application across different business levels.
Gen AI adoption by industries (use cases)
Immediate opportunities for GenAI encompass various sectors, each benefiting from its unique capabilities:
Supply chain management:
GenAI’s potential shines in supply chain management by identifying the ideal supplier from a vast pool of options, considering factors such as pricing, delivery timelines, quantity, and quality.
Pharmaceutical and healthcare:
Within the pharmaceutical and healthcare sectors, GenAI finds niche applications. It aids in computer vision-based disease diagnostics, optimizes lead molecule generation for toxicity and synthetic feasibility in drug discovery through deep learning, and generates clinical reports and discharge summaries.
Wealth management:
Wealth management leverages GenAI for real-time data analysis to understand market trends, movements, and sentiment correlations. This insight aids in predicting future prices and optimizing investment strategies.
Market research:
GenAI benefits market research applications, ranging from product and service analysis, customer segmentation, the execution of targeted campaigns, and the automated handling of customer complaints to content generation for dynamic social media platforms.
Insurance:
In the insurance sector, GenAI streamlines claims processing, with a particular focus on external damage inspection using computer vision techniques. It also automates the underwriting of insurance policy documents and investigates claim documents for eligibility and policy adherence.
Fintech:
Fintech companies benefit from GenAI in areas such as fraud transaction detection, customer churn prediction, loan default forecasting, and credit underwriting.
Edtech:
Within the education technology domain, GenAI personalizes educational content based on individual learning abilities, enhancing the learning experience.
Data Security:
By leveraging GenAI for data anonymisation and synthetic data generation, organisations can better protect sensitive information, adhere to data privacy regulations, and reduce the risk of data breaches and privacy violations
These diverse applications highlight the versatility and potential of GenAI across a wide spectrum of industries, paving the way for smarter and more efficient operations.
Navigating challenges and opportunities in enterprise adoption
GenAI is expected to revolutionize the way applications are interacted with, and enterprises have initiated pilot projects to explore its potential. Coping with technological disruption necessitates a deep understanding of customisation and a quick response to new market conditions, with clear business objectives in mind. Customisation is a top challenge in adoption, and the cost can vary significantly depending on the use case, scale, and business requirements. Security, intellectual property protection, and privacy measures are crucial when adopting GenAI, often involving collaboration with vendors and partners. Compliance with regional regulations must also be anticipated and managed.
President
Product Engineering
Inspirisys Solutions
What does a successful Gen AI adoption entail?
A successful AI adoption strategy includes assessing data readiness across the organisation, confirming data reliability, establishing a data governance framework encompassing rules, processes, role delegations, privacy, and compliance, and implementing strategies for upskilling the workforce in AI-related skills.
Close monitoring of generated outputs and continuous feedback sharing can help prevent hallucinations, factual errors, and biased results. Implementation should occur in phases, prioritizing use cases with a clear return on investment and innovation potential. Starting with time-bound pilot projects on a “Learn and Iterate” basis provides the necessary experience and confidence to scale up adoption initiatives successfully. A human feedback-based reinforcement approach is vital to ensure that outputs remain relevant and ethical without compromising accuracy. Consistent evaluation of results, tracking business impact, and fine-tuning adoptions should be ongoing processes. Ultimately, stakeholder consensus, involvement, and support are crucial for a successful GenAI adoption journey for enterprises.
