From Data to Insights: How Generative AI is Optimising Operations of Every Industry?
Though the history of Generative AI can be traced back to Hidden Markov Models and Gaussian Mixture Models developed in the 1950s, it took the world a toll when ChatGPT was introduced in 2022.
Within a week of launch, this AI language-based model that generates original content in response to user prompts crossed a million users worldwide. That was one of the major breakthroughs in mainstream consumer usage of AI and large language models, as per Sam Altman.
From content curation to code writing, from research to sales departments, and from manufacturing to the healthcare sector, every industry has started exploring the best use cases and optimizing the capabilities of tools based on generative AI like GitHub Copilot, Stable Diffusion, DALL·E 2, GPT-3, after the popularity of ChatGPT.
Let’s explore in detail how generative AI is poised to impact different industries, along with cautions that must be taken care of in detail:
- How is generative AI transforming businesses at each level?
- Impactful Industry Use Cases for Generative AI: Revolutionizing Operations across various sectors
- Addressing Key Challenges for Successful Enterprise Adoption of Generative AI
- Is Generative AI a fad or here to stay?
How Generative AI is transforming businesses at each level?
Till now, after millions of prompts, ad promotions of newly generated tools and services, and analyses by researchers, we can summarise benefits into the following listicle:
· Market research: Futuristic trends, competitive analysis, and consumer behavior can now be easily studied and analyzed
· High-Quality Content: Relevant, grammatically correct, and highly researched content production has become less challenging
· Accurate Data: Raw data is collected from various resources and human interactions to ultimately generate accurate data with minimal mistakes and correction probabilities
· Efficiency booster: Less time and fewer resources are required for better outputs and improve the productivity of people and processes
· User Experience: Automation and Human-like interaction is unmatched
Impactful Industry Use Cases for Generative AI: Revolutionizing Operations across various sectors
Be it healthcare, finance, manufacturing, or any other industry, generative AI-based tools have touched some or the other way in every industry. Let’s explore its impacts:
FINANCE INDUSTRY
Finance industry basically deals with “Money”. Every enterprise be it related to banking or fintech, face two major issues- Fraud and Risk Management. Both of these challenges can be resolved with generative AI tools like Forter, Simility, Fedzai, etc. as they help in inspecting huge transaction databases, and finding anomalies by comparing the historical and present data to indicate fraud.
Additionally, generative AI can automate manual repetitive tasks like database entry, data matching, segmentation, etc. for the purpose of statutory compliance, hence avoiding the scope of penalties. Several other tasks like credit scoring, portfolio management and trading can be efficiently done by new-age AI tools as they would help in analyzing credit history of people, market data, and returns on investments
HEALTHCARE INDUSTRY
The impact of generative AI in healthcare is still mostly on paper and rigorous trial sessions and experimentations are conducted for their respective authenticity. From medical image analysis, drug discovery, and clinical decision support to cancer and neuro diseases detection, generative AI has found its implications in every major healthcare domain.
For example, Generative Adversarial Networks (GANs) are able to generate synthetic medical images that can be used to train and improve medical imaging models, Variational Autoencoders (VAEs) are used for novel drug molecules generation that can be further tested for potential drug development.
Additionally, personal treatment plans, assistance in clinical decision-making, and medical chatbots are also used in the healthcare sector which is based on generative AI algorithms.
MANUFACTURING
Generative AI can improve the efficiency and productivity of both machines and people in the manufacturing industry. Firstly, these new-tech tools can be utilized for better product design and avoiding specific constraints.
Further, it can help in the early detection of machine failure and errors by analyzing the sensor’s historical data. Thereby, saving time and cost for repairing, thus enhancing the performance of equipment.
Not just machines, but Generative AI can handle supply chain management, automate manual processes, predict demand, optimize inventory levels, and reduce the waste included to boost the efficiency of people working in manufacturing industries.
REAL ESTATE
Generative AI is slowly getting traction in the real estate industry although it has applications from property valuation to pricing optimization domains.
Most real estate investors are benefited from generative AI as they can calculate the value of a property based on location, size, and other conditions. Also, they can find the best properties based on their search history and preferences.
Not only investors but tenants and landlords can utilize generative AI to find optimal rent amounts depending on the location and facilities present around the property. There is one interesting application of generative AI utilized by real estate agents and brokers creating customized marketing campaigns.
Some of the applications that are common to every industry and be utilized for optimizing their revenues and growth are Research or R&D of market trends, competitive data, and futuristic innovations; Automating operations from data generation, execution plans, documentation, analysis; marketing, sales, and customer support by creating personalized strategies, sales emails and pitches, communication plans and analyzing gaps.
Addressing Key Challenges for Successful Enterprise Adoption of Generative AI
Generative AI-based applications are still at a nascent stage and have several challenges which must be addressed for optimum utilization:
· Data quality and data quantity: To train generative AI models, enterprises require high-quality large volume data ensuring that the data available has the potential to resolve their stated challenges
· Integration with existing systems: Enterprises that are already functioning with their customized systems and software might encounter the integration challenge as it requires specific architecture and infrastructure.
· Ethical and legal considerations: Some AI tools might create issues related to copyright infringement, creation of misleading content, and issues that might lead to ethical and legal penalties
· Rate on Investment: Though AI solutions are cost-effective, for specific industries and resolving specific challenges, enterprises might have to pay the heavy cost required by Generative AI to adjust with software, hardware, and human resources
These challenges can be addressed when enterprises equip themselves for generative AI innovation.
Is Generative AI a fad or here to stay?
Generative AI is here to stay. The technology has already demonstrated significant potential in various industries, and ongoing advances in machine learning and natural language processing are likely to drive further growth and innovation in the years to come.
Preparing yourself for Generative AI requires properly evaluating- functions and domains of enterprises that would be impacted by implications, policies, and legal standards related to the application and its outcomes, limitations of AI models, systems for cross-checking the outputs generated by AI applications, and accepting the fact that machines can be wrong like humans.
