This article was written by Anil Kumar Arora, pursuing the Executive Certificate Course in Corporate Governance for Directors and CXOs course from Skill Arbitrage, and edited by Koushik Chittella.
Table of Contents
Introduction
Data gets accumulated on a day-to-day basis in multiple forms during an organisation’s operations. This data may pertain to product/service quality, sales, service, after-sales service, customer service, finance, accounts, and many more. It may also come from customer behaviour or internally from the operations of different departments.
In today’s competitive world, each enterprise is struggling to gain and improve its foothold. Every organisation is grappling to increase its customer base by giving customised, suitable offers and services to convert and consolidate its customers. Despite massive volumes of data in hand, organisations have been capable of doing little to augment their market size and achieve their goals. Computers and manhours are being used to analyse and deduce these data figures to derive conclusions to facilitate the achievement of their goals, to be utilised to further the customer base, revenue, and market size. Such activities take a long time and expensive efforts, but still, the results remain restricted and constrained. Managing and leveraging this strategic asset to derive business objectives remains a major challenge.
Artificial intelligence: meaning
The importance of data has grown exponentially. In 2022, the IT industry came up with a concept that analyses data and produces results that could converge the focus and vision of business enterprises and catalyse operations. This concept works as the brain of the organisation, supporting their vision of enhanced presence and trust. This concept is called artificial intelligence.
In other words, artificial intelligence analyses the provided input and creates output or results within a predefined strategy and a set framework of rules and guidelines. It does not produce anything novel or new results, but, in a better understanding, produces results from its analysis or creates a setup for a framework or a guideline for smart decisions to accelerate and streamline the operations. It refers to systems designed to work on a particular set of inputs. Artificial intelligence excels in task-specific applications. It works as a predictive analytics tool and improves efficiency across industries.
Generative artificial intelligence: meaning
Generative artificial intelligence is a derivative of artificial intelligence but represents the next generation in generating output. Generative artificial intelligence, or GAI, is capable of generating and transforming results, producing similar, improved, and advanced output on the basis of input information.
Generative artificial intelligence creates new and original content using neural networks and by identifying different patterns and structures within existing data. Generative artificial intelligence is capable of creating structured & organised output from arbitrary and asymetrical data or input on the basis of algorithms. It is also capable of generating new results or specifications from basic data by merging and evaluating a large number of different ideas to produce improvised ones. It even generates new and creative content based on a variety of inputs. These inputs can be text, sound, images or videos, animation, 3D models, any type of analytical data, or even computer codes.
Generative artificial intelligence enables companies to offer personalised products and experiences to their customers Generative artificial intelligence is fast emerging as an advanced and transformative force in data-based decision-making.
The key difference between artificial intelligence and Generative artificial intelligence
The key difference between AI and GAI lies in their capabilities and applications. Artificial intelligence solves specific tasks with predefined rules. Generative artificial intelligence uses unsupervised learning and generative models. It mimics the fundamental patterns of existing data to produce new output that resonates with the original source. Generative artificial intelligence is built upon massive neural networks (LLMs) that have been organised on enormous datasets. While artificial intelligence and generative artificial intelligence have distinct functionalities, they supplement each other to provide powerful solutions.
For instance, AI analyses user behaviour data, and GAI creates personalised content for the user. This plays an important role in shaping our future with unique possibilities. In the rapidly evolving digital landscape, embracing these advanced technologies will be the key for individuals and businesses to stay ahead of the curve.
Requirements of a successful GAI model
The key requirements of a successful generative AI model are:
- Quality
- Diversity
- Speed
Uses and Applications of Generative Artificial Intelligence
There are various uses and applications of GAI in the industry, some of which are:
- In language:
- Marketing Content
- Note Taking
- Gene Sequencing
- Code Development
- Essay Generation
- In visuals:
- Video Generation
- 3D Models
- Design
- Image Generation
- Animation
- In audio / sounds:
- Music Generation
- Voice Generation
- In data input-to-output conversion
Synthetic Data: It is extremely useful to train AI models when data doesn’t exist or is restricted and unable to address requirements with the highest accuracy. The development of synthetic data through GAI is perhaps one of the most impactful solutions for overcoming the data challenges of many organisations.
Application areas of GAI
The application areas of generative artificial intelligence can be understood by categorising them as follows:
- Content Creation:
- Text
- Images
- Videos
- Codes
- Creative Fields:
- Design
- Music
- Research & Development:
- Drug Discovery
- Scientific Exploration
- Customer Service & Marketing:
- Chatbots
- Personalised Marketing
Benefits of Generative Artificial Intelligence and Its Uses in Businesses and Organisations
Some key benefits of using generative artificial intelligence in business include:
- These algorithms are used to create new and original content.
- These algorithms are used to improve the efficiency and accuracy of existing AI systems.
- They are used to explore and analyse complex data.
- Generative artificial intelligence algorithms save time and resources for an organisation by helping it automate a variety of tasks and processes.
Generative artificial intelligence has the potential to give a significant thrust to a wide range of industries and is an important area of artificial intelligence research and development. It can create new content, insights, and ideas, catalysing innovation across diverse sectors.
Senior business professionals are increasingly and rapidly realising that generative artificial intelligence has the potential to revolutionise business models and transform the world of work. Generative artificial intelligence has the capability to make humans better at work and work better for humans.
Benefits of Generative Artificial Intelligence in a Senior Professional’s Growth
Generative artificial intelligence analyses data sources to generate detailed descriptions of required tasks, responsibilities, and skills. Generative artificial intelligence enhances the personalisation of the customer experience, customer service, fraud detection, supply chain management, predictive maintenance, business process automation, and many more. Generative artificial intelligence behaves like an architect, giving new multi-dimensional suggestions and propositions based on fundamental parameters. A senior professional controlling and leading a business or an enterprise uses this powerful tool to implement a new approach with new outcomes to deepen its hold and achieve business objectives.
Generative artificial intelligence empowers senior professionals in:
- Accelerated creative processes and Brainstorming
- Enhancing market research, Ecosystem trends & data analytics
- Rapid prototyping
- Personalised content
- Streamlines workflow
Key Areas to implement GAI for Senior Professionals
These are the key areas for a senior professional to implement GAI:
- Strategic Planning: Identification of potential disruptions, market shifts, and new opportunities.
- Research & Development: Exploration of novel product areas and refinement of concepts.
- Marketing & Sales: Creation of highly targeted campaigns and outreach.
- Operations: The optimisation of processes and identification of areas for automation
- Training & Development: Creation of engaging and personalised Training modules
Important Considerations while implementing GAI
While implementing GAI, the following are important considerations:
- Data Quality: It is important to verify the quality of the data being fed to the GAI.
- Clear Objectives and Scope: While implementing GAI, it is crucial to know what the business wants the AI to perform.
- Legal and ethical implications: Before implementing AI, it is important to check the regulatory framework, including IP, data privacy, etc.
With proper guardrails in place, generative AI can not only unlock novel use cases for businesses but also speed up, scale, or otherwise improve existing ones. Senior professionals, CEOs, and their teams reflect on the value creation case for generative AI and how to start their journey. Some may see an opportunity to leapfrog the competition. Senior professionals can use the imaginative outputs of generative artificial intelligence to spiral their businesses to achieve better team collaborations.
Lately, generative artificial intelligence models are being used to streamline searches. Models securely filter and extract all organisational documentation, like contracts, research reports, and business trend analysis, and can highlight important sections or clauses for ready reference. The excitement around generative AI is noticeable, and senior professionals can move ahead with thoughtful and intentional speed.
Caution and Misuses
Generative artificial intelligence poses a variety of risks, including:
- Algorithmic bias may be the outcome due to imperfect or incorrect input.
- Data and model outputs can lead to significant IP risks, including infringing legally protected materials (copyrights, trademarks, and patents).
- Privacy concerns could arise if, due to user-specific data input, output is in a form that makes any individual identifiable.
- It could also be used to create and disseminate malicious content such as disinformation, deepfakes, etc.
Conclusion
Generative AI has shifted far away from being a mere advanced tech concept. Today, senior professionals in organisations are actively implementing this technology to create generative artificial intelligence applications that lead to business transformation, innovation, growth, and better scalability. This helps and supports senior professionals and their businesses in their perseverance to be and remain ahead of their competition. From creating and completing videos to expediting coding and enhancing chatbots, the generative artificial intelligence use cases are continuously expanding.
References
- www.upgrad.com
- https://www.wns.com/perspectives/articles/articledetail/1121/generative-ai-a-catalyst-for-data-driven-gro
- https://www.cognizant.com/us/en/aem-i/generative-ai-future-of-work
- https://www.nvidia.com/en-us/glossary/generative-ai/#:~:text=Generative%20AI%20enables%20users%20to,or%20other%20types%20of%20data.
- https://www.linkedin.com/pulse/innovation-realized-focus-unlocking-potential-generative-harvey-lewis/
- https://www.pecan.ai
- https://hbr.org/2023/10/navigating-generative-ai-as-an-older-worker#:~:text=Understand%20the%20Opportunity&text=This%20AI%20can%20then%20use,size%2C%20orientation%2C%20and%20purpose.
- https://www.turing.com/resources/generative-ai-applications
- https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/what-every-ceo-should-know-about-generative-ai
