Generative AI for Future Business Models and Transformation

Overview :

Gen AI may be one of the biggest technological leaps in history. It is poised to unlock new business models, transform industries, reshape people’s jobs, and boost economic productivity. In most machine learning models more than 90% never went into production. Their business goals are achieving use cases to implement using AI and automate that business to increase productivity. The governance model is important to reduce security attacks, governance around workflows, and automation. The risk will manage using a governance model that how it will train and make available to validate and apply trust and transparency. CEO and CIOs are mainly concerned with data quality, and data security while enabling or implementing these generative AI use cases to transform businesses to stay ahead and competitive in industries.

Create a Generative AI Model :

Clients and customers are getting started and willing to implement these use cases in their businesses and industries. There is a huge sense of urgency around the world on Generative AI solutions and every CEO and COO is willing to see or implement Generative AI solutions to increase productivity and customer experience to stay ahead in the market. Foundation model and Generative AI capabilities are enabled products and tools by different companies. The open sources model is key to the success of these projects in smaller spaces where companies are willing to put these use cases on a smaller scale at the enterprise level that will build these solutions and transform the business.

ChatGPT brought everyone’s attention to the Generative AI model where ChatGPT is costly to build and run these computing and costly against the solution when we are looking at these applications scale at business and enterprise levels.

Generative AI evolved as we progress and research the different phases of AI, machine learning model, Deep Learning, Generative AI, and foundation model. (Figure -1) Governance – Trust and transference are basic layers that seat across these AI generations. The product and tools that scale at enterprise-level AI with governance at heart will be going to shine in AI Market.

These use cases are popular in today’s business model and continue to lead use cases :

·?????Summarization

·?????Content Generation

·?????Question – Answering

·?????Code assistance and generation of code/program

·?????Chatbot

·?????Grammar correction

·?????Test insights

·?????Classification

·?????Data Creation

·?????Help to back-office team where the client is looking to reduce manual administration.

·?????Defect detection at different construction places, roads, bridges, and airport runways.

Developer resources, tools, and techs are useful to try the use cases that will help to execute the program and build these use cases using different technologies that are available in the market at an enterprise scale ready. The nature of architecture is based on a large language and generative AI model which is trusted with a governance model.

Trust and transparency are key for this architecture solution where the business is really looking enterprise-level partner who can help to build these use cases with 100% confidence in architecture decisions and data governance.

1. Gen AI in Customer Care Services :

One of the most popular use cases in today’s market is customer care, all companies are looking to bring more automation, better customer experience, and 360-degree feedback that help to improve productivity, margin, and better market insight. Customer care services are common in every industry such as distribution, retail, pharmacy, insurance, and medicine field. It enables to provide 24 x 7 x 365 days?of service for every consumer and user.

2. Gen AI in Demand & Supply Chain:

During covid and post covid, demand and supply chains are one of the major issues and concerns in the global economy and it’s become a nightmare to every business across the globe. All companies are trying to transform their demand and supply chain that help them to sustain and top position in their industry. The industrial and automotive industry is a major concern for demand and supply chain which depends on more than 50 to 100 tier 1 and 2 contractors and suppliers.?Gen AI will help to better predict inventory as well as demand in the market through market insight and customer buying behavior.?I worked on my research and build use cases using open AI technologies that bring Gen AI model in demand and supply chain solutions to transform demand and supply chain.

3. Gen AI in Marketing :

Marketing is key in today’s competitive global market. As we transform from Global marketing to digital marketing, Gen AI is a major play in the digital market where understanding customer behavior, buying pattern, and user personas will better help to keep the inventory and adjust the marketing strategy based on market demand is key in today’s digital market. Also, customer-first and real-time 360-degree feedback from customers made available to the customer and consumers in 1 click user experience is key in today’s global market environment. I did my initial research and some prototype that enables these use cases with trust and transparency and 100% secure data governance.

?4. Gen AI in Information Technology :

Gen AI tools are implemented to generate automated code generation and conversation using different Open AI tools. It helps to perform migration and modernization of monolithic codes into microservices with 80% accuracy?of code. Code conversion from Java monolithic to microservice or COBOL, mainframe to Java microservice patterns. It will help to reduce 80% of the development lifecycle and increase the productivity of development and operation support.

AI value, capabilities, and Ethics are important when you consider ROI.?Financial returns on investments in traditional AI are edging up.AI spending is increased year by year from $1.5 B to $2.8 B by 2024 against overall IT spending over revenue.

Conclusion :

Generative AI in powering better customer experience for enterprise business needs. Gen AI on a hybrid cloud platform is a key solution to building robust, secure trust and transference solutions for enterprise-ready AI solutions for different business use cases. CIOs and CEOs are big competition fear to implement these AI models to increase productivity and stay ahead in business to provide better customer transformation and user experience.

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About Author :

Anil Patil has 25 years of IT experience in different roles such as developer, senior architect, Chief architect, and solution leader. Anil is worked on different large clients in the banking financial, insurance, and telecom domain. Anil has built different solutions and implemented a foundation model for Generative AI use cases using a hybrid cloud platform with different tools and technologies.

If any questions or help with different use cases, please reach out by email at [email protected]