GenAI is transforming various domains including software engineering. This technology enables high productivity, rapid development, and abundant innovation promising overall productivity gains of 26-30%. Embrace the future and discover the extraordinary potential of GenAI.

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What’s inside

1. An overview of GenAI’s impact on the software engineering industry
2. A detailed prognosis of GenAI’s impact on the software development lifecycle
3. Tools and useful technologies for GenAI-assisted software engineering
4. Future trends in GenAI for Software engineering

Key insights: How GenAI can impact software engineering

GenAI is expected to boost productivity in software engineering by 28% and is projected to improve task completion speed by 54% by 2025. It can also increase application development and maintenance revenue by 25%-30%, leading to a 5%-6% improvement in overall profitability. The GenAI market is expected to grow at a CAGR of 42% over the next decade, and the technology can help address the expected shortfall of 4 million developers by 2025. However, detailed study and implementation strategies are needed to consider factors such as initial installation effort, resource training, and technology integration.

GenAI’s impact on the software development lifecycle

GenAI can significantly impact the various phases of software development by generating code, improving code quality, simplifying code structures, and automating tasks such as unit testing and bug detection. It can reduce the duration of task execution across various software development stages by 20%-45%, resulting in substantial cost savings. While software engineers may be the primary beneficiaries of GenAI, other teams, such as architects, consultants, and sales teams, can also derive substantial advantages. However, implementing GenAI requires a comprehensive strategy that includes tool selection, investment, deployment, and developer training. The implementation strategy should target the highest-leverage phases of software development, and enterprises should invest in resources and training necessary for developers to leverage the technology effectively. Download Complete Research

Tools and useful technologies for GenAI-assisted software engineering

Integration of GenAI with DevOps principles can increase productivity by automating tasks, generating content, and providing intelligent insights. Adopting GenAI in the DevOps software development environment can further boost productivity through script generation, automated monitoring and alert generation, and synthetic data for pipeline load testing. GenAI can complement and enhance Low Code No Code (LCNC) capabilities by integrating visual developer interfaces, quickening development cycles, and creating text and multimedia assets. Key platforms for code generation using GenAI include Copilot by GitHub, CodeWhisperer by AWS, ChatGPT by OpenAI, Vertex AI by Google Cloud, and TabNine by Codota Dot Com Ltd.

Future trends in GenAI for software engineering

As AI continues to evolve, GenAI will become an increasingly essential aspect of the software development process. GenAI provides opportunities for innovation and creativity while also presenting new challenges. Customized benchmarking contextualized to the customer environment is crucial in refining and optimizing GenAI tools, fostering greater productivity and efficiency in software development. Enterprises must formulate a comprehensive GenAI strategy and incorporate GenAI tools in the implementation and testing phases of the software development lifecycle to maximize development cost and effort savings and improve quality. Collaboration with GenAI tools will create new roles, such as Prompt Engineers, and allow developers to focus on strategic thinking and creative problem-solving. GenAI is also an excellent learning and training tool, automating the generation of educational content and assisting in information retrieval and organization. Download Complete Research

GenAI can lead to faster development with a 28% increase in task completion speed, which is expected to reach 54% by 2025. Additionally, documenting code functionality for maintainability and writing new code can be accomplished in 50% less time. Developers using GenAI-based tools for complex tasks had a 25% to 30% higher likelihood of completing the tasks. Finally, using AI tools for testing processes resulted in a 31% increase in completion, and this is estimated to rise to 57% in 2025.

Credits
Lead Authors@lab45: Hussain S. Nayak, Anju James
Contributing Authors@lab45: Vinay Ramananda, Dattaram B A

Manufacturing is becoming more smarter, efficient, precise, and sustainable by adopting IIoT, AI, Robots, Blockchain, and 5G for operations optimization. Manufacturing business trends are enabling flexible & transparent supply chains, customer-centric & agile production, ecosystem collaborations, and new business models.

What's inside

1. Key Takeaways
2. Key drivers shaping trends in manufacturing
3. Business Trends driving innovation
4. Technology Trends aiding the business

Key takeaways

The convergence of advanced technologies with labour, supply chain, and demand challenges is driving full automation. Nearly 84% of manufacturers have adopted or are considering smart manufacturing. Manufacturers are exploring tech-enabled ecosystem partnerships, reshoring, and factory-in-a-box model to address supply chain instability. AI, IIoT, Big Data & Analytics, Robotics, 5G & Edge Computing are enabling data collection, pattern identification, and prediction for process optimization and efficiency improvement. AI in manufacturing is expected to reach $115 billion in 2032 globally. Blockchain is ensuring supply chain and ecosystem security. Driven by regulations and environmental commitments, manufacturers are adopting technologies to reduce emissions. On-demand production, mass customization, and subscription-based products are enhancing customer experience. Download Complete Research

Key drivers shaping trends in banking

• Ensuring supply chain resilience and navigating market demand uncertainty are manufacturers’ main requirements amid natural disasters and geopolitical tensions. AI and Data Analytics are key to overcoming disruptions and predicting demand.
• Manufacturers are prioritizing automation to deal with skilled labour shortage, manage rising costs and enhance efficiency while maintaining quality.
• Sustainability is another priority for manufacturers, focusing on responsible materials sourcing, environment-friendly production, and safe waste disposal. Customer preference is changing, and they are seeking sustainable products and processes.

Business trends driving innovation

• Dark factories fully orchestrate processes using automated machines while operators remotely monitor activities, enhancing performance and resource efficiency.
• The product-as-a-service business model opens new revenue streams for manufacturers while lowering the upfront costs for customers.
• Global data exchanges & platforms allow data sharing between ecosystem partners while protecting proprietary data.
• Green manufacturing is getting propelled using digital technologies and adopting sustainability policies.
• Reshoring and micro-factories have gathered pace due to wage increases, supply chain disruptions, and the advent of digital technologies.
• Prescriptive maintenance identifies patterns, performs root-cause analysis, and recommends the best maintenance approach, lowering running costs and downtime.
• Customized product manufacturing is being enabled by additive manufacturing and flexible manufacturing approaches.
• Advanced environment-friendly materials are gaining traction resulting in enhanced performance, high wear resistance, and high thermal stability of products.
• These trends impact various stakeholders, including manufacturers, suppliers, workers, and customers.

Technology trends aiding the business

• AI is transforming manufacturing through real-time machine operation optimization, problem prediction, demand forecasting, cost modelling, product quality inspection, robot navigation, etc.
• IIoT is key for prescriptive maintenance, real-time production and machine monitoring. 5G provides high bandwidth for reliable connectivity between IIoT devices, offloading computation-heavy tasks to edge infrastructure.
• Robots & Cobots enable production line process handling, intelligent material handling, and warehouse automation.
• Digital Twin enables the convergence of information technology (IT), operational technology (OT), & engineering technology (ET) in manufacturing.
• Blockchain allows for tracking materials & goods through the supply chain, inventory management, & warranty management.
• Secure and dedicated private networks, Blockchain, and AI are increasingly used to safeguard critical manufacturing operations from cyberattacks.

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Credits
Lead Authors@lab45: Parag Arora
Contributing Authors@lab45: Hussain S Nayak

GenAI is changing the banking industry through work automation, individualized customer experiences, and fraud detection. Operational cost savings from using GenAI chatbots in banking globally is 35 times more than not using GenAI. GenAI helps banks increase productivity, lower expenses, and enhance customer happiness.

What's inside

1. Key Takeaways
2. Reimagining banking through GenAI
3. GenAI transforming banking landscape
4. Challenges in GenAI implementation and their solutions

Key takeaways

Among industries globally, GenAI could add about $ 3.5 trillion annually in productivity on average, out of which the banking sector would be nearly 8 per cent. Banks are starting with applications in software development, chatbots and media content generation. GenAI has vast potential to execute business and technology processes autonomously. The operational cost savings from using GenAI chatbots in banking reached $7.3 billion globally. It is 35 times the operational savings without using GenAI. The integration of GenAI with virtual assistants has significantly enhanced customer support and experience. GenAI enables banks to automate crucial processes such as customer onboarding, fraud detection, and risk management. As a result, employees can concentrate on more intricate tasks, such as delivering exceptional customer support. The banking sector can significantly benefit from this, leading to an overall increase in efficiency. Download Complete Research

Reimagining banking through GenAI

Empowering customers through GenAI

• GenAI enables banks to provide customized products, personalized financial advice enhancing the banking experience for customers.
• GenAI automates and streamlines banking processes, reducing wait times and enhancing customer convenience.
• GenAI helps banks strengthen security measures, protecting customers from fraud, unauthorized access, and identity theft.
• AI-powered chatbots and virtual assistants provide round-the-clock customer support, offering prompt and reliable assistance.
• Through efficient processes, enhanced security, and seamless support, GenAI enhances overall customer satisfaction with the banking experience.

GenAI transforming banking landscape

Corporate banking, retail banking and software engineering are the most value-creating functions with each providing a value of about $ 50 billion. The rest of the functions include wealth management, asset management, risk, IT and finance and HR. Download Complete Research

The diagram below shows the impact of GenAI on banks' functional architecture

Future trends in GenAI

GenAI is in nascent stage, but it has the potential for vast changes in banking. The following use cases are expected to prevail in future:

• Financial forecasting: As GenAI will be able to integrate and analyze data from various sources in future, it will be able to identify data patterns and run simulations based on real-world context and hypothetical scenarios. It can help banks to make effective financial forecasts.
• Generate financial advice for customers: Training GenAI on customers’ financial goals, risk-taking ability, income, expenditure. It can be used for budgeting recommendations. Also, it can help in smarter investment and wealth management and trading advice.
• Minimize manual paperwork: Manually analyzing financial documents is costly and time-consuming. GenAI can be used to summarize large documents and significantly cut operational costs.
• Regulatory code change advisor: GenAI will be used to make developers aware about underlying regulatory changes that will require them to change code. It can assist in automating the changes and providing documentation.
• Manage risks and credit worthiness: GenAI will be used to create a more accurate picture of borrowers after analyzing vast amount of data from multiple sources.

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Credits
Author@lab45: Poonam Pawar, Hussain S Nayak