Generative AI is forming a new economic ecosystem, reshaping the behaviour of key players in the IT industry, generating opportunities for super-scalers, and unveiling numerous niches for startups. The outlines of this new IT landscape are emerging, prompting a closer examination.

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What’s inside

1. Introduction: The rise & impact of generative AI
2. The technology & business stack of GenAI
3. Business niches for GenAI startups
4. Future trends in GenAI
5. Appendix 1: Reality & expectations of GenAI
6. Appendix 2: Startups across the GenAI tech stack

Introduction: The rise & impact of generative AI

Generative AI has caused significant disruption, expanding its offerings and services well beyond traditional AI domains. This has led to an explosion of potential use cases for customers who aren’t AI experts. Unlike before, customers no longer require a team of AI experts, curated data, or precisely measurable outcomes to adopt AI tool and gain immediate benefits. The interaction with GenAI is so seamless and intuitive that the onboarding for new customers is frictionless, eliminating barriers to adoption and facilitating rapid technology spread. The high variability in potential inputs and priming of generative models allows for a diverse range of applications impacting nearly every imaginable aspect of people activities. This is a foundation of a new era of Artificial Intelligence.

In this primer we leveraged our knowledge of 50+ GenAI-related and VC-backed startups to reconstruct the technological stack of the forming GenAI space.

The technology & business stack of GenAI

Large tech companies are leveraging their existing technological and capital advantages to create the framework for the GenAI market landscape, which we are going to explore in this section.

While offering of the LLMs on the current scale and heavy focus on unstructured data are somewhat new, the other elements of the tech stack closely mirror those needed for any large computational modeling. Established companies in the field of traditional AI are at an advantage, as they can expand and repurpose preexisting software, infrastructure, and services. Download Complete Research

Business niches for GenAI startups

While large players are occupying a sizable portion of the GenAI tech stack, there remains more than enough room for GenAI startups to flourish. The landscape of AI and ML is continuously evolving, with new startups, technologies, and methodologies emerging regularly.

Bottom-right (AIOps): Here, startups may offer tools for easier adoption of LLMs, facilitating the initial process of customizing and implementing these models.

Ascending (Integration): Moving upwards represents the process of integrating LLMs into various applications and business operations. Startups could offer integration services, templates, or frameworks to streamline this, or build an entire end to end app for a selected market niche.

Moving left (Service platforms): As we move leftwards, the focus shifts from core LLM functionality to auxiliary services. This could range from platforms offering specialized training data, to marketplaces for LLM apps, to optimization tools. These firms may automate the need for certain experts.

This taxonomy can serve as a foundational overview for anyone looking to understand the current state of the LLM ecosystem. It’s also worth noting that the landscape of AI and ML is continuously evolving, with new startups, technologies, and methodologies emerging regularly. Let’s inspect each block in greater detail:

Future trends in GenAI

The future of the GenAI landscape is going to be defined by several processes:

1. Consolidation of major players
2. Rise in open-source adoption
3. Surge in service platforms
4. Expansion of skill marketplaces
5. Segment-specific applications
6. Regulatory oversight and standardization

Appendix 1: Reality & expectations of GenAI

While enhancing the users with great capabilities, the LLM-based service is neither a freebie, nor a cornucopia. Each implementation of LLMs carries its own advantages and downsides. In this section of the Appendix, we discuss what can and cannot be realistically expected from a GenAI model in each of the most popular use cases.

We start with primary properties of a pre-trained LLM model, underlying its strong sides and functionalities as well as build-in flaws. And we move to the current ways of augment LLM model to work around the flaws. Download Complete Research

Appendix 2: Startups across the GenAI tech stack

The table of 78 startups we have based our analysis on is presented in this section.
The states of startups are set to the August of 2023.

Credits
Author@lab45: Rinat Sergeev

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.

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

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

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