By Tapan Barman, Co-founder and CEO, Mihup.ai
The last decade witnessed Artificial Intelligence (AI) transform from a gimmick to a game-changer. New technological advancements have seen it reshape multiple industries, and with the emergence of generative AI, this transformation has now reached the B2B sector. With its ability to simulate human interactions and engage in natural conversations, generative AI has opened up a world of new possibilities to the B2B sector. This is especially apparent in its impact on customer experience and engagement, traditionally the most time and resource-intensive aspects of the sector.
Generative AI is a subset of AI that encompasses technologies like Generative Adversarial Networks (GANs). By analysing large quantities of data and learning from it, it can simulate human-like intelligence, understanding, and decision-making abilities. The more data it’s fed, the better its ability to mimic human behaviour and make predictions and recommendations. With that in mind, it’s easy to imagine its value in the B2B space. Customer engagement, content creation, the automation of repetitive tasks – generative AI can drive efficiency and innovation in each of these segments, and many more besides.
The Adoption of Generative AI in the Indian and Global Contexts
Business leaders in the space have already recognised the ground-breaking potential of generative AI, actively seeking to integrate it into their operations. Per LinkedIn’s ‘The B2B Marketing Benchmark’ study, 87 percent of marketing leaders in India are committed to increasing their use of generative AI to improve efficiency and create engaging content. Similarly, 9 in 10 B2B marketing leaders will push for a larger marketing budget to better incorporate generative AI technologies.
A similar trend can be seen playing out globally, driven by a belief in the ability of generative AI to profoundly alter the scale of the global economy. A report by McKinsey examining the power, reach, and capabilities of this tool estimates that generative AI could add between USD 2.6 trillion and USD 4.4 trillion to the world’s economic output annually. This figure was determined through 63 use cases. If this software was used across a wider range of applications, we could easily see that figure double.
Factors Fuelling the Rise of Generative AI
While the latest advancements in generative AI are being driven by its constantly evolving applicability in commercial contexts, its initial development relied on a variety of factors. The development of deep learning at the start of the previous decade led to a new wave of progress in generative AI models. The use of artificial neural networks that could learn from inputted data allowed for the generation of increasingly realistic output.
Open-source frameworks and datasets have also played a crucial role in the advancement of generative AI models. Developers providing the general public access to pre-trained AI models also acted as a win-win situation. Developers had the chance to experiment with the capabilities of the technology and access a wide pool of resources and data, while even as AI tools were made accessible to a wide audience. The best example of this mutually beneficial relationship is the popularity and capability of open-source tools such as image generator DALL-E and AI chatbot ChatGPT, both of which have rapidly acquired millions of users.
The Business Impact of New Technologies
In a digitally-driven world, AI has emerged as the next frontier of competitive advantage in the B2B space. Through its ability to analyse vast quantities of data and provide concise insights, AI has enabled organisations to develop more effective and targeted strategies. By enabling companies to sift through complex information, identify trends, and suggest an optimal approach, businesses are provided with a tailored plan that best suits a specific customer’s needs and preferences. As such, the demand for greater automation, personalisation, and efficiency has accelerated the demand for generative AI solutions.
The cost-efficiency of AI models has also been a critical component of its appeal. Automating labour-intensive tasks and improving the output quality of the final product or service helps improve a business’s productivity and efficiency. This technology’s potential has seen it receive significant funding from investors, further fuelling its growth. This has resulted in startups playing a crucial role in providing AI tools that are tailored to the needs of different industries. By leveraging generative AI, these startups bring innovation to AI use cases and offer solutions that address specific challenges faced by businesses.
The Real-World Applications of Generative AI
The impact of generative AI is already apparent across a wide array of B2B businesses. In marketing, this technology’s ability to rapidly create brand-specific content and copy has allowed it to create personalised and impactful campaigns that directly impact outcomes and reach. Businesses centred on customer and client servicing have seen massive improvements in efficiency by using generative AI to engage in human-like conversations. Advanced AI and machine learning (ML) systems allow for automated conversations to take place, with the AI providing context-specific responses across a multitude of customers simultaneously. These virtual agents can seamlessly mimic brand tonalities, leading to improved customer acquisition and retention.
Generative AI has also found uses in more technical fields, such as law and information technology. In the legal industry, AI models can automate document review processes, analyse contracts, and provide amendments compliant with new regulations. The automation of these time-consuming tasks allows lawyers to focus their attention on the more complicated aspects of any given case. The use of generative AI in software development has the potential to fundamentally change the field, once Large Language Models (LLMs) are trained to comprehend main functionalities and suggest feasible implementations through conversational experiences. These sorts of functionalities can similarly be applied to other verticals, such as construction and engineering, education, design, and so on.
Generative AI has rapidly established itself as an integral part of the B2B sector, offering companies an easily-accessible route to increasing economic growth and productivity. Going forward, the importance of this technology will only continue to grow, making it an indispensable tool in B2B lead generation, demand generation, and other core business activities. With startups at the forefront of this new wave of technology, businesses must decide whether to embrace the change early or lose their competitive edge.