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In 2017, Google Brain introduced transformers, which is a deep learning model that became the backbone of many NLP applications. It enables machines to understand and generate human-like text by grasping the relationship between the words in the sentence. In 2022, the release of Chatgpt created a stir in the AI world where the unexpected was achieved. Years of machine learning research advancements, notably in transformer-based models and the Large Language Models (LLMs) they permit, contributed to the unexpected birth of generative AI. Generative AI is much more than just content creation and there is so much more in this technology that is unveiling as days pass by.
A new analysis predicts that the global market for generative AI would grow at a CAGR of 35.6% over the next few years, from $11.3 billion in 2023 to $51.8 billion in 2028. Several well-known international businesses in the field, including Jasper, Runway, Lightricks, and Stability AI, have attracted more than $500 million in financing, demonstrating the potential.
Even with the possibility of a recession and significant layoffs at certain companies, many startups still struggle to locate all the people they require to bootstrap their operations. A sense of the potential of AI tools may be obtained from the possibility that generative AI offers to increase the productivity of a small crew. The technology enables startups to streamline their operations at a time when their long-term viability is at stake.
Other than NLP, present-day AI solutions offer considerable benefits to companies of all sizes. Applications for robotic process automation, which automate a range of time-consuming and repetitive corporate procedures, are sometimes powered by AI. This strategy frequently helps current staff at larger companies instead of replacing them. However, as was already mentioned, companies could profit from having fewer staff work harder. The number of use cases where automation is beneficial can grow thanks to generative AI.
It comes as no surprise that generative AI is currently making significant strides in text-based functional areas. Startups can leverage this technology to streamline tasks such as data entry, marketing copy generation, and appointment scheduling. Moreover, once a startup launches its product or service, the integration of generative AI-powered chatbots can effectively handle a substantial portion of the customer service role. By embracing this approach, startups can achieve greater efficiency and productivity with a leaner workforce.
A coin has its flip side as well. Questions have been raised about its unchecked growth. Despite the potential benefits that generative AI might have for society, others fear that we’re also on the verge of a massive startup failure wave as a swarm of generic “AI startups” spring up in response to venture capitalist’s latest fascination with Generative AI. The problem with startups entering the generative AI sector is the lack of clearly distinguishable or unique products, as many companies are jumping into the field without offering innovative solutions or addressing novel problems.
The other issue with startups entering the generative AI field is the lack of differentiation and value proposition, especially in text generation, where existing tools such as OpenAI’s offering may outperform generic copywriting tools. Additionally, startups in the B2C space face challenges due to the absence of strong customer-solution binding, making B2B applications that deeply integrate into enterprises more compelling.
While generative AI excels in generating new content, classification algorithms are better suited for identifying patterns and anomalies in data, which is crucial for detecting production line issues. One challenge for generative models is their lack of explainability, making it difficult for manufacturing experts to trust the results they produce. Hence, investors should prioritize startups that focus on solving specific problems rather than solely emphasizing the technology they employ. EthonAI, for instance, positions itself as a software tooling company developing a platform for quality management, rather than solely branding themselves as an AI startup.
It is crucial for companies to prioritize the establishment and effective management of corporate guidelines. Protecting data privacy and ensuring the confidentiality of sensitive corporate information are paramount for success in the business world. Therefore, defining and implementing appropriate guidelines at the outset is of utmost importance. Alongside the potential risks of compromising confidential, personally identifiable, or legally protected data, there is an additional concern when training publicly accessible Large Language Models with proprietary information. This risk involves the inadvertent exposure of intellectual property, particularly when the outcomes of the training are shared with others, including competitors. To strike a balance between fostering innovation and mitigating the associated risks of Generative AI, comprehensive policies and thoughtful frameworks must be put in place.
Lastly, striking the optimal balance between succumbing to exaggerated hype surrounding a technology and prioritizing initiatives with the highest potential return can pose a challenge. It is essential for organizations to effectively allocate their capital and resources to address the most critical initiatives. However, organizations that remain on the sidelines for an extended period, expecting the technology to fully mature, run the risk of missing out on the widespread adoption of AI within the industry. This can lead to falling behind competitors who leverage the latest technologies to disrupt the market and diminishing their long-lasting competitive edge.
Strategies for Success
1- Establish clear use rules and privacy guidelines that protect corporate interests while fostering innovation and facilitating the integration of Generative AI into mainstream enterprise operations.
2- Create a dedicated and focused group at the senior level to experiment with Generative AI and reengineer core business processes, aiming to disrupt existing models effectively. This requires strong sponsorship and dedicated attention to deliver tangible business outcomes.
3- Continuously evaluate emerging solutions within the Generative AI ecosystem, considering their specific strengths, weaknesses, and applicability to different industries and enterprise requirements. Incorporating Generative AI into enterprise applications requires thoughtful decision-making and effective change management to fully realize its value.
Regardless of what lies ahead for generative AI, it is evident that these tools offer substantial prospects for startups, particularly in the field of natural language processing (NLP). Entrepreneurs should closely monitor the progress and developments in this domain of AI and machine learning. By doing so, they can not only gain inspiration for innovative business ideas but also enhance the operational efficiency and effectiveness of their startups.
To conclude, Sam Altman in a recent interview with Economic Times, when asked about the opportunities for entrepreneurs today mentioned, “I think this is the most exciting time to start a company since the dawn of the internet. I think this is going to be bigger than mobile, it might turn out to be bigger than the internet. I hope it does. Which means that anything you do can be huge.”
This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill out the form here.