25 January 2023
From generating copy for social media posts to enterprise use cases, artificial intelligence (AI) and machine learning (ML) have become the largest enablers for technological advancements in organisational processes and creative ideation. The emergence of generative AI, capable of creating novel content rather than simply analysing or processing existing data, has resulted in a new wave of innovation and renewed interest in the sector. Many industry experts are pointing to the technology’s realisable value and ability to enable top-line growth opportunities for enterprises and investors alike.
|Arize AI||Observability platform that provides production ML analytics and workflows to detect model and data issues|
|Atomwise||Creator of an AI-based biotechnology engine that aids in small molecule drug discovery|
|D-ID||Leverages generative AI to create photorealistic videos by combining images with text|
|Forethought||Generative AI platform that automates the entire customer support lifecycle|
|Gretel||Offers a complete end-to-end platform that synthesises, labels, classifies and protects data|
|Inworld||Creator of an AI-based platform that allows users to create immersive virtual experiences|
|Jasper.ai||Offers an AI-based tool that writes original marketing content|
|Mostly AI||Offers a tool to synthetically generate data for the banking, insurance and telecommunications sectors|
|Regie.ai||Developer of an AI-based content creation and management system|
|Stability.ai||Developer of an open AI-based text-to-image generator|
Conversely, late-stage or pre-IPO AI companies will likely wait for more favourable macroeconomic conditions before tapping the public equities market. While the broad range of industries that pre-IPO players operate in will dictate the level and volatility of share price movement, companies with a strong technological edge, proven ability to cut costs and deliver top-line growth should earn premium trading multiples compared to peers in their respective primary categories. Companies like Scale AI, which provides software to product synthetic data, and Sparkcognition, a security-focused AI software solution provider, are companies we expect to be successful here. Both are critical expansion points in their lifecycles as their enterprise-ready AI solutions have gained traction with tech-focused and data-centric customers and are ready to leverage public funding to propel their growth.
|Bullfrog AI||Clinical-phase biotechnology company that uses ML for predictive analytics in drug development strategy|
|Dataiku||Provides an end-to-end enterprise AI platform for businesses at scale|
|DataRobot||Offers an enterprise AI cloud that automates the delivery of AI to production for any business|
|H20.ai||Offers an open-source machine learning automation platform intended to democratise artificial intelligence|
|Hive||Offers a cloud-based platform that streamlines the process of building custom business AI models|
|Scale AI||Provides a suite of data-centric, end-to-end solutions to manage the ML lifecycle|
|Spark Cognition||Delivers enterprise AI solutions that process, predict, and prevent cyberattacks|
As the sector continues to expand, commercial applications of generative AI could bring immense value to a diverse range of fields that require vast amounts of testing data to gain insights into their models, such as insurance, security and fraud protection, robotics and advanced driver-assistance systems (ADAS) & autonomous vehicles. Replacing traditional data collection methods with synthetic data generation is enabling companies to comprehensively test and train their models more efficiently and cost-effectively, benefiting both enterprises and consumers. “We are already seeing this play out in the insurance industry, where leveraging generative AI to process claims is expected to save auto, property, life and health insurance providers almost $US1.3 billion by 20232 while increased accuracy and efficiency simultaneously improves customer satisfaction,” Shah adds.
In addition to its value-add to existing business models, generative AI could give rise to a new ecosystem of AI-based professional service providers or specialised consultancies, particularly in creative fields like marketing. Generative models capable of producing original written content, such as emails, social media posts and thought leadership articles, could help organisations create and refine their output in accordance with their specific business needs. The emergence of generative design, which uses AI to autonomously create optimal design solutions, will also bring value to fields that require both creative and technical expertise, such as architecture, engineering and construction.
Innovations stemming from generative AI have proven to be investable and capable of providing meaningful workflow solutions for enterprises as well as creating new business models. As the market continues to grasp the potential impact of generative AI, Shah expects to see exponential growth and considerable benefits to enterprises and investors alike. “The AI sector is exploding with new players that are finding innovative ways to take advanced AI to the next level. We expect growth to continue as new research is coming to light everyday about the endless possibilities of the technology.”
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