DALL-E 2’s picture-perfect generations are leaps and bounds above its predecessor, despite less than a two-year gap between the release of the models.I expect the technology to continue improving at a parabolic pace. Google recently showcased its text-to-video AI model, which is likely to increase output quality at least as (if not more) quickly as DALL-E 2 has done. GPT-4 has already been announced by OpenAI, and there have been increasing hints that the technology will have multimodal capabilities, able to function across images, texts and sounds all at once. A multimodal AI would be capable of building new business models that could open the door for jobs and opportunities that we couldn’t even imagine a year ago.

Generative AI is also on track to become much more widely adopted for commercial purposes and as a part of creative workflows and practices. Companies could soon routinely leverage generative text and imagery to create entirely new content and marketing displays. Instead of spending hours churning out drafts, you could create hyper-personalized content rapidly, reducing your marketing budget and improving effectiveness in the process.

AI generative music could also contribute to this evolution in marketing, becoming a staple in commercial jingles, background video music and other online media. Furthermore, generative imagery has the potential to help create logos, social media posts and other high-quality visuals. I see these capabilities as revolutionizing the marketing industry by switching the focus to AI-assisted content that can resonate with individual user needs.The healthcare industry is also primed to undergo profound transformation due to AI. AI is currently being used to do gene-sequencing work, predicting and identifying hidden patterns in genomic data to make it easier for us to research diseases and potential treatments. According to Pfizer, AI is also "being trained to predict drug efficacy and side effects, and to manage the vast amounts of documentation and data that support any pharmaceutical product."

Machine learning can speed up regulatory filings by anticipating questions and using data-driven insights to generate proactive responses.

AI is also providing actionable insights for patient treatment, allowing for the development of more personalized treatment plans. In this way, nuanced insights from physiological data can help predict how a patient will respond to various treatment options, giving doctors the ability to choose the best course of action at the level of the individual patient.With the growing adoption of AI, there is bound to be a greater focus on responsible AI usage. Currently, image generators can create almost life-like images, which has raised concerns about artistic integrity and abuse. AI has also been accused of propagating human biases, namely because AI technologies are trained using data provided by humans. These are core issues that companies will need to address in order to successfully integrate robust AI systems into production environments.

Some nascent solutions have us mitigating the potential for bias by examining the underlying data used to train AI systems. Businesses will also need to ensure AI transparency among employees and customers, emphasizing how AI is being used in order to alleviate stakeholder concerns. Lastly, businesses will also need to bolster their data privacy, security and usage practices—proactively navigating new IP infringement and data leakage risks.AI is likely to soon transform the ways businesses interact with customers. In fact, AI-powered digital assistants and chatbots are already capable of having human-like conversations using natural language processing (NLP), answering ever more sophisticated questions and personalizing interactions based on customer intent. Soon, companies may be able to offer completely individualized and bespoke experiences for customers. Using individual customer data, AI could be used to predict customer needs and expectations better, reducing handling times and helping to identify future trends.

AI can also be used to help optimize supply chains by analyzing huge volumes of data to predict demand across multiple product segments and geographies. Early adopters have been able to use AI solutions such as processing optimization, predictive maintenance and inventory analytics to manage their wider value chain. While only a handful of businesses are currently using such AI-based solutions, these are likely to permeate across all industries as businesses strive to remain competitive.

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