With AI spending forecast to exceed $500 billion in 2023, according to IDC, rapid advances in adaptive, decentralized, and generative AI will occur. McKinsey reported that 50 percent of companies in their “State of AI in 2022” survey have already adopted AI in at least one function in their business.
On the one hand, and given the engineering complexity and the demand for faster time to market, it is critical to develop less rigid AI engineering pipelines or build AI models that can self-adapt in production. Adaptive AI responds to that as it can continuously retrain its models to learn and adapt based on new experiences without needing developers to rebuild them, leading to faster and better outcomes. Gartner predicts that by 2026, enterprises that have adopted AI engineering practices to build and manage adaptive AI systems will outperform their peers in operationalizing AI models by at least 25%.
On the other hand, recent developments led by ChatGPT or Stable Diffusion represent the maturing of AI “decentralization”, which refers to the development of advanced AI technologies not monopolized by players with access to massive, centralized, proprietary data sets. In 2023, McKinsey expects to see how this decentralization will continue to disrupt different sectors, from entertainment to gaming or media areas, where we’ve already seen new technologies make early inroads.
“The idea that companies with the biggest data sets will lead innovation (such as AI) took a hit in 2022. Numerous startups are emerging with compelling products to give some large tech companies a run for their money.” Vinayak HV – McKinsey Senior Partner, Singapore
In addition, Generative AI – which uses neural network models to create something new- is also leading the charge in AI’s second wave as the latest venture capital darling. Despite economic uncertainty, startups are back to high valuations, and technological improvements are poised to transform how content, teams, and processes function across industries. Persado estimates that at least 25% of the population interacts with generative AI content. This figure could be 35% to 40% when accounting for all companies that use generative AI in marketing copy. AI tools are already helping brands create better content at scale in creative industries, freeing up creatives by saving time on creativity and drafts.
Recent releases of text-to-image and text-to-video generators are appealing to consumers -the public release of image models like DALL-E 2 and language products like Copy.ai and Jasper sparked an explosion in generative AI adoption- but also raise significant concerns around the spread of disinformation, harmful content, copyright protections, and algorithmic biases. The conversation around ethics is evolving in real-time. AI requires new forms of trust, risk, and security management that conventional controls don’t provide. Unique AI TRiSM capabilities are here to ensure model reliability, trustworthiness, security, and privacy. Gartner predicts that by 2026, organizations that operationalize AI transparency, trust, and security will see their AI models achieve a 50% result improvement in adoption, business goals, and user acceptance.
