Back in 1956, John McCarthy defined AI as “The science and engineering of making intelligent machines.”
AI makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. AI along with its component technologies is transforming enterprise digitalization by generating insights from complex, real-time and multi-source data. AI can be structured along three evolutionary stages.
Global State-of-the-Market for AI
As per IDC, the global AI spend stood at USD 37.5 bn. in 2019 and is expected to reach USD 97.9 bn. by 2023. Significant implementation of the technology has been witnessed in banking, retail, manufacturing, healthcare and professional services sectors, accounting for more than 50% of the overall global AI spend in 2019. Sectors expected to see the fastest growth comprise media, government education, resources and utilities. From a regional perspective, US is expected to contribute more than 50% to the total AI spend forecast, riding on a large current investment base. On the other hand, Japan and China would be displaying the fastest AI spending growth by 2023, growing at CAGR of approximately 45%. The post-COVID scenarios are yet to be evaluated.
With the ongoing global crisis, AI is also known to have the potential to help us tackle the pressing issues raised by COVID-19 pandemic, helping to identify patterns in coronavirus-related research and helping with diagnostics. This will result in healthcare to be one of the fast growing sectors in terms of overall AI spend globally.
Adjacent market developments supporting AI adoption
There are certain key market developments that can act as catalyst to ensure successful enterprise and consumer adoption of AI. Some of these adjacent market developments include AutoML – using AI to build AI, AI workloads at the ‘edge’ – increase in computational capabilities of edge devices, Cloud services – rise of AI-as-a-service and Embedded AI – increase in chatbots, virtual assistants.
These market developments specifically in cloud-based services and shift in AI workloads at the edge will act as the key driver for the rise in AI software and platforms. AI software and platforms are expected to generate USD 120 bn. revenue by 2023 growing at a CAGR of ~27%. Investments in AI systems continue to be driven by a wide range of use cases. The three largest use cases – automated customer service agents, automated threat intelligence and prevention systems, and sales process recommendation and automation – will deliver 25% of all spending in 2019.
Evolution in AI and enabling/dependent technologies
Some of the evolving tech trends in AI are also driven other emerging/foundational technologies like cloud and edge computing – AI PaaS, Edge AI, AI cloud services will see immense growth in this decade.
The TECHADE is will be in the narrow AI stage or ANI, primarily machine learning, followed by AGI (Machine Intelligence) and ASI (Machine Consciousness) sometime close to 2040. The focus of this decade is on democratization of AI – bringing AI to the masses, via AI PaaS, Edge AI, AutoML amongst others. Another important focus area is building trust in AI, AI governance, ethics and moving away from the black box model by making AI more explainable.
What do you think will drive AI adoption? Share your thoughts and stay tuned for more on this topic and other emerging technologies.
Read our full section on TECHADE 2020 – Technologies driving global GDP in the decade to 2030 for more details.