AI All-Over

Key Takeaway:

While AI adoption globally today is more than double that in 2017, according to McKinsey, the proportion of organizations using AI has leveled off to around 50-60% in recent years. However, Generative AI (GenAI) marks a tipping point for AI with its ability to generate new content (such as text, audio, video, images, code, or even protein sequences) from similar formats of unstructured data. It can automate many tasks, boost productivity, reduce costs, and provide multiple growth opportunities. Those are key reasons why, according to the World Economic Forum, by 2026, mWhile AI adoption globally today is more than double that in 2017, according to McKinsey, the proportion of organizations using AI has leveled off to around 50-60% in recent years. However, Generative AI (GenAI) marks a tipping point for AI with its ability to generate new content (such as text, audio, video, images, code, or even protein sequences) from similar formats of unstructured data. It can automate man

Trend Type: Technology

Sub-trends: Democratized GenAI, GenAI Adoption, Corporate AI Agenda, Applied AI, GenAI expansion into the business world, AI Business Model, Artificial General Intelligence, AI Reality Check, Multimodal AI;

May, 2024 • Less than 4 min read

GenAI’s Enterprise Adoption

While AI adoption globally today is more than double that in 2017, according to McKinsey, the proportion of organizations using AI has leveled off to around 50-60% in recent years. However, Generative AI (GenAI) marks a tipping point for AI with its ability to generate new content (such as text, audio, video, images, code, or even protein sequences) from similar formats of unstructured data. It can automate many tasks, boost productivity, reduce costs, and provide multiple growth opportunities. Those are key reasons why, according to the World Economic Forum, by 2026, more than 80% of enterprises will have used generative AI APIs, models, or applications, representing an increase from fewer than 5% today. In fact, according to a report by Bloomberg, enterprise spending on GenAI solutions could reach $143 billion in 2027, up from $16 billion in 2023, with the world’s largest companies expected to allocate over 40% of their core IT spend to AI-related initiatives. This trend suggests an exponential rise in AI integration within corporate strategies, influenced by C-suite enthusiasm and developer engagement.
In addition, although all technologies evolve, AI’s rate of improvement will far surpass the already powerful Moore’s Law, which has successfully predicted the doubling of computing power every two years. Instead of doubling every two years, the amount of computation used to train the most powerful AI models has increased by a factor of 10 every year for the past ten years. The foundation technologies that enable AI will only get cheaper and more accessible. As with any software, AI algorithms are far easier and cheaper to copy and share than physical assets. And as AI algorithms get more powerful—and computing gets more affordable—AI models will be everywhere.
Having said this, fully realizing the technology’s benefits will take time, and leaders in business and society still must manage the risks inherent in GenAI, determine what new skills and capabilities the workforce will need, and rethink core business processes such as retraining and developing new skills. Also, ethical considerations related to AI will gain traction. While there’s enthusiasm for AI’s capabilities, concerns over its responsible usage may manifest in increased lawsuits and public demonstrations over its use to replace people.
GenAI’s Transformative Potential
This year will be pivotal for GenAI, marking a transition from the initial hype in 2023 to more realistic expectations. In 2024, many companies will find attractive ROI from GenAI, but only a few will succeed in achieving transformative value from it. McKinsey research shows that organizations that rely on innovation, data analysis, and process automation stand to benefit the most from gen AI. Within the agricultural, chemical, energy, and materials sectors, many companies are now moving beyond straightforward use cases and taking increasingly innovative approaches to adopting gen AI. Estimates show that an additional $390 billion to $550 billion of value from those companies can be created in the coming years. GenAI may appear easy to use, and many cloud service providers already embed GenAI capabilities in their offerings. However, realizing GenAI’s full potential requires more than letting employees use new capabilities in enterprise applications, no matter how powerful they may be. It requires taking advantage of GenAI’s capacity to be customized to specific needs and its remarkable scalability — while also paying close attention to its potential risks.
An AI For All
With investments flowing, AI continues to post state-of-the-art results with continuous improvements in model accuracy. For example, the cost of training image classification systems has decreased by 63.6%, and training times have improved by 94% since 2018. Venture capital investments increased 425% from 2020 until 2023, and nearly 80% of current AI research focused on generative AI with a wide range of start-ups successfully developing their own models—eg. Cohere, Anthropic, and AI21 Labs, among others, build and train their large-language models (LLMs).
Source: Deloitte Tech Trends 2024

 

On the other hand, the emergence of smaller, more efficient AI models is a key trend. While current LLMs will continue to thrive, there is also an increasing need for more cost-efficient models. These models will get smaller to run on low-footprint installations with limited processing capabilities, including on-edge or smaller enterprise architectures. In 2024, new AI platforms will also provide tools for companies to leverage generative AI without the need for deep internal technical expertise. In the long run, this will lead to the creation of interconnected networks of models designed and fine-tuned for specific tasks and the development of actual multi-agent generative ecosystems. These generative AI developments indicate an evolution towards a more accessible, versatile, and cost-effective technology. And although multimodal AI is in its infancy, models that can accept diverse inputs open new opportunities for AI in highly multimodal industries, such as healthcare, automotive, retail, and manufacturing.
AI’s Unpredictability
Lastly, as usual in technology trends, AI is evolving unpredictably. The need for synthetic data (information that’s artificially manufactured rather than generated by real-world event) and the event of artificial general intelligence (AGI) are two factors that make it more unpredictable in AI’s case. On the one hand, researchers estimate that, by 2026, we will exhaust high-quality data for training LLMs — a trend that can slow down AI progress. Scraping proprietary sources is getting more complex—cost and scarcity drive model developers to experiment with synthetic data, which can drive technology unpredictably.
On the other hand, regarding AGI – the AI that possesses the ability to understand, learn, and perform any intellectual task a human being can accomplish – the uncertainty relates to questions about the nature of intelligence and the human brain’s capabilities, as there might be an upper bound to the complexity of tasks the human brain can perform and the brain’s ability to transmit information to other intelligent entities (humans or AI) is limited by the slow speed of information transmission of our senses and our language. Therefore, what is beyond our comprehension is unpredictable.

Use Cases

How OTP Bank is using AI to market new solutions

GenAi Adoption: OTP Bank generated a Hungarian large-language model to enable more than 30 banking use cases across the organization, with an initial focus on spoken and txt customer interactions, fraud detection, and cybersecurity.

How AI helped Emirates NZ win the America's Cup

Applied AI: Emirates Team New Zealand accelerated hydrofoil design and testing by using AI to train a “digital twin” – a digital replica of a sailor – to test designs in a simulated environment.

Use Cases

How OTP Bank is using AI to market new solutions

GenAi Adoption: OTP Bank generated a Hungarian large-language model

How AI helped Emirates NZ win the America's Cup

Applied AI: Emirates Team New Zealand accelerated hydrofoil design and

Sub-Trend Sources
Democratized GenAi: Gartner Strategic Trends, Kyndryl, Kantar's Media Trends, Deloitte Tech Trends, WEF Jobs of Tomorrow GenAI, McKinsey Tech Trends Outlook, McKinsey Economic Potential of GenAI, Finance & Development
GenAI Adoption: BBVA Spark Tech Trends, Forbes Tech Predictions, McKinsey Tech Trends Outlook, Bruegel.org - Exposure of GenAI to European Labour Market
Corporate AI Agenda: HBR Tech Trends
Applied Ai: McKinsey Tech Trends Outlook, McKinsey Beyond the Hype
GenAI expansion into the business world: Cisco Trends
AI Business Model: HBR Tech Trends
Artificial General Intelligence: Benedict Evans, Future Today Institute, Finance & Development
AI Reality Check: IBM AI Trends, Future Today Institute, PWC AI Trends, McKinsey GenAi Reset
Multimodal AI: CBInsights Emerging Tech Trends
Small AI: HBR Tech Trends
GenAi: Small is the new big | Capgemini

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