Page 11 - AI for Good - Impact Report

AI for Good

AI and Generative AI Trends

This section provides an in-depth look at the evolving AI landscape, focusing on the latest AI
and Generative AI (GenAI) trends. Drawing insights from across Deloitte’s global network, it
examines key AI innovations, evaluates the adoption of AI technologies across various industries
and regions, and highlights the areas in which private sector companies are investing in AI
and GenAI. It addresses the barriers to broader adoption and implementation as well as the
sentiments at different organizational levels. Furthermore, it delves into the impact of AI and
GenAI on the workforce, talent considerations, and the skills required now and in the future.

Artificial intelligence is everywhere

The term "artificial intelligence" (AI), coined almost seven decades ago, has garnered significant
public attention in recent years due to its synergies with individuals, businesses, governments,
and legislation. However, a lack of consensus remains regarding its definition. Public perception
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often alternates between utopian promises and dystopian visions of the future. Achieving a
balance between innovation and control is crucial, highlighting the need for a fundamental
understanding of AI to participate in discussions about the latest AI and GenAI trends.

The OECD defines AI as “a machine-based system that, for explicit or implicit objectives,
infers, from the input it receives, how to generate outputs such as predictions, content,
recommendations, or decisions that can influence physical or virtual environments. Different AI
systems vary in autonomy and adaptiveness after deployment.” However, this broad definition
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may not be precise enough for categorizing individual systems. As Stuart Russel, a renowned
British computer scientist in the field of AI, notes: “It's surprisingly difficult to draw a hard and fast
line and say […] this piece of software is AI, and that piece of software isn't AI.” This provides
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a general understanding of the systems discussed in this report. AI systems are increasingly
integrated into everyday applications, spanning from voice-controlled virtual assistants and
personalized online shopping experiences to healthcare monitoring and emergency response
systems. 5

From a technical perspective, AI encompasses several key areas, each representing different
stages and methodologies in its development. The first wave, symbolic AI, involves early
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techniques that rely on predefined rules and logic and understandable symbols, like words
or numbers, to perform tasks such as reasoning, planning, and problem-solving. While no
longer dominant in AI research, symbolic AI is evolving and being integrated into modern
AI frameworks. It’s still relevant in fields requiring transparency, reasoning, and structured
knowledge such as medical diagnosis systems, robotics and natural language processing. The
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second wave, characterized by machine learning (ML) and data-driven AI, uses large datasets to
train algorithms that can make predictions or decisions without explicit programming for each
task. This has led to significant progress in fields such as image and speech recognition. For
instance, AI-driven translation tools have evolved from rule-based systems to machine learning
models that can translate text more accurately by learning from vast amounts of bilingual data. 8
Understanding the link between AI, machine learning, deep learning, and GenAI is integral to
understanding the evolution and capabilities of these technologies. AI serves as the overarching
concept and category for the other terms. Machine learning (ML) refers to a subset of methods
in AI that enable systems to learn from data and improve performance over time without being
explicitly programmed, allowing them to find solutions to problems rather than following

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