Get ready for an Artificial Intelligence metamorphosis in 2024. The progress of AI is expected to be far more than a gradual improvement. It's poised to be a significant transformation, reshaping the technological landscape, legal frameworks, ethical principles, and social interactions. Gartner predicts that AI will serve as a primary indicator of national power by 2027, driving strategies for the digital economy.
AI promises to reshape industries, unlock new possibilities across various sectors, and even accelerate scientific breakthroughs like drug discovery. The future feels closer than ever, from AI-powered assistants boosting your productivity to machines understanding the world through multiple senses. But this revolution demands responsible handling—open-source models democratize access while regulations like TRiSM ensure ethical, responsible deployments. Let’s delve into the key AI trends in 2024 that are expected to have the most significant impact across industries.
Top 7 AI trends for 2024
Generative AI remains a dominant force
The potential of generative AI to revolutionize knowledge work across industries and functions has sparked both awe and anticipation. From sales and marketing to customer service and software development, it promises to reshape roles, enhance performance, and unlock potential value in the trillions across diverse sectors. Generative AI could automate as much as 60-70% of work tasks, surpassing earlier estimates of 50%. This jump is fueled by AI's improved natural language skills, crucial for activities occupying 25% of work time. Furthermore, according to McKinsey, knowledge-intensive jobs with higher wages and education requirements are expected to feel the biggest impact of this automation wave. Previously resistant to automation, knowledge work involving crucial tasks like decision-making and collaboration stands to be dramatically reshaped by the arrival of generative AI.
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A recent McKinsey study predicts a groundbreaking economic upswing, with generative AI potentially contributing $2.6T to $4.4T annually across 63 analyzed use cases. This translates to a remarkable 15% to 40% boost in the overall impact of all Artificial Intelligence, exceeding the entire UK’s GDP in 2021. In addition, embedding generative AI in existing software beyond these specific use cases could even double the amount.
Banking, high tech, and life sciences stand to gain the most significant percentage-wise impact on their revenues. In banking alone, full implementation of identified use cases could unlock $200B to $340B in annual value creation. Similarly, retail and consumer packaged goods stand to gain $400B to $660B annually. By 2040, generative AI could increase labor productivity by 0.1 to 0.6 percent annually.
There are two emerging subtrends in generative AI: augmented intelligence and multimodal fusion. Augmented intelligence lets users collaborate with AI, while multimodal AI blends data types for richer AI context and deeper insights. Let's take a closer look at these AI trends:
1. Augmented Intelligence
Augmented Intelligence, or the use of AI and Machine Learning (ML) technologies to enhance human capabilities and decision-making, is often applied in areas where human expertise is crucial, such as healthcare, finance, and data analysis, to assist professionals in making more informed decisions. According to McKinsey analysis, generative AI's impact will extend beyond routine tasks, significantly reshaping the knowledge work that individuals with advanced education levels perform.
Among the AI trends used in the workplace, the augmented-connected workforce (ACWF) concept is gaining traction. Driven by the need for faster talent development and scalability, ACWF leverages intelligent applications and workforce analytics to provide real-time support and guidance for employee experience, well-being, and skills development. This approach aims to achieve improved individual worker outcomes and positive business results for organizations. Gartner research indicates that by 2027, 25% of CIOs are expected to implement ACWF initiatives to achieve a 50% reduction in time to competency for critical roles.
2. Multimodal AI
Artificial Intelligence that uses more than one type of data to make more accurate predictions, draw insightful conclusions, or draw comprehensive conclusions about a real-world situation is known as multimodal AI. A multimodal AI system uses images, audio, video, speech, text, and traditional numerical data sets to train and learn. Multimodal AI means using many data types in tandem to establish content and interpret context by drawing connections that Data Science, limited to singular data types, often misses. This fusion unlocks possibilities, enhancing accuracy, context awareness, and real-world interaction.
In Computer Vision, multimodality enables AI to recognize objects and understand their surroundings and context, enhancing identification accuracy. This technology benefits industries with applications in predictive maintenance in manufacturing, personalized healthcare, and driver monitoring in the automotive sector. In robotics, multimodal AI allows machines to navigate complex real-world environments by processing data from multiple sensors, enabling them to interact with pets, interpret traffic signals, and adapt to diverse settings. Multimodal AI transcends mere information processing, paving the way for a future where machines genuinely understand and interact with the world around them.
Deloitte predicts that in 5+ years, neuroadaptive AI will directly generate outputs based on the user’s brain activity. Contextual awareness, including past actions, emotions, and environment, will empower multimodal models to understand intent more deeply. This will result in AI responses truly adapted to individual personalities.
AI in material and drug discovery
In R&D, generative AI has emerged as a powerful tool. McKinsey research shows it can significantly boost research productivity by 10-15%. Industries like life sciences and chemicals lead the charge, using generative design to revolutionize development.
Think of it as brainstorming boosted with AI. Pre-trained models generate promising candidates for new drugs and materials, like molecules or composites, dramatically speeding up the process. And it doesn't stop there. Another AI technology, deep learning surrogates, is increasingly used alongside generative AI for even greater R&D power. While their integration may require specific solutions, the potential to test AI-generated designs faster is immense.
A prime example is GNoME, a groundbreaking AI tool that has dramatically accelerated the discovery of new materials. By leveraging a deep learning approach known as Graph Networks for Materials Exploration, GNoME has unearthed a staggering 2.2M new crystals, of which 380,000 possess the stability and potential to revolutionize countless fields.
The magnitude of GNoME's achievements is truly remarkable. By doubling the number of known stable materials, it has propelled the field of materials science forward by an estimated 800 years' worth of knowledge.
Now, let's look at AI trends in the pharmaceutical industry. Drug discovery is slow and risky, with a long 12-15-year journey to market and a staggering 90% failure rate in clinical trials. The cost of bringing a new drug to market can reach a staggering $2.5B. It's no wonder AI is seen as a beacon of hope. A recent BCG/Wellcome report suggests AI could bring significant time and cost savings (25-50%) in the crucial preclinical stages, paving the way for faster, more affordable drug development. Studies by McKinsey estimate a potential annual value of $60-$110B for just the pharmaceutical and medical-product industries. Generative AI is a breakthrough technology that can revolutionize R&D across industries, from pharmaceuticals to materials science.
AI in healthcare
Imagine a healthcare system where robots handle paperwork, doctors offer personalized treatment plans based on your unique needs, and data analysis guides informed decisions. Generative AI, a powerful new tool, holds the potential to turn this vision into reality. It can streamline processes, personalize care, and unlock insights from mountains of data, paving the way for a future of efficient, effective, and even more personal healthcare.
A majority of respondents perceive generative AI as having the potential to positively affect healthcare accessibility and affordability, according to a Deloitte survey. Over half (53%) of participants believed in its ability to improve access, while 46% saw it as having the potential to reduce costs. Interestingly, individuals with prior experience with generative AI held even more optimistic views of AI trends in healthcare, with 69% and 63%, respectively, expecting enhanced access and affordability.
The application of generative AI in healthcare holds potential across various segments. The BCG analysis explores potential use cases for providers, pharmaceutical firms, payers, medtech companies, services and operations, and public health agencies, categorized into three stages: existing validated solutions, early-stage or conceptual applications, and hypothetical future uses not yet under development.
Providers:
- Improving clinical outcomes and resource utilization: AI tools can assist with diagnosis, care plans, and patient monitoring while automating administrative tasks like documentation and claims handling.
- Personalized care: Digital AI solutions, like risk profiling or chatbots, can tailor care to individual needs.
- Future potential: Real-time patient monitoring, personalized nudges for prevention, and more accurate imaging across specialties.
Pharmaceutical firms:
- Faster drug discovery and precision medicine: AI speeds up drug development, from target identification to clinical trials, leading to more personalized therapies.
- Future potential: Tailoring drugs to individual patients and accelerating treatment access, even for rare diseases.
Payers:
- Reducing costs and improving member engagement: AI automates tasks like underwriting and claims management and helps identify high-risk segments for proactive care.
- Future potential: Personalized member communication and support based on health needs and preferences.
Medtech:
- More personalized and patient-centered devices: AI is helping design devices like prosthetics and pacemakers tailored to individual needs and incorporating remote monitoring.
- Future potential: Collect and analyze data via remote monitoring for more effective interventions and predicting equipment maintenance needs.
Services and operations:
- Data analytics and software optimization: AI can improve interoperability between existing applications and generate synthetic data to improve decision-making.
- Future potential: Inventory management, data sharing, and HR functions automation.
Public health agencies:
- Improving resource planning and public health interventions: AI can help anticipate needs, develop early-warning systems for outbreaks, and allocate resources more effectively.
- Future potential: Tools for public health monitoring, resource allocation, drug safety review, and outbreak prediction.
Read more: Generative AI for enterprise: challenges and integration solutions
Open source AI
In 2023, the world saw an explosion of creative possibilities with generative AI, with OpenAI's ChatGPT 3.5 Turbo leading the charge. But now, a new AI trend is emerging: the rise of open-source models. Open-source models empower organizations with transparency, flexibility, and cost-efficiency, fostering wider adoption and democratization of generative AI. While proprietary models still hold a place, we can expect an open-source future: Forrester predicts a staggering 85% of enterprises will integrate open-source AI models by 2025, signifying a shift towards collaborative, accessible AI innovation. These models, unlike their closed-source counterparts, break down barriers to entry and unleash a wave of democratization:
- Accessibility: Open-source LLMs like LLaMA, Vicuna-13B, and Mistral empower individuals and smaller organizations to leverage cutting-edge AI. As a result, previously unimaginable tools are now accessible, fostering widespread innovation and experimentation.
- Agility & innovation: Open-source nature promotes rapid evolution. With a global community collaborating, advancements like instruction tuning and multimodality emerge quickly, surpassing expensive closed-source models. Imagine achieving more with $100 and 13B parameters than giants with millions and 540B.
- Democratization of AI training: Techniques like LoRA make fine-tuning accessible, allowing anyone with a computer to participate in AI development. This empowers individuals to contribute to the evolution of LLMs, shaping them for diverse needs and real-world applications.
- User-focused development: Open-source communities are inherently user-driven. Transparency and collaboration ensure LLMs adapt to user needs and priorities, leading to more accessible and impactful applications.
While questions remain about the future of closed-source models, the open-source LLM revolution is undeniable. By democratizing access, accelerating innovation, and empowering users, open-source LLMs drive a transformative wave of AI that promises to change the world.
Quantum AI
The fast-paced evolution of AI in recent years, particularly with the emergence of generative AI, has sparked considerable excitement and anticipation. However, the current capabilities of AI are constrained by limitations inherent in conventional silicon-based hardware. Enter quantum computing, a fundamentally different approach to processing information that holds the potential to revolutionize not only AI but the entire computing landscape.
While still in its early stages, quantum computing is rapidly progressing. Major tech companies and promising startups are actively developing and offering access to quantum computing resources, albeit currently for specialized and often experimental purposes. The optimism surrounding the technology suggests that mainstream adoption could occur within the next decade, opening the door to transformative applications.
When combined with AI, quantum computing's potential becomes even more remarkable. The ability to process vast amounts of data at unprecedented speeds could empower AI to perform tasks that are currently beyond its reach, including advanced material design, personalized medicine, and autonomous systems with superior decision-making capabilities. This synergy between two groundbreaking technologies could usher in a new era of computing, fundamentally reshaping industries and driving innovation across fields. Imagine:
- Accelerating drug discovery: Quantum simulations unlock rapid molecular analysis, leading to faster, more effective drug development.
- Sharpening climate models: Unprecedented data processing accuracy enhances climate change predictions, empowering us to make informed decisions.
- Mastering financial markets: Sophisticated analyses enable businesses to navigate the market with unparalleled precision.
Quantum AI’s potential extends to areas like logistics optimization, energy management, and even advanced material design, solving problems once deemed impossible for classical computers. This technology empowers businesses with game-changing insights, revolutionizing data-driven strategies and opening new avenues for innovation and efficiency.
Edge AI
One of Gartner's AI trends predictions for 2024 highlights the rise of edge AI, where processing power migrates closer to data sources. This eliminates dependence on centralized cloud or remote data centers, facilitating faster, local decision-making. No more relying on slow cloud connections; AI algorithms execute directly at the edge, reducing latency and boosting system responsiveness.
For various types of Internet of Things (IoT) applications, edge AI leverages available local computing power. This proves transformative for scenarios demanding real-time decisions, like autonomous vehicles or smart medical devices. Data confidentiality and security also benefit: sensitive information stays local, minimizing unnecessary exposure to external threats.
This convergence of AI and edge computing fosters efficient and responsible solutions. Research anticipates a dramatic shift: Gartner predicts over 55% of deep neural network data analysis to happen at the point of capture in an edge system by 2025, compared to a mere 10% in 2021.
AI legislation and risk management
2023 has seen a surge in the adoption of AI regulations worldwide. In the US alone, over 190 AI-related bills have been introduced nationwide. Whether through global collaborations or internal security measures implemented by individual businesses, the focus on AI regulation is shifting. Now, it's not just about getting more AI; it's about getting the right AI—AI deployed ethically minimizes risks, and operates with clear transparency.
1. Global AI governance: AI policies in the EU, US, and the UK
In the face of AI's exponential growth, robust and responsive legal frameworks are becoming critical. The past year saw a global effort to bridge the gap between innovation and responsibility. The UK's AI Safety Summit culminated in the historic Bletchley Declaration, an international agreement on safe AI development signed by 28 nations. Meanwhile, the US outlined its AI Bill of Rights, the EU adopted the Artificial Intelligence Act, and China and Canada strengthened their existing regulations. Around the world, countries are actively forming their AI governance plans. As we move into 2024, expect this trend towards comprehensive and unified AI regulation to solidify, with the focus shifting towards ethical deployment, risk mitigation, and transparency. These regulatory efforts will undoubtedly be a defining element in shaping the trajectory of AI trends in the years to come, ensuring a future where AI serves humanity in an ethical and responsible manner. Businesses must remain agile and informed to navigate these evolving legal frameworks effectively.
2. AI TRiSM
The need for AI Trust, Risk, and Security Management (TRiSM) intensifies as AI permeates diverse sectors. Even well-intentioned AI models can unleash cascading negative consequences that eclipse potential benefits without proper controls. TRiSM equips organizations with a toolkit for responsible AI implementation, covering key areas like:
- ModelOps: Streamlining the AI lifecycle from development to deployment and maintenance.
- Proactive data protection: safeguarding sensitive data used in AI models.
- AI-specific security: Mitigating unique vulnerabilities inherent to AI systems.
- Model monitoring: Continuously tracking performance for deviations and unintended outcomes.
- Risk control for third-party models: Assessing and managing risks when integrating external AI models.
By implementing these TRiSM controls, Gartner estimates that businesses can achieve significant gains by 2026. Specifically, they predict up to an 80% reduction in faulty and misleading information influencing decision-making, leading to more informed and responsible AI applications.
Read more: Generative AI vs predictive AI: A comparison
Wrap-up
At the brink of 2024, it's clear that generative AI is no longer a futuristic promise but a dawning reality. Its potential to reshape industries, empower individuals, and redefine entire workflows is no longer hypothetical. AI is expected to drive innovation and progress from augmenting human capabilities to revolutionizing scientific discovery.
Despite the challenges, there are boundless opportunities. Open-source models foster democratization, empowering individuals and smaller organizations to participate in the AI revolution. Edge computing brings intelligence closer to the data, enabling faster, more responsive decisions. Quantum AI promises to tackle once-intractable problems, pushing the boundaries of scientific and technological advancement.
The time for hesitant exploration is over. The future belongs to those who dare to embrace the potential of generative AI. Businesses should work with an experienced technology partner to get the most out of AI. This will help them use AI responsibly, efficiently, and effectively to get real results.
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