As we enter 2024, the once theoretical promise of Artificial Intelligence (AI) has solidified into a tangible force reshaping industries and challenging established paradigms.
From robust large language models to cutting-edge robotics, these emerging AI forms possess the transformative potential to revolutionize every facet of doing business.
Executives across the globe now have to make a critical decision: navigate the complexities of AI or risk falling behind in the wake of its transformative power.
This article will delve into the emerging trends, as well as common challenges, that define the 2024 AI landscape. Follow Var Meta team to demystify the latest AI developments and unveil strategic business opportunities.
Key Takeaways
- Generative and Multimodal AI Growth: Significant advancements in AI that enable realistic image, video creation, and multifaceted data analysis.
- Quantum AI Emergence: The intersection of quantum computing and AI, promising smarter and faster AI systems with applications in various sectors.
- Explainable AI (XAI) Development: Providing transparency in AI decision-making processes, crucial for building user trust and addressing AI system complexities.
- Rise of Edge AI: On-device data processing for real-time decision-making, enhancing bandwidth efficacy and reducing latency.
- AI Governance Importance: Ensuring ethical AI use, respecting rights, promoting fairness, and fostering transparency in AI applications.
- AI and Sustainability: Leveraging AI to enhance energy efficiency, reduce carbon footprint, and support sustainable business practices.
- Deepfake Technology Risks: The increasing threat of AI-generated fake content, posing significant challenges to information authenticity.
- Ethical and Legal Challenges in AI: Addressing biased AI results, intellectual property issues, and ensuring compliance with legal standards.
- Scalability and Infrastructure in AI: Overcoming challenges in scaling AI initiatives and integrating AI into existing business infrastructures.
- Transformation of the Job Market: AI’s impact on employment, necessitating new skill sets and roles in the workforce.
- Shortage of AI Chips: The global scarcity of high-performance chips is crucial for AI development, affecting businesses’ capacity to innovate.
- Adaptation and Proactive Measures: The necessity for businesses to adapt transparent, ethical practices, and prepare the workforce for AI integration.
The 6 AI Trends in 2024
1/ Explosive Growth of Generative AI and Multimodal AI
In 2023, generative AI witnessed significant advances and started knocking on corporate doors.
Indeed, it has demonstrated remarkable capabilities, from delivering realistic images and videos to catchy marketing copies, that sparked great interest among businesses and end-users alike.
For example, corporate giants like Unilever and BMW have turned to generative AI to create personalized advertisements at a much lower cost.
On the other hand, the introduction of multimodal AI models, such as Google’s recent launch of Gemini, has unlocked the ability to interpret multiple data types and generate more accurate outputs.
With an AI model that can process various types of inputs ranging from visuals, and text documents to audio recordings, businesses can derive insightful data on customer behavior and other metrics that matter.
Generative AI and multimodal AI are poised to pave the way for a new era of business evolution in 2024.
They can actually complement each other: Multimodal AI provides richer data inputs for generative AI models to produce more realistic outputs; while generative AI models create new content that can be further analyzed by multimodal AI systems.
Both, if implemented strategically, will lead to substantial cost savings and more data-driven decisions for businesses.
2/ Quantum AI Is The Next Big Thing
Valued at USD 242.4 million in 2023 and projected to reach USD 1.8 billion by 2030, the Quantum AI market represents a novel intersection of quantum computing and artificial intelligence that will facilitate smarter and faster AI systems.
This holds substantial implications for sectors such as finance, healthcare, and logistics, where organizations are exploring potential applications in tasks like optimization, pattern recognition, and complex simulations.
The continuing development of quantum algorithms is poised to improve the accuracy and efficiency of existing machine learning models, resulting in more optimized data analysis.
Key players in the tech industry, including IBM and Google, are actively contributing to the advancement of quantum computing technology and encouraging innovation in the Quantum AI market.
As of now, quantum computing is still in its infancy; and the research community is actively addressing key challenges regarding the reliability, scalability, and applicability of quantum computers.
Overcoming these hurdles is a critical prerequisite for unlocking the full potential of quantum technology to accelerate AI, and enabling its integration into diverse industry applications.
3/ “Black Box” Demystified with Explainable AI (XAI)
Considering how complex AI systems are, it is understandable that even the providers and deployers of these systems aren’t always aware of how an AI algorithm arrives at a specific result – a phenomenon commonly referred to as the “black box effect”.
Explainable AI (XAI) is the answer: with the ability to provide understandable explanations for AI systems’ rationale, users can comprehend the underlying logic of such decisions to detect hallucinations and biases, if any, in the decision-making process.
With the contemporary robust proliferation of AI systems, XAI is crucial for an organization in building confidence among end-users when putting AI models into production.
AI explainability also helps an organization adopt a responsible approach to AI development, focusing on ethics, explainability, and accountability. The transparency provided by XAI not only strengthens trust between organizations and users but also aligns with core data protection principles.
Embarking on a new journey of XAI, business executives should actively encourage human involvement and intervention in decisions that have significant consequences to foster effective human-AI collaboration and address the risk of over-reliance on AI systems.
In addition, the intended audience also needs to be taken into account when providing explanations.
4/ Evolving Landscape of Edge AI
For most AI algorithms today, data must be sent to the cloud for analysis and then transmitted back to a device out in the field.
However, this process consumes a substantial amount of energy and can lead to transmission delays or security vulnerabilities.
Thanks to recent advances in computing infrastructure and the widespread adoption of IoT devices, edge AI has emerged as a paradigm shift in AI deployment.
It allows for on-device data processing and enables real-time decision-making, with improved bandwidth efficacy and reduced latency compared to cloud-based AI.
According to Fortune Business Insights, the Edge AI market is expected to grow from USD 15.6 billion in 2022 to USD 107.47 billion by 2029 at a Compound Annual Growth Rate (CAGR) of 31.7%.
As we enter 2024, it’s not just an opportunity, but a strategic imperative for businesses to start experimenting with Edge AI.
The potential benefits are manifold – streamlined operations, optimized production lines, and significant cost savings.
Edge AI also has endless industry-specific applications, ranging from transportation, and manufacturing to healthcare; with an emphasis on accelerating performance and enhanced security.
5/ AI Governance Gains Momentum
The contemporary widespread adoption of AI tools, systems, and technologies in various industries raises significant concerns about AI ethics.
Specifically, as AI becomes increasingly woven into the fabric of organizations, the urgency for robust AI governance intensifies in 2024.
Business executives need to ensure that AI is used in a way that respects rights, promotes fairness, and encourages transparency to establish trust among stakeholders.
Implementation of AI governance can enable strict data handling protocols, mitigate biased algorithms, and reduce operational risks to ensure regulatory compliance, while also fostering a culture of ethical practices.
By prioritizing responsible AI practices, businesses can reap significant benefits in improving efficiency, building trust; and ensuring a thriving AI future.
For instance, Pfizer and Mastercard have taken the initiative to design their own AI-governance processes with cross-functional accountability to oversee the company’s AI activities and ensure they fit with data responsibility principles.
6/ Intersection of AI and Sustainability
Stepping into 2024, businesses are striving to improve energy efficiency and reduce their carbon footprint.
Consumers nowadays also have a tendency to support businesses that adopt sustainable practices and introduce environmentally friendly offerings.
This is where the convergence of AI and sustainability comes into the picture.
AI empowers businesses to not only track their carbon footprint but also to actively minimize it by identifying inefficiencies and suggesting improvements in real time.
Google Deepmind, by leveraging AI to optimize energy consumption in data centers, has managed to reduce cooling costs by up to 40%.
Meanwhile, Tesla’s AI can determine the most efficient way to use battery power based on factors like speed, terrain, and outside temperature; resulting in effective emissions reduction.
In the event of climate change and shifting consumer expectations, business executives must acknowledge that sustainable practices are no longer optional – they’re a matter of survival.
AI is a great ally to showcase your genuine commitment to sustainability, establish an environmentally-conscious brand image, and build a competitive advantage against competitors.
Top 5 big AI Challenges in 2024
As we embrace the era of artificial intelligence with open arms, it’s essential to recognize that alongside its undeniable benefits, AI presents a spectrum of challenges that demand our attention.
A nuanced understanding of the challenges is paramount to harnessing AI’s potential responsibly.
1/ Deepfake Deception: The AI-fueled Fraud
With generative AI skyrocketing at an unprecedented speed, deepfake scams are now a big threat to businesses, especially those of any industry that work with customers remotely such as fintech and crypto.
For example, a manipulated audio record of a senior executive can spread disinformation and deceive customers into disclosing sensitive information, or transferring funds to fraudulent accounts; resulting in reputational damage for the company.
Elon Musk himself has been a victim of multiple deepfake scams, most of which aimed at luring investors and unwitting social media users.
In the near future, we expect that more sophisticated types of synthetic fraud will emerge.
To deal with this reality, PwC UK’s Arnav Joshi, an expert in data ethics and digital trust, suggests businesses invest in authenticity technology for digital content (e.g., video encryption, blockchain-based verification), and rely on official channels to communicate with stakeholders.
Planning a crisis response strategy in advance is also necessary to mitigate potential damage as the technology falls into the wrong hands.
In 2024, everyone, from individuals to businesses and greater society, will need to be alert in their consumption and production of online content, especially video.
2/ Navigating The Ethical Minefield
The rapid deployment of AI systems has also ushered in a range of ethical challenges, including inaccurate and biased results generated by AI models.
Meanwhile, there remain several challenges in achieving explainable AI, from high computational cost to the trade-off between explainability and accuracy.
On the other hand, intellectual property (IP) and copyright liabilities are another hurdle for businesses adopting AI.
Copyright disputes around AI, particularly around the use of protected content to train AI models without consent, have witnessed increasing features on headlines in 2023.
For example, The New York Times recently sued OpenAI and Microsoft over copyright issues associated with its written works, which were used to train their automated chatbots.
In order to avoid potential legal implications, business executives are encouraged to collaborate closely with legal advisors who specialize in IP and technology law to ensure that their use of generative AI aligns with existing regulations.
Regular audits and assessments of AI systems should also be conducted to identify any biases or inaccuracies in time.
3/ Scalability and Infrastructure Challenge
Scaling AI initiatives to the enterprise level remains a significant difficulty for companies as of now.
Some main obstacles include obtaining vast amounts of high-quality data, investing heavily in financial resources and expertise; and integrating the technology into existing infrastructure.
But the benefits offered are enormous: from cost optimization and sophisticated data analytics to improved customer service.
In 2024, scaling AI is crucial for businesses to thrive in a highly competitive economy.
To scale AI initiatives systematically, business executives should start by carefully assessing the existing business model to determine core functions that would benefit from scaling AI; and then develop a proper data procurement strategy to serve as the foundation of the entire system.
Effective communication between departments is also an important aspect to ensure a well-coordinated scaling strategy.
As these AI initiatives mature, they can deliver substantial returns on investment, boosting revenue levels and overall operational efficiency for the business.
4/ AI-transformed Job Market
The impact of continuing AI advancements on the job market soon has huge implications for employees and businesses alike.
The two most important foreseeable trends are that manual workers performing repetitive tasks would be replaced by AI; and new AI-related jobs such as AI-prompt engineer, machine learning engineer, etc. would emerge, with higher skill requirements and higher average salary.
From a business perspective, this shift in job market dynamics would require corresponding changes in human resource practices.
Businesses that are still in the process of exploring AI applications should conduct a skill gap analysis and develop training programs to equip the existing workforce with the skills needed to work alongside AI technologies.
As for businesses that are already adopting AI, they should analyze how AI has influenced their employees’ roles to redesign training programs, along with creating roles that leverage both the creativity of humans and the data analysis capacity of AI.
Proactive measures surrounding reskilling and upskilling are crucial for companies to ensure a smooth transition to an AI-centric job market.
5/ Shortage of Powerful AI Chips
To power AI products, investors and companies go to great lengths to obtain high-powered chips known as graphic processing units (GPUs).
Lying at the heart of any generative AI program, GPUs are used to run countless calculations involved in training and deploying AI algorithms. Unfortunately, these chips are currently facing a global shortage.
In 2023, the hype of online chatbots like ChatGPT has set off a wave of excitement among tech companies, leading to a massive spike in global chip demand that surpasses the supply of the world’s largest chip manufacturer Nvidia.
As AI advancements are still continuing, we expect long wait lists, even up to a year, to access these chips.
This is a frustrating reality for businesses that see the boundless growth potential of AI and don’t want to risk falling behind fierce competitors.
Meanwhile, large tech firms can get their hands on GPUs more easily due to their size, financial capacity, and market position.
Facing ongoing supply constraints, some companies try to make their AI models more efficient to reduce the number of GPUs required and maximize the utilization of available resources; while others try to diversify their supply chain to reduce dependence on a specific chip supplier.
However, it seems that the chip shortage isn’t going to ease any time soon, partly because competitors to Nvidia could take as long as 2 to 3 years to expand their offerings.
In the meantime, business executives are required to find creative ways to reduce reliance on GPUs and use available resources more smartly.
Conclusion
As 2024 dawns, the landscape of artificial intelligence shifts into high gear, presenting businesses with both ground-breaking opportunities and complex challenges.
Coupled with countless use cases, the potential advantages are endless: from cost reductions, and optimized data analysis, to swift decision-making.
Yet, alongside this boundless potential remain concerns about ethical and legal implications, the looming threat of deepfakes, and even the shortage of high-performance chips.
To navigate this landscape effectively, business executives must prioritize transparency and accountability through robust AI governance frameworks.
Legal compliance regarding intellectual property and copyright must also be carefully taken into account.
Meanwhile, investing in employee reskilling and promoting human-AI collaboration ensures a well-equipped workforce.
Only by taking such proactive measures can businesses lay a firm foundation for a sustainable future with AI.
Dear visionary leaders, are you ready to step into the complex AI landscape and guide your organization toward a new era?
Please contact us for collaborative consulting sessions and craft disruptive AI solutions to redefine your business!
Var Meta would love to hear about your visions and be a part of your success.
References
- CNN Business (2023). The big bottleneck for AI: a shortage of powerful chips, available at: https://edition.cnn.com/2023/08/06/tech/ai-chips-supply-chain/index.html
- European Data Protection Supervisor (2023). TechDispatch #2/2023 – Explainable Artificial Intelligence, available at: https://edps.europa.eu/data-protection/our-work/publications/techdispatch/2023-11-16-techdispatch-22023-explainable-artificial-intelligence_en
- Fortune Business Insights (2022). Edge AI Market Size, Share & Forecast Report [2022 – 2029], available at: https://www.fortunebusinessinsights.com/edge-ai-market-107023
- Google Deepmind (2016). DeepMind AI Reduces Google Data Centre Cooling Bill by 40%, available at: https://deepmind.google/discover/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-by-40/
- Iapp (2023). AI governance as an enterprise business initiative, available at: https://iapp.org/news/a/ai-governance-as-an-enterprise-business-initiative/
- IBM (n.d.). What is explainable AI?, available at: https://www.ibm.com/topics/explainable-ai#:~:text=Explainable%20artificial%20intelligence%20(XAI)%20is,expected%20impact%20and%20potential%20biases.
- PwC Australia (2019). From pornography to presidents: Why business needs to pay attention to deepfakes, available at: https://www.pwc.com.au/digitalpulse/how-business-should-respond-to-deepfakes.html
- RTS labs (2023). Unveiling the Challenges and Strategies of Scaling AI Initiatives, available at: https://rtslabs.com/unveiling-the-challenges-and-strategies-of-scaling-ai-initiatives/
- The New York Times (2023). The Desperate Hunt for the A.I. Boom’s Most Indispensable Prize, available at: https://nytimes.com/2023/08/16/technology/ai-gpu-chips-shortage.html
- The New York Times (2023). The Times Sues OpenAI and Microsoft Over A.I. Use of Copyrighted Work, available at: https://www.nytimes.com/2023/12/27/business/media/new-york-times-open-ai-microsoft-lawsuit.html
- The Quantum Insider (2023). Is Quantum Artificial Intelligence Close? Understanding The Challenges Of Quantum AI, available at: https://thequantuminsider.com/2023/11/23/is-quantum-artificial-intelligence-close-understanding-the-challenges-of-quantum-ai/
- Xailient (2022). What is Edge AI and Why Does it Matter?, available at: https://xailient.com/blog/what-is-edge-ai-and-why-does-it-matter/
Infographic: AI Trends and Challenges in 2024
