Today’s large language models (LLMs), powered by advanced conversational intelligence, are transforming communication experiences. These models are making interactions more human and less transactional, revolutionizing tasks such as sales and marketing pitches, answering queries, engaging in digital transactions and trades, and more.
AI has ventured into domains traditionally managed by humans, such as human resources, where it evaluates performance, skillsets, aptitude, and cultural fit to make hiring and firing decisions.
Top executives are leveraging AI to enhance their capabilities, enabling them to become highly innovative communicators, designers, and creators of future business products and solutions.
NVIDIA AI experts recently forecast the emergence of intelligent stores, a new class of robots, and advancements in healthcare, manufacturing, and other sectors—all driven by AI technology.
The impact of AI on humanity continues to grow. A 2024 OECD report reveals that AI is reshaping critical aspects of human development, including health, education, social norms, relationships, and employment.
Spotlight on Jason Alan Snyder
Jason Alan Snyder is a distinguished author and lecturer on technology topics, including spatial computing, the metaverse, artificial intelligence (AI), machine learning (ML), and cognitive robotic process automation.
With numerous patents and a proven track record of translating emerging technologies into commercial successes, Snyder has operated across entrepreneurial, corporate, finance, medical, pharmaceutical, and government sectors.
He co-founded SuperTruth, an innovative data company that leverages AI to help nationwide health systems verify the authenticity of clinical trials and patient data.
TRENDS explored the multifaceted ways AI is transforming our collective future and the essential factors for realizing its full potential in an exclusive interview.
In what ways can multi-model AI be used for business training and upskilling?
Multi-model AI can make training more immersive, dynamic, and personalized than ever before. Integrating text, visuals, and audio can simulate real-world scenarios, such as onboarding new employees or upskilling existing teams to handle emerging technologies.
These AI systems analyze real-time performance data and tailor content to individual learning styles. However, to unlock the potential of AI-driven upskilling, businesses must feed their systems with accurate, up-to-date, and well-structured data. Without this, insights and outcomes risk becoming irrelevant or misleading.
What new work roles will AI create, and where will funding for AI skills be sourced?
AI will introduce roles such as AI ethics officers, data stewards, synthetic data engineers, and AI trainers. These positions focus on ensuring that systems are fair, reliable, and aligned with human values. Data custodians will play a critical role in managing data quality and mitigating decay, which is essential for maintaining AI’s long-term effectiveness.
Funding for AI skills development will primarily come from the private sector, where the return on investment is evident. Governments must also integrate AI education into K-12 and higher education programs to prepare future generations. Nonprofits will be crucial in extending these skills to underserved communities, closing the gap between those who have access to AI-powered opportunities and those who do not.
How important will prompt engineering be for innovative AI-human solutions in the future of work?
Prompt engineering is becoming the critical bridge between human creativity and AI’s computational power. However, even the most well-crafted prompts depend on high-quality data.
At SuperTruth, the broader challenge is aligning human intent with AI’s capabilities. Prompt engineering isn’t just about generating good answers; it’s about teaching people how to ask the right questions and ensuring that AI operates on a foundation of clean and accurate data.
As this field evolves, it will expand into broader AI orchestration roles, where professionals manage complex workflows across multiple AI systems.
What risks and threats could multi-model AI pose to the digital economy?
Alongside misinformation, bias, and privacy concerns, a major risk is data decay—the gradual loss of data relevance and accuracy over time. Multi-model AI depends on vast datasets, but poorly maintained data can lead to errors that undermine trust in AI systems.
Another concern is data monopolization, where access to high-quality data is concentrated among a few large organizations, creating barriers for smaller innovators. Transparency and accountability are also vital; without explainability, particularly in sensitive sectors like finance and healthcare, trust in AI systems could erode.
