The Rise of #AI: From Rule-Based Expert Systems to Autonomous Agents

Artificial Intelligence (AI) has rapidly evolved from simple rule-based systems to advanced autonomous agents capable of performing complex tasks. Over the past year, AI has dominated discussions across industries—every pundit, futurist, conference speaker, magazine, academic, and expert has weighed in on its potential and implications. Meanwhile, social media is flooded with a mix of real, speculative, and sometimes misleading insights about AI’s trajectory. Keeping up with this fast-moving landscape can be overwhelming.

I recall working in research and development for a multinational corporation between 1992 and 1996, building expert systems—early forms of AI designed to mimic human decision-making in specific domains. Our projects included:

• An AIDS awareness system,
• A fire protection and safety advisory tool, and
• A system for psychiatrists working with mental health patients.

At the time, we were working with heuristic rule-based expert systems, which relied on predefined "rules of thumb" to make decisions—primitive by today’s standards, but groundbreaking back then. The excitement of watching these systems produce results felt like magic. Compared to today’s advanced AI tools, which simplify and automate complex processes, those early models seem rudimentary.

The Evolution of AI: From Data Analysis to Autonomous Action

AI has long been used for data analysis and decision-making, but recent advancements have ushered in two powerful new categories:

• #GenerativeAI
• #AgenticAI

So how do AI, Generative AI, and Agentic AI Differ?

1. Artificial Intelligence (AI): AI refers to systems that mimic human intelligence to analyze data, recognize patterns, and make predictions or decisions. Examples: Machine learning, computer vision, speech recognition. Use Cases: Fraud detection, predictive analytics, automation of repetitive tasks.
2. Generative AI: A subset of AI that creates new content, including text, images, audio, and video, based on learned patterns. Examples: GPT-based chatbots, AI-powered design tools, automated content creation. Use Cases: All potential use cases can be classified into the following 5 categories: Essay Writing, Summarization, Translation, Information Retrieval, Invoke APIs and actions.
3. Agentic AI: A more advanced form of AI that autonomously sets goals, takes actions, and adapts to changing environments with minimal human intervention. Examples: AutoGPT, BabyAGI, AI-powered personal assistants. Use Cases: Fully autonomous financial advisors, AI travel planners, self-improving software agents.

The rapid pace of AI development has sparked debate over the best approach. While some argue that large AI models—trained on vast datasets—are necessary for meaningful progress, others advocate for a more efficient strategy using smaller models.

Large vs. Small AI Models: A Shifting Paradigm

Large models, despite their versatility, come with challenges such as:

• High resource demands,
• Expensive infrastructure,
• Performance bottlenecks, and
• Scaling limitations.

Smaller models, while more resource-efficient, excel in specific domains rather than broad, general-purpose applications. They require extensive fine-tuning to adapt to new tasks, as content is highly dynamic and vectorized embeddings only offer limited generalization.

The Role of Small Models in Agentic AI

Smaller and nano-models are particularly well-suited for Agentic AI, where a network of specialized models can work in tandem to replicate the capabilities of larger models—while consuming fewer resources.

A useful analogy is Big Data vs. Small Data. At its peak, Big Data was heralded as a transformative force, capable of solving countless problems. However, organizations soon realized that implementing Big Data solutions required massive infrastructure investments. Similarly, while large AI models have their place, the future may favor a distributed, modular approach using specialized AI systems.

Potential Industry applications of AI, Generative AI, and Agentic AI

1. Banking & Financial Services

• AI: Fraud detection, risk assessment, algorithmic trading.
• Generative AI: AI-generated financial reports, automated contract creation, behavioral insights.
• Agentic AI: Autonomous portfolio management, AI-driven compliance monitoring, loan approvals.

2. Hospitality

• AI: Dynamic pricing, chatbot-based customer service, demand forecasting.
• Generative AI: AI-created promotions, customer sentiment analysis, AI-generated hotel interior designs.
• Agentic AI: AI-powered concierge for bookings, guest requests, and itinerary planning.

3. FMCG (Fast-Moving Consumer Goods)

• AI: Inventory optimization, demand forecasting, automated supply chain analytics.
• Generative AI: AI-created product descriptions, advertising campaigns, virtual influencer content.
• Agentic AI: Autonomous procurement agents, AI-driven customer engagement models.

4. Medicine & Healthcare

• AI: Disease diagnosis, robotic-assisted surgeries, drug discovery.
• Generative AI: AI-generated medical reports, synthetic patient data for trials, AI-driven treatment recommendations.
• Agentic AI: Autonomous health monitoring, AI-powered research assistants.

5. Travel & Tourism

• AI: Chatbots for travel bookings, dynamic pricing, flight delay predictions.
• Generative AI: AI-generated travel itineraries, virtual tour content, travel recommendations.
• Agentic AI: AI travel planners that book, reschedule, and optimize trips based on real-time conditions.

Risks, Ethical Issues, and Challenges

1. Data Privacy & Security Risks
2. Bias & Fairness Issues
3. Job Displacement & Workforce Disruption
4. Ethical Concerns & Accountability
5. High Costs & Complexity

Conclusion

AI, Generative AI, and Agentic AI are transforming industries, introducing efficiency, creativity, and autonomy. While the benefits are immense—ranging from enhanced customer experiences to autonomous decision-making—businesses must navigate challenges related to privacy, fairness, workforce impact, ethics, and costs.

The key to successful AI adoption lies in responsible AI development, regulatory compliance, and continuous human oversight to ensure AI remains an enabler rather than a disruptor.

By strategically leveraging AI’s potential while mitigating risks, businesses can drive sustainable innovation and achieve long-term success in an AI-driven world.