Enterprise AI Analysis
A Systematic Review of Generative AI: Importance of Industry and Startup-Centered Perspectives, Agentic AI, Ethical Considerations & Challenges, and Future Directions
Authors: Kinjal Patel, Milind Shah, Karishma M. Qureshi, Mohamed Rafik N. Qureshi
Publication: Artificial Intelligence Review (2026) 59:7 | Received: 5 July 2025 / Accepted: 19 October 2025
Executive Impact at a Glance
Generative Artificial Intelligence (GenAI) is rapidly redefining the landscape of work organizations and society at large. GenAI has rapidly evolved from rule-based symbolic systems of The 1940s to advanced deep learning architectures capable of producing human-like content across modalities, such as text, images, audio, and video. This review focuses on current emerging trends, such as large concept models and critical comparisons of tools, including ChatGPT, Gemini, and Claude.
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GenAI Market CAGR (2023-2028)
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Projected Market Size by 2028
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Studies Included in Review
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
GenAI Evolution Timeline
Early Computing (1940s-1980s)
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Classical Machine Learning (1980s-2000s)
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Deep Learning Revolution (2010s)
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Transformer Architecture (2017s)
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Large Language Models (2018s-2021s)
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MultiModel Frameworks (2021s-2023s)
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Agentic AI (2023s-Present)
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Advanced Multimodal Foundation Models Emerge
Case Study: BloombergGPT in Finance
Bloomberg has launched a generative model dedicated to the finance sector called the Bloomberg generative pretrained transformer (GPT). This model assists with various financial operations, from sentiment analysis to report generation and regulatory compliance. It leverages internal datasets and research, providing precise information to clients and financial professionals.
This initiative highlights GenAI's essential role in automating financial processes, enhancing decision-making, and improving overall efficiency within a specialized industry domain.
Advancing
Patient Care, Research, and Administrative Services with GenAI in Healthcare
GenAI Product Development Lifecycle for Startups
1. Requirements & Design
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2. Implementation Testing
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3. Deployment & Feedback
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4. Customer Value & Impact
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5. Continuous Improvement
Increased
Business Automation Levels through GenAI
| Parameter | ChatGPT | Gemini (Bard) | Claude |
|---|---|---|---|
| Developer | Open AI | Anthropic | |
| Model Architecture | Transformer-based (GPT), Generative Pretrained Transformer | LaMDA and PaLM Architecture | Transformer-Decoder only LLM (Haiku, Sonnet, and Opus) |
| Key Strength |
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| Bias, Ethics & Safety | Active safety management, potential for inappropriate content if not monitored | Transparency regarding sources, variable factual reliability | Advanced bias minimization, safety protocols, critical for healthcare and finance |
| Best Use Cases | Creative writing, brainstorming, general knowledge Q&A | Real-time news, research requiring current info | Extended research, technical/legal work, ethical decision support |
Mitigation
Strategies for Factual Accuracy & Hallucination Reduction
Calculate Your Potential GenAI ROI
Estimate the economic impact of GenAI implementation within your organization. Adjust parameters to see potential annual savings and reclaimed operational hours.
Annual Cost Savings
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Annual Hours Reclaimed
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Your Strategic GenAI Implementation Roadmap
A phased approach ensures responsible integration, maximizing benefits while mitigating risks. This roadmap outlines key stages for successful GenAI deployment.
Phase 1: Initial Assessment & Strategy
Conceptualize requirements, define use cases, and design initial GenAI frameworks. Focus on identifying high-impact areas and establishing ethical guidelines.
Phase 2: Pilot Development & Testing
Implement and test GenAI solutions in controlled environments. Evaluate performance, accuracy, and identify potential biases. Gather user feedback for iteration.
Phase 3: Deployment & Feedback Loop
Roll out GenAI applications to target user groups. Establish mechanisms for continuous feedback, performance monitoring, and rapid iteration based on real-world usage.
Phase 4: Optimization & Scalability
Continuously refine models, expand to new use cases, and scale infrastructure. Ensure long-term ethical compliance, data security, and adaptability to evolving GenAI capabilities.
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