Global Entrepreneurship Summit 2027

Agentic artificial intelligence (AI) extends generative AI from responding to individual prompts toward pursuing goals through planning, tool use, information retrieval, and coordinated actions. This shift creates new possibilities for startups seeking to operate with limited resources and established enterprises managing complex workflows. However, automating more steps does not necessarily produce measurable business value. This paper examines the business potential and implementation risks of agentic AI through a focused review of technical research, industry adoption evidence, and current governance guidance. It proposes the Scalable Agentic Governance and Execution for Business (SAGE-Biz) framework, which links organizational objectives, trusted data, bounded agent orchestration, permission tools, human approval, and continuous performance monitoring. The paper compares applications in customer operations, sales, financial planning, software development, and enterprise service management. Two transparent, assumption-based scenarios illustrate how organizations might estimate time savings and implementation economics without mistaking projected benefits for observed results. A controlled evaluation design is proposed to test task completion, quality, cost, reliability, and safety against manual and conventional automation baselines. The central conclusion is that agentic AI should be adopted as a governed operating capability, not as unsupervised replacement labor. Its value depends on task suitability, workflow redesign, reliable data, and economically meaningful outcomes. Keywords: agentic AI, autonomous agents, startups, enterprise automation, business process management, AI governance, digital transformation

AI & Predictive Analytics Researcher | IT Consultant | Doctoral Researcher

Sakera Begum is an artificial intelligence and predictive analytics researcher based in the United States, specializing in machine learning, intelligent decision-support systems, financial technology, and cybersecurity. She is currently pursuing a Doctor of Computer Science at the University of the Potomac, USA, and holds a Master of Science in Information Technology and a Bachelor of Business Administration in Finance and Accounting.

Her research combines artificial intelligence with practical business and economic challenges, particularly the development of predictive models and intelligent frameworks to improve capital accessibility, financial resilience, and strategic decision-making. She has authored numerous scholarly publications and a research book, developed AI-powered analytical platforms, and presented her work at international academic conferences.

Her research contributions have been recognized through prestigious academic distinctions, including the Best Paper Award at INDEA 2026, the Best Researcher Award in Artificial Intelligence, Data Analytics and Business Intelligence, and the Enterprise Solutions Innovation Award.

Beyond her research activities, she contributes to the international academic community as a peer reviewer for Springer Nature, Elsevier journals, and international conferences, including SEMIT. She has also served as a conference speaker, panel chair, and scientific committee member. She is a member of IEEE, ACM, and the Analytics Society of INFORMS.

Her ongoing research focuses on advancing responsible, explainable, and practical AI solutions that address real-world challenges in business, finance, and emerging technologies.