﷽
Embedded Files
Strategic Assets Advisors (ISO 55000)
AI-QUANTUM
Quran and Quantum Physics
﷽
"وَهُوَ الَّذِي خَلَقَ اللَّيْلَ وَالنَّهَارَ وَالشَّمْسَ وَالْقَمَرَ ۖ كُلٌّ فِي فَلَكٍ يَسْبَحُونَ"
"It is He (The ALLAH, The CREATOR of The COSMIC) Who Created the Night and the Day, and the Sun and the Moon, Everything Swimming in an Orbit."
The Divine Holy Book Al-Quran: Chapter: 21 Verse: 33
Divine Knowledge >> Perpetual Scientific Advancement
Our Focus Sectors
Aerospace | Agriculture | AI-Ilm | AI Economy | Deep Space
Economy | Education | Energy | Logistics | Manufacturing
Mining | Optical Systems | Technology | Water
All hyperlinks open new window
Group URLs: ABOUT >> AI-FINANCIAL MODELING >> LEGAL ENTITIES
Quantum Science and Technology
Our Project: Quantum-AI Algorithms Optimization (QAAO)
(Update as of June 30, 2025)
Program Lead: Mohammad A. Mirza
President: Afro Eurasian Coalition (USA)
Our Focus
Our Unique Methodological Process
Evaluation > Analysis > Design > Implementation > Optimization
Our Priority Focus: Algorithm Engineering
Deep Dive
Gravitational Waves are invisible Ripples in the Fabric of Space-Time. They are caused by some of the most violent and energetic events in the universe.
Gravitational Waves include Colliding Black Holes, Collapsing Stellar Cores, Merging Neutron Stars or White Dwarf Stars, possibly the remnants of gravitational radiation created by the birth of the universe.
15 Years of Radio Data Reveals Evidence of Ripples In Space-Time. Source: NASA
U.S. NSF Lab LIGO on September 14, 2015, Detected Merger of Two Black Holes in the Distant Universe.
Algorithmic Research focuses on designing, analyzing, and implementing efficient and effective solutions for computational problems.
It involves exploring different Algorithmic Approaches, analyzing their complexity, and comparing their performance.
Our focus in this field is that how to make Algorithms more robust, secure, and scalable, especially in areas like:
MUGHALS Panels of Experts
MUGHALS Panels of Experts
Eight Panels of Experts
Eight Panels of Experts
1) Attorneys; 2) Academia - Students; 3) Community Activists;
4) Economists; 5) Financial Experts; 6) Scholars; 7) Philanthropist;
8) Technologists (Scientists-Engineers-Technicians)
Our Focus: Quantum Query Model (QQM)
Algorithm Engineering is a multidisciplinary field that combines elements of Mathematics, Computer Science, and Software Engineering to design, analyze, and implement efficient algorithms for solving real-world problems.
Algorithm Engineering bridges the gap between Theoretical Algorithm Design and Practical Application, focusing on optimizing algorithm performance and reliability in real-world settings.
In essence, it's about creating algorithms that not only perform well theoretically but also work efficiently in real-world applications.
Algorithm Engineering is a methodology for Algorithmic Research that bridges the gap between theory and practice in software engineering.
It combines the design, analysis, implementation, optimization, and experimental evaluation of algorithms.
What are AI Algorithms?
Artificial Intelligence (AI) refers to the Simulation of Human Intelligence in machines, allowing them to perform tasks that typically require human cognitive functions such as learning, reasoning, problem-solving, perception, and decision-making.
AI Systems analyze vast amounts of data, adapt to new information, and enhance their performance autonomously over time.
The ultimate goal of AI is to develop machines that can think, reason, and act autonomously, and in some cases, surpass human capabilities across various domains
AI Algorithms Encompass a diverse range of techniques, enabling machines to learn from data, identify patterns, make decisions, and solve complex problems autonomously.
Categorized based on functionality and learning approach, these algorithms include:
In essence, AI algorithms are the engine that drives artificial intelligence, enabling machines to learn, adapt, and perform a wide range of tasks autonomously or semi-autonomously.
Muslims and Evolution of Science
During the Islamic Golden Age, significant advancements were made in various scientific fields, including Physics, Chemistry, Biology, Algebra, Mathematics, Astronomy, Cosmology, Geometry, Medicine, Optics etc.
For example Al-Khwarizmi laid the groundwork for algebra, Ibn al-Haytham (Alhazen) revolutionized optics, and Al-Zahrawi made major contributions to surgery and medicine.
Minimum 1001 Plus Muslim Scientists Inventions, Pioneering Discoveries
The Father of Mathematics, Algebra, Algorithm, Computers, Computational Sciences,
Pioneer: Algorithmically-based Technology
The Father of Modern Scientific Method
Using Experiment to Verify Theory
Pioneer: Optics: The True Nature of Light,, Vision
Real-World Case Studies
Case Study: Amazon Quantum Algorithm:
Quantum Computing: IBM Perspective
IBM's perspective on quantum computing focuses on both developing and democratizing the technology. They offer cloud-based access to quantum computers via their Qiskit Platform, enabling broader access for research and development. IBM also actively researches and develops quantum computing hardware and software, aiming for quantum advantage and Fault-Tolerant Quantum Computing.
IBM Quantum Platform
Quantum Algorithms and Programming Languages used by IBM are part of their effort to advance quantum computing, primarily through the open-source Qiskit software development kit (SDK) and the IBM Quantum Platform.
Quantum Algorithms at IBM
Quantum algorithms leverage the principles of quantum mechanics—such as superposition, entanglement, and quantum interference—to solve problems more efficiently than classical algorithms for specific use cases. IBM has been actively researching and implementing quantum algorithms, particularly for near-term noisy intermediate-scale quantum (NISQ) devices. Below are some of the key algorithms IBM explores:
Quantum Query Algorithms
Deutsch-Jozsa Algorithm: Determines whether a function is balanced or constant with a single query, showcasing quantum speedup over classical methods requiring multiple queries.
Grover’s Algorithm: Provides a quadratic speedup for unstructured search problems, useful for tasks like database searches or optimization.
Simon’s Algorithm: Solves a problem related to finding periodicity in functions, demonstrating exponential speedup over classical approaches.
Shor’s Algorithm:
Used for integer factorization, which has implications for cryptography (e.g., breaking RSA). It offers an exponential speedup over the best-known classical algorithms.
Quantum Fourier Transform (QFT) and Inverse QFT:
A key component in algorithms like Shor’s and phase estimation, QFT transforms quantum states to analyze periodicities, enabling applications in cryptography and signal processing.
Phase Estimation:
Estimates the eigenvalues of unitary operators, critical for algorithms like Shor’s and quantum simulation. It’s used in applications like quantum counting and solving linear systems.
Variational Quantum Algorithms (e.g., VQE and QAOA):
Variational Quantum Eigensolver (VQE): Used for quantum chemistry to find the ground state energy of molecules (e.g., simulating H2, LiH, and BeH2 molecules, as demonstrated by IBM in a 2017 Nature paper). VQE is hybrid, combining quantum and classical computation to optimize parameters.
Quantum Approximate Optimization Algorithm (QAOA): Designed for combinatorial optimization problems, such as portfolio optimization or graph-based problems, offering potential advantages in finance and logistics.
Quantum Machine Learning:
IBM explores quantum algorithms for training machine learning models, potentially improving applications like disease diagnosis, fraud detection, and energy management by leveraging quantum speedups in data processing.
These algorithms are implemented and tested on IBM’s quantum processors, which use superconducting transmon qubits. IBM’s focus is on demonstrating quantum utility—where quantum computers can compete with classical methods for specific tasks—and eventually achieving quantum advantage, where quantum systems outperform classical computers for meaningful problems.
Quantum Algorithms leverage the principles of Quantum Mechanics—such as Superposition, Entanglement, and Quantum Interference—to solve problems more efficiently than classical algorithms for specific use cases. IBM has been actively researching and implementing quantum algorithms, particularly for Near-term noisy Intermediate-Scale Quantum (NISQ) devices. Below are some of the key algorithms IBM explores:
Deutsch-Jozsa Algorithm: Determines whether a function is balanced or constant with a single query, showcasing quantum speedup over classical methods requiring multiple queries.
Grover’s Algorithm: Provides a quadratic speedup for unstructured search problems, useful for tasks like database searches or optimization.
Simon’s Algorithm: Solves a problem related to finding periodicity in functions, demonstrating exponential speedup over classical approaches.
Used for integer factorization, which has implications for cryptography (e.g., breaking RSA). It offers an exponential speedup over the best-known classical algorithms.
Quantum Fourier Transform (QFT) and Inverse QFT:
A key component in algorithms like Shor’s and phase estimation, QFT transforms Quantum States to analyze periodicities, enabling applications in Cryptography and Signal Processing.
Estimates the eigenvalues of unitary operators, critical for algorithms like Shor’s and quantum simulation. It’s used in applications like quantum counting and solving linear systems.
Variational Quantum Algorithms (VQA): e.g., VQE and QAOA
Variational Quantum Eigensolver (VQE): Used for quantum chemistry to find the ground state energy of molecules (e.g., simulating H2, LiH, and BeH2 molecules, as demonstrated by IBM in a 2017 Nature paper).
VQE is hybrid, combining quantum and classical computation to optimize parameters.
Quantum Approximate Optimization Algorithm (QAOA): Designed for combinatorial optimization problems, such as portfolio optimization or graph-based problems, offering potential advantages in finance and logistics.
Quantum Machine Learning:
IBM explores quantum algorithms for training machine learning models, potentially improving applications like disease diagnosis, fraud detection, and energy management by leveraging quantum speedups in data processing.
These algorithms are implemented and tested on IBM’s quantum processors, which use superconducting transmon qubits. IBM’s focus is on demonstrating quantum utility—where quantum computers can compete with classical methods for specific tasks—and eventually achieving quantum advantage, where quantum systems outperform classical computers for meaningful problems.
IBM Programming Languages and Tools
IBM primarily uses Qiskit, an open-source quantum computing framework, to program and interact with their quantum hardware and simulators. Below is a detailed look at the programming languages and tools IBM employs:
Qiskit:
Overview: Qiskit is IBM’s flagship open-source SDK for quantum computing, launched in 2017. It allows developers to create, manipulate, and execute quantum circuits at the level of circuits, pulses, and algorithms. It’s the most widely used quantum SDK globally, with over 600,000 registered users and 700 universities incorporating it into curricula.
Primary Language: Qiskit is Python-based, leveraging Python’s simplicity and extensive libraries (e.g., NumPy, SciPy) to make quantum programming accessible.
Components:
Qiskit Terra: Core module for constructing and optimizing quantum circuits.
Qiskit Aer: Provides high-performance simulators for testing quantum programs on classical computers.
Qiskit Ignis: Tools for noise characterization and error mitigation in quantum systems.
Qiskit Aqua (now part of Qiskit Applications): Supports domain-specific applications like chemistry, optimization, and machine learning.
Qiskit Pulse: A machine-level programming interface for controlling quantum hardware at the pulse level, enabling precise manipulation of qubits (e.g., defining how a quantum gate is physically enacted).
Execution: Qiskit programs can run on IBM’s quantum processors (via the IBM Quantum Platform) or local/cloud-based simulators. It supports hybrid quantum-classical workflows, crucial for NISQ-era algorithms like VQE and QAOA.
Features: Includes AI-assisted tools like Qiskit Code Assistant for error detection, code autocompletion, and circuit visualization. It also supports third-party hardware (e.g., IonQ’s trapped ion quantum computers via Google Cloud Marketplace).
OpenQASM (Open Quantum Assembly Language):
Overview: OpenQASM is a hardware-agnostic intermediate representation for quantum circuits, introduced by IBM. It translates high-level quantum programs (written in Qiskit) into machine-level instructions for execution on quantum processors.
Versions:
OpenQASM 2: Supports basic quantum operations (gates, measurements, resets) and is widely used for circuit construction.
OpenQASM 3: Extends functionality with classical feed-forward control, gate modifiers, and pulse-level implementations, catering to advanced needs like circuit optimization and hardware debugging.
Use Case: Acts as a bridge between high-level Qiskit code and physical quantum hardware, ensuring compatibility across different quantum software tools. Qiskit provides utilities to convert between OpenQASM and its QuantumCircuit class.
Quantum Composer:
A graphical user interface (GUI) for building quantum circuits via a drag-and-drop interface, resembling a musical score (hence, circuits are called “quantum scores”). It allows users to construct algorithms visually or write OpenQASM code directly.
Users can run circuits on IBM’s quantum processors or simulators, making it accessible for beginners and educators.
IBM Quantum Platform:
A cloud-based platform providing access to IBM’s quantum processors (12 devices as of June 2025, with 100+ qubit systems available). It supports Qiskit and OpenQASM, offering free access to 10 minutes of execution time per month on 100+ qubit processors.
Includes tutorials, interactive courses, and tools like Jupyter notebooks for programmatic circuit design.
Other Tools:
Qiskit Functions: Provided by IBM and third-party partners (e.g., Q-CTRL’s Fire Opal for performance management and optimization solvers), these accelerate development by offering pre-built solutions for common tasks.
Qiskit Transpiler Service: Uses AI to optimize quantum circuits for specific hardware, improving performance and reducing errors.
Integration with Classical Tools: Qiskit integrates with industry-standard tools like Jupyter notebooks, Python libraries, and cloud platforms (e.g., qBraid, OVHCloud), enabling seamless quantum-classical workflows.
How IBM Uses These Tools for Quantum Algorithms
Development Process: Developers use Qiskit to write quantum algorithms in Python, leveraging its libraries for circuit construction and optimization. These programs are compiled into OpenQASM for execution on IBM’s quantum processors or simulators.
Applications:
Chemistry: Qiskit Aqua (now part of Qiskit Applications) translates chemistry problems into quantum inputs, enabling simulations of molecular structures (e.g., H2, LiH, BeH2).
Optimization: Algorithms like QAOA are implemented for financial applications (portfolio optimization, risk analysis) and logistics.
Machine Learning: Quantum algorithms enhance data processing for tasks like fraud detection and disease diagnosis.
Accessibility: IBM’s Quantum Platform and Qiskit’s open-source nature make these tools available to researchers, developers, and educators worldwide. The Quantum Composer lowers the entry barrier for non-specialists.
Performance Optimization: Qiskit Pulse and the Transpiler Service allow fine-tuned control over quantum hardware, addressing noise and connectivity constraints in NISQ devices.
Key Features and Benefits
Ease of Use: Python-based Qiskit reduces the learning curve, making quantum programming accessible to those familiar with classical programming.
Open-Source Community: Qiskit’s open-source model fosters collaboration, with contributions from IBM Research, the Qiskit community, and partners like Q-CTRL.
Hardware Access: IBM provides free access to 100+ qubit processors, enabling real-world experimentation.
Educational Resources: IBM offers extensive learning materials, including the Qiskit Textbook, tutorials, and courses via IBM Quantum Learning, supporting beginners and experts.
Limitations
NISQ Constraints: Current quantum hardware has limitations in qubit count and stability, requiring hybrid quantum-classical approaches and error mitigation.
Learning Curve for Advanced Features: While Qiskit is beginner-friendly, mastering pulse-level control (Qiskit Pulse) or advanced algorithms like QAOA requires expertise in quantum mechanics and mathematics.
Hardware Access: While IBM offers free access to smaller systems, more powerful processors require membership in the IBM Quantum Network or access awards.
Sources
IBM Quantum Learning:
IBM Quantum Platform and Qiskit:
OpenQASM:
Qiskit Pulse and collaborations:
Quantum algorithms paper:
Qiskit Textbook and educational resources:,
This overview covers IBM’s quantum algorithms and programming tools as of June 2025, reflecting their commitment to advancing quantum computing through accessible, Python-based frameworks and cloud-based hardware access. If you need deeper details on specific algorithms or tools, please contact peis@themughals.net
AI, Quantum Computing Impact on Global Economy
Quantum Computing, particularly when integrated with Artificial Intelligence (AI), is projected to have a substantial impact on the global economy, potentially creating $1 trillion in value by 2035 and generating millions of new jobs. This transformative technology promises to accelerate AI training, enhance risk analysis, and revolutionize industries like finance, healthcare, and cybersecurity.
Artificial Intelligence (AI) is profoundly influencing Global Economies; driving Innovation; and Productivity while posing challenges like Skill Gap; Job Displacement; Socioeconomic Inequality and Regional Disparity.
Global AI Economy
Global AI Economy
2025 = US $ 294.16 Billion
2030 = US $ 19.9 Trillion
Compound Annual Growth Rate (CAGR) = 29.2%
(Source: Fortune Business Insights, 2024)
AI Global Impact
AI Global Impact
Geopolitical Competition
The race for AI dominance has Economic and Strategic Implications. China aims to lead global AI by 2030, challenging the U.S.A.This competition is reshaping Trade, Supply Chains, and Innovation leadership worldwide.
Automation and Job Displacement
AI is automating routine tasks in industries such as manufacturing, logistics, and customer service. A McKinsey report estimates that by 2030, up to 375 million workers worldwide may need to switch occupations due to automation. At the same time, AI is creating new jobs in areas like technology development, data science, and AI system maintenance.Economic Growth and Productivity
By optimizing processes, improving decision-making, and fostering innovation, AI boosts productivity.The World Economic Forum predicts AI could contribute $15.7 trillion to the global economy by 2030, with the largest gains in countries leading AI adoption, such as the U.S. and China.
Inequality and Skills Gap
AI risks widening income inequality as high-skill workers benefit from new opportunities while low-skill workers face job losses. Regions with advanced tech ecosystems (e.g., Silicon Valley USA, Shenzhen China) may prosper, while others fall behind. Addressing this divide requires investment in education and retraining.
Multiple research organizations predicts that business spending to adopt Artificial Intelligence (AI) will have a Cumulative Global Economic Impact of $19.9 trillion through 2030 and drive 3.5% of Global GDP in 2030. As a result, AI will affect jobs across every region of the world. This includes direct, indirect, and induced effects from AI adoption.
As of April 2025, according to U.S. Congressional Budget Office:
👉...“General attribute of AI is its ability to identify patterns and relationships and to respond to queries that arise in complex scenarios for which the precise computational algorithm that is needed cannot be specified in advance”...
👉Research into the performance of businesses that have implemented AI is still in its early stages.
👉Surveys show that only 5 percent of businesses in the United States currently rely on AI to produce goods and services.
:👉Department of Defense's 2024 budget request dedicated to AI is less than 1%, signaling a cautious approach, fiscal year 2025 only $1.8 billion.
AI Adoption
AI adoption is the use of Artificial Intelligence (AI) in businesses and organizations to improve efficiency and productivity. AI is software that mimics the human brain to solve problems.
How is AI used?
Customer service: AI is used to answer questions and provide recommendations
Data processing: AI can quickly process large amounts of data
Product and service improvement: AI can help enhance products and services
Automation: AI can automate tasks in human resources, logistics, and more
Social data mining: AI can analyze social data
Language translation: AI can translate languages
Challenges
Getting started with AI can involve finding the right tools and training employees.
There are challenges with adoption, including ROI and resolving adoption challenges.
Examples of AI in use
Financial Sector uses AI to detect fraud etc.
ACI Worldwide uses AI to summarize meetings and generate code
The Global AI Economy is projected to experience significant growth between 2025 and 2030, driven by advancements in AI technology, increased adoption across industries, and substantial investments.
AI Market Segmentation
AI Market Segmentation
AI market segmentation reveals a diverse and dynamic landscape, with technology, applications, and industry verticals driving growth. Machine Learning (ML), Natural Language Processing or Neuro-Linguistic Programming (NLP), and Computer Vision lead in technology, while Healthcare, Banking, Financial Services and Insurance (BFSI), and Retail dominate in industry early adoption.
North America and Asia-Pacific are key regions, with cloud-based deployment and SME adoption shaping market trends. Understanding these segments is crucial for stakeholders to capitalize on growth opportunities and address challenges.
Market Size and Growth Projections
Market Size and Growth Projections
2025 = US $ 294.16 Billion
2030 = US $ 19.9 Trillion
Compound Annual Growth Rate (CAGR) = 29.2%
(Source: Fortune Business Insights, 2024)
Regional Contributions
Regional Contributions
North America
Expected to hold the highest market share, with a 32.93% share in 2024,
The U.S. AI market was valued at USD 42 billion in 2023 and is a leader in AI innovation, particularly in robotics and software development.
Asia-Pacific
Accounted for 25.6% of global AI revenue in 2023.
China's AI market is projected to grow at a CAGR of 43.5% from 2024 to 2030, driven by applications in natural language processing, computer vision, robotics, and autonomous vehicles.
Pakistan, Malaysia and India's AI market is supported by government initiatives like the National AI Strategy.
Middle East and Africa (MEA)
The AI market in MEA is expected to reach US $166.33 billion by 2030, with a focus on Arabic Language Processing (ALP) and Natural Language Processing (NLP).
Europe
The European AI market is anticipated to grow at a CAGR of 33.2% from 2024 to 2030, particularly in the financial sector.
Economic Impact and GDP Contributions
Economic Impact and GDP Contributions
Global Economic Impact
AI is predicted to contribute USD 19.9 trillion to the global economy by 2030, driving 3.5% of global GDP in 2030 (IDC, 2024).
Regional GDP Boost
China is expected to see a 26% boost to GDP by 2030 due to AI, equivalent to significant economic value (PwC, 2017).
North America could experience a 14.5% GDP boost by 2030, driven by AI adoption in industries like healthcare, finance, and retail (PwC, 2017).
Emerging markets and developing economies may face fewer immediate disruptions from AI but could lag in harnessing its benefits due to limited infrastructure and skilled workforces (IMF, 2024).
Job Market and Employment Impact
Job Market and Employment Impact
Job Creation vs. Job Displacement:
The World Economic Forum (WEF) projects that AI could create 170 million new jobs globally by 2030 while eliminating 92 million positions, resulting in a net increase of 78 million jobs (WEF Future of Jobs Report 2025).
Mohammad Mirza, [2/12/2025 3:56 AM]
Skills and Workforce Transformation
By end of 2025, 97 million people may be needed to fill AI-related roles globally.
AI is expected to boost employee productivity by 40% by 2035, particularly in knowledge work.
Women’s adoption of Generative AI is projected to match or exceed men’s in the U.S. by 2025, closing the gender gap in AI usage (Deloitte, 2024).
Industry-Specific Impacts
Industry-Specific Impacts
Healthcare:
Expected to gain dominance by 2030, with use cases like robot-assisted surgery, virtual nursing assistants, and automated image diagnosis.
AI-powered diagnostics and personalized medicine are key growth areas.
Finance (BFSI):
AI is used for financial analysis, risk assessment, and investment management, with 88% of finance firms reporting revenue increases due to AI.
The financial sector in Europe is undergoing significant transformation due to AI adoption.
Retail and Advertising:
The advertising and media segment led the AI market in 2023 and is expected to grow at the fastest CAGR from 2024 to 2030.
AI personalizes shopping experiences and enhances marketing campaigns, driving sales.
Technology and Software:
Growth in AI-as-a-Service (AIaaS), Deep Learning, and data mining is driving the software segment (Fortune Business Insights).
Generative AI-enabled smartphones and laptops are expected to see significant adoption, with 30% of smartphones and 50% of laptops having local GenAI processing capabilities by 2025 (Deloitte, 2024).
Investment Trends
Global AI Investments:
Projected to approach USD 200 billion by 2025, with investments concentrated in AI model development, infrastructure (e.g., data centers), and software (Goldman Sachs, 2023).
By 2028, global AI investments are expected to nearly triple to USD 630 billion, growing at a 30% 5-year CAGR (posts on X, citing Beth Kindig, 2024, inconclusive but aligns with industry trends).
Energy and Sustainability:
AI's energy demands are significant, with global data center electricity consumption forecasted to double to 1,065 terawatt-hours (TWh) by 2030, or 4% of total global energy consumption (Deloitte, 2024).
Tech companies are investing in energy-efficient chips, innovative cooling solutions, and renewable energy to mitigate AI's environmental impact (Deloitte, 2024).
Challenges and Risks
Inequality and Digital Divide:
AI may widen inequality, particularly between advanced and developing economies, due to disparities in digital infrastructure and skilled workforces (IMF, 2024).
Emerging markets and developing economies need investments in digital infrastructure and education to harness AI's benefits (IMF, 2024).
Trust and Ethics:
Trust in AI companies has dropped to 53% globally, highlighting systemic risks (posts on X, citing Raiinmakerapp, 2024, inconclusive).
Ethical concerns, including data privacy and bias, remain significant, with regions like the EU and China imposing stricter regulations (Grand View Research, IDC).
Job Displacement Risks:
While AI creates jobs, it also poses risks of wage pressure and job loss, particularly in routine and high-skilled roles (IDC, 2024).
The IMF notes that 40% of global employment is exposed to AI, with advanced economies facing greater risks (IMF, 2024).
Mohammad Mirza, [2/12/2025 3:56 AM]
Policy and Preparedness
AI Preparedness Index:
The IMF's AI Preparedness Index assesses readiness in areas like digital infrastructure, human capital, innovation, and regulation (IMF, 2024).
Advanced economies are better equipped for AI adoption, while low-income countries lag, though there is variation across countries (IMF, 2024).
Policy Recommendations:
Advanced economies: Prioritize AI innovation and integration while developing robust regulatory frameworks to ensure ethical use (IMF, 2024).
Emerging markets and developing economies: Focus on building digital infrastructure and a digitally competent workforce (IMF, 2024).
Strengthen social safety nets and reskilling programs to mitigate job displacement risks (IMF, 2024).
Mohammad Mirza, [2/12/2025 4:05 AM]
AI Market Segmentation
AI market segmentation involves dividing the global AI market into distinct categories based on various factors such as technology, application, industry vertical, deployment mode, and region. This segmentation helps stakeholders understand market dynamics, identify growth opportunities, and tailor strategies to specific segments. Below is a detailed breakdown of AI market segmentation based on current trends and projections for 2025–2030.
1. Segmentation by Technology
AI technologies are categorized based on the underlying methods and systems driving AI solutions. This is one of the most critical segmentation criteria.
Machine Learning (ML): Dominates the AI market, accounting for over 40% of the market share in 2023 (Grand View Research).
Includes supervised learning, unsupervised learning, reinforcement learning, and deep learning.
Growth drivers: Increased adoption in predictive analytics, fraud detection, and recommendation systems.
Projected CAGR (2024–2030): 35.8% (Grand View Research).
Natural Language Processing (NLP): Accounted for 25.6% of the AI market revenue in 2023 (Grand View Research).
Applications: Chatbots, virtual assistants, sentiment analysis, and language translation.
Growth drivers: Rising demand for multilingual NLP solutions, particularly in regions like the Middle East and Africa (e.g., Arabic language processing).
Projected CAGR (2024–2030): 37.2% (Grand View Research).
Computer Vision: Held a significant share in 2023, driven by applications in facial recognition, autonomous vehicles, and medical imaging.
Growth drivers: Advancements in image recognition and object detection, especially in healthcare and retail.
Projected CAGR (2024–2030): 36.5% (Grand View Research).
Robotics: Includes industrial robots, service robots, and collaborative robots (cobots).
Growth drivers: Adoption in manufacturing, logistics, and healthcare (e.g., robot-assisted surgery).
Projected CAGR (2024–2030): 34.8% (Fortune Business Insights).
Others: Includes expert systems, neural networks, and AI-as-a-Service (AIaaS). AIaaS is gaining traction due to its scalability and cost-effectiveness for small and medium enterprises (SMEs).
Projected CAGR for AIaaS (2024–2030): 38.1% (Fortune Business Insights).
2. Segmentation by Application
AI applications span various use cases across industries, driving demand in specific areas.
Predictive Analytics and Forecasting:
Used in finance, retail, and energy sectors for demand forecasting, risk assessment, and trend analysis.
Growth drivers: Increasing need for data-driven decision-making.
Projected CAGR (2024–2030): 35.5% (Statista Market Forecast).
Customer Service and Engagement: Includes chatbots, virtual assistants, and personalized marketing.
Growth drivers: Rising demand for 24/7 customer support and personalized experiences.
Projected CAGR (2024–2030): 36.2% (Grand View Research).
Healthcare Diagnostics and Treatment
Applications: Medical imaging, drug discovery, personalized medicine, and virtual nursing assistants.
Growth drivers: AI's ability to improve diagnostic accuracy and reduce healthcare costs.
Projected CAGR (2024–2030): 37.8% (Grand View Research).
Autonomous Systems
Includes self-driving vehicles, drones, and robotic systems.
Growth drivers: Advancements in computer vision and sensor technology.
Projected CAGR (2024–2030): 38.5% (Fortune Business Insights).
Cybersecurity
Applications: Threat detection, anomaly detection, and fraud prevention.
Growth drivers: Rising cyber threats and the need for real-time security solutions.
Projected CAGR (2024–2030): 36.9% (Statista Market Forecast).
Others: Includes supply chain optimization, energy management, and smart city applications.
Growth drivers: Increasing adoption of AI in emerging markets for infrastructure development.
3. Segmentation by Industry Vertical
AI adoption varies significantly across industries, with some sectors leading in implementation and others catching up.
Mohammad Mirza, [2/12/2025 4:05 AM]
Healthcare
Expected to dominate by 2030, with a market share of over 20% in 2023 (Grand View Research).
Applications: Diagnostics, drug discovery, robot-assisted surgery, and virtual health assistants.
Projected CAGR (2024–2030): 37.8% (Grand View Research).
Banking, Financial Services, and Insurance (BFSI)
Held a significant share in 2023, driven by AI in fraud detection, risk assessment, and algorithmic trading.
88% of finance firms reported revenue increases due to AI (explodingtopics.com, 2025).
Projected CAGR (2024–2030): 36.5% (Fortune Business Insights).
Retail and E-commerce
Applications: Personalized recommendations, inventory management, and dynamic pricing.
Growth drivers: Rising demand for personalized shopping experiences.
Projected CAGR (2024–2030): 35.9% (Grand View Research).
Manufacturing
Applications: Predictive maintenance, quality control, and supply chain optimization.
Growth drivers: Adoption of Industry 4.0 and smart factories.
Projected CAGR (2024–2030): 34.8% (Statista Market Forecast).
Automotive
Applications: Autonomous vehicles, driver assistance systems, and connected car technologies.
Growth drivers: Advancements in computer vision and sensor technology.
Projected CAGR (2024–2030): 38.5% (Fortune Business Insights).
Advertising and Media
Led the AI market in 2023, driven by personalized advertising and content recommendation systems.
Projected CAGR (2024–2030): 37.2% (Grand View Research).
Others
Includes Energy, Agriculture, Education, and Government sectors.
Growth drivers: AI's role in smart grids, precision farming, and e-learning platforms.
4. Segmentation by Deployment Mode
AI solutions are deployed either on-premises or through cloud-based platforms, each with distinct advantages.
Cloud-Based Deployment
Dominated the market in 2023, accounting for over 60% of the market share (Grand View Research).
Growth drivers: Scalability, cost-effectiveness, and ease of access for SMEs.
Projected CAGR (2024–2030): 37.5% (Fortune Business Insights).
On-Premises Deployment
Preferred by industries with strict data security and compliance requirements (e.g., healthcare, BFSI).
Growth drivers: Need for data privacy and control.
Projected CAGR (2024–2030): 34.2% (Grand View Research).
5. Segmentation by Region
Geographic segmentation highlights regional variations in AI adoption and growth.
North America:
Held the largest market share in 2024 (32.93%, Fortune Business Insights).
Growth drivers: Strong AI innovation ecosystem, high investment, and adoption in healthcare, BFSI, and retail.
Projected CAGR (2024–2030): 34.5% (Grand View Research).
U.S. AI market valued at USD 42 billion in 2023 (Grand View Research).
Asia-Pacific:
Accounted for 25.6% of global AI revenue in 2023 (Grand View Research).
Growth drivers: Rapid adoption in China (CAGR of 43.5%), India's National AI Strategy, and Japan's focus on robotics.
Projected CAGR (2024–2030): 38.2% (Grand View Research).
Europe
Significant growth in the financial sector, with applications in fraud detection and investment management.
Growth drivers: EU's focus on ethical AI and regulatory frameworks.
Projected CAGR (2024–2030): 33.2% (Grand View Research).
Middle East and Africa (MEA)
Expected to reach USD 166.33 billion by 2030, driven by Arabic language processing and NLP.
Growth drivers: Government initiatives and investments in AI infrastructure.
Projected CAGR (2024–2030): 35.8% (Grand View Research).
Latin America
Emerging market with growing adoption in agriculture, retail, and energy sectors.
Growth drivers: Increasing digitalization and AI awareness.
Projected CAGR (2024–2030): 34.5% (Statista Market Forecast).
6. Segmentation by Organization Size
AI adoption varies based on the size of the organization, with different needs and capabilities.
Mohammad Mirza, [2/12/2025 4:05 AM]
Large Enterprises
Dominated the market in 2023, accounting for over 55% of the market share (Grand View Research).
Growth drivers: High investment capacity, need for advanced analytics, and automation.
Projected CAGR (2024–2030): 35.2% (Fortune Business Insights).
Small and Medium Enterprises (SMEs)
The global AI economy is projected to experience significant growth between 2025 and 2030, driven by advancements in AI technology, increased adoption across industries, and substantial investments. Below is a detailed overview based on available data and forecasts from various sources. Note that projections vary depending on the methodology and focus of each study, but they collectively highlight AI's transformative economic impact.
Market Size and Growth Projections
Market Size Estimates:
The global AI market is projected to grow from USD 294.16 billion in 2025 to USD 1,771.62 billion by 2032, with a compound annual growth rate (CAGR) of 29.2% (Fortune Business Insights, 2024).
Another estimate suggests the AI market will reach USD 826.70 billion by 2030, with a CAGR of 27.67% from 2025 to 2030 (Statista Market Forecast).
Grand View Research estimates the AI market size at USD 196.63 billion in 2023, growing at a CAGR of 36.6% from 2024 to 2030, potentially reaching USD 1,811.75 billion by 2030.
Posts on X also reflect optimism, with some users citing projections of the AI market reaching USD 1.8 trillion by 2030, though these claims are inconclusive and align with broader industry estimates.
Regional Contributions
North America:
Expected to hold the highest market share, with a 32.93% share in 2024 (Fortune Business Insights).
The U.S. AI market was valued at USD 42 billion in 2023 and is a leader in AI innovation, particularly in robotics and software development (Grand View Research).
Asia-Pacific:
Accounted for 25.6% of global AI revenue in 2023 (Grand View Research).
China's AI market is projected to grow at a CAGR of 43.5% from 2024 to 2030, driven by applications in natural language processing, computer vision, robotics, and autonomous vehicles.
India's AI market is supported by government initiatives like the National AI Strategy, aiming to foster innovation and economic growth.
Middle East and Africa (MEA):
The AI market in MEA is expected to reach USD 166.33 billion by 2030, with a focus on Arabic language processing and natural language processing (Grand View Research).
Europe:
The European AI market is anticipated to grow at a CAGR of 33.2% from 2024 to 2030, particularly in the financial sector (Grand View Research).
Economic Impact and GDP Contributions
Global Economic Impact:
AI is predicted to contribute USD 19.9 trillion to the global economy by 2030, driving 3.5% of global GDP in 2030 (IDC, 2024). This includes direct, indirect, and induced effects from AI adoption.
Another estimate suggests AI could add USD 15.7 trillion to the global economy by 2030, with USD 6.6 trillion from productivity gains and USD 9.1 trillion from consumption-side effects (PwC, 2017). This aligns with posts on X citing similar figures, though these are inconclusive.
Goldman Sachs Research forecasts that AI will boost U.S. GDP by 0.4 percentage points by 2034, with similar effects in other developed markets (0.3 percentage points) and advanced emerging markets (0.2 percentage points) (Goldman Sachs, 2023).
Regional GDP Boost:
China is expected to see a 26% boost to GDP by 2030 due to AI, equivalent to significant economic value (PwC, 2017).
North America could experience a 14.5% GDP boost by 2030, driven by AI adoption in industries like healthcare, finance, and retail (PwC, 2017).
Emerging markets and developing economies may face fewer immediate disruptions from AI but could lag in harnessing its benefits due to limited infrastructure and skilled workforces (IMF, 2024).
Job Market and Employment Impact
Job Creation vs. Job Displacement:
The World Economic Forum (WEF) projects that AI could create 170 million new jobs globally by 2030 while eliminating 92 million positions, resulting in a net increase of 78 million jobs (WEF Future of Jobs Report 2025, cited by Ars Technica, 2025).
About 77% of surveyed firms plan to launch retraining programs to help workers collaborate with AI systems, and 70% plan to hire AI specialists (WEF, 2025).
However, AI's impact on jobs varies by region:
Advanced economies face greater risks but also more opportunities, with 60% of jobs potentially impacted by AI (IMF, 2024). Half of these jobs may benefit from AI integration, while the other half could see reduced labor demand.
Emerging markets and low-income countries have lower AI exposure (40% and 26%, respectively), but they may struggle to leverage AI's benefits (IMF, 2024).
Skills and Workforce Transformation:
By 2025, 97 million people may be needed to fill AI-related roles globally (explodingtopics.com, 2025).
AI is expected to boost employee productivity by 40% by 2035, particularly in knowledge work (explodingtopics.com, 2025).
Women’s adoption of generative AI is projected to match or exceed men’s in the U.S. by 2025, closing the gender gap in AI usage (Deloitte, 2024).
Industry-Specific Impacts
Healthcare:
Expected to gain dominance by 2030, with use cases like robot-assisted surgery, virtual nursing assistants, and automated image diagnosis (Grand View Research).
AI-powered diagnostics and personalized medicine are key growth areas (PwC, 2025).
Finance (BFSI):
AI is used for financial analysis, risk assessment, and investment management, with 88% of finance firms reporting revenue increases due to AI (explodingtopics.com, 2025).
The financial sector in Europe is undergoing significant transformation due to AI adoption (Grand View Research).
Retail and Advertising:
The advertising and media segment led the AI market in 2023 and is expected to grow at the fastest CAGR from 2024 to 2030 (Grand View Research).
AI personalizes shopping experiences and enhances marketing campaigns, driving sales (Grand View Research).
Technology and Software:
Growth in AI-as-a-Service (AIaaS), deep learning, and data mining is driving the software segment (Fortune Business Insights).
Generative AI-enabled smartphones and laptops are expected to see significant adoption, with 30% of smartphones and 50% of laptops having local GenAI processing capabilities by 2025 (Deloitte, 2024).
Investment Trends
Global AI Investments:
Projected to approach USD 200 billion by 2025, with investments concentrated in AI model development, infrastructure (e.g., data centers), and software (Goldman Sachs, 2023).
By 2028, global AI investments are expected to nearly triple to USD 630 billion, growing at a 30% 5-year CAGR (posts on X, citing Beth Kindig, 2024, inconclusive but aligns with industry trends).
Energy and Sustainability:
AI's energy demands are significant, with global data center electricity consumption forecasted to double to 1,065 terawatt-hours (TWh) by 2030, or 4% of total global energy consumption (Deloitte, 2024).
Tech companies are investing in energy-efficient chips, innovative cooling solutions, and renewable energy to mitigate AI's environmental impact (Deloitte, 2024).
Challenges and Risks
Inequality and Digital Divide:
AI may widen inequality, particularly between advanced and developing economies, due to disparities in digital infrastructure and skilled workforces (IMF, 2024).
Emerging markets and developing economies need investments in digital infrastructure and education to harness AI's benefits (IMF, 2024).
Trust and Ethics:
Trust in AI companies has dropped to 53% globally, highlighting systemic risks (posts on X, citing Raiinmakerapp, 2024, inconclusive).
Ethical concerns, including data privacy and bias, remain significant, with regions like the EU and China imposing stricter regulations (Grand View Research, IDC).
Job Displacement Risks:
While AI creates jobs, it also poses risks of wage pressure and job loss, particularly in routine and high-skilled roles (IDC, 2024).
The IMF notes that 40% of global employment is exposed to AI, with advanced economies facing greater risks (IMF, 2024).
Policy and Preparedness
AI Preparedness Index:
The IMF's AI Preparedness Index assesses readiness in areas like digital infrastructure, human capital, innovation, and regulation (IMF, 2024).
Advanced economies are better equipped for AI adoption, while low-income countries lag, though there is variation across countries (IMF, 2024).
Policy Recommendations:
Advanced economies: Prioritize AI innovation and integration while developing robust regulatory frameworks to ensure ethical use (IMF, 2024).
Emerging markets and developing economies: Focus on building digital infrastructure and a digitally competent workforce (IMF, 2024).
Strengthen social safety nets and reskilling programs to mitigate job displacement risks (IMF, 2024).
Conclusion
The global AI economy is poised for explosive growth between 2025 and 2030, with market size projections ranging from USD 826.70 billion to USD 1,811.75 billion by 2030, and economic contributions potentially reaching USD 19.9 trillion by 2030.
While AI offers immense opportunities for productivity gains, job creation, and industry transformation, it also poses challenges related to inequality, job displacement, and ethical concerns. Policymakers, businesses, and societies must work together to ensure AI's benefits are equitably distributed while addressing its risks.
Mohammad Mirza, [2/12/2025 4:05 AM]
AI market segmentation involves dividing the global AI market into distinct categories based on various factors such as technology, application, industry vertical, deployment mode, and region. This segmentation helps stakeholders understand market dynamics, identify growth opportunities, and tailor strategies to specific segments. Below is a detailed breakdown of AI market segmentation based on current trends and projections for 2025–2030.
1. Segmentation by Technology
AI technologies are categorized based on the underlying methods and systems driving AI solutions. This is one of the most critical segmentation criteria.
Machine Learning (ML):
Dominates the AI market, accounting for over 40% of the market share in 2023 (Grand View Research).
Includes supervised learning, unsupervised learning, reinforcement learning, and deep learning.
Growth drivers: Increased adoption in predictive analytics, fraud detection, and recommendation systems.
Projected CAGR (2024–2030): 35.8% (Grand View Research).
Natural Language Processing (NLP):
Accounted for 25.6% of the AI market revenue in 2023 (Grand View Research).
Applications: Chatbots, virtual assistants, sentiment analysis, and language translation.
Growth drivers: Rising demand for multilingual NLP solutions, particularly in regions like the Middle East and Africa (e.g., Arabic language processing).
Projected CAGR (2024–2030): 37.2% (Grand View Research).
Computer Vision:
Held a significant share in 2023, driven by applications in facial recognition, autonomous vehicles, and medical imaging.
Growth drivers: Advancements in image recognition and object detection, especially in healthcare and retail.
Projected CAGR (2024–2030): 36.5% (Grand View Research).
Robotics:
Includes industrial robots, service robots, and collaborative robots (cobots).
Growth drivers: Adoption in manufacturing, logistics, and healthcare (e.g., robot-assisted surgery).
Projected CAGR (2024–2030): 34.8% (Fortune Business Insights).
Others:
Includes expert systems, neural networks, and AI-as-a-Service (AIaaS).
AIaaS is gaining traction due to its scalability and cost-effectiveness for small and medium enterprises (SMEs).
Projected CAGR for AIaaS (2024–2030): 38.1% (Fortune Business Insights).
2. Segmentation by Application
AI applications span various use cases across industries, driving demand in specific areas.
Predictive Analytics and Forecasting:
Used in finance, retail, and energy sectors for demand forecasting, risk assessment, and trend analysis.
Growth drivers: Increasing need for data-driven decision-making.
Projected CAGR (2024–2030): 35.5% (Statista Market Forecast).
Customer Service and Engagement:
Includes chatbots, virtual assistants, and personalized marketing.
Growth drivers: Rising demand for 24/7 customer support and personalized experiences.
Projected CAGR (2024–2030): 36.2% (Grand View Research).
Healthcare Diagnostics and Treatment:
Applications: Medical imaging, drug discovery, personalized medicine, and virtual nursing assistants.
Growth drivers: AI's ability to improve diagnostic accuracy and reduce healthcare costs.
Projected CAGR (2024–2030): 37.8% (Grand View Research).
Autonomous Systems:
Includes self-driving vehicles, drones, and robotic systems.
Growth drivers: Advancements in computer vision and sensor technology.
Projected CAGR (2024–2030): 38.5% (Fortune Business Insights).
Cybersecurity:
Applications: Threat detection, anomaly detection, and fraud prevention.
Growth drivers: Rising cyber threats and the need for real-time security solutions.
Projected CAGR (2024–2030): 36.9% (Statista Market Forecast).
Others:
Includes supply chain optimization, energy management, and smart city applications.
Growth drivers: Increasing adoption of AI in emerging markets for infrastructure development.
3. Segmentation by Industry Vertical
AI adoption varies significantly across industries, with some sectors leading in implementation and others catching up.
Mohammad Mirza, [2/12/2025 4:05 AM]
Healthcare
Expected to dominate by 2030, with a market share of over 20% in 2023 (Grand View Research).
Applications: Diagnostics, drug discovery, robot-assisted surgery, and virtual health assistants.
Projected CAGR (2024–2030): 37.8% (Grand View Research).
Banking, Financial Services, and Insurance (BFSI):
Held a significant share in 2023, driven by AI in fraud detection, risk assessment, and algorithmic trading.
88% of finance firms reported revenue increases due to AI (explodingtopics.com, 2025).
Projected CAGR (2024–2030): 36.5% (Fortune Business Insights).
Retail and E-commerce:
Applications: Personalized recommendations, inventory management, and dynamic pricing.
Growth drivers: Rising demand for personalized shopping experiences.
Projected CAGR (2024–2030): 35.9% (Grand View Research).
Manufacturing:
Applications: Predictive maintenance, quality control, and supply chain optimization.
Growth drivers: Adoption of Industry 4.0 and smart factories.
Projected CAGR (2024–2030): 34.8% (Statista Market Forecast).
Automotive
Applications: Autonomous vehicles, driver assistance systems, and connected car technologies.
Growth drivers: Advancements in computer vision and sensor technology.
Projected CAGR (2024–2030): 38.5% (Fortune Business Insights).
Advertising and Media:
Led the AI market in 2023, driven by personalized advertising and content recommendation systems.
Projected CAGR (2024–2030): 37.2% (Grand View Research).
Others:
Includes energy, agriculture, education, and government sectors.
Growth drivers: AI's role in smart grids, precision farming, and e-learning platforms.
4. Segmentation by Deployment Mode
AI solutions are deployed either on-premises or through cloud-based platforms, each with distinct advantages.
Cloud-Based Deployment:
Dominated the market in 2023, accounting for over 60% of the market share (Grand View Research).
Growth drivers: Scalability, cost-effectiveness, and ease of access for SMEs.
Projected CAGR (2024–2030): 37.5% (Fortune Business Insights).
On-Premises Deployment:
Preferred by industries with strict data security and compliance requirements (e.g., healthcare, BFSI).
Growth drivers: Need for data privacy and control.
Projected CAGR (2024–2030): 34.2% (Grand View Research).
5. Segmentation by Region
Geographic segmentation highlights regional variations in AI adoption and growth.
North America
Held the largest market share in 2024 (32.93%, Fortune Business Insights).
Growth drivers: Strong AI innovation ecosystem, high investment, and adoption in healthcare, BFSI, and retail.
Projected CAGR (2024–2030): 34.5% (Grand View Research).
U.S. AI market valued at USD 42 billion in 2023 (Grand View Research).
Asia-Pacific:
Accounted for 25.6% of global AI revenue in 2023 (Grand View Research).
Growth drivers: Rapid adoption in China (CAGR of 43.5%), India's National AI Strategy, and Japan's focus on robotics.
Projected CAGR (2024–2030): 38.2% (Grand View Research).
Europe
Significant growth in the financial sector, with applications in fraud detection and investment management.
Growth drivers: EU's focus on ethical AI and regulatory frameworks.
Projected CAGR (2024–2030): 33.2% (Grand View Research).
Middle East and Africa (MEA):
Expected to reach USD 166.33 billion by 2030, driven by Arabic language processing and NLP.
Growth drivers: Government initiatives and investments in AI infrastructure.
Projected CAGR (2024–2030): 35.8% (Grand View Research).
Latin America
Emerging market with growing adoption in agriculture, retail, and energy sectors.
Growth drivers: Increasing digitalization and AI awareness.
Projected CAGR (2024–2030): 34.5% (Statista Market Forecast).
6. Segmentation by Organization Size
AI adoption varies based on the size of the organization, with different needs and capabilities.
Large Enterprises
Dominated the market in 2023, accounting for over 55% of the market share (Grand View Research).
Growth drivers: High investment capacity, need for advanced analytics, and automation.
Projected CAGR (2024–2030): 35.2% (Fortune Business Insights).
*S[mall and Medium Enterprises (SMEs)](https://x.com/i/grok?text=mall%20and%20Medium%20Enterprises%20(SMEs)):
Growing rapidly due to cloud-based AI solutions and AIaaS.
Growth drivers: Cost-effective solutions and government support for SME digitalization.
Projected CAGR (2024–2030): 37.8% (Grand View Research).
Key Trends and Insights
Technology Dominance:
Machine learning and NLP are the leading technologies, with NLP expected to grow fastest due to demand for multilingual solutions.
Computer vision and robotics are critical for autonomous systems and healthcare applications.
Industry Leaders:
Healthcare and BFSI are the fastest-growing verticals, driven by diagnostics, fraud detection, and personalized services.
Advertising and media lead in revenue share due to AI's role in personalized marketing.
Regional Dynamics:
North America remains the largest market, but Asia-Pacific is catching up rapidly, driven by China Pakistan, Malaysia and India.
MEA and Latin America are emerging markets with significant growth potential.
Deployment Preferences:
Cloud-based deployment dominates due to scalability, but on-premises solutions are preferred in regulated industries.
SME Growth
SMEs are increasingly adopting AI due to affordable cloud solutions, creating opportunities for AIaaS providers.
Growth drivers: Cost-effective solutions and government support for SME digitalization.
Projected CAGR (2024–2030): 37.8% (Grand View Research).
Key Trends and Insights
Technology Dominance:
Machine learning and NLP are the leading technologies, with NLP expected to grow fastest due to demand for multilingual solutions.
Computer vision and robotics are critical for autonomous systems and healthcare applications.
Industry Leaders:
Healthcare and BFSI are the fastest-growing verticals, driven by diagnostics, fraud detection, and personalized services.
Advertising and media lead in revenue share due to AI's role in personalized marketing.
Regional Dynamics:
North America remains the largest market, but Asia-Pacific is catching up rapidly, driven by China, India and Pakistan.
MEA and Latin America are emerging markets with significant growth potential.
Deployment Preferences:
Cloud-based deployment dominates due to scalability, but on-premises solutions are preferred in regulated industries.
SME Growth:
SMEs are increasingly adopting AI due to affordable cloud solutions, creating opportunities for AIaaS providers.
Conclusion
Conclusion
AI market segmentation reveals a diverse and dynamic landscape, with technology, applications, and industry verticals driving growth. Machine learning, NLP, and computer vision lead in technology, while healthcare, BFSI, and retail dominate in industry adoption. North America and Asia-Pacific are key regions, with cloud-based deployment and SME adoption shaping market trends. Understanding these segments is crucial for stakeholders to capitalize on growth opportunities and address challenges.
Strategic Partners: AMCO Engineering + AEC + PEIS
Project: Quantum Compatible Interfaces Program (QCIP)
QCIP Taskforce Business Scope
QAI Algorithms + AI Entanglement + Generative AI
QCIP Taskforce Direction
Alignment + Lawful Collaboration + Strategic Integration
AMCO-AEC-PEIS Taskforce Eight Panels of Experts
1) Attorneys; 2) Academia - Students; 3) Community Activists; 4) Economists; 5) Financial Experts; 6) Scholars; 7) Philanthropist; 8) Technologists (Scientists-Engineers-Technicians)
Lead Policy Advisor: Mohammad Afzal Mirza
AMCO-AEC-PEIS Core Professional Team (CPT) provide Data-driven Policy Recommendations (DPR), using custom algorithms, based on applicable Regulations so that Organization Strategic Priorities (OSPs) are achieved by aligning Grassroots-Level-Work (GLW) with Organization Core Values and Principles (CVPs).
AMCO-AEC-PEIS Taskforce R&D Primary Sources
1) Governments (Policies) 2) Industry 3) Academia
Project Lead: Afro Eurasian Coalition (AEC) LLC USA
Program Lead: Mohammad Afzal Mirza, President, AEC LLC USA
Certifications: USA DOD CAGE CODE: Active
AEC-PEIS NAICS Code: 541690 Scientific and Technical Consulting Services
AEC-PEIS SIC Code: 87420501; PEIS USA FCC FRN #: 0034792853
Neural Networks: A method to train computers to process data in a way that’s inspired by the human brain, using a layered, interconnected neuron-inspired structure.
A Human-Machine Interface (HMI) is a hardware or software system that allows a person to interact with a machine, system, or device.
HMIs can be used to:
*Control Machinery
*Monitor Machine Inputs and Outputs
*Track Production Time, Trends, and Tags
*Oversee KPIs
*Review and Monitor Processes
*Diagnose Problems
*Visualize Data
HMIs are used in many industries, including Agriculture; Aerospace; Aviation; Automotive; Logistics; Manufacturing; Pharmaceuticals, etc. HMIs can take many forms, including:
Physical Control Panels: These panels have buttons and indicator lights.
Industrial PCs: These PCs have color graphics displays and dedicated HMI software.
Touch Screens: These screens allow for intuitive parameter configuration.
Specialized Keyboards: These keyboards can be used in conjunction with touch screens.
HMIs are designed to be user-friendly and efficient, allowing operators to translate complex data into useful information. They often use icons, sounds, pictures, and colors to illustrate the current status and operating conditions of the machinery.
AI is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy. Applications and devices equipped with AI can see and identify objects. They can understand and respond to human language
Focus Sectors
1) Agriculture 2) Aerospace 3) Aviation 4) Finance
5) Health 6) Education 7) Manufacturing 8) Supply Chain
AI Algorithms
AI Algorithms
AI Algorithms are the foundation of AI Technology, which allows machines to learn and adapt to their environment. AI Algorithm is the programming (set of instructions) that tells the computer how to learn to operate on its own.
Three Main Categories of AI Algorithms
Three Main Categories of AI Algorithms
Reaml-World Examples
Reaml-World Examples
Convolutional Neural Networks (CNNs)
A deep belief network is a stack of multiple Restricted Boltzmann Machine (RBM) structures that form the foundation of deep architectures. Each of these RBMs consists of a visible layer and a hidden layer. The visible layer accepts the input from the previous layer, while the hidden layer stores the processed output.
Generative Adversarial Networks (GANs)
Long Short Term Memory Networks (LSTMs)
A Long Short-Term Memory (LSTM) neural network is a type of Recurrent Neural Network (RNN) specifically designed to handle sequential data by effectively remembering information over long periods of time, overcoming the "vanishing gradient" problem common in standard RNNs, making it ideal for tasks like language translation, speech recognition, and time series forecasting where long-term dependencies are crucial; it achieves this by utilizing "gates" that selectively control the flow of information through a "memory cell" within each LSTM unit, allowing it to store relevant data and discard irrelevant information across time steps.
Multilayer Perceptrons (MLPs)
Radial Basis Function Networks (RBFNs)
Recurrent Neural Networks (RNNs)
Restricted Boltzmann Machines (RBMs)
Self Organizing Maps (SOMs)
AI Applications
AI Applications
AI Entanglement (AI-E)
AI Entanglement (AI-E)
AI Entanglement refers to the Interconnectedness and Interdependence of Artificial Intelligence (AI) Systems, Data, and Humans (AI-SDH).
Real-World Applications
Real-World Applications
1. Human-AI collaborative decision-making in healthcare.
2. AI-assisted data analysis for climate change mitigation.
3. Entanglement-based cybersecurity systems.
4. AI-driven supply chain optimization.
By understanding and embracing AI Entanglement, we can create more harmonious, efficient, and beneficial relationships between humans, AI, and data.
Our Research Focus
Our Research Focus
1. How can we balance human and AI capabilities?
2. What are the implications of AI Entanglement on societal structures?
3. How can we ensure explainability and transparency in AI systems?
4. What are the potential applications of quantum AI Entanglement?
May Allah continue to guide humanity in harnessing AI for peace, mercy, and blessings!
AI Entanglement Foundational Pillars
AI Entanglement Foundational Pillars
The foundational pillars of AI Entanglement include:
The foundational pillars of AI Entanglement include:
Pillar 1: Cognitive Entanglement
Pillar 1: Cognitive Entanglement
1. Human-AI Collaboration
2. Shared Decision-Making
3. Cognitive Biases and Debiasing
4. Explainability and Transparency
Pillar 2: Data Entanglement
Pillar 2: Data Entanglement
1. Data Interconnectedness
2. Data Quality and Validation
3. Data Provenance and Lineage
4. Data Privacy and Security
Pillar 3: Systemic Entanglement
Pillar 3: Systemic Entanglement
1. Complex Systems Thinking
2. Emergence and Self-Organization
3. Feedback Loops and Dynamics
4. Resilience and Adaptability
Pillar 4: Ethical Entanglement
Pillar 4: Ethical Entanglement
1. Value Alignment and Ethics
2. Fairness and Accountability
3. Transparency and Explainability
4. Human Rights and Dignity
Pillar 5: Quantum Entanglement
Pillar 5: Quantum Entanglement
1. Quantum Computing and AI
2. Quantum Machine Learning
3. Quantum Information Processing
4. Quantum-Inspired Optimization
Key Concepts
Key Concepts
1. Non-duality: Integrating human and AI capabilities.
2. Holism: Considering AI systems as part of larger ecosystems.
3. Contextuality: Understanding AI behavior in context.
4. Relationalism: Focusing on relationships between AI, humans, and data.
Research Directions
Research Directions
1. Developing entanglement-inspired AI models.
2. Investigating cognitive biases in human-AI interactions.
3. Designing explainable and transparent AI systems.
4. Exploring quantum AI applications.
The U.S. National AI Advisory Committee (NAIAC) consists of experts with a broad and interdisciplinary range of AI-relevant experience from across the private sector, academia, non-profits, and civil society.
Technologies Trends up to 2050
10 Tech Developments are attracting the most Venture Capital and producing the Most Patent Filings.
They include next-level Process Automation and Virtualization, and seamless connectivity through 5G and the Internet of Things (IoT).
Other areas to watch include Trust Architecture – which verifies the Trustworthiness of Devices as Data Flows Across Networks – Next-Generation Smart Materials and Artificial Intelligence (AI) Algorithms that Train Machines and Strategic Human Capital.
Research: PEIS | Source: USA Government and McKenzie
Autonomous Economics At Scale: Capability Assessment & Analysis Processes (CAAP)
Deliverable: Quantum Compatible Autonomous Digital Infrastructure (CADI)
Product: Digital Infrastructure Distributed Clusters (DIDC)
Cybersecurity Framework: Post-Quantum Advanced Encryption Standard (PAES)
User Friendly Interface: Autonomous Digital Assets Management (ADAM)
MUGHALS Development Focus: Economic Development Programs (EDPS)
1) Advanced Computing
2) Advanced Engineering Materials
3) Advanced Gas Turbine Engine Technologies
4) Advanced Manufacturing
5) Advanced and Networked Sensing and Signature Management
6) Advanced Nuclear Energy Technologies
7) Artificial Intelligence
8) Autonomous Systems and Robotics
9) Biotechnologies
10) Communication and Networking Technologies
11) Directed Energy
12) Financial Technologies
13) Human-Machine Interfaces
14) Hypersonics
15) Inter Satellite Communication Algorithms
16) Networked Sensors and Sensing
17) Quantum Information Technologies
18) Renewable Energy Generation and Storage
19) Semiconductors and Microelectronics
20) Space Technologies and Systems
As per one legal definition, Underlying Technology MEANS "the level of technology that Underlies Multiple Applications, at least one application of which is outside of the Business, as of the Closing Date, rather than being directed to only a specific application, but only to the extent such technology is common to such applications."
👉 In an environment of rapidly evolving cybersecurity threats, the continued reliance on the Data Encryption Standard (DES) and other Non-Standard Encryption Algorithms poses a significant threat to the security of sensitive Data and Information Systems.
👉 In accordance with the OECD and the U.S. Laws, Policies and Guidelines, we are developing Suits of Algorithms to Lawfully Strengthen and Integrate Interoperability and Compatibility of existing Digital Infrastructures.
👉 As currently, one of the major challenges include to overcome the vulnerabilities of the continued use of the deprecated DES and other non-standard algorithms.
Augmented Reality (AR) | Blockchain |
Combined Heat & Power (CHP) | Digital Twin |
Directed Energy Deposition (DED) | Distributed Ledger |
Electric Propulsion | Geothermal | Infrared Remote Sensing | LiDAR |
Microwave Reaction Technology (MRT) |
Machine Learnings (ML) | SMART Contracts | Virtual Reality (VR)
By 2025, more than 50 billion devices will be connected to the IoT, generating 79.4 zettabytes of data yearly. Annual installations of Industrial Robots, which have increased two times to about 450,000 since 2015, will grow to about 600,000 by 2022, even as 70 percent of Manufacturers will be regularly using Digital Twins by 2022. Gradually, across Industries, manufacturing processes will be replaced by Additive Manufacturing (AM).
Critical and Emerging Technology (CET) Subfields
Each CET Area, as identified by U.S., includes a set of Key Subfields
Advanced Computing
Advanced Computing
Supercomputing
Edge computing
Cloud computing
Data storage
Computing Architectures
Data Processing and Analysis
Advanced Engineering (Materials)
Advanced Engineering (Materials)
Materials by design and material genomics
Materials with new properties
Materials with substantial improvements to existing properties
Material property characterization and lifecycle assessment
Gas Turbine Engine Technologies
Gas Turbine Engine Technologies
Aerospace, Maritime, and Industrial Development and Production Technologies
Advanced Manufacturing
Advanced Manufacturing
Additive Manufacturing
Clean, Sustainable Manufacturing
Smart Manufacturing
Nanomanufacturing
Advanced, Networked Sensing and Signature Management
Advanced, Networked Sensing and Signature Management
Payloads, Sensors, and Instruments
Sensor Processing and Data Fusion
Adaptive Optics
Remote sensing of the Earth
Signature Management
Nuclear Materials Detection and Characterization
Chemical Weapons Detection and Characterization
Biological Weapons Detection and Characterization
Emerging Pathogens Detection and characterization
Transportation-sector Sensing
Security-sector sensing
Health-sector sensing
Energy-sector sensing
Building-sector sensing
Environmental-sector sensing
Advanced Nuclear Energy Technologies
Advanced Nuclear Energy Technologies
Nuclear energy systems
Fusion energy
Space nuclear power and propulsion systems
Artificial Intelligence (AI) - Autonomous Systems and Robotics
Artificial Intelligence (AI) - Autonomous Systems and Robotics
Air
Maritime
Space
Surfaces
Hypersonics
Hypersonics
Propulsion
Aerodynamics and Control
Materials
Detection, Tracking, and Characterization (DTC)
Defense
Renewable Energy Generation and Storage (REGS)
Renewable Energy Generation and Storage (REGS)
Renewable generation
Renewable and Sustainable Fuels
Energy Storage
Electric and Hybrid Engines
Batteries
Grid Integration Technologies (GIT)
Energy-Efficiency Technologies (EET)
Biotechnologies
Biotechnologies
Nucleic Acid and Protein Synthesis
Genome and Protein Engineering including design tools
Multi-omics and other Biometrology, Bioinformatics, Predictive Modeling, and Analytical Tools for Functional Phenotypes
Engineering of Multicellular Systems
Engineering of Viral and Viral Delivery Systems
Biomanufacturing and Bioprocessing Technologies
Communication and Networking Technologies
Communication and Networking Technologies
Radio-frequency (RF) and Mixed-Signal Circuits (MSC), Antennas, Filters, and Components
Spectrum Management Technologies
Next-Generation Wireless Networks including 5G and 6G Optical Links and Fiber Technologies
Terrestrial / Undersea Cables (TUC)
Satellite-Based Communications
Hardware, Firmware, and software
Communications and Network Security
Mesh Networks / Infrastructure Independent Communication Technologies
Financial Technologies (FinTech)
Financial Technologies (FinTech)
Distributed ledger technologies
Digital assets
Digital Payment Technologies
Digital Identity Infrastructure
Human-Machine Interfaces
Human-Machine Interfaces
Augmented reality
Virtual Reality
Brain-Computer Interfaces (BCI)
Human-Machine Teaming (HMT)
Quantum Information Technologies
Quantum Information Technologies
Quantum Computing
Materials, Isotopes, and Fabrication Techniques for Quantum Devices
Post-Quantum Cryptography
Quantum Sensing
Quantum Networking
Semiconductors & Microelectronics
Semiconductors & Microelectronics
Design and Electronic Design Automation Tools
Manufacturing Process Technologies (MPT) and Manufacturing Equipment
Beyond Complementary Metal-Oxide-Semiconductor (CMOS) Technology
Heterogeneous Integration and Advanced Packaging
Specialized / Tailored Hardware Components for Artificial Intelligence, Natural and Hostile Radiation Environments, RF and Optical Components, High-Power Devices (HPD), and other Critical Applications
Novel Materials for Advanced Microelectronics
Wide-Bandgap and Ultra-Wide-Bandgap Technologies (WUBT) for Power Management, Distribution, and Transmission
Space Technologies and Systems
Space Technologies and Systems
On-Orbit Servicing, Assembly, and Manufacturing
Commoditized Satellite Buses
Low-Cost Launch Vehicles
Sensors for Local and Wide-Field Imaging (LWFI)
Space Propulsion
Resilient Positioning, Navigation, and Timing (PNT)
Cryogenic Fluid Management (CFM)
Spacecraft Entry, Descent, and Landing (SDL)
Research References Sources
GOV OECD POLICY Autonomous Regulators
GOV USA DOD SPACE ELECTROMAGNETIC SPECTRUM OPERATIONS
GOV USA DOD CYBERSPACE 2023-2027 Cyber Workforce Strategy - Implementation Plan
Framework for “Investment Contract” Analysis of Digital Assets1
Framework for “Investment Contract” Analysis of Digital Assets1
Cybersecurity Framework's five Functions:
Cybersecurity Framework's five Functions:
Standards for Critical and Emerging Technology
Standards for Critical and Emerging Technology
What is an Initial Coin Offering? https://www.marylandattorneygeneral.gov/Securities%20Documents/ICOs_051818.pdf
What is an Initial Coin Offering? https://www.marylandattorneygeneral.gov/Securities%20Documents/ICOs_051818.pdf
EXECUTIVE OFFICE OF THE PRESIDENT
EXECUTIVE OFFICE OF THE PRESIDENT
Additional Sources
Additional Sources
Framework for “Investment Contract” Analysis of Digital Assets1
Framework for “Investment Contract” Analysis of Digital Assets1
Cybersecurity Framework's five Functions:
Cybersecurity Framework's five Functions:
Standards for Critical and Emerging Technology
Standards for Critical and Emerging Technology
What is an Initial Coin Offering? https://www.marylandattorneygeneral.gov/Securities%20Documents/ICOs_051818.pdf
What is an Initial Coin Offering? https://www.marylandattorneygeneral.gov/Securities%20Documents/ICOs_051818.pdf
EXECUTIVE OFFICE OF THE PRESIDENT
EXECUTIVE OFFICE OF THE PRESIDENT
Page updated
Report abuse