Artificial intelligence
Computer systems that observe, analyze, and learn from data to perform complex tasks requiring human-like intelligence, helping enterprises work smarter and unlock new possibilities. The global AI market is projected to explode from $638.23 billion in 2024 to $3.68 trillion by 2034, representing nearly a 6x increase in just one decade.
How artificial intelligence works in practice
AI systems operate through three core capabilities: observation, analysis, and learning. They gather new data from their environment, extract meaningful patterns from complex information, and adapt their decision-making logic over time without explicit reprogramming.
Modern AI leverages machine learning algorithms, neural networks, and statistical models to process vast amounts of data and improve performance automatically. These systems create rules for completing specific tasks, choose the right approach for each job, and continuously adapt based on new information they encounter.
Core artificial intelligence capabilities for enterprises
These capabilities extend to intelligent document processing, workflow automation, and robotic process automation—all designed to enhance how enterprises operate. The robotic process automation market experienced explosive growth from $1.4 billion in 2019 to $6.2 billion in 2023, more than quadrupling in size. Organizations use these technologies to automate repetitive tasks, streamline business functions, and extract valuable insights from their accumulated data.
These capabilities extend to intelligent document processing, workflow automation, and robotic process automation—all designed to enhance how enterprises operate. Organizations use these technologies to automate repetitive tasks, streamline business functions, and extract valuable insights from their accumulated data.
Types of artificial intelligence systems
AI systems fall into distinct categories based on their scope and capabilities. Narrow AI focuses on specific tasks like speech recognition or recommendation systems. It's highly efficient for targeted problems but doesn't generalize beyond its designed purpose. Most current enterprise ai implementations use narrow AI focused on specific business functions.
General AI (AGI) represents systems with human-like adaptability, capable of understanding and solving a wide range of tasks across domains. While AGI remains largely theoretical, it represents the long-term vision for AI development.
Enterprise ai spans supply chain management, finance, marketing, customer service, human resources, and cybersecurity. It facilitates data-driven decision-making, boosts operational efficiency, and elevates customer experience. Customer issue resolution leads enterprise ai applications, appearing in 35% of generative AI projects. Generative AI is revolutionizing customer service by resolving 35% of enterprise projects and improving response times by up to 80%.
Real-world artificial intelligence applications
Enterprise ai spans supply chain management, finance, marketing, customer service, human resources, and cybersecurity. It facilitates data-driven decision-making, boosts operational efficiency, and elevates customer experience. Customer issue resolution leads enterprise ai applications, appearing in 35% of generative AI projects.
Worker trust remains one of the biggest barriers to effective AI adoption, with 61% of respondents either ambivalent about or unwilling to trust AI systems. A striking 61% of people remain ambivalent or unwilling to trust AI systems, citing major concerns about safety, fairness, and transparency. AI algorithms depend heavily on data quality and can inherit biases from training data, leading to faulty results or inappropriate responses.
Worker trust remains one of the biggest barriers to effective AI adoption, with 61% of respondents either ambivalent about or unwilling to trust AI systems. AI algorithms depend heavily on data quality and can inherit biases from training data, leading to faulty results or inappropriate responses. Despite the AI hype, 42% of enterprises abandoned most of their AI projects in 2025 due to cost and data privacy concerns.
Key challenges in artificial intelligence implementation
Worker trust remains one of the biggest barriers to effective AI adoption, with 61% of respondents either ambivalent about or unwilling to trust AI systems. AI algorithms depend heavily on data quality and can inherit biases from training data, leading to faulty results or inappropriate responses.
Technical challenges include interpretability—many AI systems operate as "black boxes" with unclear decision-making processes. Systems can also struggle with robustness, failing when encountering small input changes, and adaptability, finding it difficult to transfer knowledge to new settings. Accuracy concerns persist as generative AI systems sometimes produce inaccurate information, requiring careful assessment of outputs.
Glean's approach to artificial intelligence
At Glean, we believe AI should enhance human capabilities rather than replace them. Our approach focuses on building AI systems that understand organizational context and respect data permissions, ensuring employees can access and act on information securely.
We've evolved from enterprise search to agentic AI, combining retrieval-augmented generation (RAG) with advanced reasoning capabilities to help users complete complex workflows. Our AI architecture includes three key components: a self-learning language model that continuously adapts to company-specific terminology, a hybrid search system that balances semantic and lexical approaches, and a knowledge graph that understands relationships between people, content, and activities.
This foundation enables our agents—configurable, AI-powered workflows that automate tasks, answer questions, and integrate with various tools. These agents can plan, execute, and reflect on tasks while maintaining enterprise-grade security and permissions, ensuring AI works within the boundaries of what each user should access.
Frequently asked questions about artificial intelligence
What's the difference between artificial intelligence and machine learning?
Machine learning is a subset of AI where systems learn from data to improve performance automatically. AI encompasses the broader field of creating systems that can perform tasks requiring human-like intelligence, including but not limited to machine learning approaches.
How can I tell if a system is truly using artificial intelligence?
True AI systems use large, varied datasets including unstructured data, update their knowledge base over time, adapt their decision-making logic autonomously, and can identify and correct biases in their outputs.
What are the main types of machine learning in artificial intelligence?
The three main types are supervised learning (trained on labeled data), unsupervised learning (finds patterns in unlabeled data), and reinforcement learning (learns through trial and error using reward systems).
Is artificial intelligence safe for enterprise use?
Enterprise-ready AI must meet stringent requirements for data privacy, regulatory compliance, and system interoperability. It should be robust enough to handle large data volumes and flexible enough to adapt to changing business environments while maintaining security and accuracy standards.
How is generative artificial intelligence different from traditional AI?
Generative AI creates new content—text, code, images, audio, or video—using deep-learning techniques. Traditional AI typically focuses on classification, prediction, or automation of existing processes rather than content creation.
