Since the 1970s, Artificial Intelligence (AI) has evolved through multiple phases, from niche academic research to a strategic imperative reshaping industries worldwide. As of 2025, AI is no longer a futuristic concept but a business necessity, driving innovation, efficiency, and competitive advantage across sectors. For companies just beginning their AI journey, grasping the foundational concepts and current landscape is critical to unlocking AI’s transformative potential.

To understand how to unlock the power of AI, let’s start by going through basic concepts. For learning purposes, we can reduce the scope to commonly used buzzwords, offering you clarity on what are their characteristics.

After you finish exploring the fundamental concepts and get a better understanding of AI’s common concepts, your next step is to become aware of the common challenges of enterprise adoption of AI, and understand how to overcome them by strategically use different AI systems and not having to choose between them.

What Is AI and Why Is It Different Now?

Artificial Intelligence refers to systems or software capable of performing tasks that typically require human intelligence, covering abilities such as learning, reasoning, problem-solving, perception, and language understanding. Although AI research dates back to the 1950s, recent leaps in computing power, data availability, and algorithmic sophistication, especially in the area of deep learning, have dramatically accelerated its evolution.

Today, AI is a powerful tool for expanding human creativity and productivity. In enterprises, it enables automation of complex operations, accelerates decision-making, and allows offering uniquely personalized customer experiences at scale.

However, most enterprises still struggle balancing AI innovation with the enterprise requirements for governance, explainability, and reliable execution of core business requirements.

TIP: The architectural decisions made today will determine whether AI initiatives deliver long-lasting business value or introduces new risks to the business, with accumulated minor errors and derived huge losses.

Deciding what is the best possible strategy, tool and way to innovate using AI, is a complex task. We can reduce the AI vast landscape to a smaller scope of commonly used terms trending across enterprises, and introduce each concept with its surrounding context. We’ll also break down the concepts involved in the overall steps of the decision making throughout this and future articles.

Breaking Down AI Essential Concepts: AI/ML, GenAI, Agentic AI, LLMs, Symbolic AI

Symbolic AI has been largely used across the industry for many years, and stands as a solid enterprise approach for tackling needs that range from simple to complex mission-critical requirements. It’s based on programming systems with explicit rules and logic to perform tasks. This method has been working well and targets clearly defined requirements seeking assurance of predictable results, transparency and governance. However, it was not designed to handle ambiguous, unstructured, or high-dimensional problems that require learning from vast amounts of data. 

As technology advanced, other Artificial Intelligence (AI) research fields brought us systems that could learn from data rather than relying solely on predefined rules. Within this space, Machine Learning (ML) was quickly perceived by its powerful technique that unlocked computers’ ability to self-improve, by identifying patterns in large datasets. These patterns gave them probabilistic abilities to predict or classify, without being explicitly programmed for every scenario.

Note: Until this point, AI/ML was not mainstream as it required different areas of expertise, computing power and involved other concerns that were usually enough to slow down its adoption. However, it was the key enabler of a new branch of AI that disrupted the market.

Generative AI (GenAI) is a branch of AI that uses machine learning models to create new, original content based on learned data patterns models are designed to create new content. It can create text, images, music, or other digital artifacts based on patterns learned from vast amounts of data. Unlike traditional analysis or classifications done by the traditional AI/ML,   GenAI generates original outputs when given specific instructions or prompts. 

Here’s how it works: The input is a prompt, instructions describing what the GenAI solution is expected to generate. Prompts can bring guidance on the generation to some degree. Think of it as the question you ask ChatGPT, Gemini or Claude. The GenAI model processes the prompt and using its learned knowledge, generates a relevant response, predicting word after word based on probabilities. 

TIP: At the core of many generative AI systems are Large Language Models (LLMs). These are massive neural networks trained on enormous amounts of text data from books, websites, and conversations. LLMs learn statistical relationships between words and phrases, enabling them to predict what comes next in a sentence or generate coherent paragraphs on a wide range of topics, being capable of producing human-like language without explicit programming for each task.

For example, if you give a generative AI a prompt like “write a poem about the ocean”, the quality and clarity of the prompt heavily shapes the quality of the output. As generative solutions were used by more and more people, the concept of prompt engineering became the next “big thing” in terms of career, since GenAI was now becoming naturally used to increase productivity and accelerate businesses. 

Prompt engineering refers to the practice of crafting prompts that effectively guides generative models in creating customized, polished responses that are closer to expected results. It ultimately was a way to seek answers that were factual, useful, relevant or even, to control the results. 

While GenAI systems require inputs to operate, Agentic AI takes it a step further. An AI Agent is a system that can act autonomously. It is aware of its environment and data, and without any external intervention, it sets goals, makes decisions, and executes one or a series of tasks required to achieve a particular goal. All with minimal human intervention. 

Here’s an example to clarify the concept of an AI Agent: Imagine working with an assistant (but a digital one) that not only answers your questions, it understands when and how to schedule meetings, address straightforward questions you receive via email, automatically replying them without your involvement, ordering supplies, and adapting its prioritized tasks on its own. 

Such an advanced behavior, requires a pre-existing set of systems and APIs, acting at some level, as an orchestrator. Having this clearly defined set of “possibilities” made available through integration endpoints, agents can act as an independent figure and pursue objectives. Additionally, what makes this possible, is the system’s ability to learn, reason, plan, and interact with humans and external systems, efficiently operating on every data exchanged. 

With the rise of AI Agents and GenAI, the need to make systems available to AI systems became latent. Aiming to avoid vendor lock-in and create standard ways to interact with external tools, the Model Context Protocol (MCP) was created and quickly got traction. 

MCP defines a standard method for systems integration with AI. For an AI agent to perform a task like booking a meeting, it needs to know what’s available to be used, and have structured and permissioned access to the relevant API. In addition to transparency, this standardization accelerates developers with common and simple ways to augment systems and to consider using guardrails to mitigate potential risks of AI hallucination.

Overcoming Challenges: Enterprise Path to Intelligent Solutions

Risks of financial losses, regulatory exposure, brand damage, and flawed operations, are some of the concerns regarding AI innovation, especially in enterprise software. Such risks are imminent when a company opens space to a possible lack of compliance with business policies or governance standards. 

To avoid common pitfals in Enteprise AI adoption, here’s what you need to know before engaging on your next effort with AI: Overcoming Challenges: Enterprise Path to Intelligent Solutions. Understand the risks and limitations of intelligent systems, their potential impact on enterprise organizations, and most importantly, how to overcome each limitation without giving up on trust, compliance, transparency and control, and skipping the tough process of making a choice between different AI options.

Key Concepts Summary

Symbolic AI: AI based on explicit rules and logic, designed for clear, explainable reasoning in well-defined domains.

Artificial Intelligence (AI) / Machine Learning (ML): AI is the broad field of creating intelligent systems; ML is a subset where machines learn patterns from data to make predictions or decisions.

Generative AI (GenAI): A branch of AI that creates new, original content (text, images, music) based on learned data patterns.

Prompt: The input or instruction given to a generative AI model to guide content creation.

Large Language Models (LLMs): Massive AI models trained on extensive text data, capable of understanding and generating human-like language.Agentic AI: Autonomous AI systems that perceive, plan, decide, and act independently to achieve complex goals.

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