Artificial intelligence has quickly become the sweetheart of the boardroom. While business leaders are asking their teams to incorporate it into their strategies and processes, many still remain in the dark about the fundamentals. Perhaps you feel you already missed the boat, you’re too scared to ask, or you don’t know where to begin learning. No matter who you are, this guide breaks down the key concepts, terminology, and applications of AI in a way that's accessible to those who need a crash course on the basics of AI.
Artificial intelligence is a field of computer science dedicated to creating machines that can perform tasks that typically require human intelligence. These tasks may include learning, reasoning, problem solving, perception, and language understanding.
At its core, AI systems rely on several key components to function:
Algorithms: These are sets of instructions that computers follow to solve problems or perform tasks. AI systems use algorithms to learn from data, make decisions, and improve their performance.
Data: AI systems learn from vast amounts of data, identifying patterns that might not be apparent to the human eye. The quality and quantity of this data has a huge impact on the system's effectiveness.
Training: AI systems analyze examples to learn relationships between inputs and desired outputs. This process allows them to recognize patterns and make predictions when presented with new data.
To be conversant in AI discussions (and to set yourself up for success while reading the rest of this post) you should familiarize yourself with these key terms:
Machine learning is a subset of AI that involves teaching systems to learn from data. By analyzing and interpreting large sets of information, these systems can identify patterns and make informed decisions without being explicitly programmed for specific tasks. ML enables machines to improve their performance over time through experience.
Deep learning is an advanced form of machine learning that uses artificial neural networks with multiple layers to analyze data. This approach allows systems to process information in layers, with each layer transforming the data in meaningful ways to extract higher-level features.
Large Language Models are an advanced form of artificial intelligence designed to understand, generate, and manipulate human language on a large scale. These models are trained on vast amounts of text data, enabling them to produce coherent and contextually relevant responses to prompts. An example of this you’re probably familiar with is ChatGPT, which is an LLM that’s used behind the scenes of many chatbots.
LLMs operate through sophisticated machine learning techniques, specifically deep learning, which enables them to discern intricate patterns within language data, which is why the content they generate sounds pretty close to the way humans actually speak with one another.
Generative AI is a subset of artificial intelligence that focuses on creating new content – things like text, images, audio, video, and even code – based on user prompts. Unlike traditional AI systems that analyze existing data, generative AI can produce entirely original content that never existed before. In other words, it generates something (get it?)
Generative AI is very helpful when trying to answer complex questions, summarize information, or automate tasks.
Agentic AI is the next evolution in artificial intelligence (and maybe you think so, too, since you’re reading this on a platform for AI agents). Agentic AI is a way of describing systems that can operate autonomously – making decisions and performing tasks – without the intervention of humans. In this way, agentic AI does more than generative AI because beyond just generating content, it can also actively think, reason, and take action toward a specific goal.
Agentic AI is unique in that it is autonomous, adaptable to new information, and goal-oriented.
Now that we’re all speaking the same language, let’s talk about why people are so excited about AI – particularly generative AI and agentic AI.
Generative AI tools can help draft emails, reports, blog posts, and other written content, saving time on research, editing, even providing creative recommendations for when you’re just burnt out. For example, when creating social media content after your company releases an important report, you might share that report with a generative AI tool and receive some ideas for social media content, tailored to each platform.
Similarly, agentic AI can take this a step further by autonomously managing entire content workflows—from ideation to publishing—adapting to feedback and improving over time. For instance, an agentic system might not only draft a social media post, but also push it to your social publishing tool, format it, schedule it, monitor performance, and report back performance metrics to you.
While traditional automation handles repetitive tasks in fixed ways, generative AI can adapt to variations in tasks and produce customized outputs. For example, it can summarize IT service logs to identify potential issues without requiring predefined rules for every scenario.
Agentic AI takes this to the next level by independently making decisions about workflow optimization. Agentic AI can perceive bottlenecks, reason through solutions, and implement changes while continuously learning, improving processes, and operating with minimal human oversight.
Generative AI excels at personalization, because it’s capable of customizing offers and content for different audiences. This translates to surfacing highly customized content across channels, suggesting the most relevant upsell opportunities, and surfacing the best offers for prospects and customers.
Agentic AI can take it a step further by offering even more intuitive customer interactions by proactively identifying needs and adapting responses in real-time. For example, in customer service, autonomous agents can handle multichannel support that accesses multiple databases and information sources at once, while continuously learning from each interaction to provide increasingly personalized experiences.
While there’s no shortage of exciting ways to integrate AI into your business and workflows, people are equally abuzz about the ethical considerations and possible shortcomings of AI. Here are a few ways you can ensure you’re staying safe as you dip your toe into AI:
Remember that human oversight remains essential. Even with agentic systems that can act autonomously, human supervision is crucial. And the more consequential the decision you entrust your AI with, the more supervision is needed. AI should augment human capabilities, not replace critical thinking.
Recognize AI’s limitations. AI can make mistakes. Outputs can be biased. AI may misunderstand context and produce inaccurate results. Generative AI is a good starting point, not an end point.
Continue to invest in data privacy and security. Both generative and agentic AI systems may interact with sensitive business information. Ensure proper data governance practices are in place, especially when using third-party services.
If you’re excited about AI, it may feel hard to move past asking ChatGPT to create a used car dealership jingle using your kids’ names. (But there is a next step, we know from experience.)
First, start with accessible AI tools. Play around with generative AI platforms (yes, like ChatGPT), but try giving it more complex prompts and see what it can do. One prompt I like to recommend is asking it to write a cover letter for you. See what types of outputs you get with different prompts – more detail, less detail, different job types and job levels, different company environments. It helps rip the bandaid off, and bring AI into a professional context for people.
Once you’re comfortable playing with AI, come up with a list of a few use cases for your own job, and commit to trying it out in your own work processes. When you experiment, use an iterative approach. Write prompts, review outputs, refine your approach, and repeat until you see what it takes to get to a good final result.
Now, build on existing workflows. Identify areas in your work where AI can enhance productivity within existing structures. For example, you may start by using generative AI to draft emails to executives, because you struggle to get the tone right and waste time perseverating over the blank page. You may even start to play with the Agent.ai platform and identify agents that you can bring into your workflows (or build your own agent, if you’re feeling particularly spicy).
As you get more comfortable with AI, remember that you must still commit to human intervention. While AI is impressive, it can also be inaccurate. It’s only as good as the prompts we put it, the data it has access to, and the guardrails we set. In this rapidly evolving field where new capabilities emerge every day, the more you immerse yourself into this world and experiment with the tools you have on hand, the more comfortable you’ll feel with this revolutionary technology that has the potential to change the way you do your job and reach your goals.