AI isn’t Magic:

Arthur Clarke famously stated “Any advanced technology is indistinguishable from magic” and artificial intelligence (AI) is no exception. However, while AI might seem like magic, it’s no different than any other technological breakthrough (cloud computing, no-code applications, etc.). Like other technologies, AI needs a powerful engine, and that’s where a robust AI infrastructure comes in. With the proper infrastructure in place, solving even the toughest business challenges isn’t just feasible — it’s seamless. 

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How does AI work?

AI analyzes mind-numbingly large amounts of data, identifies patterns or relationships, and surfaces actionable insights to move your business forward. At first glance, this might sound comparable to a business intelligence (BI) tool (which is a common misconception). However, while a BI tool can help you draw insights from historical data, AI goes further by analyzing your data, identifying new patterns and relationships, and advising you on what to do next.

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Most people are familiar with artificial intelligence from using tools like ChatGPT, Gemini (formerly known as Bard), or DeepSeek. While impressive, these chatbots only scratch the surface of what AI is capable of. Industry experts estimate that there are hundreds, if not thousands, of applications for AI just for boosting office productivity. However, when you embed AI into your business's core strategic initiatives, the use cases are practically endless. The first step is to create the necessary AI infrastructure. 

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How do you create an AI infrastructure? 

Having the right AI infrastructure in place is critical, regardless of how you plan to use AI. But what exactly is it doing behind the scenes? Creating an AI infrastructure typically requires five steps:
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1. Collecting relevant data: The first step is to identify the data sources you plan to use to train your models. Remember, AI systems become more powerful as you feed them more data. The best results occur when the system can leverage data from both internal sources (either structured or unstructured) and external sources (data from public databases, industry news, social, etc.).

2. Cleaning the data: Once you’ve identified your data sources, you’ll likely need to clean, enrich, and categorize the data before it can be used effectively by AI. Think of it like refining crude oil before it can be used to power a car. (Yes, this sounds tedious. And it is. This is why VirtuousAI built a “data janitor” that can accept unstructured data and enrich it for you.)

3. Training your model: The AI model then trains itself using the historical data you provided to learn how to address the problem at hand. This could be something simple (like automating manual tasks) or more complex (like embedding AI into your core business strategies to get custom strategy recommendations). Regardless of the use case, it’s important to feed the model as much data as possible during this stage to help it learn more quickly.  

4. Building the knowledge graph (KG): You can think of the knowledge graph as the brain for your AI system. It serves as the central hub for all strategic decision-making and action. The KG draws on and contextualizes data from LLMs, the internet, and internal/external sources to help drive successful decision-making for your AI system. Just like a real brain, the knowledge graph automatically learns and improves over time.

5. Putting AI to work in your business: The final step in creating an AI infrastructure is accessing the knowledge you created and putting it to work to address your use cases. This takes the form of a user interface (most often a chatbot, similar to ChatGPT) and/or an API to connect your applications and front-end tools. 

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Creating an AI infrastructure without assistance can be a daunting task. This is why many companies choose to buy a pre-existing, customizable AI infrastructure instead of building it themselves.

Building vs. buying an AI infrastructure

Building an AI infrastructure from the ground up usually requires a team of data scientists, which already poses a significant challenge. Additionally, most medium-sized enterprises can expect to spend approximately $1M to get up and running and $500k in people and compute costs annually. These costs make it nearly impossible for most small and medium businesses to leverage custom AI solutions.

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The faster, more cost-effective option is to buy an existing AI-infrastructure-as-a-service, lean on the expertise of external professionals, and customize it to meet your use cases. The time and money involved are a tiny fraction of what you would devote to building from the ground up. In fact, here at VirtuousAI, we have proven processes to take enterprises from 0 to AI in just 90 days. 

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Let’s explore a few other best practices for introducing AI to your organization.

What else to know when onboarding AI

Many companies are eager to start harnessing the power of AI and want to dive in headfirst. But, it’s easy to underestimate the work (and resources) involved in creating a custom AI solution for your company (especially if you plan on using AI to assist you with core strategic initiatives). This is why it’s important to establish a plan to prove the ROI of AI spend. At VirtuousAI, our approach is to prove the value of AI with your first use case while establishing the strategic foundation from which you will drive exponential ROI as you scale use cases.

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There’s no doubt that implementing an AI infrastructure is an investment that requires careful thought and planning. But, when done correctly, the benefits of AI can feel like magic. 

Continue your journey to AI adoption 

Interested in learning more about the right path to AI? Download our guide AI Unboxed: The business leaders’ guide to accelerated AI adoption to learn how to pinpoint the highest-value AI use case for your company and put your AI initiatives on the fast track.

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