Enabling AI: How to Bring AI into Your Business

Getting Started Is Hard

We have talked to hundreds of teams at various stages of their AI journey. We have talked to folks from across the entire landscape of business from small retailers to giant Fortune 100 companies.

The biggest commonality we see is a lot of market confusion. What is AI and what is NOT AI? Many companies have board mandates or executive buy-in to do "something" with AI but the noise in the market makes it hard to figure out where to invest time and energy.

Your Data and Processes: Your Leverage

When you deploy an off-the-shelf model, you are getting a model trained on data that is not yours. This can lead to "hallucinations" or results that simply do not apply for the context of your business.

Having data that isn't yours is great for enriching your data with information that a human would bring to the data. There are many examples of this, such as understanding how to put the tags S, M, L, XL in order without having to pre-program for it, or understanding what an address looks like, or whether or not a company description sounds like an international freight company.

But the real opportunity is to leverage what is proprietary in your business: your own data. This can come in many forms; you may have run a number of marketing campaigns and your sales data reflects whether or not they were effective. Perhaps you tried a new pricing strategy, or a new product offering, and now you can extrapolate whether your customers would also like to buy related products, or maybe your customers only buy certain products at certain times of year.

Every business has hidden patterns in their data; there is always a fundamental rhythm to the business, whether it's days of week that sales are higher, or certain times inventory runs low or out.

Featrix lets you find and unlock these patterns so that you can get an edge you previously didn't have.

Mid-Market Challenge

For mid-market enterprises, the journey to adopting AI is both an opportunity and a challenge. Unlike large corporations with vast resources or startups with inherent agility, mid-market companies must navigate limited budgets and competing priorities while staying competitive.

From automating repetitive tasks to delivering predictive insights, AI can empower mid-market enterprises to make smarter decisions, enhance efficiency, and unlock new revenue streams. The key to success lies in starting small, focusing on high-impact use cases, and gradually scaling efforts as confidence and expertise grow. By embracing a step-by-step approach—beginning with clear goals, building foundational capabilities, and integrating AI into core workflows—mid-market companies can evolve into AI-driven organizations.

Data Matters; Compute Matters More

• A sales rep learns that a prospect has an incorrect phone number in Salesforce.
• A procurement manager discovers that a specific vendor is off-limits because a competitor uses them.
• A marketing analyst notices that a single partner company is driving half of the revenue in a particular segment.

Business Engineering

Engineering is fundamentally about designing systems that are useful and efficient. In the future, successful businesses will operate more like computers, embracing structured systems to enable seamless and scalable operations.

Computation, at its core, involves three key steps: collecting inputs, processing those inputs, and producing outputs. Adopting a "computation mindset" allows businesses to break down complex workflows into modular, interconnected processes. By treating each part of the business as a computational system, you can design workflows that are not only efficient but also adaptable and scalable.

With this mentality, businesses can connect computations—sales data, customer feedback, inventory levels, financial forecasts—into larger, integrated systems. This approach enables organizations to iterate, optimize, and ultimately build anything they need to stay competitive in a rapidly evolving landscape.

What AI is—and what AI is not

AI simulates human intelligence in machines, enabling them to learn, think, and perform tasks with minimal intervention. AI analyzes data, identifies patterns, and makes decisions or predictions. Examples include:

• Machine Learning: Learns from data to improve over time (e.g., recommendations, fraud detection).
• Natural Language Processing: Understands and generates human language (e.g., chatbots).
• Computer Vision: Interprets visual data (e.g., facial recognition).
• Generative AI: Creates new content.
• Autonomous Systems: Operates independently using real-time data.

Not everything labeled AI qualifies. Examples of non-AI include:

• Rule-Based Systems: Follows predefined instructions without learning.
• Basic Automation: Repeats tasks without intelligence (e.g., macros).
• Analytics Tools: Displays trends but doesn’t adapt (e.g., dashboards).
• Programming Logic: Solves problems without mimicking human intelligence.
• Buzzwords: Claims of AI without actual learning or adaptability.

The key difference is adaptability—AI learns and improves, while non-AI follows static rules.

The Net Net

The journey to adopting AI can feel complex and overwhelming, but it doesn’t have to be. Whether you're a mid-market business navigating limited resources or a larger organization looking to unlock hidden patterns in your proprietary data, the key to success lies in taking deliberate, scalable steps. AI isn't just about adopting technology—it's about transforming your business into a learning, adaptive system that thrives on insights and iteration.

Featrix is here to guide you every step of the way. With our expertise in AI and a deep understanding of business dynamics, we help you cut through market noise and focus on what truly matters: leveraging your unique data to gain a competitive edge.

Start with Featrix today and unlock the full potential of your business with AI-driven insights and solutions. Let’s build your future together.