What mid-market businesses need to know to unlock the potential of artificial intelligence.

Artificial intelligence (AI) is everywhere—from customer service chat bots to tools that manage supply chains. Although the AI revolution is still in its infancy, it has quickly become an integral part of companies all over the world, helping them transform industries, boost productivity, and achieve vast amounts of growth.

While AI technologies hold immense promise to revolutionize the future of work, they have also been misused to propagate unfounded claims—such as tools that claim to predict employee performance from a brief 30-second video interview or the assertion by a mayoral candidate that an AI bot could govern a city more effectively than human leaders.

With the growing buzz around AI technologies, it’s become difficult to sift through the hype and discover what really works—especially if you’re a mid-market company that’s considering implementing AI. That’s where we come in.  

At BUILT, we help you demystify enterprise AI techniques and understand what will truly transform your business. Here’s our guide to understanding AI, and how to separate the hype from reality. 

What artificial intelligence is and isn't

AI is comprised of software and algorithms that analyze data to imitate human intelligence. They then generate new insights from the information they’re given. Think of AI as a new layer added to your software and systems, one that can help you make smarter, better, and more data-driven decisions.

Here are some common terms that will help you better understand the AI landscape:

• Machine learning (ML): A type of AI that allows systems to learn from training data without being explicitly programmed.

• Generative AI (GenAI): AI that can generate new content such as text, images, or even music, based on patterns in data.

• Deep learning (DL): A type of machine learning that uses neutral networks with many layers to analyze various factors of data.  

• Natural language processing (NLP): A branch of AI that focuses on the interactions between computers and humans through natural language.  

• Large language model (LLM): An advanced AI model trained on vast amounts of text data to understand and generate human-like language.

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What artificial intelligence can do

• Automate complex tasks and accelerate their completion: AI can make processes more efficient. For example, AI chatbots can generate responses to customer inquiries, freeing up people for other tasks.  

• Predict outcomes: Using predictive analytics, AI systems can learn and forecast customer behavior, market trends, and production needs based on data patterns.  

• Enhance decision-making: AI can extract insights from vast amounts of data, allowing organizations to make better informed decisions.  

What artificial intelligence can’t do

• Replace human judgment: Complex decisions often require context, ethics, and emotional intelligence—all things AI doesn’t possess.

• Work without quality data: AI systems depend on accurate, structured data to deliver reliable results. Flawed data will lead to poor performance.

Understanding these limitations is key to successfully using AI across your mid-market business.

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The Top 5 reasons why artificial intelligence projects fail

In the summer of 2024, Rand interviewed 65 data scientists and engineers with at least five years of experience in AI technologies and machine learning. Its goal was to understand the root causes of AI projects that failed. What the study found speaks to the pitfalls of not differentiating between AI hype and AI reality.  

Here at the top five reasons why AI projects fail:
1. Stakeholders don’t understand or don’t communicate the problem they were using AI to solve.  
2. Companies don’t have enough training data for an AI model to accomplish their goals.
3. Organizations don’t consider their customers’ wants and needs.
4. Companies don’t have the right resources to manage data and use AI.
5. Leaders try to use AI to fix problems it isn’t designed to solve.

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How mid-market businesses are using artificial intelligence

AI is having a significant impact across industries and departments. Here’s how mid-market enterprises are leveraging enterprise AI:

Customer service
AI-driven chatbots and virtual assistants provide instant, personalized support. They answer customer queries with accuracy while allowing teams to focus on issues that need more attention. ‍

Data analysis
AI simplifies data collection and analysis, uncovering insights—fast. Tools like machine learning algorithms help transform raw data into predictive models. ‍

Human resources
From identifying top talent to recruit to improving the onboarding process, AI tools streamline human resources (HR). Personalized learning plans, powered by AI, also boost employee engagement and productivity. ‍

Marketing
AI technologies craft marketing campaigns by analyzing audience behavior and generating targeted content that can save money and keep customers engaged. ‍

Supply chain management
By predicting demand and improving operational transparency, AI minimizes waste, reduces costs, and ensures timely delivery, all of which are crucial for staying competitive. ‍

Healthcare
AI can improve patient outcomes, streamline triage processes, and reduce burnout. AI-driven tools can also help clinicians prioritize urgent care cases, offer real-time decision support, and personalize care to meet patient needs.

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Four common myths about artificial intelligence

While it’s true that some aspects of AI have been overhyped, there are also some myths and misconceptions. Here are the four most common ones:‍

Myth 1: Artificial intelligence will replace humans  
AI automates repetitive tasks, allowing humans to focus on higher-level strategic, creative, and interpersonal aspects of their jobs.‍

Myth 2: Artificial intelligence solves every problem  
AI’s strength lies in solving specific, data-dependent challenges. It’s not magic. ‍

Myth 3: Artificial intelligence requires a massive budget  
Today, AI is accessible even for smaller enterprises. Affordable tools offer features that can help your business grow. ‍

Myth 4: Artificial intelligence delivers perfect results
AI is highly effective but can make mistakes. It relies on high-quality data and human oversight to make sure it’s adhering to ethical standards.

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A strategic artificial intelligence approach for mid-market businesses

Making AI a useful tool for your business requires careful planning and strategic execution. Here are some guidelines for structuring your AI strategy to meet the needs of your mid-market enterprise:

Identify opportunities  
Analyze the potential use cases and prioritize those that fit with your business goals. Focus on areas where AI can reduce inefficiencies or create new value.

Analyze your readiness  
Make sure you have the necessary infrastructure and data quality to support AI. Also, invest in training teams to make sure you get the most out of your AI tools.  

Act responsibly  
Test, test, test, and then test again. AI presents a whole new paradigm for your systems and processes. That’s why it’s critical to experiment with AI tools in controlled settings on a limited scale before you move them over to every aspect of your business. Doing so will allow you to troubleshoot and create best practices.

Be prepared to iterate—and reiterate  
As AI evolves, so should your strategy. The new technology requires a lot of experimentation, not only with your prompts but also with your systems. So you’ll need to stay on top of the latest trends to improve organizational decision making—your competition likely will.  

Get your artificial intelligence readiness assessment today
With thousands of new AI-powered tools on the market, identifying what might work for you can be daunting. BUILT is here to help.  

We use generative AI to both build products for our clients and as a component within the products we build for our clients.  

To get started, take our AI opportunity assessment. It simplifies the process by pinpointing the optimal technologies to enhance revenue and advance key capabilities. The assessment covers areas such as:

• Streamlining operations and boosting productivity

• Assessing AI readiness and data environment insights

• Reducing risk (risk identification and mitigation)

• Enhancing return on investment

• Identifying pain points and AI applicability

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Discover how AI can drive the greatest value for your organization by consulting with our AI experts today.  

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