In this episode of the One Step Beyond Cyber Podcast, hosted by Scott Kreisberg, Founder and CEO of One Step Secure IT, you’ll hear a practical discussion on how small and mid-sized businesses can adopt AI without sacrificing security. This blog features the full transcript and key takeaways from Scott’s conversation with Christopher Carter, Founder of AppRoyo and a global AI expert. You can read through the highlights below or watch the complete episode linked at the end.
Artificial Intelligence has dominated business conversations throughout the last two years. Yet for many small and mid-sized business leaders, all that buzz still hasn’t turned into a clear plan. You might still be asking, “How can we use this technology while keeping our business safe?”
We explored this challenge at the season finale of the One Step Beyond Cyber podcast. This written recap shares useful ideas, expert tips, and easy steps from the episode. You can start using AI and cybersecurity strategies in your business today.
AI is reshaping the benchmarks for business productivity and operational excellence. Implementing AI should be about liberating your team. When AI handles data processing and routine daily tasks, it frees your team's time to focus on strategic work.
This shift is the new frontier of competitive advantage. It creates bandwidth for your company to innovate faster, pivot to new strategies, and move beyond generic interactions. This kind of automation helps reduce waste and lower costs. As your business grows, AI systems can easily scale to meet higher demand without adding the same amount of extra work or expense.
Scott Kreisberg, CEO of One Step Secure IT, sits down with Christopher Carter, Founder of Approyo and global AI expert. Together, they break down what it takes for SMBs to use AI safely, efficiently, and profitably.
How to Prepare Your Organization for Machine Learning Deployment
Carter’s message is clear: small and mid-sized businesses are important to the American economy. He says, “small businesses are the true innovators."
What a business needs are the right tools, clean data, and a solid plan. To ensure your AI project succeeds, begin with a clear and well-structured plan. Think of it as the blueprint for your entire project.
A strong plan acts as your guide, outlining:
What do you want AI to achieve?
What is the timeline for each phase of the project
What data, people, and tools do you need?
A clear plan helps your AI project stay aligned with your business goals. When you list each step, from preparing your data to launching the final model, you lower the risk. This also improves efficiency and gives you a better return on your investment.
Step One: Start with One Clear Use Case
Carter explained that the biggest mistake companies make with small and large language models is trying to do too much at once. Many organizations attempt to solve every problem instead of starting small and building from there.
The right approach is to pick one specific business problem to solve first. Most start with vague goals, such as:
- Improving customer service
- Getting better sales insights
- Automating finance tasks
But these are categories, not tangible goals. Real success starts small and with a measurable target.
To get this right, pinpoint a precise area where AI can deliver a clear impact. Turn your general idea into a specific, trackable goal. For example:
Instead of: "Better customer service."
Try: "Decrease average response time for customer inquiries by 40% using AI chatbots."
How you measure it: Comparing the average time to resolve issues before and after you implement the chatbot.
Instead of: "Smarter marketing."
Try: "Increase conversion rates for personalized marketing campaigns by 25%."
How you measure it: Track the conversion rates of campaigns using AI recommendations versus your traditional methods.
Instead of: "Better forecasting."
Try: "Reduce inventory holding costs by 20% through AI-driven demand forecasting."
How you measure it: Measure the change in holding costs and compare it against historical data.
This clarity gives you a defined finish line. As Carter puts it, “Start with a use case, clean your data, and test your model. Build on what works.”
Step Two: Build on Clean, Reliable Data
An AI system can only perform well if it learns from good data. Similar to a car engine. It requires clean fuel to operate efficiently.
For many businesses, this means cleaning up years of messy data. That includes removing duplicates, fixing missing details, and updating old information before using any new AI tools. “Every company has some ‘yuck’ in its database,” Carter says. “Clean it first, and the AI will reward you.”
The High Cost of "Dirty Data"
Dirty data isn't just problematic; it's a liability. It actively works against you by causing:
Inaccurate Analytics: Your AI will spot patterns that aren't real, leading to bad business decisions.
AI "Hallucinations": When an AI tool receives confusing or incomplete data, it may produce incorrect or made-up answers.
Data security risk: Poorly managed data can expose sensitive customer or company information.
On the other hand, clean data improves accuracy, speeds up your results, and keeps your business secure.
What Does "Dirty Data" Look Like?
The challenge usually comes in two forms:
Data Quality Issues: This is the "yuck" Carter mentioned. It includes inaccurate, incomplete, or inconsistent information. Think of missing customer phone numbers, duplicate entries, or different spellings for the same client.
Data Silos: This is when your data is trapped in different, disconnected systems. For example, your sales data may be in your CRM.
Your marketing data could be on an email platform. Your customer service data might be in a helpdesk system. If your AI tool cannot access all the information together, it will not be able to give you a full or accurate answer.
How to Get Your Data AI-Ready
Getting your data clean doesn't need to be a massive, multi-year project. Your data-cleaning process should follow two main phases:
1. Collect and Integrate: Name your key data sources (e.g., customer lists, sales numbers, support tickets). Use the right tools to bring this data together into a single, unified view.
2. Clean and Prepare: Review your data to make sure it is accurate. Remove or merge duplicates, fix formatting issues, and fill in any missing information.
Step Three: Keep Your AI Private and Secure
Public AI tools such as ChatGPT, Grok, Gemini, or Claude can help generate ideas. However, they also pose serious risks to your company’s confidential data.
When employees upload internal documents or customer data to a public AI model, that information is no longer private.
As expert Christopher Carter explains, “When you upload it, they own it. Use your data. Keep it in your box.”
His advice for small and mid-sized businesses is to create a private AI environment, often called a “Black Box.” In this setup, company data stays internal, encrypted, and fully under your control.
Businesses can make a safe AI environment. They can use reliable cloud platforms. Some examples are Microsoft Azure, Amazon Web Services (AWS), and Google Cloud. Another option is to use on-premise servers with limited access and strong verification controls.
Carter recommends SMBs focus on small language models (SLMs) over large, public LLMs.
Why? Because SLMs are faster, cheaper, and purpose-built for your company’s unique data.
“We don’t have petabytes of data like enterprises,” he explains. “We need models that go deep, not wide.”
By training small language models with your own clean and secure data, your business can gain accurate insights. This helps you get real-time information while keeping your data safe from unnecessary risks.
Step Four: Measure ROI with Small Wins
AI success doesn’t come overnight, but it does come from measurable wins.
Carter highlights three fast-track examples:
- Sales & Marketing AI: Automate lead scoring, content creation, and personal research.
- Developer AI Tools: Detect code errors and speed up problem-solving.
- HR & Finance Automation: Streamline scheduling, payroll, and PTO tracking.
Each win compound: improving efficiency, saving costs, and boosting employee satisfaction.
Carter calls this the “smiles lead to smiles” effect. When one department succeeds with AI, that success inspires others and builds momentum across the entire company.
Build Your AI Strategy on Security and Simplicity
Artificial Intelligence can transform how small and mid-sized businesses operate, but only if done intentionally.
Begin with a focused approach. Ensure your data is accurate. Use secure, private AI systems. Safeguard your business operations.“AI isn’t here to replace people,” Carter reminds us. “It’s here to make people better.”
Making a private language model may not be possible for every business. However, they can still take steps to protect sensitive data when using public AI tools.
Remove or anonymize personal data
Replace or mask personally identifiable information (PII) using techniques like anonymization, pseudonymization, or synthetic data. This prevents exposure of real customer or employee details during AI training or use.
Define strict data boundaries
Establish clear policies for which data can be accessed, by whom, and for what purpose. Limiting data workloads helps prevent unauthorized use or accidental leaks.
Enforce strong data governance
Develop and maintain policies for how data is stored, accessed, and retained. Effective governance ensures compliance with privacy and security standards.
Monitor and audit regularly
Continuously track AI activity for unusual patterns and schedule regular security audits to detect and fix vulnerabilities early.
Train employees on AI safety
Train staff on how to use data responsibly. Teach them about privacy rules and why it is important to protect confidential information when using AI tools.
Manage the data lifecycle
Retain data only as long as necessary for business use. Delete outdated or unnecessary records to reduce exposure and maintain compliance.
AI adoption is within reach for every small and mid-sized business. By taking the right steps, you can use AI to grow smarter, work faster, and stay secure. When data protection is strong and customers trust the company, technology becomes a real advantage, not a risk.
Tune in to the One Step Beyond Cyber Podcast on:
BuzzSprouts | Spotify | Apple Podcast | Amazon Music | YouTube
Share this article
