Public AI Models

A good starting point is with public AI models like ChatGPT, Gemini, Claude and Co-Pilot for creation of templates, processes, social media posts, images and content. Avoid submitting customer or confidential data to these models. There are security and privacy concerns — think of this as sharing a computer with everyone around the world, where all your data is used for the benefit of others’ questions. In some cases, your data is fully accessible.

Public AI models have limitations, with most training data being at least two years old. Despite this, they remain highly powerful, delivering detailed responses based on user prompts. For very high-quality relevant answers, public AI usually requires much more complex question phrasing and sometimes user-supplied data, which leads into privacy concerns.

Open public AI is not free. For real business applications, even for small to mid-sized businesses, costs often reach $500+ monthly with models like ChatGPT. Use of the very cutting-edge Grok AI could jump that same use to $1,000-$2,000 monthly. Additionally, data submitted remains unsecured, posing risks for businesses.

Private AI for Business

For even small businesses, private AI is the best option to avoid potential public data exposure, lawsuits, and the risk of a major public relations problem. Following the above public computer example, private AI is like having your own private personal computer for your business. Your business will have its own AI enhanced with the power of the public models plus significant privacy, security & data control. 

Because a dedicated private AI is trained exclusively on your data, products, services and policies, it delivers the best responses for your business even with short questions. While more expensive than public AI, it offers key advantages.

Private AI ensures enhanced security, encrypting question/response data and preventing exposure to public models. It becomes highly specialized, enabling broader business use while allowing control over training data, access, limitations, accuracy and response bias (so competitors don’t show up in answers). For businesses without in-house AI expertise, partnering with a reputable vendor ensures a structured, supportable solution.

Secure AI

For high-security needs, military-grade AI can be installed locally, even on hardened Android devices without internet access. However, due to high costs, these solutions have limited business applicability. We are involved with a number of military AI and UAV bids and these solutions are incredibly expensive.

Typical Retail AI Use Cases

Businesses typically use separate AI environments — one for customer-facing and one for internal staff needs. The first step is almost always training a private AI on your business to allow flexibility for many use cases. Once trained, AI can support various interfaces, with chatbots, email and SMS automation being the most common. Typical applications in manufacturing, retail and distribution include:

• AI chatbots and virtual assistants offer 24/7 scalable support, instantly handling inquiries. They can be embedded in websites and apps or used on staff computers and mobile devices, improving response times and efficiency. Result: Customers get fast, comprehensive answers based on your business and processes. 
• Automated email and SMS responses streamline communication by reading inbound emails or SMS, generating personalized replies, and escalating to human support if needed, reducing response times and workload. Result: Our customers use show that 95%+ of answers are satisfactorily addressed through automated AI response in under four minutes.
• AI-driven content creation tools generate blog, social media and ad copy, ensuring brand consistency while saving hours on other jobs like sales email creation and product value messaging. Result: Much higher productivity and brand messaging consistency, plus the ability to workflow updates to thousands of products. 
• Process automation reduces manual tasks, errors, and inefficiencies by using AI to capture website form data, manage account setup, approve credit, and send customer notifications seamlessly. Result: Near limitless scaling; faster, more complete customized responses; and improved customer service.
• Use AI to train new and existing staff through micro-training with the ability to answer nearly any process or product/service question as they come up for employees. Even state employment and health code information can be loaded for staff access. Result: Micro-training has shown to deliver higher retention. When delivered in a simple chat app, staff get the training when they need it and when they have questions.
• AI-powered pricing and inventory optimization paired with automation can dynamically adjust prices and stock levels based on demand, market trends, and competitor activity. Result: Prices change constantly for a business, and this capability allows the business to maintain and improve profitability.
• The possibilities are limitless due to the existing flexibility of AI and automation.

Data Privacy & Confidentiality

Do common open public AI models save/retain any submitted data? Yes. Public AI models pose a major risk of exposing sensitive data. They rely on vast datasets that may include confidential business information, customer data, proprietary algorithms, and even malicious code. Without strict safeguards, companies face higher risks of data breaches and unauthorized access, leading to severe financial and reputational damage.

One head of finance we talked with was very proud of themselves that they used ChatGPT for trend analysis of all customer account information. We had the unfortunate opportunity to inform her and the head of IT that they have just breached 100% of their customer account data publicly. If they had completed the same process on a private AI, there would have been no issue.

Lack of Control and Customization

Are open public AI models specifically trained on your business? No. Open AI models are neither specially trained on all your services/products nor have limiters to offer competing solutions as answers.

Open public AI models are designed to be versatile and widely applicable, but this can also be a limitation for businesses with specific needs. The lack of control over the model’s architecture, input data sources, output and parameters can deliver inaccurate or poor information. 

Most public AI models rely on outdated datasets, with minimal recent training data. This makes it difficult to align AI with current business needs, leading to poor performance and inaccurate responses. Companies also lack control over training data, risking technically correct answers that include competitors’ information. Additionally, without control over AI creativity, these models are prone to hallucinations, otherwise known as lying.

 

Security Vulnerabilities & QC Control

Do open public AI models expose business users to additional security risks? Yes. 

Public AI models are widely accessible, including to hackers, increasing security risks. Non-vetted AI apps, often used freely by businesses, further expose companies to adversarial and ransomware attacks. The lack of source data validation prevents reliable control over AI responses, compromising output integrity and decision-making. Some models, like China-based DeepSeek AI, have undefined security practices, prompting experts to recommend blocking them entirely from business networks.

 

Compliance & Regulatory Challenges

Are open public AI models compliant? No. Generally, they do not pass internal security requirements, privacy, data exposure, risk, GDPR, HIPAA, Soc 2 Type II or data control requirements.

Retailers must meet baseline privacy and security standards. Even accidental submission of customer data to a public AI model violates PII regulations and has led to major lawsuits. The lack of encryption and security in open AI models remains a critical threat to AI adoption in business.

 

Ethical Considerations

Are open public AI models biased? Yes. Public models do not provide control or validation of the rules or bias and will deliver results based on whatever training data they have. It is also important to note that businesses want bias control to retain brand and messaging control for responses. Indeed, the ability to control bias is relevant when the best solution should be your business instead of presenting a competing option. There are several considerations for using AI in business which span brand and messaging control to controlling answers based exclusively on your data, services and products.

Lack of bias control is often misidentified as incorrect answers by users. These models are often trained on diverse datasets, which can inadvertently introduce biases that affect decision-making processes. Businesses do need full control of the biases to ensure the answers provided align to the brand, products and messaging.

 

Final Thoughts

We have now entered a period of daily AI advancements estimated to be 2,500% faster than the entire internet boom. The six major AI solutions offer many model options. There are good applications for open public AI models that can improve efficiency, sales and customer service; however, private AI greatly minimizes overall risks. Businesses should look for AI solutions partners who can deliver robust data protection measures, data control and validation, compliance and have extensive experience tuning AI for business. By striking the right balance between innovation, generative AI creativity, and security, AI can deliver unimaginable productivity and competitive advantages for businesses.