Less time on tasks. More time for purpose.
25N Coworking Lunch and Learn
A practical guide for small businesses
Proud member of the Schaumburg Business AssociationAI is moving quickly. This session gives small-business owners a practical way to understand privacy, control, and useful AI before deciding what belongs in their work.
About Raj
I spent more than 20 years helping businesses understand, build, and support technology. At Flexera, I worked from consulting to engineering leadership, learning how to listen before proposing a solution.
It keeps me present, curious, and focused on what matters before reaching for a solution.
It reminds me that meaningful progress comes through patience, consistency, and small steps.
It has taught me how to listen closely and make room for the human story behind the work.
I teach from real work. The goal is not more technology for its own sake. It is a clear path to something useful for the people and business in front of us.
Why I do this work
Rajneesh (Raj) SehgalMindful AI Guy
People deserve a clear, practical understanding of AI before deciding what belongs in their business.
In conversations with SBA members and local business owners, I kept hearing real uncertainty about AI and privacy. Education comes first. Understand the choices, ask better questions, and decide what makes sense for your business.
The questions behind the concern
What happens when I give it a prompt?
Where does it go, who can access it, and how is it handled?
Not every tool, setting, or use case has the same privacy tradeoffs.
The conversation becomes more useful when we separate two things. First, what AI is and how it works. Second, how a particular tool handles information.
How AI learns
Once it has learned the pattern, it can estimate the most likely output for an input it has not seen before. For example, 10 miles becomes about 16.1 kilometers. This is a probability-based prediction, not human understanding.
How large language models learn
A model learns which pieces of language tend to follow one another in different contexts, then generates a response one piece at a time.
A distinction that matters for privacy
It learned broad patterns from a large collection of information.
How your information is stored, retained, reviewed, or used later depends on the tool, plan, settings, and agreement.
“Will my data train the model?” is important. It is not the only privacy question. We also need to ask where information is processed, who can access it, how long it is kept, and what controls the business has.
Why business context matters
Common language and patterns across many industries.
Useful AI needs the right context. The question is what it needs for this task, and how to share that information responsibly.
A model can provide a reasonable general answer without knowing your business. To help with real work, it usually needs some business-specific context.
The information path
A question, document, spreadsheet, customer detail, or other information
The tool receives the information and returns a response
You review the response and decide what to use
The exact path varies by product. Information does not become private simply because the prompt is short or the response appears in a chat window.
Before choosing a tool
A business account can change this answer.
A chat, a file, and an API request can follow different rules.
Your team, the provider, connected systems, or an administrator.
Do not begin by asking which AI model is best. Begin by asking what information the work needs and what will happen after it leaves the business.
The practical starting point
For OpenAI and Anthropic commercial offerings, business inputs and outputs are not used to train the model by default.
The work still goes to a cloud service. Chats and files can still be stored under the plan and product rules.
This is a sensible baseline for everyday business work. It is better than asking employees to use personal AI accounts, but it is not blanket approval to upload every business document.
The middle path
It stays inside your business system.
A redacted policy excerpt or short summary goes to the cloud service.
AI returns a draft. A person decides what to use.
Hybrid is a workflow that keeps the full record under business control while sending only the minimum useful context to a cloud AI service.
The highest-control option
Stays on hardware you control.
OpenAI open-weight models can run on infrastructure you control.
Stays within your environment.
The tradeoff More direct control over the path, with more responsibility for hardware, security, updates, backups, and support.
Fully local AI can be the right answer for highly restricted work. It is not automatically the right answer for every business or every workflow.
Matching the approach to the work
One business may use more than one approach. The goal is not to declare the cloud safe or unsafe. Match the information, the work, and the level of control the business needs.
A practical next step
Make it the place for business work.
Customer records, financial information, employee information, legal documents, and proprietary material need a deliberate decision.
Learn what information it needs, where that information goes, and how the business will review the result.
Responsible AI begins with clear boundaries and one useful experiment. The technology should fit the business requirements, not the other way around.
The takeaway
The goal is not to use more AI or less AI. It is to use AI in a way that protects the information, relationships, and responsibility that matter to the business.
How I work
A mindful path from understanding to action.
Workshops like this one help people understand AI and uncover worthwhile workflows.
Evaluate value, privacy, risk, adoption, and the context the work needs. BizMind can become the business-context foundation.
Why AI needs business context →Implement the strongest place to start with clear human ownership and judgment.
Improve what helps, keep context current, and decide what comes next.
Align → Understand → Realize → Advance