The more I talk with people about artificial intelligence, the more I realize that we are using the term “AI” to describe many very different things. Someone may say their company is using AI because employees use ChatGPT or Microsoft Copilot to help write emails. Another company may have AI updating its CRM, responding to customers, processing documents, or running entire workflows without an employee being involved. Those are not the same thing. The distinction matters because the potential value increases as AI becomes more capable—but so do the security, privacy, financial, and operational risks. A simple way to understand today’s AI tools is to look at what they are actually being allowed to do.
1. AI That Helps You Think
This is the type of AI most people are familiar with today. You ask it a question. You give it information. It researches, summarizes, analyzes, organizes, or drafts something for you. It might:
- Summarize a lengthy report
- Research or analyze a business issue
- Draft emails, presentations, or other content
- Pull key points and action items from information
This is how many people currently use tools such as ChatGPT, Claude, Gemini, Copilot, or Perplexity. The important distinction is that the AI is helping you do the work. It studies the information and gives you a response, but you still decide what happens next. For example, you might ask AI to draft a customer email, but you review and send it. You might ask it to summarize a sales call, but you decide what should be added to the CRM. You might ask it to help build a business plan, but you remain responsible for the assumptions and decisions. This type of AI is extremely useful, but it is not risk-free. Employees can accidentally share confidential information. The AI can misunderstand the request, leave out important details, or confidently provide incorrect information. Companies still need policies defining what information employees can share and how AI-generated work should be reviewed. However, the human remains directly involved. That provides an important level of control.
2. AI That Takes Action
The next level is very different. Instead of merely helping you decide what to do, the AI actually does something on your behalf. This is often called agentic AI. The term simply means that the AI has been given the ability to take action. An AI agent may be able to:
- Find and organize files
- Update a CRM, spreadsheet, or database
- Send communications or create follow-up tasks
- Navigate connected systems to complete work
Consider the difference between these two examples. In the first example, you give AI a customer conversation and ask it to summarize the discussion. You then copy the summary into HubSpot and create the follow-up task yourself. In the second example, the AI accesses the meeting recording, creates the summary, updates HubSpot, assigns the follow-up task, and possibly drafts or sends the customer an email. The first AI helped you work. The second AI completed the work. That is a significant change.
To perform those tasks, the AI needs access to company systems, files, email, databases, or applications. That access creates value, but it also creates exposure. What happens if the AI misunderstands the instructions? What happens if it sends information to the wrong person? What happens if an attacker manipulates the AI into sharing sensitive information or performing an unauthorized action?
This is not a hypothetical concern. Gartner predicts that more than 40% of agentic AI projects will be canceled by the end of 2027, largely due to escalating costs, unclear business value, or inadequate risk controls. That is a reminder that giving AI the ability to act is a significant step, and it deserves the same planning and oversight as any other major operational decision.
One of the growing concerns is prompt injection. This occurs when hidden or misleading instructions are placed inside an email, website, document, or other content that an AI agent is reading. The attacker’s goal may be to trick the agent into ignoring its original instructions, revealing confidential information, or taking an action for the attacker. This does not mean businesses should avoid AI agents. It means they should be deployed with the same care used when giving a new employee access to important systems. The agent should have only the access it needs. High-risk actions should require approval. Activity should be logged. Results should be reviewed. There should also be a way to quickly stop the agent if it begins behaving unexpectedly.
The more an AI tool can access and the more it can do, the more important these protections become.
3. AI and Automation That Run Without You
The next distinction is whether the technology waits for a person to start it or runs automatically. These processes can start automatically when an email arrives, a form is submitted, information changes, or a scheduled time is reached.
There are two basic ways to create this type of automation.
A Step-by-Step Workflow
In a traditional workflow, the company defines each step. For example:
- A customer submits a support form.
- The information is added to the ticketing system.
- The customer receives a confirmation.
- The appropriate team is notified.
- The ticket is assigned according to predefined rules.
Because each step is defined in advance, the process can be predictable and repeatable. AI can still be included. It might summarize the customer’s message, categorize the issue, or suggest a response. But companies should be thoughtful about where AI is inserted. AI reasons rather than simply following a fixed formula. That means it may not produce exactly the same result every time. For activities that must always be exact, like billing, reporting, payroll, or compliance deadlines—a rule-based process may be safer than allowing AI to make decisions. AI can assist around the edges by summarizing, identifying unusual results, or explaining the outcome without controlling the underlying calculation.
An Autonomous AI Worker
The other approach is to give the AI a goal and allow it to decide how to achieve it.
Instead of defining every step, you might tell it:
- Keep our sales pipeline accurate.
- Follow up with leads that have gone quiet.
- Review incoming invoices and flag anything unusual.
- Make sure new customers complete the onboarding process.
- Identify support tickets that may require escalation.
The AI then determines which steps to take. This makes it more adaptable than a fixed workflow. It can respond to changing information and handle situations that were not specifically programmed in advance. But flexibility also creates uncertainty. When you define every step, you generally know what the process will do. When you give AI a goal, it may choose a path you did not expect. That is especially important when the AI can operate automatically, without someone sitting in front of it. A small error made once may be inconvenient. A small error repeated hundreds of times before anyone notices can become a serious business problem. Automated AI workers need clear boundaries, monitoring, exception reporting, audit trails, and defined situations that require human approval.
4. AI That Builds Software
AI is also changing who can create software. People can now describe an application in plain English and ask AI to build a customer portal, reporting dashboard, workflow tool, or quoting application. This is sometimes called “vibe coding.”
Some platforms build and host the application for you, making the process more accessible to nontechnical users. Other tools generate code that can be reviewed, modified, and hosted elsewhere, offering greater flexibility but requiring more technical expertise.
This can help businesses create solutions faster and at a lower initial cost. However, “AI built it” does not mean the normal responsibilities of software development disappear. Security, data protection, user permissions, backups, regulatory requirements, ongoing maintenance, and ownership of the code and data still need to be addressed. Businesses must also understand their dependence on the platform hosting the application and what happens if the software fails or the provider changes its terms.
AI can make software development more accessible. It does not eliminate the need for responsible technology management.
The Connections Are Where Much of the Risk Lives
For AI to do meaningful work inside a business, it usually needs connections to other systems. These connections may allow it to reach email, cloud storage, accounting software, a CRM, a ticketing system, a database, or another business application. The connection gives the AI access. Separate instructions tell it what to do with that access. This is an important distinction. An AI tool may be well protected on its own, but connecting multiple systems creates additional paths for information to move.
Traditionally, companies focused heavily on protecting the network perimeter—the traffic coming into and leaving the organization. Today, much of a company’s information moves directly between cloud applications. Those systems may communicate without the activity ever passing through the company’s traditional firewall. That makes visibility and control more difficult.
Businesses need to know:
- Which systems have been connected?
- Who authorized each connection?
- What information can move through it?
- What actions the connected tool can perform?
- Whether the access is still necessary
- How the connection is monitored
- How quickly it can be disabled
Employees should not be independently connecting company systems to new AI tools without review. A connection that looks harmless can create access to far more information than the employee realizes.
A Simple Way to Evaluate an AI Tool
Before approving an AI tool, companies should ask a few basic questions:
- Is it only providing information, or can it take action?
- What company information can it access?
- Which systems can it connect to?
- Can it send, change, move, or delete information?
- Does a person approve important actions?
- Can it run when no one is present?
- Can we see a record of everything it did?
- How do we stop it if something goes wrong?
- Who is responsible for reviewing its work?
- What will it cost when used at scale?
The answers help determine both the value of the tool and the level of protection it requires.
The Bottom Line
AI is not one thing. Some AI tools help people research, think, write, and analyze. Others take action. Some run automatically. Others build software or operate across multiple business systems. The easiest way to remember the progression is:
AI can advise. AI can act. AI can operate. AI can build.
As AI moves through those stages, it becomes more powerful. It also requires more access, stronger controls, better monitoring, and greater accountability. The goal should not be to avoid AI. The opportunity is too significant for that. The goal is to understand exactly what type of AI you are using, what authority you are giving it, and what protections need to be in place before it begins working on behalf of your business. ATCOM can help you evaluate AI tools, identify security and privacy risks, develop practical policies, and create a responsible implementation plan. Whether you are beginning to explore AI or already considering agents and automation, contact ATCOM to make sure your strategy is productive, secure, and aligned with your business.
