The latest news from the major AI companies has me thinking about a question every business owner should ask: If AI can take on more of the work, are we ready to manage it?
For a long time, most people used AI by asking it a question and waiting for an answer. We asked for a draft, a summary, a spreadsheet formula, or help with an idea. We stayed close to the process and decided what happened next.
Now the companies building these tools are aiming for something broader. They want AI to take on ongoing work, use business systems, and carry a task through several steps.
That could be useful for a small company. It also changes what the owner or manager needs to do. As AI takes on more work, we need a clearer way to assign that work, check it, and handle the cases that need human judgment.
What the latest announcements tell us
At its September 29, 2026, DevDay, OpenAI introduced agents designed to take on ongoing responsibilities. Its announcement described Dots as always-on agents and included updates to cloud-based development and tools for business decisions.
On October 8, Google announced its Gemini agent for work. Google described an agent that can plan tasks, connect to systems, use tools, and work across familiar business applications. The same announcement covered agent identities, permissions, audit trails, and spending controls.
Anthropic’s October 7 release of Claude Haiku 5.5 adds another piece to this picture. The company describes it as a small model built for high-volume, cost-sensitive work. That matters because many business tasks are routine and repeated.
These announcements point toward AI taking a larger role in getting work done. They do not mean every tool is available to every business, or that every task can run safely on its own. But they give business owners a reason to think beyond the next prompt.
My view is that the next challenge will be learning to manage this work well.
Start with a job that someone can explain
In my years working in and advising businesses, I have seen how much trouble an unclear assignment can create. Two people can leave the same meeting with different ideas about what they are supposed to deliver.
AI can run into the same problem.
“Help us improve sales” is a goal. It leaves a lot of open questions. Which customers should be reviewed? What counts as an opportunity? What information is available? Can the AI contact anyone? How will the sales manager know the review is complete?
Consider a fictional food manufacturer that wants to spot customers who may be overdue for another order.
A useful assignment would be to review order history each weekday, flag customers who have passed their usual reorder window, and prepare a short list for the account manager. Each item should include the last order date, the expected window, and the reason for the flag. The account manager would review the list before any customer contact.
That gives the AI a defined role. It also gives the manager something concrete to assess.
The task might still need changes. Some customers buy only during certain seasons. Some have moved to a different product. Others may have placed an order that has not yet reached the system. Those details belong in the operating rules as the business learns what affects the result.
Map the work before handing it over
Before assigning a task to AI, I want to know what starts the process, which facts it needs, where those facts come from, and who owns the result.
That sounds basic. It is basic. It is also where many business problems begin.
Imagine asking AI to prepare a product specification sheet. The product’s weight is in the ERP system, ingredients are in a separate file, and the case dimensions are in an old email. Two documents contain different shelf-life values.
The AI may produce a polished sheet. The business still needs to decide which facts are correct.
For this kind of work, the manager should define the approved sources and what happens when they disagree. A conflict could stop the draft and create a request for review. A missing field could remain clearly marked until someone supplies it.
A good AI worker should make these problems easier to see. If it fills gaps with plausible guesses, it creates more work for the person who has to find and correct them later.
Give access that fits the assignment
Google’s latest announcement puts attention on agent identities, permissions, and records of actions. I think those ideas matter for small businesses as well as large ones.
Access should follow the job.
An AI worker that reviews orders may need permission to read order history and prepare an internal report. That assignment does not call for changing prices, updating customer records, or sending messages.
As the role grows, the permissions can grow with it. But each new action should have a business reason and a way to check whether it was done properly.
It helps to separate reviewing information, drafting something, recommending a choice, and taking action. A business can gain value at each stage.
For example, an AI worker could review overdue invoices and prepare a proposed follow-up list. A person could then check disputed balances and approve the next step. The business gets help with the review while keeping control over customer relationships.
Test the difficult cases
A good-looking result from one simple example tells us very little about ongoing performance.
Before giving AI a regular assignment, I would test the cases that make the job hard. What happens when a record is missing? What happens when two sources disagree? What happens when a customer has an open dispute? What happens when the proposed action falls outside the worker’s authority?
Use fictional or properly approved test data and make the expected result clear before running the test.
For the reorder example, the AI might correctly flag a customer with a steady buying pattern. It should also recognize that a seasonal customer needs a different rule. If there is too little history to support a useful estimate, it should say so.
The manager needs to see both the successful work and the cases that stopped for review.
That record helps answer a practical question: Is the AI ready for this responsibility, or does the assignment still need work?
Count the manager’s time
One of the easiest ways to overstate the value of AI is to measure only how quickly it produces something.
A report may take seconds to generate and twenty minutes to check. If the old process took fifteen minutes, the new process has not saved time.
From a controller’s point of view, I want to measure the full cost of the work. That includes the AI’s cost, the time spent reviewing its output, and the cost of correcting mistakes.
The right measure depends on the task. It might be the cost of a checked product sheet, the number of useful reorder flags, or the time needed to clear an invoice exception.
Quality matters too. A process that saves a few minutes but introduces wrong product facts can be a poor trade.
For a small business, the owner’s attention is often one of the scarcest resources. An AI worker should be judged partly by how much useful work it completes without creating a new stream of questions and repairs for the owner.
Keep a record people can use
When AI takes on ongoing work, the result needs a home.
A manager should be able to see what was reviewed, what the AI produced, which issues remain open, and who owns the next step. That information should be available to the people who need it.
For a simple process, a shared log may be enough. A larger workflow may need a task queue inside the business system.
The format can vary. The purpose is to make the work visible and prevent it from getting lost between people, tools, and chat sessions.
A statement such as “the review is complete” is less useful than a result showing which records were checked, what was found, and where the manager needs to act.
Expand responsibility as the evidence improves
I am interested in giving AI room to do useful work. That includes testing ideas and finding better ways to complete an assignment.
But broader responsibility should follow evidence.
A worker might begin by preparing an internal report. After repeated checks show that the report is useful and accurate, the business could allow it to prepare drafts of follow-up tasks. Later, a narrow class of low-risk actions might be allowed under clear rules.
There should also be a way to reduce that authority when performance changes. A new data source, a changed process, or repeated errors may call for more review.
This is normal management. We adjust responsibility when the job changes or the results show that support is needed.
What I would do first
If I were helping a small business get started, I would choose one recurring task with a clear owner, dependable inputs, and a result that can be checked.
I would document how the task works today, including the time it takes and the problems that occur. Then I would give AI a narrow assignment and compare the results.
The first goal would be modest: complete that task better, with less effort or fewer missed details, while keeping review time reasonable.
That creates a basis for the next decision. Expand it, improve it, or stop it.
The latest AI tools make this worth exploring. My view is that an intelligent company will be built through clear assignments, reliable business facts, visible results, and steady improvements in how work gets done.
The big AI companies are building tools that can carry more responsibility. Business owners now have an opportunity to decide which responsibilities are worth giving them—and how to manage them well.
What recurring task in your business needs more reliable follow-through? If you run a manufacturer, distributor, or service business, I would welcome a conversation about where AI could help.
John Wheeler
Wheeler Intelligent Systems
https://wheeleris.com
Sources
OpenAI, “DevDay 2026 Recap,” September 29, 2026.
https://openai.com/index/devday-2026-recap/
Google Cloud, “Welcome to Gemini at Work 2026: Introducing the Gemini agent,” October 8, 2026.
https://cloud.google.com/blog/products/ai-machine-learning/welcome-to-gemini-at-work-2026
Anthropic, “Introducing Claude Haiku 5.5,” October 7, 2026.
https://www.anthropic.com/claude-haiku-5-5