For the last few years, most people have used AI in basically the same way. You open ChatGPT or another chatbot, ask it a question, get an answer, and then move on to whatever you were doing before. That alone has been a huge change in the way people work, but what we are seeing now feels like the beginning of something much bigger because the major AI companies are no longer just focused on making chatbots smarter. They are starting to build agents that can actually take responsibility for work and continue making progress without needing someone to sit there and prompt them every step of the way.
OpenAI’s new Dots are a good example of where this is heading. Instead of thinking of a Dot as another assistant you talk to, it makes more sense to think of it as an AI teammate that can be given an ongoing responsibility. A business could eventually assign a Dot to something like sales research and have it continuously look for companies that match a specific customer profile, organize the information it finds, prepare personalized outreach, and keep the process moving without requiring someone to restart that workflow every morning.
What makes Dots especially interesting is that OpenAI is not the only company betting on this idea. xAI has introduced Grok Bot, while Meta has been pushing Muse in a similar direction. The products are not identical and they will probably evolve very quickly, but they are all pointing toward the same larger shift, which is that AI is moving away from simply answering questions and toward actually participating in the day-to-day work of a business.
What Changes When AI Becomes a Worker Instead of a Chatbot?
The biggest difference between traditional AI assistants and this new generation of agents is the level of responsibility they can potentially take on. Right now, a salesperson might use AI to help write an email or summarize a meeting, but that still leaves most of the actual workflow in the hands of the employee. Someone still has to research the prospect, find the right person to contact, update the CRM, remember when to follow up, and make sure nothing gets lost along the way.
An agent changes that relationship because it can potentially handle several of those steps as one connected process. Instead of asking AI to help with one small task, you can start assigning it a broader outcome and letting it work through the steps required to get there. The human still matters, especially when approvals or judgment are involved, but the amount of repetitive work that needs to be done manually can start shrinking.
That is why we think this shift matters more than another model getting slightly better at writing or answering questions. The real value of agentic AI will come from reducing the amount of time people spend moving information between systems, repeating the same administrative tasks, and constantly checking whether something has been completed.
Grok Bot and Muse Are Chasing the Same Opportunity
We recently wrote about Grok Bot, and one of the things that stood out most was how xAI is framing the product. They are not presenting it as a better version of a chatbot. They are presenting it as something closer to a digital teammate that can have its own computer, remember previous work, and continue operating throughout the day.
That distinction is important because it changes what businesses should expect from AI. A chatbot might help your sales team write better outreach, but an agent could eventually help research the account, identify the right contact, prepare the message, update your CRM, and keep track of when that prospect needs to be contacted again. The value is not simply that the writing gets better. The value is that several pieces of a workflow that used to require someone sitting in front of a computer can start happening automatically.
Meta is approaching the same opportunity from a slightly different angle with Muse, but the underlying idea is very similar. Muse is designed to work across the applications people already use, which is particularly interesting for small businesses because most SMB owners already rely on a collection of different tools to run their companies. One platform might handle accounting, another handles ecommerce, another manages communication, and another is responsible for marketing. The problem is that someone still has to constantly move between all of those systems and figure out what needs attention.
If an AI agent can understand what is happening across those tools and take action based on that information, it becomes much more useful than simply adding another dashboard to the business. Instead of giving the owner one more system to monitor, the AI can start becoming the thing that does some of the monitoring.
Why This Could Matter Even More for Small Businesses
We think small businesses could end up benefiting more than almost anyone from this shift because they usually have the least amount of extra bandwidth. A large company can respond to growth by hiring another analyst, another coordinator, or another salesperson. A five-person company usually cannot do that every time the workload increases, which means the same few people end up absorbing more and more responsibilities as the business grows.
That is why so many SMB owners spend their days jumping between sales, customer service, operations, marketing, scheduling, and finance. The individual tasks are not always complicated, but there are so many of them that they consume a huge amount of time. Researching prospects, updating CRM records, following up with customers, finding inventory online, scheduling meetings, creating reports, and reviewing spreadsheets might each seem manageable on their own, but together they can easily become the workload of another employee.
If AI agents can reliably take a meaningful portion of that work off someone’s plate, the economics start becoming very interesting. A small business does not necessarily need an AI to replace a full-time employee. It may simply need AI to remove ten or fifteen hours of repetitive work every week so that the existing team can spend more time selling, serving customers, and growing the business.
That is a much more realistic way to think about the opportunity.
The Best Place to Start Is With the Workflow
Whenever a major new AI product launches, there is usually a rush to figure out how to use it immediately, but we think businesses should approach this from the opposite direction. Instead of starting with Dots, Grok Bot, Muse, or any other tool, start by looking closely at the way work actually moves through the company.
Find the processes that your team repeats every day or every week and ask which ones take a lot of time without requiring much human judgment. Maybe the sales team spends hours researching accounts before they ever make a call. Maybe someone is constantly copying information from email into a spreadsheet. Maybe the same weekly report gets rebuilt from scratch every Friday, or someone is spending a large part of the day searching different websites for inventory at the right price.
Those are the kinds of problems that are worth solving first because they already have a clear business impact. Once the workflow is understood, then it becomes much easier to decide whether an off-the-shelf agent like Dots, Grok Bot, or Muse can handle it, or whether the process needs something more customized.
The Workflow Will Matter More Than the Brand
This is also why we do not think businesses should get too caught up in which AI company is winning the agent race right now. Dots, Grok Bot, and Muse will all continue changing, and there will almost certainly be more competitors entering the market over the next year.
The more important question is whether a business understands its own processes well enough to use these tools effectively.
Two companies might use the exact same CRM and still need completely different automations because their sales process, approval rules, pricing structure, customer expectations, and internal workflows are different. The AI still has to understand those differences if it is going to be useful.
That is where we think a lot of the real value will be created. The companies that benefit most from agentic AI probably will not be the ones with the largest collection of AI tools. They will be the ones that know where their teams are wasting time and build systems around those specific problems.
The agent matters, but the workflow matters more.