AI workflows should be managed more like employees than software.
For the past two years, we've talked about artificial intelligence using the language of software. We call it a tool, a platform, a copilot, or an assistant. Those descriptions made sense when most companies were experimenting with prompts and chatbots. They still suggest, however, that AI exists to help people work a little faster.
Last week we were reviewing implementation of the second AI workflow for a client when I realized that we weren't reviewing another software application. We were reviewing a new member of the revenue team.
That may sound like a small difference, but I believe it represents one of the biggest changes AI will bring to business over the next decade. Every technology wave changes the tools people use. AI is beginning to change something even more important: who does the work. Once you watch an AI workflow perform meaningful business tasks every day, it becomes difficult to think of it as just another application. It starts to feel more like a teammate with a clear role, measurable responsibilities, and the ability to improve over time.
Every Revenue Team Has Invisible Work
After leading sales organizations for more than twenty years, I've learned that selling takes up only part of a salesperson's week. The rest of the time is spent getting ready to sell. Reps research companies, identify decision makers, monitor buying signals, update CRM records, prepare for meetings, and write follow-up emails. None of those activities closes deals by itself, but together they make great customer conversations possible. Most companies accept this work as simply part of the job because there has never been another practical way to do it.
The second workflow we built with this client started with a different question. Instead of asking how AI could help a salesperson research faster, we asked whether the salesperson should be researching at all. The new workflow now identifies renewable energy projects, determines which company is behind each project, researches the developer, identifies the best executive to contact, drafts personalized outreach, and prepares everything for review before a salesperson reaches out. It follows the same process every time and improves through human feedback.
That isn't software helping someone work.
That's software doing work.
The difference matters because it changes how leaders think about growth. Until now, companies had only a few ways to increase sales capacity. They could hire more salespeople, ask their current team to do more, or buy software that saves a little time. AI workflows create a fourth option. Companies can add digital teammates that permanently take over specific categories of work. The organization becomes more productive without adding more people.
Notice what the workflow is replacing. It isn't replacing the salesperson. It is replacing dozens of small tasks that surround selling but rarely appear in a job description. Reading public filings. Researching companies. Connecting information from different sources. Finding the right executive. Organizing everything into a useful summary. None of those tasks takes very long on its own, but together they consume hours every week. When a workflow performs them continuously, salespeople begin each day ready for customer conversations instead of another round of research.
The Conversation Changed
During our customer review, the workflow reached an important milestone. Earlier that morning, the team fixed an issue that had been hiding research results. Once it was corrected, the workflow successfully identified the right contact for twenty of twenty test projects. This changed the conversation immediately. Nobody questioned whether the workflow could do the research accurately anymore. Instead, the customer asked for LinkedIn links, source citations, additional contacts, and improvements that would make the workflow even easier to use.
Watching that discussion reminded me of onboarding a new salesperson. During the first few weeks, managers ask whether the new hire understands the job. After that, the questions change. How can they improve? How can they ask better questions? How can they build stronger customer relationships? Once the basic skills are proven, coaching becomes the priority.
That is exactly what happened with the workflow.
One thing surprised me. The technology almost disappeared from the conversation. Instead of asking, “Can it do the job?” everyone started asking, “How can we make it even better?” The workflow had crossed an important line. It was no longer being evaluated as software. It was being coached to become better at its job.
Digital Teammates Need Management Too
I think this is where many companies misunderstand AI.
Most implementation projects focus on choosing the right technology. Once a workflow begins doing real work, however, technology becomes only part of the story. The workflow starts behaving much more like an employee than a software application.
It needs onboarding.
It needs supervision.
It benefits from coaching.
Its work should be reviewed.
Its performance should be measured.
Those responsibilities should sound familiar because they are exactly how we help people improve.
During the meeting, the customer decided to keep the workflow in a supervised learning phase. The AI drafts outreach. People review it. Their edits become new training examples. Every customer interaction helps the workflow produce better work the next time.
Watching that process changed the way I think about AI deployments. When companies install traditional software, success usually means employees use it correctly. AI workflows are different. Success means the quality of the work improves over time. Managers are no longer coaching only people. They are beginning to coach workflows as well. Which recommendations led to better conversations? Which outreach earned replies? Which research sources produced the strongest insights? Those lessons no longer stay inside the heads of individual salespeople. They become part of the workflow, making the entire organization better.
What struck me during the meeting was how quickly individual judgment became organizational knowledge. Every improvement the customer suggested immediately became part of the workflow. The next salesperson wouldn't have to rediscover that lesson. In good sales organizations, managers have always tried to spread best practices across the team. AI simply does it faster.
The Organization Chart Is About to Change
Perhaps the biggest shift isn't technological at all. It's organizational.
For decades, growing a revenue organization meant hiring more people. A company added another SDR, another account executive, another sales engineer, or another customer success manager. As revenue grew, headcount grew alongside it. AI workflows introduce a new option. Instead of asking, “Who should we hire next?” leaders will increasingly ask, “Should the next member of the team be a person or a workflow?”
That doesn't make people less important. It makes their time more valuable. Digital teammates take responsibility for research, preparation, documentation, and other repetitive work that surrounds customer conversations. People spend more time building relationships, solving customer problems, negotiating agreements, and earning trust. Each focuses on the work they are best equipped to do.
I don't think organization charts will look the same five years from now. Alongside salespeople, marketers, and customer success managers, companies will manage portfolios of AI workflows responsible for specific business outcomes. Leaders won't measure only employee productivity. They'll measure how effectively people and digital teammates work together. That may become one of the most important competitive advantages an organization can build.
A Different Way to Think About AI
Artificial intelligence is no longer just another tool employees use. It's becoming a member of the team.
In our customer review last week, I realized nobody was evaluating the workflow as software anymore. The discussion sounded like a coaching session with a new employee. The customer wanted better recommendations, clearer evidence, additional capabilities, and continuous improvement. Those aren't the conversations organizations have about applications. They're the conversations they have about teammates.
I think that's where AI is heading. The companies that create the greatest advantage won't necessarily own the newest language model or the largest technology budget.
They'll become better at deciding which work belongs to people and which belongs to digital teammates. They'll build workflows that improve every day, give employees more time for higher-value work, and make the entire organization smarter over time.
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