Emilia Gette serves as a marketing associate at Workast, specializing in crafting innovative strategies to engage with the audience. With a focus on creativity and attention to detail, she contributes to driving impactful results across various marketing channels.
For the past few years, businesses have been flooded with AI products. Every week there's a new chatbot, meeting assistant, writing tool, coding assistant, or automation platform promising to transform the way teams work. As a result, many business owners feel pressure to "start using AI".
The problem is that most companies begin their AI journey by asking the wrong question:
What AI tools should we buy?
It sounds reasonable, but it's usually the reason AI initiatives fail to deliver meaningful results.
Imagine trying to hire an employee before deciding what job they should do. You wouldn't hire someone and then spend weeks figuring out how to keep them busy. You'd first identify the work that needs to be done and then find the right person for it.
The same principle applies to AI, yet many organizations do the opposite. They purchase software first and only afterward begin searching for ways to use it. The result is predictable: the tool gets tested, a few people experiment with it, some impressive demos happen, and then everyone quietly returns to their old way of working.
Not because the AI wasn't capable, but because there was never a clearly defined problem to solve.
Instead of asking which AI platform to adopt, ask a different question:
What's the most repetitive, painful, and high-volume process in our business?
The best opportunities for AI are usually the tasks your team repeats every day. They're predictable, time-consuming, and follow the same process over and over again.
A digital agency might spend hours every week organizing new client requests before any real work begins. A law firm may repeat the same client intake process for every new matter, while an accounting or bookkeeping firm often follows the same onboarding checklist for each new client.
Other common examples include:
Lead qualification
Support ticket triage
Meeting follow-ups
Weekly reporting
Recurring reminders
Automating one of these processes will almost always create more value than trying to introduce AI across the entire organization from day one.
Not every task is a good candidate for AI. The biggest gains come from processes that happen frequently, follow clear rules, and require the same steps every time.
Think about how your team handles work every day. An accounting firm may need to collect the same financial documents for every new client. A recruitment agency might review every application using the same hiring process. A real estate team may qualify every new lead before assigning it to an agent.
These processes are predictable, follow clear rules, and happen over and over again. That's exactly where AI workflows have the greatest impact. Instead of saving a few minutes on a single task, you're improving an entire process your team repeats dozens—or even hundreds—of times every month.
One of the biggest misconceptions about AI adoption is that it needs to happen company-wide. In reality, the businesses seeing the best results usually start much smaller.
A bookkeeping firm might automatically create an onboarding checklist whenever a new client signs up. A recruitment agency could move every new application into the correct hiring stage and assign it to the appropriate recruiter. A digital agency may automatically categorize incoming project requests, assign an account manager, and create subtasks for design, development, and content review.
Once you've automated one process successfully, it becomes much easier to identify the next one. Teams gain confidence, adoption increases, and automation starts spreading naturally across the business.
One of the biggest barriers to AI adoption isn't learning the technology—it's changing habits.
If your team already communicates in Slack, introducing a completely separate platform often creates more work instead of less. Conversations happen in one place, tasks live somewhere else, and someone still has to copy information between them.
The best AI workflows don't ask people to change where they work. They fit into the tools your team already uses and automate what happens next.
Imagine a digital agency discussing a new client request in a Slack channel. Instead of asking someone to copy that conversation into a project management tool, the conversation itself becomes the starting point for the workflow. A task is created automatically, the account manager is assigned, subtasks for the creative and development teams are added, due dates are set, and everyone involved is notified.
The same principle applies to emails, forms, and other business tools. Rather than replacing them, AI workflows connect them, allowing information to move automatically from one step to the next.
The easier it is to automate work inside the tools your team already knows, the easier AI adoption becomes. Instead of asking people to learn a new way of working, you're simply removing the repetitive steps from the one they already have.
Many companies focus on using AI to complete individual tasks. They'll use it to write an email, summarize a meeting, or draft a document. Those are useful capabilities, but they rarely change how a business operates.
The biggest improvements happen when AI becomes part of an entire workflow instead of a single action.
Writing one email isn't a workflow. Summarizing one meeting isn't a workflow. A workflow continues after AI generates the response.
Imagine a new client submitting an onboarding form to an accounting firm. Instead of simply summarizing the information, the system automatically creates a task, assigns it to the account manager, requests the necessary tax documents, schedules a kickoff call, creates an onboarding checklist, and moves the client into the next stage of the process.
The same idea applies to recruitment agencies managing candidate pipelines, law firms handling new client matters, real estate teams following up with new leads, or digital agencies organizing incoming project requests. Rather than helping with one step, AI helps move the entire process forward.
Successful AI adoption rarely happens through one large initiative. It's usually the result of solving one operational problem after another.
Businesses that see the greatest impact don't try to automate everything at once. They identify repetitive work, build a reliable workflow around it, measure the results, and repeat the process elsewhere.
Over time, those small operational improvements compound. Teams spend less time on repetitive administrative work, fewer tasks fall through the cracks, and processes become easier to scale as the business grows.
AI adoption isn't measured by how many tools your company uses. It's measured by how much repetitive work your team no longer has to do.
The companies seeing the biggest results aren't the ones experimenting with the most AI products. They're the ones systematically improving the way work gets done, one workflow at a time.
That's why the biggest mistake companies make when adopting AI isn't choosing the wrong platform.
It's starting with the tool instead of the problem.
Workast
