We’re staring at the most powerful productivity tool in history, yet most of us are using it like a slightly better version of Google. Here’s how to actually change the way you work with AI.
Across every industry and every department, there’s a massive gap between the promise of AI and how it’s actually being used.
When I talk to teams — from HR specialists and marketing managers to operations leads — I see the same pattern. They have an account with ChatGPT, Claude or Copilot. They use it occasionally to answer a question or rewrite a sentence.
Essentially, they’re using AI as an advanced search engine. Helpful? Sure. But it’s also the equivalent of using a Ferrari to drive to the mailbox.
To find real value, we need to stop thinking of AI as a tool for retrieving information and start treating it as a tool for processing work. We need to move from search to synthesis.
The good news: you don’t need a computer science degree or a corporate mandate to start seeing benefits. You just need to change your weekly routine.
The ‘one task a week’ challenge
The biggest mistake organizations make is automating everything at once. That leads to big visions, long meetings — and no change.
Instead, I suggest a simple challenge for every employee, regardless of role:
Every week, pick one task that’s time-consuming, repetitive or drains energy. Then use AI to complete 80% of that task for you.
We’re not looking for perfection. We’re looking for momentum. If you can reduce a 60-minute task to 10 minutes, you’ve bought 50 minutes to focus on high-impact work.
Do that once a week, and your job starts to feel very different very quickly.
A tangible example is the Friday reporting grind. Let’s look at a task that plagues everyone: consolidating data and status reports.
Whether you’re in sales, project management or operations, you probably spend time every week chasing updates from different people or systems to create a summary report.
The old way is the manual grind. It’s Friday morning. You open 15 different emails and Slack messages from your team. You open a spreadsheet. You copy and paste updates. You skim them to find red flags. You spend an hour writing a summary paragraph for your boss.
It’s tedious, prone to copy-paste errors and eats 60 to 90 minutes of your best focus time.
The AI-assisted way also is known as “The Synthesis Approach.” Instead of doing the heavy lifting, you act as the reviewer.
You take the raw inputs (project logs, email updates or data exports) and feed them into a secure AI tool. Then you give a prompt such as this:
“I’m pasting the weekly status updates from five different project streams below. Please analyze these updates.” And then:
▶ Create a bulleted summary of progress for each stream.
▶ Identify any risks, delays or blockers mentioned (look for keywords like “delayed,” “waiting on” or “issue”).
▶ Draft a professional executive summary paragraph at the top, highlighting the overall health of the projects.
The result: in about 30 seconds, the AI does the reading, sorting and drafting. You’ve turned a 90-minute drain into a 15-minute review.
You still apply judgment, but you’re no longer doing all the manual labor.
A personal example: Evaluating vendors in half the time
I’ve been using the same approach in my own work, especially when I’m evaluating new vendors.
In the past, if we were choosing a new tool, I’d spend hours doing research: scanning websites, reading case studies, sitting through long demos and trying to compare feature lists and pricing across five or six different options.
By the time I got to a recommendation, I’d already sunk a couple of evenings and multiple meetings into the process.
Now, I treat AI as my first-pass analyst.
When I’m looking at vendors, I’ll paste in material like website copy, product one-pagers, demo notes, and pricing pages and ask the AI to:
1. Pull out the core value proposition for each vendor;
2. Summarize key features and limitations in a simple comparison table;
3. Flag anything that looks like a risk (complex implementation, unclear pricing, missing integrations, etcetera); and
4. Draft a short “pros and cons” summary for each option.
Instead of spending hours manually stitching everything together, I get a structured comparison in minutes. I still attend demos and ask my own questions, but I go in knowing where to focus.
The result is fewer meetings, less time lost in research and faster, more confident decisions.
It’s the same pattern as the Friday reporting example. I’m no longer doing the grunt work of reading and sorting. I’m reviewing, checking and deciding.
How small experiments turn into bigger wins
The magic here isn’t just saving an hour on Friday.
It’s what happens after you succeed at the first task.
Once you’ve tried a few small AI experiments like the reporting example, a natural progression tends to happen.
You’ve gone from one-off experiment to repeatable playbook. Once you refine the prompt for your weekly report and see that it works, you stop writing it from scratch. You save it as a template — your “Weekly Status Synthesis” playbook.
Suddenly, this isn’t just a trick for you; it’s a tool for the team. You share that prompt with your junior staff. Now everyone is reporting in the same format, with the same level of clarity, in a fraction of the time.
Then you advance from individual productivity to team workflows. As confidence grows, you start connecting tasks together.
Step 1: You use AI to synthesize a weekly report.
Step 2: You feed that summary back into AI to draft the Monday meeting agenda based on the risks and priorities in the report.
Step 3: You reuse the same data to generate a client update email.
Without a major system overhaul, AI quietly becomes the connective tissue of your workflow, moving work from “raw input” to “client-ready output” in a few hops.
From operations to better decisions
This is where it gets really interesting. Over time, you build a library of AI-generated summaries. You can stop asking, “What happened last week?” and start asking better questions of your historical data — questions such as:
▶ Review the last 10 weekly status reports. What are the top three recurring bottlenecks that cause us to miss deadlines?
▶ Based on three months of summaries, which project stream has the highest volatility?
At this stage, AI isn’t just saving time. It’s helping you make better calls.
How to get started today
You don’t need permission to be more efficient.
Identify the pain. Look at your to-do list for this week. Circle the one thing you’re dreading because it involves tedious data processing or writing.
Sanitize the data. Make sure you are not uploading sensitive private info. Remove names, IDs or secrets if you’re using public tools.
Prompt for action. Don’t ask the AI how to do the task. Ask it to do the task.
Review the work. Always check the output. You’re the pilot, AI is the co-pilot.
The future of work isn’t about AI replacing people.It’s about people using AI to replace drudgery in their day-to-day lives.
Pick your task. Start this week.
Tyler Cahill is senior manager of data and AI for BDO USA, which provides assurance, tax and advisory services.
