In most companies, the work your team does falls into two broad categories:
Both are essential. Everyday tasks keep the business moving. Deep thinking drives growth, solves big problems, and creates competitive advantage.
AI can help with both — but in very different ways. Understanding that difference is key to understanding how work is likely to change in the coming years.
When companies think about AI, they often lump all tasks together and imagine “automation” will apply in the same way across the board. It won’t.
Mixing these up leads to missed opportunities and frustrated teams. If you put an idea-generation tool in charge of invoice processing, you’ll get inconsistent results. If you try to get a process automation platform to help you refine a market entry strategy, you’ll get a blank stare — or the software equivalent.
Let’s look at each type of work in detail.
Everyday tasks are the ones you can describe step-by-step. If the input and output are consistent, AI can handle them faster, cheaper, and with fewer errors than humans.
Examples:
Here, AI isn’t “thinking” in the creative sense — it’s recognizing patterns and executing rules. The most value comes when you combine AI’s pattern recognition with automation platforms that integrate directly with your systems.
Let’s say your team receives dozens of vendor contracts each month. You need to:
With today’s tools, you can build a process like this:
Once set up, the process runs unattended, freeing your team to focus on exceptions instead of handling every file manually.
The benefit to you: reduced manual handling, faster turnaround, fewer errors — all measurable on your operational KPIs.
Deep thinking tasks can’t be boiled down into a repeatable recipe. They’re about exploration, creativity, and judgment. That’s where interactive AI tools shine.
Examples:
In these cases, AI tools like ChatGPT, Claude, or Gemini don’t replace human expertise — they amplify it. They give your team:
Personally, I spend about an hour each day working through problems and ideas with ChatGPT Pro. It’s not just a search engine. It’s a sparring partner that helps me clarify my thinking, spot blind spots, and pressure-test scenarios.
The real value here isn’t speed alone — it’s quality of thought. The AI doesn’t get tired, impatient, or defensive. It asks questions, suggests frameworks, and can summarize or expand on complex topics instantly.
The benefit to you and your team: better ideas, faster iterations, and more confident decision-making.
Because everyday and deep thinking tasks are so different, the way you integrate AI into each is different too.
| Type of Task | Best AI Approach | Example Tools | Success Measure |
|---|---|---|---|
| Everyday tasks | Process automation with AI components | Power Automate, Azure AI Foundry, UiPath | Reduced cycle time, fewer errors, cost savings |
| Deep thinking tasks | Interactive AI for collaboration | ChatGPT, Claude, Gemini | Better ideas, faster insights, improved decisions |
Trying to use one approach for both often leads to underwhelming results. The word around town these days is the very definition of work will likely change in the coming years. When you’re thinking of what that means for you and your team, design your new idea of what work is with both lanes in mind.
Sit down with your managers and frontline employees. List major activities and decide whether each is:
You’ll probably find that 60–80% of work is everyday, and 20–40% is deep thinking — but those proportions vary by role and industry.
Pick one or two everyday tasks that:
These are your best candidates for process automation with AI components.
Example: Invoice processing, employee onboarding, recurring reporting.
For deep thinking work, the best first step is giving your team access to an AI thought partner and teaching them how to use it effectively.
This is not about replacing expertise — it’s about augmenting it. Show them:
Encourage them to start with non-critical problems so they can experiment freely.
Don’t assume that because someone has learned to use Power Automate for everyday tasks, they automatically know how to get value from ChatGPT for deep thinking work (or vice versa). The skill sets are different.
Automating a flawed process just makes the flaws happen faster. Always review and streamline the process before adding AI.
You still need human judgment. AI can give you options, but you decide what’s valid. Think of AI tools like an excavator instead of a shovel: in the hands of a trained professional, you get a beautiful swimming pool; in the wrong hands (mine, for example), you get a burst pipe, media coverage, and a very expensive accidental swimming pool.
Trying to solve a deep thinking problem with an everyday process tool — or vice versa — wastes time and frustrates users. Many Agentic projects struggle today precisely because they’re treated as process bots, when in reality they require different design principles and success measures.
AI capabilities are advancing quickly. The gap between companies that integrate AI effectively and those that don’t will widen fast. Your competitors are experimenting with these tools today. The question is not if, but how quickly you can put them to work in a way that drives your team’s performance.
By consciously separating everyday and thinking tasks, you:
AI is not one thing. It’s a set of capabilities that can be applied in different ways depending on the nature of the work.
The companies that will get the most value from AI are not those that “use AI everywhere” — they’re the ones that know when and how to use the right kind of AI for the job.
If you start thinking about your team’s work in these two lanes, you’ll see clearer opportunities, faster payback, and a workforce that’s both more efficient and more capable of tackling your biggest challenges.
If you’d like to explore how this approach could work in your business—with practical, measurable results—we can help you identify how AI will transform work in your specific environment.