From the Everyday to Deep Thinking: How AI Impacts Your Team’s Work

In most companies, the work your team does falls into two broad categories:

• Everyday tasks – predictable, process-driven, and often repetitive.
• Deep thinking tasks – creative, analytical, problem-solving work that requires judgment, exploration, and synthesis.

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.

Why the distinction matters

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.

• Everyday tasks benefit most from structured, process-oriented automation.
• Deep thinking tasks benefit from flexible, interactive AI tools that act as thought partners.

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: Automating the repeatable

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:

• Extracting data from incoming supplier documents and updating records
• Moving files between systems
• Sending standard notifications or approvals
• Checking for compliance against set rules

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.

A practical example

Let’s say your team receives dozens of vendor contracts each month. You need to:

1. Extract key terms (dates, rates, parties involved).
2. Store that information in SharePoint with correct metadata.
3. Trigger internal reviews based on contract type.

With today’s tools, you can build a process like this:

• Use Power Automate to detect when a file is added to your Contracts folder.
• Call Azure AI Foundry to run a large language model (LLM) that extracts the needed data.
• Send that data back into Power Automate to update SharePoint properties and trigger follow-up tasks.

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: AI as a thought partner

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:

• Designing a new customer onboarding experience
• Analyzing market signals to inform strategy
• Troubleshooting an unexpected supply chain issue
• Exploring product innovation opportunities

In these cases, AI tools like ChatGPT, Claude, or Gemini don’t replace human expertise — they amplify it. They give your team:

• Instant access to structured and unstructured knowledge
• Fresh perspectives and alternative approaches
• The ability to test and refine ideas quickly

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.

Two different modes, two different setups

Because everyday and deep thinking tasks are so different, the way you integrate AI into each is different too.

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.

How to start integrating AI effectively

1. Map your team’s work into the two categories

Sit down with your managers and frontline employees. List major activities and decide whether each is:

• Everyday: repeatable, rule-based, predictable
• Deep thinking: complex, creative, or judgment-based

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.

2. Prioritize high-impact everyday processes

Pick one or two everyday tasks that:

• Happen frequently
• Take significant time
• Have clear, consistent rules
• Cause delays or errors when done manually

These are your best candidates for process automation with AI components.

Example: Invoice processing, employee onboarding, recurring reporting.

3. Give your team a deep thinking “sandbox”

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:

• How to break down a complex question into smaller prompts
• How to iterate with the AI (don’t accept the first answer)
• How to use AI to test scenarios and surface blind spots

Encourage them to start with non-critical problems so they can experiment freely.

4. Train for both modes separately

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.

• Everyday AI training focuses on process mapping, system integration, and exception handling.
• Deep thinking AI training focuses on prompt design, critical evaluation, and idea iteration.
  
  

5. Measure results — but measure the right things

• For everyday AI: Track cycle time, error rate, and cost per transaction.
• For deep thinking AI: Track decision speed, number of viable options generated, or improvements in final outcomes.
  
  

Avoiding common pitfalls

Pitfall 1: Over-automation

Automating a flawed process just makes the flaws happen faster. Always review and streamline the process before adding AI.

Pitfall 2: Treating deep thinking AI as a black box

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.

Pitfall 3: Mixing modes without realizing it

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.

Why this matters now

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:

• Match the right tools to the right problems
• Get faster returns on your AI investments
• Build a workforce that’s both more productive and more innovative
  
  

The takeaway

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.

• For everyday tasks, focus on process automation integrated with AI components.
• For deep thinking tasks, focus on interactive AI that works with your team as a collaborator.

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.