Artificial intelligence has made it easier than ever to automate tasks, generate ideas, and build software. However, having access to powerful AI tools does not automatically make us more productive. Without clear goals and good habits, we can spend hours experimenting with AI and accomplish very little of what we originally intended.
In this week’s Develpreneur Weekly Challenge, Rob Broadhead and Michael Meloche reflect on their conversation with Kevin Riedl about AI-native development and the changing role of artificial intelligence in business. The discussion moves beyond simply adopting AI and focuses on building productive habits that help developers and entrepreneurs get meaningful results.
As part of Season 29, Build a Career Beyond the Code, we challenge you to examine how you currently use AI, identify one task where it can provide real value, and establish a repeatable habit that improves your workflow.
AI Is Changing Business, but Are We Using It Effectively?
During our conversation with Kevin Riedl, we explored what it means for a company to become AI-native. Rob points out that the concept goes beyond adding AI tools to existing business processes. Instead, organizations should consider how AI could fundamentally change the way they operate.
That is an important distinction. A company might use AI to write emails, summarize meetings, or generate reports while continuing to operate exactly as it always has. Those improvements can save time, but they may not take full advantage of what the technology makes possible.
An AI-native mindset asks a different question: How would we design this process if we were starting today with AI already available?
However, Michael raises an equally important concern. For small businesses and entrepreneurs, completely rebuilding operations around AI can become an expensive distraction. The technology offers so many possibilities that it becomes easy to explore interesting ideas without making meaningful progress.
The challenge is finding the balance between innovation and practical business value.
The AI Rabbit Hole: When Productivity Becomes a Distraction
One of the easiest traps to fall into with AI is losing track of the original objective.
Michael describes sitting down with an idea and beginning to work through it with AI. Several hours later, he may have created something interesting, but it is nowhere near the original minimum viable product or business goal.
This happens because AI makes experimentation incredibly easy. Each response can introduce another possibility, another feature, or another direction worth exploring. Before long, what started as a focused task becomes an entirely different project.
The experience can feel productive because you are constantly creating something. However, activity does not necessarily equal progress.
Developers encounter this problem when they begin adding features that were never part of the original requirements. Entrepreneurs experience it when they spend more time experimenting with tools than talking to customers or delivering services.
AI accelerates both good decisions and bad habits.
Without a clear objective, it can help us move in the wrong direction faster than ever.
Build AI Habits Before Building More Automation
Michael suggests that developers and business owners should begin by examining their existing AI habits.
How are you using AI today? Are you using it constructively, or are you simply experimenting because the technology is available? Are your AI activities supporting your business goals, or are they pulling your attention toward unrelated projects?
These questions matter because AI works best when it supports a defined process.
If your workflow is already disorganized, introducing AI may amplify that disorganization. Automating an inefficient process does not necessarily make it better. In some cases, it simply allows the inefficiency to happen more frequently.
Before adding another tool or agent, consider whether you understand the task you want to improve.
What does the task accomplish? How often do you perform it? What would success look like? How much time would you save if AI handled part of the work?
Answering these questions helps turn AI from an interesting experiment into a useful business tool.
Treat AI Like an Employee You Need to Manage
Rob introduces an interesting way to think about artificial intelligence: consider it an employee working for you.
When you delegate work to an employee, you should provide a clear objective, explain the expected result, and establish a way to verify that the task was completed correctly. You should not assign the work and then immediately do the same task yourself.
The same principle applies to AI.
If you use AI to review your calendar and generate a daily task list, there is little value in manually repeating the entire process afterward. You have created additional work rather than eliminating it.
That does not mean you should blindly trust every AI-generated result. It means you should distinguish between reviewing completed work and unnecessarily duplicating it.
For example, if an AI agent summarizes your upcoming meetings, you can review the summary for accuracy without rebuilding it from scratch. If the output contains errors, correct the process and improve the instructions.
Over time, the goal is to develop enough confidence in the workflow that you can spend less time supervising routine tasks.
Good delegation requires both trust and accountability. AI is no different.
Start Small and Focus on One Meaningful Task
One of the biggest mistakes developers make when exploring AI automation is attempting to automate too much at once.
It is tempting to imagine an entire collection of agents handling emails, calendars, customer communications, reporting, development, and business operations. While these systems may eventually provide value, building them all at once introduces unnecessary complexity.
Rob recommends beginning with one simple task.
Look for something repetitive, predictable, or time-consuming that does not require constant human judgment. The task does not need to be complicated or impressive. It simply needs to provide a useful result.
For example, you might use AI to summarize your calendar each morning, organize meeting notes, create a daily learning exercise, or identify action items from a document.
The goal is to establish a habit that produces a measurable benefit.
Once the process works reliably, you can begin exploring additional opportunities.
Small improvements accumulate over time, especially when they become part of your normal workflow.
The Weekly Challenge: Create One Productive AI Habit
This week’s challenge is straightforward: choose one task where AI can provide meaningful value and turn it into a repeatable habit.
Rather than spending the week exploring every new AI tool, focus on one practical improvement. Identify something you already do regularly and determine whether AI can help you complete it more efficiently.
Follow these five steps:
1. Review your current AI habits. Identify how you use AI today and whether those activities support your personal or professional goals. Look for situations where experimentation has replaced meaningful progress.
2. Choose one task. Select a repetitive activity, learning opportunity, or administrative responsibility that AI could help simplify. Start with something manageable rather than attempting to redesign an entire workflow.
3. Define the expected result. Write down what you want AI to accomplish, what information it needs, and how you will determine whether the output is useful.
4. Use AI consistently throughout the week. Establish a daily or recurring routine. Let AI perform the assigned task, review the results, and make adjustments when necessary.
5. Evaluate the value. At the end of the week, determine whether the new habit saved time, improved your work, helped you learn something, or reduced unnecessary effort.
The objective is not simply to use AI more often. It is to develop a habit that makes your work or personal life better.
Examples of AI Habits You Can Start This Week
Not every AI habit needs to involve software development or complicated automation. Rob discusses several simple examples that can help people become more comfortable using AI productively.
A developer might ask AI to explain one unfamiliar programming concept each morning. A business owner could use it to summarize relevant industry news. Someone learning a language might request five new phrases each day.
Other practical examples include:
- Daily planning: Generate a prioritized task list from your calendar and upcoming commitments.
- Meeting follow-up: Summarize meeting notes and identify action items that require attention.
- Professional development: Learn one new technical concept or business skill each day.
- Business research: Create a short summary of relevant industry developments.
- Documentation: Organize notes or produce an initial draft of routine project documentation.
- Personal learning: Practice a foreign language, explore a new topic, or develop a daily learning routine.
The specific task matters less than the value it provides.
Choose something that fits naturally into your existing routine. A simple habit you maintain is more useful than an elaborate automation system you abandon after a few days.
Avoid Micromanaging Your AI Workflow
Once you have established a task for AI, the next challenge is learning when to step away.
Michael compares this to managing employees. If you delegate a task and then perform the same work yourself, you create unnecessary overhead. You may also introduce conflicting results that make the process more complicated.
AI workflows can suffer from the same problem.
Suppose you use AI to generate a daily summary of important emails. If you then spend the same amount of time reviewing every message and rebuilding the summary manually, you have not gained much.
Instead, establish a review process appropriate for the task.
Check whether the output is accurate, complete, and useful. If it meets your expectations, use it. If it contains errors, identify what went wrong and improve the workflow.
This approach allows AI to earn your trust gradually.
You may need to review results closely when introducing a new process. As the workflow becomes more reliable, you can reduce unnecessary supervision while maintaining appropriate quality checks.
The important distinction is between validating work and duplicating it.
Measure Results Instead of Counting AI Activities
One of the recurring themes throughout this season is the importance of measuring meaningful outcomes.
Rob uses the example of dashboards filled with hundreds of widgets. AI makes it easy to generate impressive reports, visualizations, and automated processes. However, the real question is which information actually helps someone make a decision.
The same principle applies to personal AI habits.
Creating ten new automations does not necessarily make you more productive. Saving thirty minutes on a recurring task might.
Before investing more time in an AI workflow, ask whether it is producing something useful.
Are you saving time? Are you learning something? Are you making better decisions? Are you completing work more consistently? Are you reducing repetitive tasks that previously consumed your attention?
These are better measures of success than the number of prompts you write or the number of AI agents you create.
The goal is meaningful improvement, not simply increased AI activity.
Building a Career Beyond the Code Starts With Better Habits
As artificial intelligence continues changing software development, developers need to adapt more than their technical skills.
They also need to improve how they manage work, evaluate technology, delegate responsibilities, and focus on business outcomes.
These are skills that extend beyond programming.
Learning how to use AI effectively is similar to learning how to manage a team or improve a business process. You need clear expectations, reliable communication, measurable outcomes, and the discipline to stay focused.
Developers who build these habits will be better positioned to use AI as their responsibilities grow. Instead of constantly reacting to new tools, they can evaluate whether those tools support their objectives.
The technology will continue evolving, but productive habits provide a foundation that remains useful.
Final Thoughts: Make AI Work for You
AI offers an incredible number of opportunities, but those opportunities can quickly become distractions without clear direction.
This week’s challenge is about moving beyond experimentation and creating something useful. You do not need to build an elaborate AI platform, purchase expensive hardware, or automate your entire business.
Start with one task.
Define what success looks like, use AI to help accomplish it, and review the results. If the process works, make it part of your routine. If it does not, adjust the approach and try again.
Most importantly, remember that AI should help you move toward your goals rather than pull you away from them.
Building better AI habits is not about doing more with technology simply because you can. It is about using technology intentionally so you can spend more time on the work that matters.
Your challenge this week: Pick one task, build one AI habit, and measure the value it creates.
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