AI Is Here to Stay - Use It Wisely
AI Is Here To Stay - Use It Wisely
AI is here to stay. It behooves us to use it and learn it. A place to start is by thinking about our everyday tasks and seeing how we can manage them more efficiently. The question is no longer “Should I use it?” but “How can I use it wisely?”
I’ve been reflecting on this a lot in my consulting work. Tools like Otter.ai capture meeting notes—the best part being the action items. Instead of scrolling through long transcripts, I walk away with a clear list of next steps. For emails, I use AI not just to check grammar but to refine tone so I come across as collaborative rather than abrupt. For posts, AI helps me create images that bring ideas to life without spending hours designing. I am using it to auto-generate subtitles for video clips. This small step dramatically increases engagement since so many people scroll with the sound off. I also get short clips from long interviews. In addition, I use it to rapidly generate visual flowcharts for processes and systems for consulting gigs.
But this is bigger than just saving a few hours a week. We are at a turning point, and ignoring it carries risk.
The Productivity Promise — and the Pitfalls
McKinsey’s 2023 report on generative AI estimated that software engineers could see a 20–45% boost in productivity by using AI for tasks like boilerplate code, debugging, and documentation. That’s a staggering gain. Imagine the impact on product development cycles if engineers deliver features weeks earlier.
But the same report offered an important caution: productivity can drop when people spend too much time crafting the “perfect” prompt, double-checking every AI suggestion, or struggling to integrate AI into existing workflows. In other words—AI isn’t magic, and blind trust can backfire.
The lesson: AI gives us leverage, but only if we use it with intention.
Real-World Examples of Work Roles Using AI
So what does wise use of AI look like in different roles? Let’s go beyond the generic “save time” and highlight concrete, practical applications:
1. Software Engineers
Generate starter code, unit tests, and documentation in seconds.
Use AI pair programmers like GitHub Copilot to suggest solutions.
Spot bugs faster by letting AI flag anomalies in logic.
2. Product Managers
Analyze thousands of customer feedback comments and group them into actionable themes.
Draft user stories that can be refined instead of starting from scratch.
Visualize roadmaps or feature prioritization with AI-driven tools.
3. Program Managers
Automatically generate risk summaries from project updates.
Spot cross-team dependencies and bottlenecks early.
Use AI dashboards to update stakeholders instead of spending hours on manual reporting.
4. Customer Experience Teams
Draft initial responses to customer inquiries while maintaining tone consistency.
Detect recurring pain points in tickets that suggest a deeper product issue.
Personalize customer engagement at scale.
5. Data Analytics Teams
Automate data cleaning, anomaly detection, and first-pass analysis.
Surface unexpected trends or outliers for deeper human review.
Generate visualizations that can be quickly adjusted.
6. Consulting & Content Work (My Example)
Meeting notes → Action items neatly surfaced for follow-up.
Emails → Polished tone and clarity.
Post images → Quick, professional visuals.
Video subtitles → Auto-captions so people engage even with sound off.
What Happens If We Don’t Adapt?
History gives us a clear answer.
Email Adoption (1990s): Teams who resisted email for fax or phone lost speed, transparency, and credibility. Within a decade, email wasn’t optional—it was essential.
The Internet (late 1990s/early 2000s): Companies that failed to establish an online presence quickly fell behind competitors who did. Think about Blockbuster versus Netflix.
Search Engines (early 2000s): Professionals who learned how to query effectively gained an edge in research, productivity, and knowledge work.
Each wave of technology rewarded early adopters with efficiency, reputation, and influence. Those who held back found themselves scrambling to catch up—or never catching up at all.
The difference today? The speed.
It took nearly 10 years for the Internet to become mainstream. Email adoption stretched across decades. By contrast, ChatGPT reached 100 million users within two months of launch—making it the fastest-growing consumer app in history (UBS, 2023).
What once took a decade to normalize is now happening in a year—or less.
A Framework for Wise AI Use
So how do we avoid the pitfalls and use AI well? Think of it as a three-step filter:
Clarity First → Define what you’re trying to achieve before asking AI for help. Vague input = wasted time.
Trust but Verify → Use AI for drafting, summarizing, or surfacing insights—but don’t outsource final judgment.
Focus on Value → Let AI handle the noise so you can spend time on higher-value activities: innovation, collaboration, problem-solving.
The Human Edge
The temptation is to see AI as a replacement. But history shows that technology augments more than it eliminates. Email didn’t kill communication—it amplified it. The Internet didn’t replace businesses—it changed how they reached customers.
AI will not erase the need for human creativity, judgment, and empathy. But it will change what we consider “baseline” productivity.
Those who embrace it thoughtfully will have more time to do meaningful work. Those who don’t may find themselves stuck, like workers who refused to learn spreadsheets in the 1980s.
Closing Thought
AI is here to stay. The question is no longer “Should I use it?” but “How can I use it wisely?”
For me, that means letting AI capture the action items from meetings, fine-tune my communication, generate visuals, and subtitle my video clips so more people can engage. For software engineers, PMs, CX teams, and analysts, it means using AI as a powerful partner—not a crutch.
The real danger isn’t using AI poorly. The real danger is not using it at all.
👉 How are you making AI your ally in work—not just for speed, but for clarity and impact?
