During the past few years, with the advancement of generative AI, we can definitely say – it is doing impressive things. AI can generate code, write blog posts (not this one, though), summarize meetings, design landing pages, and even pretend to understand your brand voice. It’s fast, tireless, and almost always confident – until you point out the mistakes, and it says: “You’re right. That was wrong”.
So, naturally, many people increasingly wonder from time to time (from one viral AI news article to another): if it can do our job, where does that leave us?
What AI Is Actually Good At
AI thrives in structured environments, repetitive tasks and predictable patterns. Summarizing Jira tickets? Love it. Generating twenty descriptions for a button style? Dream job. Auto-generating documentation from existing code? Sign it up.
But here’s where it falls short: AI operates without context, intuition, or real understanding of stakes. It can’t sense when a project is headed in the wrong direction before anyone says so out loud. It doesn’t recognize that a client’s enthusiasm feels forced, or that a design solution technically works, but misses the emotional mark entirely. It won’t question whether you’re solving the right problem, or if that problem even matters to your users.
Don’t Chase Every Shiny AI Tool
Here’s the thing about AI in business: it’s not about having every shiny new tool. It’s about solving real problems.
AI isn’t magical. It is a combination of engineering, math, and business knowledge. The companies that get real value, most often focus on specific areas where generative AI genuinely helps: analyzing big datasets on challenges to find automation opportunities, building prototypes for strictly defined tasks, integrating models into workflows that already exist, using AI to practice learning, and continuously fine-tuning performance as needed.
At Lerpal, we’ve learned this the hard way – by building custom solutions that fit into existing ecosystems rather than chasing the latest trend. The businesses that future-proof themselves don’t just invest in AI; they invest equally in what AI can’t replicate.
Even the best AI needs direction.
What Humans Still Own
Deep user empathy: AI can analyze behavior patterns, but it doesn’t know what it’s like to navigate your app with 3% battery in a noisy subway. Your team does, and that insight builds better products.
Strategic judgment: AI completes tasks brilliantly but doesn’t choose priorities. It won’t ask if you’re building the right thing or if anyone actually wants what you’re making.
Transparency of incentives: Your teammates will tell you what drives their decisions and what constraints they’re working within. AI systems? Their rules and priorities are locked away in corporate vaults, leaving you guessing what’s really shaping your results.
Contextual communication: AI drafts emails but can’t bridge departments, navigate office politics, or know when to push back on a bad idea. That takes people who simultaneously understand the work complexity and can read the room.
This is exactly how the human touch keeps the major competitive edge – things that don’t become irrelevant when tools change.
The Hidden Cost of Letting AI Run the Show
If you’re thinking “we’ll just generate 10 versions of everything and pick one” – congrats, you’ve just invented mediocrity at scale.
What you get is a design that “looks fine” but doesn’t connect. Writing that «sounds okay» but has no edge. Products that technically work, but feel like they came from nowhere – because they kind of did. AI doesn’t care about your users. It doesn’t fight for clarity, obsess over details or get chills from a perfect microinteraction. That is still your job.
So… Are We Going To Lose Our Jobs?
No. But our jobs are changing fast. In the best cases, GenAI is like a brutally efficient intern. One that works 24/7 and doesn’t complain, but also doesn’t know what it’s doing. You still need to steer the ship. You still need to know what matters.
At Lerpal, we’re not betting on AI instead of people. We’re betting on smart people who use AI on purpose. With taste, context and with a clear understanding that the tools don’t replace the work, they just make the best teams even better.
