The hype surrounding AI has fueled both wild optimism and deep anxiety. Some believe it will solve every business challenge overnight, while others worry it will take their jobs. From what I’ve seen in real-world settings, both extremes miss the point.
I’ve implemented AI tools in my company and helped clients do the same across various industries. And here’s what I’ve learned: there are plenty of areas where AI delivers real, measurable value and just as many where it doesn’t live up to expectations.
In this article, I’ll share my hands-on experience with AI in business: what worked, what didn’t, and why. What is more, I’ll also break down five key principles to help any company adopt AI more effectively, and share my perspective on the future of AI in business.
Where AI Actually Delivers Value
In my practice, the most meaningful AI use cases are rarely the most high-profile. Rather than being found in flashy front-end tools, the true value of AI emerges in background processes – those that are repetitive, data-intensive, and susceptible to human error. It is in these areas that AI demonstrates its greatest strengths.
Let me give you a few examples from my experience:
1. Lead Research & Qualification
One area where AI can truly shine is lead research. Let’s say someone subscribes to the newsletter using a corporate email address. Now imagine if – automatically – an AI system (something similar to ChatGPT Deep Research) could scan the web and gather all available information about that company.
It could identify the industry, estimate company size, and even pinpoint the individual behind the email. From there, it could assess how well the lead matches the ideal customer profile (ICP) or, in a more advanced use case, make a judgment like: “This company doesn’t exactly fit your ICP, but it’s operating in a closely related industry with similar needs. Worth a look.”
That kind of AI-powered insight would be incredibly valuable for any business with lots of incoming leads, helping teams personalize follow-ups and prioritize outreach more effectively.
2. Content Creation Support
Another area where AI has proven invaluable is in content production. Paradoxically, I have discovered that AI excels not at creating original content independently, but as a powerful complement to human creativity.
My current content creation process exemplifies this: I capture my thoughts and opinions through voice recordings, AI converts speech to text, then use an AI model (specifically Claude, trained on my tone of voice and writing style) to generate initial drafts. This phenomenal synergy significantly accelerates content creation, not necessarily in time saved, but in the reduced effort required, allowing me to focus on ideation and core messaging.
3. Quality Control for Critical Operations
AI also plays a crucial role in high-stakes environments where precision is non-negotiable. For example, I’m currently working on a fascinating project for a major chemical company where quality and 100% control are absolutely critical.
I’m implementing AI for supply chain control to double-check human work. Even a small error – one mistake in 100,000 orders – could result in the wrong chemical being delivered to, say, a swimming pool, creating enormous public safety risks and health hazards. AI helps reduce these errors to zero, not just lowering the rate but eliminating mistakes entirely.
This shows AI’s greatest value often lies in small, behind-the-scenes improvements that quietly make the world safer. While these gains may go unnoticed, the cost of preventing even one error is enormous, proving that subtle AI-driven safety measures can have a far bigger impact than flashy innovations.
4. Administrative Process Automation
AI also brings clear value when it comes to handling repetitive, low-value tasks that drain human time and focus. A strong example is in logistics, where one of the most effective use cases has been automating form filling. Much of the day-to-day work involves transferring information from one document to another – a purely mechanical process.
AI can easily take over these tasks with precision, minimizing errors and freeing up staff to concentrate on more impactful areas like safety, organization, training, and overall process improvement. It’s a simple shift, but one that creates noticeable gains in efficiency and team productivity.
Where AI Doesn’t Deliver Results
There are many tasks where AI can truly help. However, there are also specific cases where it falls short. I’ve faced two major examples that highlight AI’s current limitations:
1. Content Creation with LLMs
At Muncly, I produce a high volume of content every week, including articles and YouTube scripts. You’d think this is where AI would excel. But in reality, text creation using large language models turned out to be one of the most disappointing use cases.
I’ve experimented with various tools like ChatGPT, Claude, Jasper AI. I fed them detailed outlines, solid research, and even tried to fine-tune the output to match my voice and writing style. Still, the results felt off. AI-generated text is too polished, too repetitive, and lacks something essential: personal experience and real opinion. Since our audience is human, this kind of sterile, overly structured content simply doesn’t connect.
2. AI Agents for Customer Interaction
Using AI agents isn’t a perfect solution. I’ve seen many companies try to implement AI agents on their websites, and I’ve learned a clear lesson: just don’t. Unless you have a very comprehensive and well-structured knowledge base and can train the AI to give genuinely helpful answers – it’s not going to work. So far, I haven’t seen anyone succeed at this (with some exclusions from those who actully know what they are doing).
People notice when they’re talking to AI. LLMs tend to follow the same sentence structures, use the same kinds of phrasing, and even format responses in similar ways – like those long em dashes everyone recognizes. Once users realize there’s a bot on the other end, they often stop the conversation altogether.
So while AI is powerful in the right context, it’s important to know its limits. For anything that requires real emotion, subtlety, or connection – AI still falls short.
The Five Implementation Principles That Actually Work
Based on my experience, successfully integrating AI tools requires more than just picking the latest solution. Here are five practical principles businesses should follow to get real results:
1. Test Tool Quality Thoroughly Before Implementation
Always verify that the AI tool actually performs well for your specific use case. In my practice, I’ve seen some freelancers struggle because they used AI tools without proper testing, resulting in generic, uninspiring content like email campaigns that read like filler and don’t convert. Poor output wastes time and damages credibility. Make quality testing your first priority before rolling out any AI solution.
2. Test Extensively Before Live Deployment
Don’t limit testing to internal teams or controlled environments. Deploy your AI tool to a small, controlled group of real users first – like 10% of your customers – and gather their feedback. For example, when implementing a chatbot for a trading company, my team and I went through this phased rollout despite the technical complexity involved. This process helps identify issues early and protects your brand from negative user experiences.
3. Implement Continuous Quality Control with Human Oversight
Never rely solely on AI – maintain ongoing human supervision. For instance, when using AI for email recognition, we randomly verify samples by having humans check data entry. If discrepancies arise, the issue is escalated for technical review. This approach is crucial, especially in tricky cases like handwritten forms where AI can make mistakes.
4. Prepare for Iterative Implementation and Continuous Improvement
AI tools rarely work perfectly out of the box. Start small by limiting the scope – for example, restrict your AI customer service bot from handling complex contract queries initially. Focus on mastering one area before expanding. AI requires constant training and iteration, so plan for an ongoing improvement process rather than a “set and forget” mindset.
5. Account for AI Detection by Adding Intentional Imperfections
People can easily spot AI-generated content, especially when it’s too polished or uses common AI phrases like “I hope this email finds you well.” To avoid this, introduce natural imperfections such as minor grammatical variations, abbreviations, or casual language. This makes communication feel more authentic and human, helping your AI-driven messages connect better with your audience.
Looking Forward: Augmentation, Not Replacement
All in all, I’ve seen many different cases of AI use, and based on the experience I gained, I have a clear view of what the future holds for AI in business.
In my opinion, AI’s most significant impact will be in areas that involve repetitive, mechanical tasks. Think data processing, form filling, initial quality control checks, and routine customer service responses. These are exactly the types of tasks where AI can take over the heavy lifting – speeding things up and reducing errors.
But the human touch? That will remain absolutely indispensable. There are many areas where AI simply can’t replace the depth and nuance that only people bring:
- Complex problem-solving that requires creative thinking and understanding of context
- Building relationships and managing interpersonal communications
- Strategic decision-making based on experience, intuition, and a deep grasp of business subtleties
- Handling exceptional cases that don’t fit into standard procedures
- Leadership and team management, where emotional intelligence and judgment matter most
- And of course, innovation and vision creation – the original thinking and long-term perspectives that drive progress
The future of AI in business is not about replacement; it’s about augmentation. AI will handle the mechanical, routine work, freeing humans to focus on high-value activities that require creativity, empathy, and complex reasoning.
In my practice, this symbiosis between AI and humans is already shaping the way we work and I believe it will define successful business operations for years to come.
