As AI continues to dominate headlines, more and more products are marketing themselves as “AI-powered” — from productivity tools to CRMs, chatbots to ecommerce platforms. But when you look under the hood, many of these so-called “AI features” are little more than automated workflows or scheduled scripts.
So it is the time that we ask the hard question: What really counts as AI, and what is just an automation?
The Overuse of “AI”
Today, you’ll find companies claiming to use AI for things like:
- Sending a discount offer when sales drop below a threshold.
- Triggering an alert when stock is low or creating Purchase order.
- Auto-sending a WhatsApp message after a payment fails.
These are useful features, but they are not artificial intelligence. They are plain IF-THEN rules in a computer program, scheduled logic/cron-jobs, or filters that run at set times or conditions.
This is automation. And there’s nothing wrong with it, but we shall not mislabel it as “AI” to ride the hype.
What Is Real AI?
True AI, especially the kind that powers modern breakthroughs, includes technologies like:
- LLMs (Large Language Models) : like GPT, Claude, or Mistral
- Generative AI : text, image, video, audio generation
- Machine Learning: models that learn from data patterns
- Computer Vision : models that interpret visual data
What sets real AI apart?
Real AI does at least one of the following:
- Understands messy, unstructured input
- Learns or adapts from new patterns
- Generates context-aware output
- Infers new knowledge insights into data based on interrelationship
- Perform data summarization
Automation vs AI: A Clear Comparison
Automation is powerful, reliable, fast, and rule-based. But AI is much beyond these rules. For instance, it can reason, infer, summarise and adapt.
Why It Matters
Calling everything AI is more than just misleading as it affects:
User trust: When people expect intelligence and see simple triggers, trust is lost.
Team clarity: Developers, PMs, and stakeholders can misunderstand what’s actually being built.
Funding misuse: Investors may fund startups thinking they’re buying deep tech when it’s just logic wrapped in buzzwords.
If your product uses GPT or other LLMs to summarize contracts, generate content, or answer questions in context — that is real AI. If it just does things on a timer or after a condition is met, that is plain logic. It is useful, but let’s call it what it is.
Where AI Should Be Used
Instead of forcing AI into every feature, let’s focus on where it really adds value:
Document understanding (contracts, policies, research, legal)
Customer service (contextual replies, not just question answer pair responses)
Summarization and analysis of text or numerical data
Personalized content generation
Intelligent search over data
In these areas, AI is not just a marketing layer, it solves the real business problems.
Summary and Final Thoughts
AI is transforming the software landscape, but let us not dilute its meaning. Not every useful feature is intelligent. And not every intelligent type looking tool is using AI, it could just be a plain automation based on plain software logical steps.
We as builders, founders, and developers, we have responsibility to our users and to ourselves, to stay honest and clear.
Let automation do what it is good at, and let AI get it’s due respect where it makes a difference.
Written by : Rishi Pal Singh
Please feel free to share your thoughts on this, as where have you seen “fake AI” being hyped? And where do you think real AI adds the value?
Connect with me on my LinkedIn: www.linkedin.com/in/rishi-singh-b8674751
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