Artificial Intelligence (AI), the empowering Technology

Artificial Intelligence

Post By : Achyut Labs

Sept 25, 2020

Updated Aug 16, 2026

The Human Life and Artificial Intelligence (A.I): The Future Awaits

As a child, I was fascinated by the story of a knife because a knife can be used when needed while also dangerous when it falls into the wrong hands. You can either chop vegetables with a knife to make a good meal or harm someone with it. Therefore, we can say that a knife is not good or bad, but it is just a tool but the user's behaviour decides the experience of the tool.

This is perhaps simple human psychology regardless of the tools we use. Every one of us has come across some sci-fi movies where the robots fight and take control over planets. That's Artificial Intelligence on an extreme scale. But soon we see that the human consciousness awakens and the human takes back the control. That's humanity. What Technology does is that it creates a balance between both ends.

Artificial intelligence has created some terrific buzz in the business world wherein large companies are heavily investing in A.I. technology. M2M (Machine-to-Machine), Deep Learning, IoT, Machine Learning, Computer Science, and Big Data are the fields of work within the purview of Artificial Intelligence.

"SIRI, ALEXA, NETFLIX and TESLA Cars are few pioneers who have successfully applied for the A.I. technology in its products."

You too can leverage the power of A.I for your business by better identifying the current demands and predicting the trends with pinpoint precision. Try to understand the trends better and how your business can benefit from it and how you can have customers use it better. Implement latest A.I technology by adapting new trends with your current business operations. Sophisticated A.I services can proactively change your business model without prompting any anticipated challenges by solving prospective issues before occurrence.

Moreover, this happens without the emotions, prejudices, myopia, and egoism that clouds much of the human judgment as we stand today on the crossroads, whether to fear or reject the A.I. It all goes down the same dark road as did Pullman, Woolworths, and Marshall Field, Kodak, and Blockbuster names once associated with success, now forever but are now tinged with the patina of failure.

To embrace A.I. is to welcome the future that may well go beyond the scope of our imagination.

Update · August 2026

What changed since this was written

When this article first went up in 2020, the everyday examples of AI were recommendation engines and voice assistants. The shift since then has been generative AI — large language models that write, summarise, translate, answer questions against your own documents, and increasingly take actions on your behalf. The change that matters for business is not that the models got cleverer; it is that they became available through an API, so using them no longer requires a data-science team.

Where it is actually paying off

The projects that succeed tend to be narrow and measurable rather than sweeping. In practice that looks like:

Customer support triage — classifying and routing enquiries, drafting first-response replies for a human to approve.
Document and data extraction — pulling structured fields out of invoices, contracts, forms and PDFs that previously needed manual re-keying.
Internal search — answering staff questions against your own policies, manuals and historical records instead of a shared drive full of files nobody can find.
Forecasting and anomaly detection — the older machine-learning use cases, which have quietly become far cheaper to run on cloud infrastructure.
Content and code assistance — first drafts that a person edits, not final output that ships unreviewed.

What to be careful about

The knife metaphor at the top of this article has aged well. A few things are worth settling before a pilot becomes a production system:

Where your data goes. If customer or employee information is being sent to a third-party model, that belongs in your privacy policy and, for Australian businesses, under your Privacy Act obligations.
Confident wrong answers. Language models do not signal uncertainty reliably. Anywhere an error is expensive, keep a person in the loop and make the source of each answer checkable.
Cost at volume. A pilot that costs cents can cost real money at production traffic. Model the unit economics before you scale it.
Lock-in. Design so the model behind an API can be swapped. This space moves faster than most procurement cycles.

A sensible starting point

Pick one process where the work is repetitive, the inputs are already digital, and someone can tell straight away whether the output is right. Measure how long that process takes today. Build the smallest version that helps, put it in front of the people who do the work, and only then decide whether it is worth extending. Most of the value in the projects we see comes from the boring parts — clean data, a clear success measure, and somebody who owns the outcome.

At Achyut Labs, we endorse the use of Artificial Intelligence and are working towards applying AI technology in most of our service and product offerings.