[karimtr]

The reality is that RPA, AI, ML, etc ... are still years away when it comes to the industrial sectors (e.g. manufacturing, energy, oil & gas, etc).

Sure there are many PoCs, smaller projects, but in my experience, working on data centric projects with a few fortune 500 industrial companies, none of these technologies have been successfully implemented in production.

Perhaps for some back office & business-level applications, but certainly not for any production related use-cases (e.g. predictive maintenance).

[pantulis]

My take is that RPA by itself is useful as most big companies have mind blowingly manual processes in their back offices (or even worse they don't have mapped their processes).

The combination of RPA with AI/ML, usually called "Intelligent Process Automation" is merely dropping some OCR or NLP engine here and there to help in points of the workflow when there is no other possiblity other than some person reading a document and trying to come up with the next step. Calling this "AI" is a stretch.

[pantulis]

Oh and don't get me started with the McKinsey quotes the likes of "workforce doing clerical work will be liberated to do higher value tasks".

They are simply going to be fired --if you squint a little while reading the slides you see that is precisely the business case.

That's going to be technology for the rest of the office workforce in the next few years.

[jwally]

The hardest part of successful RPA implementation IMHO is in not automating stupidity.

It’s too easy to send EY or whoever in to replace some god awful process by “Larry in AP” with a “bot” that just replicated what he does - no matter how pointless any step might be.