Optimising financial processes

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Can AI Help Companies Redesign Business Processes?

Once again, I was woken from my reverie by a thought-provoking Harvard Business Review (HBR) article.

Entitled “How AI Is Helping Companies Redesign Processes” raises some important themes. I share the link below.

I mused as to whether it should have been titled “How Data Is Helping Companies Redesign Processes”.

Those of you with a long memory (or Google) will know that, in the 1990s, business process reengineering was all the rage, largely associated with the rise of ERP systems and the commercialization of the internet.  

All of this was going to change the world.

And it did, but not quite as fast or in the way many expected.

Ralph Aboujaoude Diaz made me smile this morning with a LinkedIn post that showed a 1988 news image subtitled “Math teachers protest against calculator use” and linking this to the news that in today’s 2023, teachers protest against ChatGPT use. There was also wave of fear in 1999 that Google search would make everyone more stupid . . . 

Amusing irony, but worthy of deeper thought about the nature of technology change.

Bear with me – this is relevant!   

Gartner, the respected technology research analysts, popularized the technology “Hype Cycle” a few years ago, which describes the relationship and cycle time between innovation triggers, inflated expectations, disillusionment, enlightenment and productivity. 

Don’t worry, I am weaving these streams of consciousness together!

Back to “Process Re-engineering”: Whilst the vision evangelised by Michael Hammer and James Champy in their seminal work “Reengineering the Corporation” was one of enterprise transformation, in reality the processes of that era that were “re-engineered” were largely the subset of activities supported by the new ERP systems. These were not “end to end” value streams that we might consider today, but important subsets such as “order processing”, “accounts payable” etc. This, it turned out, was the “plateau of productivity” that emerged.

If, as the authors of the article below suggest, that process re-engineering is making a comeback (which I believe it has been for some time), then that is a good thing. They argue that this new wave will result in an appreciation and understanding of AI as well as a renewed appreciation of business processes as a structure for improving work and business outcomes.

There is no doubt that AI, in all its forms, has been, and is, an important foundational technology.

But at its core, AI is about Data. It is easy to conflate the two topics.

I agree that the future is about enabling better, faster, and more automated decisions. In essence, most AI deployments in large organizations involve Machine Learning from large datasets to make a prediction or classification, which in turn helps business with making a better operational decision. Pure “Data Driven Decision Making“.

The core of this is the DATA.

With access to enough of the right, trusted data, many business (and societal) problems can be solved. But not necessarily always with AI.

The data problem is also about “which data” and what “Key Business Questions” are we trying to answer, or make our decisions around? This is a tough challenge and far harder than many assume.

You can run the argument that data is the core innovation trigger we need to exploit, and AI is just one mechanism.

Data is ubiquitous. Data with shared, common meaning, less so. This is the challenge for AI as well as for Data Driven Decision Making in all its forms.

AI is also fast becoming ubiquitous.

I echo (and add the “data” to) the sentiment in the closing paragraph of the article . .

“Once the hype recedes, AI will become as standard as ERP systems, statistical packages, or even spreadsheets.

Data (and AI platforms) can be used by a much larger pool of companies to reengineer their processes.

AI is a means to an end, not an end in itself.

Firms that understand how to use data effectively in the broader context of process reengineering will also arguably get the most from AI in the long run.”

Thank you to Tom Davenport, Matthias Holweg and Dan Jeavons for your HBR article on this that really gets us thinking. You can read it (5 minutes) here . . . 

Thanks for reading . . .