Following new horizons through Work Intelligence

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A vintage Royal Mail crew van from the London Postal Museum.

Following new horizons through Work Intelligence

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The insights and experience the workforce brings to process discovery, analysis, validation, and operation are invaluable.

Where December might be the time for predictions, January is typically the time when I choose to get stuck into renewal. In this case, I’m currently working through the updates to the “Work Intelligence Market Analysis” we first launched in April 2023.

Work Intelligence pulls together existing technology markets for automation, process analysis, and workflow and – through the lens of artificial intelligence – deploys them to make our workplaces healthier. 

Indeed, in the preceding research paper “Work Intelligence: Improving Processes by Balancing Human Intelligence and AI” (which you can download for free in return for a few bits of information), we state right up-front;

Work intelligence describes a balanced partnership of human and artificial intelligence to accurately and efficiently understand, analyze, monitor, and improve an organization’s work. Put another way, work intelligence is the combination of ‘inform and validate’, where machine intelligence is validated by human workforce intelligence, and neither is supplicant.

As the way my brain chooses to operate insists that I’m never totally off work, planning the updates to this report was very much in mind over the festive holiday break, especially when, as a result of a widely watched TV drama*, the Post Office Horizon scandal was once again in the news here in the UK. The scandal itself has been unfolding through the pages of Computer Weekly since 2008 and, since then, gradually through the courts as the scale of the miscarriages of justice becomes clear.

This has generated a significant volume of comment, but this, on the platform that I’m afraid I refuse to call by its new name from Alan Finlayson, Professor of Political and Social Theory at The University of East Anglia, best encapsulates my thoughts.

“…the conviction that workers are intrinsically a problem, never a solution, assumed to be ignorant and on the make…” in particular is an analysis that you can apply not only to this specific situation but if you wish also to other contemporary industry employer concerns, such as that around flexible and remote working. Let’s not dwell on the stated ability of organizations to buy software successfully.

The default position for many organizations is mistrust to the point of outright confrontation toward the concerns of their workers, which should itself be a scandal. Especially when, in this case, the workers were correct, but listening to them early would have ensured that no party would now be paying our significant compensation and calculating the much larger, more damaging reputational impact on their organizations. Involving them more thoroughly during earlier stages of the project might have prevented the entire sad story from unfolding.

That the latest installment of this scandal is playing out against a backdrop of the UK government attempting to pin down how generative AI might find a future role in delivering citizen services has not gone unnoticed; long-time technology commentator John Naughton is particularly incisive on the broader subject.

Here at Deep Analysis, we could not have been clearer in our research around Work Intelligence that the balance between human and machine intelligence is of paramount importance to extract the maximum value from their combined work. The insights and experience the workforce brings to process discovery, analysis, validation, and operation are invaluable. It’s no secret that what underpins the implementation of many contemporary approaches to validate generative AI is the use of grounding/retrieval-augmented generation, largely using the prior recordings of human thought and deed (however ugly that recording might sometimes be).

Automation, whilst making up a significant chunk of our collective research, is not a goal in and of itself. It’s in part why the term “hyperautomation” doesn’t figure in our work. Instead, automation should be how organizations can harness technology to improve outcomes for their workers, clients, and partners.  Doing so as equitably as possible is a much healthier goal and one which, as we look through the short days of the mid-winter toward that renewal, it is worth reminding ourselves of its importance.

* It’s currently the law in the UK that all significant dramas, whether designed for the small screen or the cinema, have to feature Toby Jones or Olivia Colman (or both, which also meets legal compliance). Luckily, they tend toward brilliance, both performatively and through their choice of projects, so I am happy to see this law as a positive contributor to the national product.

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