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There's Tech... and then there's Tech Hype!

In / By Charles Christian / 21 February 2020

I know I shouldn’t, but I can’t help smiling when I see lawyers falling hook-line-and-sinker for the latest tech industry hype. It’s made all the more ironic as these are the self-same people who in their day-jobs earn large amounts of money advising other business people against making foolish mistakes.

Without a doubt, last year (2019) was when AI or Artificial Intelligence hit peak hype - everyone was either selling it or buying it. All of this is kind of unfortunate as we do not yet have AI in its true sense of machines that can think for themselves. What we do have is Machine Learning (ML), in the sense of machines that can be trained to recognize or learn patterns in data including trends, images and sounds.

Machine Learning

Any law firm that is running speech recognition software (a technology that’s been around for over 25 years) is running a Machine Learning application – the more you use the system, the more it learns your speech patterns and the more accurate it becomes. That is also why other applications making use of ML include playing board games such as chess and Go (what would we have done without technology) and identifying human faces via CCTV type systems.

Although the latter case does not without some controversy, as some systems seem to struggle with differentiating the faces of women and people of colour. The new system the Metropolitan Police want to introduce in London is coming in for criticism for its relatively poor accuracy rate.

Why is there a Hype Over AI?

In a nutshell, because it has become a buzzword that marketing departments have latched on to. By comparison, out in the more academic world of AI research where it is widely recognized, we still have a long way to go – perhaps decades – before machines become truly intelligent; there’s genuine concern we’re heading into an “AI Winter”. In particular, businesses are going to start reacting against the excessive hype of the last five years – especially those that have bought so-called AI vendors or AI systems and are seeing no obvious benefit. Indeed, I know of one vendor in the legal tech sector selling an “AI solution” that is just based on Excel spreadsheet macros.


And it’s not just AI – I’m also hearing a lot of talk about “systemisation” (sometimes in conjunction with AI for a double dose of hype) in legal circles. Now the definition of business systematisation is basically the creation of repeatable systems to manage the operations of your business, or what since the late 1970s (when they first appeared on the legal tech scene) have been known as case management systems. Well that’s not entirely true because in the early-to-late 1990s, management consultants were forever wanting to bend my ear about Business Process Re-Engineering (or BPR) which focussed on the analysis and design of workflows and business processes with an organisation. In other words, case management systems again. Call it case management, matter management, BPR or systemisation, they are all essentially the same thing but given a new spin by successive generations of companies and consultancies whose business is selling their products and services to end users. Just to complete the picture, the very nature of law firms with their constant cycle of new generations of partners and managers making IT procurement decisions means there is very little long-term institutional intelligence. This is potentially a polite way of saying they don’t remember the lessons of the past and don’t realise the shiny new systemisation project they are being sold in 2020 is no different to the BPR project of 1995, or the case management system of 1980.

Seven Myths of Machine Learning

Forrester Research recently published a report called ‘Shatter the Seven Myths of Machine Learning’, which warns:

“Unfortunately, there is a pandemic of ML misconceptions and literacy among business leaders who must make critical decisions about ML projects.”

Issues include not fully understanding the terminology, not understanding the limitations of the technology (machine learning is definitely bad at identifying past biases hidden in data), falling for the science fiction/advertising myth that ML can do something truly remarkable whereas in reality it’s better at handling un-sexy mundane tasks and a general belief that ML will be able to do something “magical” – in defiance of common sense.

Yes, explore new technologies but if you are investing money in new tech, buy systems that can deliver benefits now rather than pie in the sky systems which will never live up to expectations.

If you’d like more information or advice on the best technologies to invest in to benefit your business, speak to Nasstar’s strategic consultancy team.

Charles Christian

Charles Christian

Charles is a former barrister and Reuters correspondent turned writer and the founder of the Legal IT Insider newsletter, website and resource.

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