Harvey Nash's Tech Survey

Last night was the London Launch Event for the Harvey Nash Global Tech Survey Report - Race for Your Life.

The Report itself contains some obvious points, e.g. Younger Companies tend to be more innovative than established ones. However there were some surprising findings Construction and Engineering is the leading sector for innovation, with twice the preportion of respondents claiming their organisation is innovative than say Finance (which is investing heavily in FinTech). The FMCG  and Consumer led sector came bottom with less than 5% claiming any innovation.

Encouragingly enough the proportion of respondents who claimed that their organisation is innovative had also grown since the previous survey. Happily enough CTOs came out as more innovative than CIOs (which they should be, as its part of the job description), but CEOs also came a nose ahead of CIOs too.

As the survey is run by a recruitment specialist there is a focus on a number of recruitment centric issues. There are stong sentiments that Ageism dominates recruitment with adverse impact from your 40s onward. People still beleive that a human recruiter is far better than a machine at matching people with the right jobs. There is an increasing emphasis on completely refreshing skills every 5 years with many people investing personally in their skills.

In fact a lot of the panel discussion focussed on how organisations look at people, culture and mixed / diverse teams when building them. It seems that there is a frustration that older people cannot seem to get their CVs past recruitment consultants to the hiring manager. So what is the bottleneck there?

Close to 40% of people felt that automation would affect their jobs. Whilst this is quite high, considering that the last 30 years have been spent trying to automate IT people out of their jobs (without much success), it is probably quite necessary as there is a worldwide shortage of talent.

Two things struck me from the panel discussion however. One was positive, given that the panelists were from Tech start ups and Digital Model based companies; they all considered themselves to be the guardians of their business's appraoch to ethics. 

The negative was how much they failed to convey an understanding of innovation. They were seduced by the acquisition of technologies (by one means or another), but none of them mentioned how they empathised with customers to get at their real needs. There was no discussion of design thinking.

The other issue which came alongside this was how all of them were only just getting to grips with the idea of designing and operating to avoid technical debt. Previously, they had all been in too much of a hurry to just deliver something.

So in terms of how mature is the average operator in the new Digital As Usual (DAU) world. It's looking like 5 out of 10 to me.

The Productivity Problem and Machine Learning

One of the things that few mainstream politicians will admit to is the fact that Economic Policies which promote Productivity, lead to short to Medium Term Unemployment. Likewise the converse is true. If a government promotes policeies for High Employment, they tend to undermine Productivity.

Longer term, productivity has benefits all of its own. Long term productivity growth leads to more investment and hence higher employment. Along with this comes better quality jobs demanding higher skills and hence wage growth.

North America has long enjoyed high growth, largely due to the abundance of land and resources and a relative low density in population, leading to skills shortages and therefore the need for higher productivity (gained from investment in automation technologies). This puts Britain's recent economic performance in perspective. The UK economy largely weathered the most recent economic downturn well. Though one of the consequences was large scale imigration and a drop off in productivity growth.

This means that British Industry needs to invest in significant increases in productivity, if it wishes to maintain its long term competitive position. Also, it poses challenges as Britain has shifted to a services dominated economy, as it is often difficult to achieve high productivity within service industries and traditional approaches such as work flow (or Business Process Management) and rules engines tend to be limited in applicability and affordability. Typical problems are activities which deal with subjective judgements involved in tasks such as categorisation or recognition where people are able to deal with inconsistencies and sometimes incompleteness of data. These steps are usually not value adding in themselves, but important to being able to carry out subsequent value adding tasks.

A simple example would be in pest control. when on a job, the pest controller may find a cockroach during an inspection which indicates a probable infestation problem. Different strains of cockroach however need to be treated with different chemicals to eradicate them. Often samples need to be sent to a lab to be examined and identified, so that the operative can then return and take appropriate action. This adds time, delay and effort to the process with impact on costs and productivity. It also may impact the customer, especially if his or her business is in catering or another business which may have to close down until the infestation is cleared up. Use of picture recognition technology, based on training a machine learning system and providing field access via, say, a mobile app would enable a much quicker and cheaper response. As on the spot diagnosis could take place, leading to instantaneous treatment. The avoidance of going away to come back again, takes a couple of steps out of the process.

Other examples occur in the legal profession, where initial analysis of case files may help decide whether a case is worth pursuing based on existence or not of a number of attributes which relate to previous experience and case law. This allows lawyers and clients to focus on valuable activity rather than value destroying cases.

So, it is strange to see that some politicians want to tax such applications as people, in order to protect jobs. As inevitably this will erode productivity growth and damage long term employment opportunities. Are they trying to keep everyone poor?