Do Something!

Many firms coast along - especially if they are (at least moderately) successful. They work on the principles that "If it ain't broke, don't fix it" and 'Doing nothing is easier than doing something'.  

However, being productive snd competitive is more like running a race.  If you coast, sooner or later someone will overtake you - and then it might be too late to react and respond.

Similarly with technology.  Technology moves on.  If you don't move with it, you get left behind. 

So, keep an eye on your environment and changes that are happening, or are about to happen.  Keep an eye on your competitors.  But perhaps more importantly, keep an eye on your internal systems, processes and working practices.  What opportunities are there for positive change?  How can you improve those systems and processes? How can you improve the skills of your workforce?

Then do something. By all means plan it carefully and introduce it slowly.  But do it!

UK Workers Don't Work Hard Enough

This is a statement you will often see in the UK press (and those in other countries will see similar statements with their country replacing the UK.)

IBu is it true?

Well, firstly the statement is normally made because someone has read the latest official productivity statistics.  This shows that in the aggregate, output per worker is down. However, most workers whose output contributes to the figures are not working as individuals but as part of s team within a section  department that is s pert of an  organisation

Does this mean that we should be attributing the low productivity to teams of workers rather than individuals?

No!

In most organisations, the work of individuals snd teams forms a component part of some larger overall  process or working system.Moat likely, it is this process or system which is 'at fault'. The various component parts may be out of balance meaning we get bottle necks snd queues of materials or parts waiting for the next operation … or we get one component of the system that produces errors (perhaps as a result of machine or equipment failure) which means work has to be redone or corrected….. or equipment breaks down causing delays … or individuals do not have all the skills that would enable them to provide optimal performance.    

You get the picture. 

Few working systems or business processes are perfect (all the time).  The resulting drop in performance and productivity has nothing to do with how hard individuals (or teams) are working, but is related to the overall performance of 'the system'.

This is the responsibility of the management team, not the workers.  Yet they still get their quarterly or annual bonus when the workers are using money on their output-related incentive scheme.

When you next read about poorly-performing workers, correct this to 'poorly-performing management teams.  

Let's shift any blame to where it should lie.

Can governments handle healthcare AI?

AI has significant potential to help improve healthcare.  It can (hopefully) save lives, improve the work and job satisfaction of health professionals’, and make health systems more people centred. 

It can help address some of health’s largest challenges including a depleted and disheartened workforce, future threats to public health, ageing populations, and increasing complexity of health due to co-morbidities. 

So, bring it on.  The advantages look clear.

However, the arrival of AI does mean an incredible amount of personal data will br sloshing sabout the various system and putting people at risk of their personal (and valuable data being borrowed or stolen by 'bad actors'.  

There is also a risk of assuming that all data being fed to the AI agents is of high quality - but, especially in the early days of healthcare AI, this may not be the case.  AI trained ion poor quality data is not effective and may put patients at risk and lesd to biased or skewed results.

So governments their agencies and their technology partners have the triple. tasks of creating effective and efficient AI system to provide the claimed benefits, ensuring that data is used appropriately,  whilst simultaneously protecting the vast data sources and stores that will arise.

Do we trust them to be able tro do that?