I Give Up

 I've been using AI systems for a while now.  I mainly use Chat GPT but I have tried a few others to see how they compare.  My executive summary is that particular systems may be better at specific tasks but ChatGPT is the best all-rounder.

However, these systems are not perfect.  I have had problems with ChatGPT failing to start handling a query when I hit 'Go'.  When I subsequently get fed up of waiting and complain to ChatGPT about the lack of progress, ChatGPT either apologises or replays my prompt back to me to confirm my request and parameter choice.  If I had not responded, ChatGPT would have done nothing.  On some systems I have had problems with the system seemingly not able to meet my needs giving me a 'substitute answer' or 'holding answer'. 

I realise that the level of 'intelligence' in an LLM-based system is not high (though they are adept at handling and presenting knowledge) so I do not expect perfection. 

But when s system (or a member of my staff) cannot meet my request for completion of a task, I expect to be told so I can make other arrangements.  If I am not told sand the task is left to 'wither' until I, or someone else, notices it has not been done I getb annoted and/or frustrated.

So, AI, if you cannot meet my request within an acceptable timeframe, just say "I give up" and we can both move on.

Like Me

 There has been a tendency since forever for companies to hire new people that are like the existing people.  This results partly from both conscious and unconscious bias but also from a 'reasoned' argument that like people will get on better so the new staff will not disrupt existing relationships snd cultures.

This is still taking place in an era where the DEI movement has never been stronger - and where most senior managers and execs have had unconscious bias training. - and would probably profess their support for increasing diversity.

I remember many years ago when studying innovation being told that: 

If you have an engineering problem and you ask an engineer to solve it, you should get a competent solution.  If you ask non-engineers to solve it, you might get an innovative solution idea which can be made to work by an engineer.

So, diversity can bring different strengths to play on problems and issues, and help you relate to different segments of the market.

So look around your organisation - and especially at new hires. Are they all out of the same mould?  If so, you might be missing out on a variety of views that could increase your capacity to innovate, grow and develop.

In The Right Job

 During the pandemic many people changed jobs - though some people also lost jobs. Quite a lot of the changes invalid a switch to a different ,location and/ or to a different industry as people sought out  jobs they thought might be secure until at least after the pandemic.

The situation has been rather like that where people get made redundant and start a new role/job/career/business, saying subsequently that the redundancy was the best thing that happened to them.

So, hopefully, n we have more people in more suitable and more enjoyable jobs. suitable and more enjoyable roles.  People who are satisfied with their job/role are generally more productive. If only the economy would grow, these people would form the basis of a mini productivity boom.

So the government and then fiscal guardians and policy-setters in particular have a very important (and not particularly easy) task to accomplish - to grow the economy before then potential of his current situation is dissipated.

If governments can do this, you might look back on the pandemic as an opportunity taken, rather than as an unfortunate catastrophe. Your business would be stronger - and you would be happier.

Think longer-term

 In the UK (snd many other Western countries) companies find it difficult to plan further ahead than their next 3 month reporting period. If their reported performance drops, so does their share price - end their value.  This makes long-term planning difficult for CEOs and Boards of Directors.  This results in what  is termed 'short-termism'

Now things are getting worse. Though many companies have signed up to various carbon neutral or net zero strategies, they must commit to long (sometimes very long) planning horizons before they will see significant change, never mind the goal of zero-carbon. 

How does all this impact productivity.

Well short-termism is a productivity drag - it does not aid revolutionary innovative thinking except that a few 'rogue thinkers' will actually arrive at previously unseen ways of making positive change which directly supports a net zero aim, while benefiting economic performance..  This also has the advantage that firms which support such thinkers and reap the benefits are also likely to anticipate developing sustainability  legislation (rather than having to react to it after the event) and be in position to lead the charge to both sustainability snd higher profits.

So, look to the longer-term; lead the movement to net zero and take the innovative steps forward.  You should get improved performance, satisfaction and success.

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 are Not Working 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. 

And think about your won orgsnisation.   Take action rsther htan simply laying blame elsewhere.

Do we trust governments to deal with 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 to do that?