AI may not be a productivity revolution

There have been quite a few claims over the last five years that artificial intelligence (AI) will result in huge gains in productivity.   These huge gains don’t, though, seem to be arriving any time soon.

When one looks back at the claims, many have been put out by AI start up companies keen to laud AI and their product in particular. 

Very few of them, however, have hard data of productivity measurements to back up their claims. 

I would expect a start up to have confidence in its product but after a year or two in the marketplace, I would expect confident claims to be replaced by case examples with measured, preferably verified, data.

Until we see such case examples, we have to take the claims with a pinch of salt. We can hope for a productivity revolution, but should expect productivity evolution.  

The Importance of Analytics

We’ve talked quite a few times about measuring productivity - to highlight (relative) inefficiencies, to set benchmarks and targets for improvement - but mainly to understand just what is happening in a process.  We measure throughput, quality levels, error levels, downtime, waiting time - anything that affects overall productivity. 

Where possible, we try to use measures that already exist for some other purposes - to avoid the extra cost of a monitoring/measuring regime. 

Now, life is starting to get a little easier. Many modern processes have some form of analytics built in -  but it is surprising  how many firms do not take advantage, usually because they do not have an overall measurement plan into which the analytics can be incorporated.

So next time you invest in new plant or equipment, makes sure you know what analytics it can provide - and make sure key employees are trained in how to use - and interpret - these figures.  

If you make the data part of a wider plan to measure and monitor performance and productivity, so much the better.  Your data analysts just might become as important as your process engineers.

Feeling Good is Not Enough

Many of us use multiple messaging and productivity platforms in our work - Zoom, Teams, Slack and so on. Quite a few also regularly drop into Twitter or Facebook.

Each time we read an interesting post or contribute to a discussion  we get a little feeling of satisfaction. We are participating and contributing. We feel good. 

However, we may not be achieving. Completing a task is not the same as producing an appropriate outcome. Completing the task becomes the goal instead of the means to achieving a goal.

So beware of simply feeling good, satisfied that you have done something.  Try to measure outcomes, not outputs.

To do lists don't tell you much

Many people use ToDo lists and apps to keep track of their outstanding tasks.  

There are also lots of apps available, with different flavours of operation.

However, you need to remember that  a ToDo list (even one contained within a sophisticated app) is just that  - a list of tasks. It has no understanding of priority or urgency and certainly no knowledge of how a task should best be carried out.

So, you need to apply all of this extra, external knowledge to the tasks in the list if the list is to prove useful. Don’t expect to become more productive by simply adding tasks to your ToDo list.  Even AI can’t help here - it takes good old, human knowledge, experience and judgement to properly prioritise and organise. So, Think.  Plan. Shape your ToDo list to match that thinking and you should get good results.  But don’t thank the app; thank yourself for thinking through the issues and addressing the tasks according to established criteria.  And remember, the list is always flexible; it can be changed as circumstances change to affect priorities.

ToDo lists are good; but only when supported and backed up by a thinking human being.