A prerequisite for value generation through knowledge is the proper identification of an employee’s comptetencies. However, this is only the first part of the deal. The actual deployment of these competencies can only be facilitated when the management is listening to the ideas of the employee and allows for changes.
Any costs created by acquiring new and fresh competencies may be in vain when dreadlocked habits inhibit change and even ignore work already done. If the work is not leveraged due to a resistance toward the new approach, sunk costs are created. Additionally, frustration and demotivation might arise and inhibit further innovation introduced by the concerned or other new talents.
When you’re good at something – wouldn’t you want to try to perfect it furthermore to please yourself and others? Wouldn’t the pursuit of new challenges be the most important fulfillment possible to achieve? Where would you meet these new challenges: in your old job where you have been for 2+ years and where your work has become routine (and thus, masterly efficient) – or at a new job?
Probably, the highest risk factor for losing talents is their boredom. Additionally, the talents will very likely already know that they are welcomed pretty much anywhere (due to darn good job offerings). Stability and risk-avoidance is not a good argument for convincing a talent to stay.
What to offer?
What can you offer as an employer? Either more money or more challenging work. The latter is often not possible and also often not the most efficient and value-adding alternative for your company – a talent doing routine work would be way more efficient. Thus, you offer more money.
You also offer more money because you fear change. You fear that you might not find such a good talent again or that it would take a new employee too long to become as efficient or creative (if ever).
The problem with offering more money is that you’ll easily hit a number with the remuneration that might even make the best talent unprofitable. You build a bubble – the talent is overpaid. Additionally, you increase the market value of similar talents because a talent will expect the next company to pay at least the current oversized remuneration. And since that company has probably just lost a talent, they are willing to pay it (that oversizing of incomes and reputation is well documented in the movie 39,90).
But isn’t paying more money just a prolongment of the problem that the talent will soon be bored again? Even with more money? This will become a vicious circle until nobody can pay for these talents (and also pretenders by then) anymore and the market turns into either a winner-takes-all situation – one company has got the money for all talents – or talent remunerations burst and level out again with the larger companies having survived it.
When talent remunerations have leveled out again (still higher than for non-talents) the best strategy is probably to let talents go – because there’s always a talent in another company being bored and willing to leave and come to your place. Remember: they will leave anyway. So instead on focussing all too hard on retaining talent you should also spend quite a lot of time on scouting and obtaining talent. Get to know the common talent remuneration in your industry. Pay it – but only for real talents. Don’t overpay but don’t become inattractive for talents either.
An alternative, albeit very negative and inhumanistic strategy would be to convince the talents that they are not talents and every once in a while to assign unaccomplishable tasks. Is this a productive long-term strategy?
Nice idea. IBM’s Institute for Business Value and the Human Capital Institute did the math reports Seeds of growth. What they determined is what you would expect: invest in talent management… Surprising: Especially the small companies seem to have an advantage in talent management (employees were asked).
I like this “number crunching” as the book Super Crunchers puts it. Very inspiring. Who wouldn’t love to do some “correlating”? It’s totally logical, seemingly easy and yet very impressive. I guess the most difficult thing is to get all of the data. Get it semantical. Get enough data for a representational meaning.