As an executive with an influence over whether your company implements AI and which projects it embarks on, there’s a lot of pressure on you to be successful. The future of AI within your company could rest on you on how your chosen projects perform.
The facts are that not all projects succeed, so even some of these carefully chosen projects will need to be shut down, but this can be hard to recognise by the project leader when they are so heavily invested in the project’s success. It is, however, vital to be able to determine when to pull the plug — in the spirit of Silicon Valley, failing fast is paramount so that success can be harvested elsewhere.
Research by McKinsey & Company found several companies taking a similar approach toward identifying failed projects by using some form of external judgement to guide resource allocation decisions — transparency in this process being key to avoiding political conflicts when dismantling a project.
The core idea among various companies was to change the burden of proof. Some did this by creating a job role to objectively hunt down failing assets. Others used a ranking system to assign projects as;
When a project is noted as underperforming or lands in the "dispose" category, it is then down to the project leader to demonstrate potential project revival, or accept the ruling.
This approach eliminates the emotional attachment to projects and the bias that is often felt towards the loss of the resources that have been poured in thus far and instead brings focus around to whether or not the asset can be profitably reformed.
We believe that it is important to create a process like this within your organisation to make it easy to review whether funding is being sensibly allocated and to be able to identify when to walk away from a project.