Happy New Year to all.. 2020 - seems to be an adequate place to start a conversation where AI enabled tools can be included in the workplace to work alongside as peers to help ensure fairness & accuracy in evaluation at each step of a companies major portfolio's.
Before going into the 'how', let's talk about the 'why' & how these why's are emerging now.
Let me start with an example of a scenario similar to what I saw in a couple of cases while working & discussing events at different organisational workplaces.
Consider a scenario wherein a there is a person who works with dedication never gets acknowledged - let's say in current tech world, one techie develops and application but the moment development completes the people who had no to little input in that development jump in and take the credit.
Or another scenario - wherein a techie person who solves a problem by jumping into the code and fixing it gets no credit instead they get pulled into more places where problems exist & expectation is they will solve it but the credit in the end goes to a person who is the front face of the talk.
With all the policies of fairness in place & rules against workplace ethics, still educated humans use the tools when & where to their little advantage. Many techie Jesus's die each year to let a person who doesn't do the actual work take the credit & this is not uncommon in workplace tactics. Plus there are other places where community favouritism & personal talk are given more importance than the judging someone based on the amount of work done right. One can argue either ways but business alway's suffer's in the long run & the company or product takes the hit eventually or the customers which are using the product.
This results in loss of creative productivity & eventually building a product which needs to be rebuilt but is always behind the timeline in terms of its build quality with modern trends. All this is money & experience gone down the drain in time.
That explains the 'why', now let's talk about the 'how'.
If an AI tool or assistant is put in place to measure the quantity & quality of work being done which proves to be beneficial in the long run - then a correct judgement can be made for the credit which then would probably go to the techie Jesus who was crucified - but don't worry - the knaves will eventually find out a mechanism to bug this too.
Anyways' let's target on the savings right now. So how we do this -
1. Providing code review by an AI assistant & alternating the review with human reviewers.
2. Providing a performance review using an AI assistant, let the assistant set goals and perform the review, then that review will be alternated or cross checked with human inputs.
3. Providing mechanism to monitor login-logout times and work done using the tracking mechanisms in place.
4. Let humans work alongside AI assistants to do pair programming and then decide if the person is correctly using the logic or has a long term thought.
5. Use AI analytics alongside human analytics to understand if the quality of feature makes sense in long run.
6. Use business focussed AI assistants to give insights on the long term functionality from a business perspective from time to time and change the goals or stories to accommodate the same.
In these scenarios' a technical lead would be an inspiration and would develop leaders than being just a sampler of picking up all the good things and blaming the negatives on another member of the team. Eventually we want to get to a point where the team as a whole functions in a way to be productive, some variation would always be there but algorithms which would focus on key variations suggesting bias like for example -
1. if more people are going out of the team.
2. newly inducted members in the team don't stay too long.
3. a low level of morale within the team.
would immediately trigger an alert to examine the processes of that team more deeply.
This is just one of the scenarios where going into a world with intelligent data analysis being used actively in processes would get the business to turn-around faster & excel in their areas.
But the important bit here is not to over do it...
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