Data-driven decision-making solutions: Technologists AND our Life Sciences / Healthcare Partners see the Redline speeding in the right direction, why are so many of the train cars empty?
Making choices with the brain and evidence instead of the gut and experience requires more than innovative software, it is crucial that there is a mindset change as well. And this change will be harder for some than for others.
How do Technologists ensure that in sowing the seeds of the digital future we don't sacrifice our empathy, our ability to acknowledge the inherent value inbued all of us.
There is a fundamental basic human need for resources, and a universal desire to productively contribute to society, the creeping vines of Data-Driven Decision-Making solutions, driven by Artificial Intelligence and/or various Machine Learning methods, will undoubtedly choke the ability for some people to access resources, to contribute to the world.
There are at least three fires burning that we have some ability to bring under control.
1) Companies delivering cutting edge insight generation tools need to respect and recognize that these solutions will render many traditional roles redundant.
After the implementation of “Awesome Future System 3000” Susan will contribute less relative to Sally. And the system will contribute 10x of Susan and Sally combined. I, personally, have no idea how to address these valid fears. Does anyone have an idea you are able to share? With regards to implementing enhanced data-driving decision-making models, do you see that that the life sciences/healthcare industry is leaning into the efficiency and margin benefits (thereby validating individual’s concerns of redundancy) OR do you see companies looking to increase top-line growth (which when well positioned and explained could potentially solve the issue)?
2) The “Insights Arms Race” among technologists is getting out of hand. And a sizable percentage of it is non- differentiating verbal nonsense that is confusing and alienating.
Use terms correctly and with consistency.
Have a simplified glossary for your client audience.
And please don’t pitch a product as delivering Artificial Intelligence if underlying capabilities are driven with Machine Learning. Doing so will cost you the most critical segments, such as "growth-minded business decision-maker", ask they will never champion a concept that they can't grasp, at least at the top level, themselves.
3) Even the most humble product development teams recognize the intellectual power and social influence of their colleagues and network.
Surely we must be able to develop/modify some approaches that can be leveraged to address the "Great... what the heck am I good for now?" that is question creeping into the mind of so many professionals.
The time is now. Machine Learning/NLP/Machine Visual Learning tools have become standard expectations in most industry domains. And while the widespread realistic application of AI is a ways off, maybe it isn't... I mean, if it works the way it very well could - some tipping point TBD be all it takes collapse timelines as we know them.
A rising tide raises all ships, let’s work together on this.
Good Luck DeepThinking, societ depends on it.
Rebecca
Great post!