Recently surveyed executives anticipate a 20% to 30% improvement in their company’s productivity with the application of Artificial Intelligence. Right now, we’re seeing the harvesting of the low hanging fruit; marketing, customer service (not a great place for AI, by the way), and a seemingly unending myriad of administrative tasks are being impacted by AI. These are kind of “one and done” initiatives that may save costs, but don’t necessarily do anything for productivity...or innovation.
For companies to fully realize the promise of AI, they’ll need to adopt the technology in the form of human/machine teaming, resulting in augmented intelligence. This isn’t just my opinion, it’s drawn from Mercer’s Global Talent Trend, 2024 Report.
For example, in the Medical Device industry, we’re seeing AI being integrated into systems to anticipate the need for preventive maintenance or calibration needs. Having launched STERIS’ Scientific Service business unit in the late nineties, I can attest this is no small accomplishment. It will save healthcare systems money in the short and long run. Maintenance is teaming with AI.
Wherever there’s a data stream controlling or supporting a medical device, there is now an effort to capture and build Big Data sets for AI mining. This could have a significant impact on evidence-based care. That’s an example of healthcare providers teaming with AI for improved patient outcomes.
So, we can see how medical technologies are using AI to build out features and benefits of their devices, but I’d like to point out where the truly transformational opportunities lie for companies themselves; in terms of collaboration, innovation and accelerated valuation growth.
I’ll stay with medical device development as our example. There are areas involving complex, engineering calculations that could benefit from human/machine teaming:
I can see applications in Design for Risk Management Throughout the Critical Chain (actually, throughout the entire program management process).
I can see applications in Design Failure Mode & Effects Analysis (risk management).
Rapid prototyping, from CAD/CAM modeling to 3D printing, could benefit.
So could things as complex as Finite Element Analysis, early modeling for Verification & Validation, accelerated aging models, etc.
There’s another byproduct of human/machine teaming. You know all that institutional knowledge that’s been running feral throughout your organization all these years? Yeah, you can finally capture and leverage that strategic knowledge in new and innovative ways.
One of the major takeaways from the Mercer report was, in order to adopt and optimize human/machine teaming, you first need to address the persistent people issues within the organization. It is imperative that company leadership prepares their workforce for this transition. Laying AI over existing silos and fiefdoms isn’t going to get you to the promised land. You need to capture keen insights into where your people are today, in order to lead them forward into tomorrow. How work is being done, and who is doing it.
In speaking to an AI expert I deeply respect the other day, he commented, “Implemented properly, there’s no user adoption in the traditional sense. You’re alleviating tedious burdens and enabling knowledge workers to thrive.”
© 2024, Performance Transformation, Inc.
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