Nicely written! So much to ponder and discuss... Conventional wisdom often claims that only government has these inefficient dependency structures and handoffs - and I am certainly seeing them now in my current role... And interesting to reflect on the qual vs quant data divide now, given the new tools and their accessibility, that our former beleaguered machinations on this seem silly. But I'm in the midst of an implementation utilizing a stacked, rather than waterfall, process, and I'm seeing quite a bit of misalignment that is taxing even the most talented players on the scene. The sheer volume of information and activity is overwhelming and drags on everyone's faith that there will be time to align, that it will all come together once we all get a bit further down our parallel roads... Highly skilled/experienced knowledge workers are already reporting superior efficiencies with AI tools - not the stuff we hear in the news, but the more highly specialized folks who are doing as you say, using these tools to write snippets of code to run analytics on their own data sets, fast-track their own presentation slides, help them mine specialized knowledge databases... I think your overall thesis has been about generalists amplifying their skills with these tools, not specialists. But I see specialists doing their own amplification, too.
Thanks for your comment, @lpc! My thesis is that specialists have to start behaving and looking a lot more like Generalistas, which, it seems, is what you are observing as well. The professional makeup of most orgs is by specialized role. In the age of AI, they need to start thinking and acting more like Generalistas if they are going to evolve (based on the three forces I outline: Role Consolidation, Democratization of Specialization, and AI Empowering Novices). In terms of skills people will need in the future, that's certainly up for debate. Still, I think it comes down to what we were discussing earlier: Liberal Arts, Human-Centered Design, Reading Comprehension, and Management.
A lot of "there there" you're touching on, here. ;) Much to elucidate on this front wrt Management, skills specialization vs general, education/training pathways, communication vehicles/norms/standards, and effectiveness or productivity... We are revisiting that "hybrid T-skilled" worker of IDEO articulation, which was debated in the background as to how realistic that was as a goal. "Depends on the business" was the final excuse I heard. ;) In the face of AI, I think you're asking very relevant questions that need more exploration, so - 加油！jiayou! Keep going!
Nicely written! So much to ponder and discuss... Conventional wisdom often claims that only government has these inefficient dependency structures and handoffs - and I am certainly seeing them now in my current role... And interesting to reflect on the qual vs quant data divide now, given the new tools and their accessibility, that our former beleaguered machinations on this seem silly. But I'm in the midst of an implementation utilizing a stacked, rather than waterfall, process, and I'm seeing quite a bit of misalignment that is taxing even the most talented players on the scene. The sheer volume of information and activity is overwhelming and drags on everyone's faith that there will be time to align, that it will all come together once we all get a bit further down our parallel roads... Highly skilled/experienced knowledge workers are already reporting superior efficiencies with AI tools - not the stuff we hear in the news, but the more highly specialized folks who are doing as you say, using these tools to write snippets of code to run analytics on their own data sets, fast-track their own presentation slides, help them mine specialized knowledge databases... I think your overall thesis has been about generalists amplifying their skills with these tools, not specialists. But I see specialists doing their own amplification, too.
Thanks for your comment, @lpc! My thesis is that specialists have to start behaving and looking a lot more like Generalistas, which, it seems, is what you are observing as well. The professional makeup of most orgs is by specialized role. In the age of AI, they need to start thinking and acting more like Generalistas if they are going to evolve (based on the three forces I outline: Role Consolidation, Democratization of Specialization, and AI Empowering Novices). In terms of skills people will need in the future, that's certainly up for debate. Still, I think it comes down to what we were discussing earlier: Liberal Arts, Human-Centered Design, Reading Comprehension, and Management.
A lot of "there there" you're touching on, here. ;) Much to elucidate on this front wrt Management, skills specialization vs general, education/training pathways, communication vehicles/norms/standards, and effectiveness or productivity... We are revisiting that "hybrid T-skilled" worker of IDEO articulation, which was debated in the background as to how realistic that was as a goal. "Depends on the business" was the final excuse I heard. ;) In the face of AI, I think you're asking very relevant questions that need more exploration, so - 加油！jiayou! Keep going!