AI-driven data convergence enables another collateral convergence of informed learning and support design with multi-datapoint granularity or worker performance results at Point-of-Work. What L&D team would not want to know who needs help…when and where in the workflow they need it…visibility to investigate why they need it…and what the solution should be?
When a worker leaves the organization, some companies complete routine exit interviews. Then what happens to those exit interviews? Are the interviews HR checkbox events to be filed away in a personnel folder? Are any actionable data points leveraged outside of HR to affect change? Does this practice represent a pattern that we do not fully address? This leads me to some potentially radical thoughts…
Purpose-built Artificial Intelligence (AI) represents a bridge between high data volumes and actionable design attributes. Digging into the data will give us visibility to the things listed above and at the individual level. The result is the creation of Prescriptive Performance Support that is data-driven workflow instruction to the individual worker in the workflow and moment of need.
When it seems like we’re pushing a stone uphill over and over…maybe we should stop…leave the stone where it is… go up to the top (of the organization) and confirm if a stone like we’ve been pushing for years is even needed. We must change the conversation if new thinking will ever emerge. More specifically, enable a strategic re-think…to drive our efforts to enabling and sustaining measurable performance results in the workflow and multiple Points-of-Work.
Point-of-Work is a Destination where task-centric and role-specific workflows exist. The brokenness is what happens at that destination, not the destination itself. In fact, most all workflows may represent a number of Points-of-Work…a number of sub-destinations…where actions and decisions to take action must happen effectively and efficiently to generate value in the form of measurable results. A destination cannot be viewed as broken…but the conditions at said destinations (Point(s)-of-Work) represent the collective attributes that enable or restrain performance outcomes.
The key is consistency of Discovery from the top of the organizational hierarchy to the bottom. Comparing results of a cascade down through the hierarchy provides source data that can facilitate an Alignment Conversation from dependable, validated reconnaissance. Nobody dies if we neglect to negotiate a discovery cascade, but how many precious hours and resources do we spend to build an ineffective one-size-fits-all solution?
To this camper, it is clear the data flow from the Point-of-Work to Informed Insights critical to drive effective Decision Making must be two-way. Obviously, we need data from actual execution activities to track Point-of-Work effectiveness, but there is something else we need to inform and optimize – Workforce Engagement.
Despite thirty plus years in the corporate learning space I am sensing an overwhelming demand to reiterate the critical nature of shifting emphasis to a post-training ground zero – Point-of-Work. If you’ve read any of my postings of the last several years on the blog at my website – Living in Learning – you will […]
Given the “sky-is-falling” lament due to the Great Resignation it makes one wonder how epic is our collective failure. Are we failing or is this a long overdue cross-industry correction? Are we embracing this self-inflicted down-sizing? Are we hearing the urgent message to do more with less? Are we recognizing we need to do a better job with enabling stronger engagement with our remaining workforce? Funny thing how failure can be an urgent call-to-action to better connect our existing workforce. Connect them to what?
Over the last several years, I have written about the concept of convergence; specifically, convergence of learning with work. Emphasis was placed on assets Learning & Development (L&D) produced to enable performance support closer to actual work. Workflow Learning is the more current manifestation of convergence and is a new rallying point for L&D. My fear is that there is potential that L&D may be saddled with a blind spot that will not fully optimize solutions integrating newer technologies like Artificial Intelligence (AI) directly into workflows. Does this negate the viability of Workflow Learning? Absolutely not. In my opinion, it postures an additional critical outcome beyond training that expands our headset and our scope – Workflow Performance.