The Great Convergence Of Thought And Action At Point Of Work

The Great Convergence Of Thought And Action At Point Of Work
Summary: An organization’s culture will face a shift as a product of convergence when emphasis requires embracing innovations that impact workflows and often accompany the integration of digital technology. Digital transformation comes to mind, and unfortunately with the not-so-good news of often half-failing.

Convergence Is Happening With Us...Or Without Us

"Great" things are popping up lately, like the Great Resignation, the Great Retirement, and the Great Reshuffle as three examples. I am not swayed by the hype because these all point to one common denominator—change, and the need to lead it. It is my opinion that we are seeing the side effects surface in the form of a "Great Convergence" of thought and action forming influencers that directly impact culture. In other words, resignations, retirements, and reshuffling within and among organizations are driving the need for a strategic rethink and adopting tactical actions that are agile and responsive to the need for a treasure trove of opportunity: reshaping, upskilling, and reskilling to optimize the workforce at the point of work. Convergence is happening with us or without us. What is converging in our world of L&D and how should we manage it? How does it impact culture?

If you follow my blog, the concept of point of work has been the subject of many posts and carries an important link to the concepts of convergence. I say “concepts” in the plural because there are a number of variables undergoing the impact of convergence either directly or as a collateral by-product. The opportunity for Learning and Development (L&D) therefore is that there are multiple influencers to consider, not the least of which is embracing a strategic rethink. As a result, new innovative solution potentials present as many challenges as viable solutions. This short article will hopefully plant some seeds of thought around a key question: what are the faces of convergence and what do they produce as opportunities to think and act differently?

Learning Culture Of Change

An organization’s culture will face a shift as a product of convergence when emphasis requires embracing innovations that impact workflows and often accompany the integration of digital technology. Digital transformation comes to mind, and unfortunately, with the not-so-good news that upwards of 80% fail. The technology does not fail, we fail. We fail to accomplish leading transformational change effectively. Unfortunately, we seem to have mastered only the first of the four phases of transformational change.

  • Deployment
    We deploy new training. IT deploys new technology. We train for GoLive, cut the ribbon, and move on to the next training project. What we seem to fail at regularly is the second phase that follows.
  • Implementation
    Implementation happens at a new ground zero of point of work. That is why assessing the point of work is less deployment and event focused, and more post-GoLive implementation, and focused on change insurance. And if we fail to implement, we fail to reach the critical mass, which compromises reaching the third phase that follows.
  • Adoption
    Adoption represents that time and place where solution utilization is accepted as best practice and regularly and effectively applied by the user population. The final phase following is often overlooked as well.
  • Sustainability
    Have protocols been established and integrated to ensure the change adoption is not just a temporary flash in the pan? Have we created flexibility and resilience into the process of change to endure the dynamics of change? Are we equipped to keep the change alive and agile enough to respond to ongoing business fluctuations?

I submit there is a significant phase that should always precede deployment: discovery. Without performing that step of completing due diligence, we are left with responding to requests of realities that are often different throughout the leadership cascade as we head downwards to the workforce's points of work. This knowledge is essential for successful transformational change.


We too often default to responding to requests without performing due diligence as to what prompted those requests. We should accomplish due diligence specific to answering several pivotal questions:

  • What needs to be achieved?
  • Why does it need to be achieved? What is a driving business need?
  • Who engages at all levels to ensure achievement?
  • What are the current states of leadership readiness, workforce readiness, technology readiness, validation readiness, and monitoring readiness?
  • What are the measurable results we need in order to validate and monitor achievement?
  • What are the underlying root cause factors preventing change from being achieved?

Again, what I am suggesting is the addition of a fifth phase to transformational change—discovery. We must look beyond requests for pursuing change, which is often based on assumptions and hypotheses despite being well-intended, and uncover root causes behind the motivations that drive the request for change. Prioritization should then follow to ensure we start small and scale. Omitting these steps of "discovery" can spell failure to optimize something as complex as digital transformation, and they are at the core of transformational change leadership.

Do these phases impact culture? Absolutely they do because we cannot launch into an extended journey (like digital transformation) as a series of deployments left to flounder after deployment, and neglect the momentum to gain critical mass and strategic thinking to include all subsequent phases of change. I used digital transformation as an example; however, any project of any size should experience all five phases of change for the sake of consistency of expectations and process workflows.

Is this convergence? Absolutely it is, by virtue of a strategic rethink keyed on workforce performance converging with the tactical demands of optimizing workflows throughout all five phases of change. Embracing a repeatable change model sets organizational expectations and supports consistent best practice application regardless of size and complexity.

Workflow Learning

Thanks to the pioneering work by Bob Mosher and Conrad Gottfredson of APPLY Synergies and their well-known "five moments of need," we see a growing acceptance of workflow learning. This is a dynamic real-time convergence of learning with work. In fact, the convergence of learning and/or support within live workflows is enabled by leveraging agile design methodology and delivered via digital adoption platform technology solutions. Workflow learning exemplifies leading transformational change because all five phases of change are addressed, from initial "discovery" (including complete due diligence), to "deployment" (of learning and support solutions), to "implementation" directly into workflows at points of work (at specific moments of need), to full "adoption" (via supporting and refining best practice performance), to "sustainability" via protocols designed to maintain and adapt agile solution content (responsive to changing business conditions).

Artificial Intelligence (AI) Integration

Recently, the buzz around AI is surfacing frequently. To many, AI is a mystery but it comes into better focus when we consider specific applications where it can be used and controlled. Augmentir has done some groundbreaking work in the manufacturing sector with purpose-built AI in conjunction with performance pattern recognition within connected worker performance data at points of work, yielding actionable analytics to enable targeted learning to upskill, reskill, and offer performance support solutions within the workflow.

The targeting is not limited to workflows and/or processes, but through the added power of AI, data points can be captured within workflows at the step level and by individual workers. This level of granularity can produce more data points than it is humanly possible to analyze and give too much data to easily extract patterns that can optimize performance and enable effective upskilling and reskilling within workflows specific to identified workers. This is a convergence of data analytics and purpose-built AI technology.

AI-driven data convergence enables yet another collateral convergence of informed learning and support design from the multiple data point granularity of worker performance results at the point of work. What L&D team would not want to know who needs help, and when and where in the workflow they need it? What L&D team does not want the visibility to investigate why the employees need the help they do and find out what the solution or refinement should be?

Closing Thoughts

This is an exciting time to be in the L&D discipline. I believe the call to action has become a business imperative for the many faces of convergence, with the most prominent being the urgent demand to be responsive to business risk converging with the demand for optimizing agile, measurable, and sustainable workforce performance at the point of work. This business convergence is driving the need for performance consulting disciplines to converge with the operational stakeholder population with the capability to assess performance at point of work and serve as liaison (still more convergence) with L&D design, development, and delivery capabilities as well as with other functional resources like HR-OD, process improvement disciplines like lean six sigma, and IT technology resources.

A parting word of caution: do not allow our (L&D) readiness to fail to materialize and force us into relying upon the best intentions of our requestors to influence our solution outcomes. Trust but verify. Discover. We must be prepared to shepherd transformational change as a core deliverable of our job function and we must be fully informed to do so effectively.