The Two Speeds of An Organization

Having spent close to three decades working with various types of organizations (Retail, CPG, Business Consulting, AI Consulting), one feature that has consistently stood out is the two speeds at which most organizations operate.

While there is always an urgency at the Board or C-suite level, the middle and lower part of the organization moves at a pace which is a step slower. Why does this happen?

The higher echelons of any organization are more attuned to external signals like market and industry dynamics and financial commitments to the Street and shareholders which is usually a quarterly ritual. Theirs is a hard-nosed numbers-based approach to meet or exceed targets and thus get to the win-win of increased market share and higher shareholder value. However, in order to achieve these twin objectives, there is an inherent expectation of nimbly adapting to shifting market and other external factors. When the executives see the broader organization not moving at this level of agility, there is exasperation followed by an acquiescence of the ground reality often rationalized by phrases like “culture”, “ways of working”, “let’s take everyone together” etc. It doesn’t have to be this way.

The middle and lower echelons of the organization are the engine that drives a company. These are the people on the ground, on the shopfloor, in the warehouse directly contributing to the revenue and profit of the organization. A majority of these people have well-defined operational jobs consisting of a set of activities and a certain cadence associated with them. In other words, there is a comfort in familiarity and regularity of their daily routine that, over the years, gets embedded into their psyche. Just like any human, any change or disruption to this routine will encounter some level of resistance that is proportional to the degree of change. This is no different than our personal lives where most of us are creatures of habit and averse to change.

Over the years, I have seen this speed gap widen across different levels of the organization as the pace of market changes has quickened and external shocks have become more common and frequent. Attempts to mitigate this gap have manifested in actions tantamount to window dressing which lasts one CEO cycle to more thoughtful organization changes that promote experimentation and embrace failure as an option.

The meteoric ascendance of AI in the last few years has compounded this speed gap with boards and C-suites being bombarded with messages that are both transformative but also have an existential undercurrent. In some cases, this has led to a FOMO mindset that is driving a “leap before you look” attitude which leads to AI sprawl with little value to show for it. Media is rife with examples of corporations that ploughed in hundreds of millions of dollars in the last couple of years only to take a step back and rethink their AI adoption strategy.

So how does one narrow this speed gap effectively without causing heartburn at either ends of the organization? While “change management” is a much bandied (and has often led to the unintended consequence of “changed management”!), increasing organizational speed requires a more deliberate approach that focuses on how work actually gets done. It requires an understanding of the tasks that each employee does and how do these tasks interact with other employees’ work, systems and external entities. Before anyone cries out BPR (Business Process Re-engineering) or process modeling; let me make it clear that there are good parts to both of them. You can’t effect change without understanding the underlying process and the impact. This is painstaking and deliberate work but one that is essential for identifying opportunities where the organization can be made more agile or efficient. Using technologies like Agentic AI that is already available (& evolving rapidly), one can semi-automate creation of most of an organization’s critical processes and human and systems interactions. This will allow the executives to simulate the effect of changes and decisions before taking one. While technology may never be able to model the human element of a process or job like resistance to change, it would offer an objective view into the promise and the peril of change.

As to how organizations address the prevalent fear of AI related job insecurity rather than proffering platitudes like “AI augmenting workers”; that is a separate and a deeper discussion that will require a fundamental relook at how a “job” and “worker” is defined in the future. This would draw inspiration from the foundational precepts of cybernetics to encourage a policy-level rethink for organizations and governments alike. AI will, in all likelihood, result in significantly higher efficiency. Whether and how the benefits of productivity improvements are distributed for the greater societal good in a capitalist society is a complex, multi-disciplinary discussion that has to happen sooner rather than later.