In The Age of AI, Management Modeling Is Vital
It’s a bit of a misnomer to say digital transformation leads to new business models. In most cases, the business models are the same; the delivery models or execution of those models are all that have changed. Truly new business models are hard to come by, and even if a model is innovative, it is extremely difficult to gain long-term advantage because competitors are adept at responding quickly. Consequently, companies require new forms of competitive advantage that are enduring, sustainable, and hard to copy.
A winning business model is central to the creation of every company – it’s a company’s management model that allows it to adapt to marketplace changes in order to ensure long-term success. In today’s world, there are two compelling forces requiring companies to critically review their time- tested management models.
Among all the technologies, AI is the most transformative with the potential to provide the senior management team with greater insights into how its company operates and performs. Here’s a closer look:
AI changes how often corporate goals are reviewed. How a company sets and pursues goals is no longer a quarterly or yearly exercise. Traditionally, elaborate planning exercises are conducted annually to set objectives, resulting in plan creation and goal setting. With this technology, the ongoing review of massive data sets allows senior management to respond and adapt to a company’s performance, as well as market conditions.
AI changes how tasks and activities are executed. AI will not replace employees but the technology can significantly impact the activities done. Managers were given the role of reviews, trouble shooting and course corrections. With this tech, many tasks are redefined as prediction tasks with the potential to automate anything where there are clearly defined set of inputs, business rules and measurable outputs.
AI changes how corporate decisions are made. Traditional management models call for top-down decision making. Enlightened companies created feedback mechanisms to gain insight from mid-level managers and the rank-and-file. And, this type of decision making is usually made at a glacial pace. In age where tasks are executed at machine-speed, the “human in the loop” that can slow down the process is removed, and a sanitized approach is created, replacing bias and emotions with cold data analysis.
With AI embedded into more processes, managers need to rethink how they allocate tasks, validate outcomes and, in general, how they measure the efficiency of the task execution and quality. With AI, managers need to clearly articulate machine responsibility vs. employee responsibility. The interesting area will be conflict management – how conflicts are managed when employees knowingly come in the way of machine efficiency or machines come in the way of human judgment!
Practice 2: Free Employees to Take on Higher-order Judgment Work
While AI is extremely efficient at systemized tasks, there are many scenarios where empathy plays a significant role in the outcome, and decision-making requires insight beyond what AI can derive from data alone.
