In The Age of AI, Management Modeling Is Vital

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. Managers are so engulfed in operational-level tasks that they have little time to influence critical business decisions. With AI dispassionately and objectively delivering consistent outputs, there is a critical need to enable the “human in the loop” to apply more judgment-related skills to validate AI outputs. HR practices, learning management and developmental training have traditionally emphasized technical skills. Now judgment-oriented skills, which include creative thinking and experimentation, data analysis and interpretation and design thinking, are required among managers to succeed in the future.

Practice 3: Implement an Inclusive Culture to Treat AI as a “Colleague”

Compared to employees, AI is scalable, more efficient, and predictably rational. But, change a few parameters and the same AI will be clueless. Human workers in this scenario are be versatile enough to adapt to the changing conditions. It’s important to understand that AI needs humans as much as humans needs AI.

Take a manufacturing or retail setting as an example. Tracking production schedules, inventory management, customer orders and optimal distribution routes almost at real time, for sure, is something you would be gladly hand over to the intelligent machines to manage. However, drafting a strategy for your long tail inventory items remains unmistakably human. Simply put, AI’s role should be seen as assistive intelligence to augment employees’ capabilities, not to replace a person’s judgment.

The key to successful human-machine collaboration is to treat AI as a capable colleague. And for that to happen, the machines will have to explain why they predicted what they predicted in order to gain trust.

Despite the hype around AI as a panacea, AI solutions are narrow and solve only those problems for which they were designed and trained. Managers are mistaken if they believe they can successfully apply AI solution into any or all of their processes or tasks. To address specific problems, AI requires the collecting of data, selecting the right methodology, applying the right algorithms, and applying enough end-user validations through feedback loops. Only then will the final tech solution be ready.

It is often seen that metrics drive behaviors. AI is disruptive and, at the same it is data hungry. For AI to unleash its true transformative potential, companies need to demolish organizational boundaries and data hoarding mentalities. New KPIs need to be established to promote collaboration, information sharing and experimentation. The traditional business metrics covering top-line and bottom-line will probably stay, but the real measure of the business now shifts toward how well a company can consistently adapt to changing business conditions and technology-led disruptions. Individuals or teams should be rewarded if they demonstrate learning and decision-making effectiveness and reach beyond the organization for insights and ideas.

In conclusion, AI is one of the transformative technologies that transcends typical boundaries. It effects internal operations, external relations, and even product development. When everybody has access to technology capabilities, it is the AI-embedded management model that will differentiate between winners and losers.

Read more: How To Accelerate Innovation In The Age of Disruption

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