Business leadership needs the most human-specific approach to achieve success. Since its conception, Artificial Intelligence has proved to be the flag bearer of automated technologies. Automating business leadership and decision making will transform the businesses forever.
With the boon of automation, business leaders can generate revenue savings up to $155,395 per year. Such is the power of Artificial Intelligence and its technological advancement. Business leadership has its challenges to overcome in this fiercely competitive era.
Most business leaders use a set of patterns to face these challenges, and there lies the opportunity to automate specific decision making through these patterns.
In some business paradigms such as marketing, AI has been pivotal in the decision making, and 67% of Marketing Leaders use an automated marketing platform for the same. So, let us begin with what is AI leadership? And then, we will discover how it can affect decision-making through patterns in decision making?
AI leadership is a cognitive approach to use algorithms of machine learning for decision making in business-related activities. In machine learning, businesses can train algorithms through three different learning methods:
With AI leadership, critical decision making, planning, and administrative activities are automated. Algorithms will use their training module to learn the data patterns to keep their business activities in context with the scenario.
Any design pattern is a set of frequently occurring problem and solution elements for that problem. Pattern language or data patterns are data sets that represent the issues and problems occurring frequently. These data also have a specific solution to the problem, which can be used repeatedly, forming a pattern.
Pattern-based AI leadership uses these pattern language or data patterns as a resource for the training of its algorithms. Algorithms use these pattern language as empirical data to learn the outcomes on the application of solution elements to the problems that occur repeatedly.
There are three distinct benefits of using pattern language for AI leadership.
A pattern language can provide a standardized structure for describing all the relevant aspects of leadership interventions in business activities. It allows induction of
A pattern language can be a shared language where all the leaders can have common grounds on interventions in business activities.
It will provide a platform for scaling of leadership interventions across scenarios and contexts. It will also enhance the cognitive efforts of incorporating different leadership interventions.
AI can be leveraged to structure several leadership patterns and code them into codified leadership practices or trends. To amalgamate AI and leadership interventions(actions), we need to understand the structure of any leadership intervention.
Let us take an example of a mobile app development process. Here there is a team of developers and a leader who initiates an intervention, and the group responds with the task performed.
We can apply these codified leadership patterns as data sources for algorithms to learn effective responses to the employee feedback or work responses for every intervention. Let us first use the supervised learning module to establish an algorithm to automate leadership intervention.
In supervised learning, the algorithm learns the pattern language with all its recurring issues and solutions under the mentorship of leaders or specifically designated leaders. This module provides full control over the outcome with adequate mentoring.
While in unsupervised learning, the algorithms can learn themselves from several codified pattern practices without any mentoring from the experts and business leaders. Here, the process becomes automated without human intervention.
Reinforced learning is all about using specific pattern language for particular scenarios and context. It allows algorithms to learn from solutions to definite problems that occur in specific conditions and how to deal with that problem.
Take an example, where we can teach algorithms to decide on the purchase of scrap steel when there is an offseason, to save on the costs. So, the algorithm will then follow the same pattern each time to buy the scrap steel according to the market conditions.
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Though there has been significant stress over the use of AI for automation of operational activities and not management activities. It has undoubtedly led to advancements in the field of automated operations and manufacturing. But AI can be of more significant help in performing business decisions.
The use of such technologies can help incorporate strong leadership skills into any business domain or market for successful automation. AI leadership provides brighter prospects for business leaders and administrative decision-makers.
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