Introduction: New Leadership
Traditionally, management involves resource allocation, risk management, and steering employees towards a goal. In almost all scenarios, it came down to gut feeling, experience, and hindsight. Predictive models based on the previous quarter’s performance would likely end poorly in today’s environment.
Today’s management environment relies on data in volumes that far exceed human comprehension. It is almost pointless to rely on just instinct to run a department. The goal of a modern management strategy is to guide teams in a way that uses the maximum potential of technology. The primary example of this is through the use of AI. It is more beneficial to use AI and maintain control over how it is used, instead of waiting for the next technological paradigm.
Today’s AI Management
As with any emerging technology, the conversation surrounding AI is still evolving. What practicality this technology will bring remains to be seen, regardless of the transformed potential of efficiency, streamlining, and elevated resourcing that businesses will experience.
Analyzing the Changes: AI Landscape for 2023 and Following Years
Until 2023, the use of AI in business was almost exclusively the domain of data scientists and IT departments. However, 2023 marked the beginning of the democratization of these tools. Generative AI and low-code predictive platforms gave algorithms power straight into the hands of managers without technical backgrounds.
This change can be seen in three areas:
- Ease of Use: No more SQL. Simply ask the system in everyday language.
- Embedded: AI is finally integrated into CRMs, ERPs, and other tools.
- Real-Time Actionable Insights: Instead of weeks, actionable insights can be extracted from data in minutes.
Why Leaders Can’t Ignore AI
The other options are detrimental. Efficient and forward-thinking competitors will outperform you. Your competitor will gain an insurmountable advantage if they use AI to optimize their supply chain and reduce waste by 15%, and if they use AI to predict sentiment and reduce attrition.
AI not only allows for new ways of operating in all businesses, but is also becoming a necessity to retain existing ways of operating in all businesses. Leaders need to embrace AI in their forecasting. It is a necessity for the modern business ecosystem, as the demands from boards and investors are increasing and the margins for errors are tightening.
Key Advantages of the Role of AI in Management
AI is an advanced technology, and the products of AI are highly useful when the number of potential customers, the number of possible interactions, and the number of potential outcomes, as well as the range of varied factors that are involved in the interactions that can result in a multitude of outcomes, are very great. AI offers three major advantages to managers that result in significant operational improvements.
AI: Business Management Decision Making
Traditionally, a business manager would simply react to the problems that emerged in the processes of their business. They would essentially wait until something went wrong, and only then would they begin to adjust their business processes to resolve the issue. With AI, an entirely different approach can and should be taken. AI allows business leaders to think several steps ahead of a situation and intervene before a problem occurs. AI provides the ability to predict future outcomes based on the patterns of past outcomes.
An example of the application of AI analytics is in the case of a sales director. Traditionally, they would make an educated guess as to which of the sales leads was most likely to convert to a customer. With AI, instead of making an educated guess, the director can use a sales lead scoring algorithm that reviews thousands of interactions and then ranks the leads based on the likelihood of conversion. The result is that the sales director avoids wasting efforts on low-probability leads and instead invests his efforts in the leads that are predicted to yield the greatest return. AI can provide the evidence for the director to justify his sales efforts, whereas before AI, those efforts could only be justified by belief.
AI for Business Managers: Increased Productivity and Efficiency
Business managers are responsible for the administration of many projects. A significant portion of their valuable time that could be spent on strategic initiatives or coaching activities is consumed by the administration of tasks such as scheduling, compliance reporting, and allocation of resources.
AI agents are capable of managing complex multi-stakeholder meetings, transcribing and summarizing meetings, and automatically spotting compliance issues in expense reports. By removing some of the bureaucratic friction of management, AI allows leaders to shift their focus to higher-order problem solving.
An Unlikely Application of AI: Human Resources and Team Management
AI’s role in Human Resources and team management has been somewhat of a surprise, but high employee turnover is incredibly expensive. Most employee burnout signals go unnoticed, and their absence is often the first signal of a deeper problem.
AI-enabled tools can pick up changes in communication and workflow patterns as well as output metrics to pinpoint areas of stress, disengagement, and burnout. This triggers the opportunity for a manager to offload or reassign tasks to relieve pressure before the employee collapses. It acts as a safety mechanism to preserve the company’s greatest resource: its people.
A Successful AI Leadership Plan Requires More Than Software
Purchasing software is a straightforward process. However, developing a comprehensive management plan that leverages that software is where the real challenge is. Success is based on alignment and context in the marketplace.
Aligning AI with the Organization’s Strategy
AI should not create business opportunities in the absence of real business needs. A sound strategy should always start with the business goal rather than the the means to the end. Leaders should think along the lines of: “What is the main constraint in our ability to scale?”
If the constraint is around how much support can be given to customers, then the AI strategy is the focus of automated triage and chatbot. However, if it is around how volatile the supply chain is, then the focus shifts to logistics with prediction. The KPI should guide the development of the technology and not the opposite.
Strategically Integrating AI: Moving Past The Hype
For integration to happen, the focus should be on managing the change. This requires auditing processes currently in use to establish the areas where AI can be of value, and those where it would be counter-productive. Outdated processes will create more of the same if AI is simply added to the processes. A strategic integration will in many cases involve the manager putting order into the data and processes beforehand so that AI processed clean data, and not the mess that it would otherwise be.
The Competitive Environment: Managing Norms with AI
The table below shows the extent of change in the norms of management as a result of adopting AI.
| Operational Area | Traditional Management Norms | AI-Enhanced Management Norms |
|---|---|---|
| Forecasting | Linear projections based on past quarters. | Dynamic modelling accounting for market variables. |
| Risk Management | Passive damage control after problems arise. | Active anomaly detection and reporting. |
| Talent Oversight | Annual appraisals and evaluation. | Continuous appraisal and evaluation, along with gap analysis. |
| Resource Allocation | Annual budgets are rigid. | Assessment and evaluation of current demands are flexible. |
Essential AI Skills for Managers
Managing in the era of AI does not necessitate coding skills, but requires acquiring alternative form of skills.
Data Literacy and Algorithmic Understanding
Managers need to comprehend the existence, origin, and biases of data, along with an explanation of the algorithms in use. For instance, an AI pricing tool that suggests a price increase requires managerial scrutiny. Is the recommendation based on a data set that overlooks the ongoing corporate public relations crisis? Algorithms should not be followed blindly.
Emotional Intelligence in a Tech-Driven World
Emotional intelligence (EQ) is the human element that machines cannot do. AI cannot negotiate a sensitive partnership, calm an angry client, or mentor a struggling junior employee.
Future managers will spend less time on spreadsheets and focus more on employee relations. Becoming an empathic, persuasive, and change-leading individual will be the greatest asset a manager will offer.
Managing AI Ethics
AI brings new issues in privacy, discrimination bias and accountability. If an AI hiring tool discriminates against a specific group, the manager is liable, not the software vendor. Leaders must steer AI use with ethics, transparency, and fairness. This means defining boundaries for data use and ensuring a human is in the loop for critical decision making.
Integrating AI in Management: A Step-by-Step Process
A big change, such as adopting a new style of management which is enhanced by AI, involves many small steps.
Determining Readiness for AI
Before applying AI, you need to address the current state of your data. Is your data fragmented across departments? Messy and chaotic? AI requires clear, easily accessible data to work. Often, the first step is data governance: cleaning out the digital files. It will be the first step you take instead of automation.
How to Build an AI-First Company Culture
An AI-first culture is about empowering employees, not replacing them. Leaders must communicate that the aim is to eliminate drudgery, not jobs. Foster a culture of experimentation. Give teams the freedom to trial AI tools for specific tasks and report back. When employees see AI easing their workload, adoption is more likely to be organic.
Overcoming Resistance and Fostering Adaptability
Resistance is a natural response to fear of the unknown. Invest in training to ease that fear. When you bring in a new AI tool, provide training to help employees use it effectively. This shows them you are investing in their future relevance.
The Future of Management with AI
“Man vs. Machine” is not a correct description of reality. The future scenario is “Man plus Machine”.
Collaboration vs Replacement: The Human-AI Partnership
The most successful companies will be the ones that harness the most effective feedback loops between human judgment and machine processing. The AI provides the probability, and the human provides the context and ethical concerns.
Future of Management: AI vs Human Responsibilities
| AI Responsibilities (The Engine) | Manager Responsibilities (The Steering) |
|---|---|
| Data Processing: Adding and analyzing millions of data points at once. | Deciding which data points are important for the strategy. |
| Problem Solving: Identifying various possible approaches. | Choosing the approach that fits the company’s values. |
| Creativity: Thinking of different alternatives and versions. | Adjusting to create the final picture. |
| Team Dynamics: Checking and monitoring the workflow. | Solving problems and giving guidance. |
The Importance Of AI For Modern Business Leaders in the Next Decade
In the next 10 years, there will be no difference between ‘tech companies’ and ‘non-tech companies’. Every business will be data oriented. Leaders who learn AI now will be better prepared for the future. They lay out the infrastructure for speed and flexibility, which will set the winners apart in the 2030s.
Conclusion: Taking the Leap into AI-enhanced Leadership
The application of Artificial Intelligence in business is not awaiting a passing trend. It shows a primary change in how to build and capture the value of business. The current and the future leaders have one clear choice: To gauge the tools which reduce ambiguity. By carrying out the extremely complex task which involves the computational efficiency of AI, together with the human element of nuanced decision making, a manager moves from merely executing operational activities to problem solving in an orchestra of a value creating system. It is not the adoption of AI that poses the greatest danger for managers; it is the danger of falling behind without having the opportunity to catch up because they waited too long.
Frequently Asked Questions (FAQ)
AI’s biggest contribution to management is increasing the level of efficiency associated with human decision making. AI also removes the burden of certain administrative tasks by automating those tasks. AI can process trillions of data points and can find trends and patterns otherwise unseen by the human eye. With these skills, AI allows managers to spend their time on more important roles incorporating higher order functions (and strategy) of managing people.
While AI is able to replace managers, it is not able to replace business managers. In fact, the only managers that will be replaced by AI will be the managers who do not use AI. While managers need to use AI to replace themselves, managers need to use AI and managers need to be able to manage (with) AI. The role of managers is and will continue to evolve while also encompassing the skills of soft (or interpersonal) skills, strategy, and more.
To manage AI systems, it is not necessary to be a computer scientist; however, having the following skills is important: data literacy (being able to distinguish different data types and the data’s influence), algorithm comprehension (being able to determine AI-generated data), and AI ethics (bias and privacy management). With the advancement of technology, automation will leave people management with emotional intelligence as its most important component.
The former strategy is based on historical data and annual planning. In contrast, the AI leadership strategy is forward-thinking and uses predictive data and real-time analytics to develop strategies while mitigating possible risks through predictive modelling. This is more focused on the use of data for flexibility through constant change rather than the same execution over and over.
Without training, AIs create “black box” systems where understanding how the AI came to that decision is impossible. This will lead to fabricating and reinforcing data bias, breaches of privacy, and, possibly, violence committed against the management. Furthermore, without proper training in change management, the use of AI will create anxiety and resistance, which will affect the culture of the organization negatively.
