Business decisions have traditionally depended on experience, market research, financial reports, customer feedback, and the judgment of senior leaders. Those factors still matter, but artificial intelligence is changing how leaders collect, evaluate, and act on information.
Artificial intelligence (AI) systems can process large volumes of data, identify patterns, generate forecasts, and provide recommendations in a fraction of the time required for manual analysis. This shift is already visible across marketing, sales, customer service, finance, operations, and other business functions.
For business leaders, the biggest change may be less about replacing human judgment and more about improving the information available before making a decision. AI can help executives ask better questions, identify risks earlier, compare alternatives, and respond to changing conditions more quickly.
Why Business Leaders Need Strong Analytical Skills
AI can process information quickly, but leaders still need to understand what the information means. A recommendation generated by an AI system is useful only when an executive can evaluate its assumptions, limitations, and potential consequences.
This is where advanced business education and professional development can become valuable. Professionals pursuing a Doctor of Business Administration (DBA) can develop these skills as part of their advanced business education. According to Saint Leo University, these programs can feature the following in the curriculum:
- Global leadership and ethics
- Analytics for decision-making
- Seminar in economics and finance
All these help develop strong analytical skills. Working professionals looking to accelerate their careers can also opt for an online DBA to gain 24/7 access to course materials.
The value of advanced management knowledge becomes especially apparent when AI produces several possible courses of action. Leaders still need to determine which option aligns with company objectives, available resources, customer expectations, and long-term priorities. AI can provide information, but the responsibility for applying it remains with the decision-maker.
From Intuition-Based Decisions to Data-Supported Decisions
An experienced executive may spot a market opportunity based on lessons learned from similar situations in the past. However, relying heavily on intuition can be challenging when markets move quickly or when businesses operate across multiple customer segments and regions.
AI gives leaders another source of insight. As a result, an increasing number of businesses are turning to artificial intelligence solutions.
A McKinsey & Company global survey found that nearly 90% of organizations now use AI in some form. Despite this growth, most companies remain in the initial stages of testing AI through limited pilot programs. Only around one-third of organizations have successfully expanded AI beyond individual use cases.
This shift extends beyond large multinational corporations. Data show that AI adoption has been increasing consistently across businesses of different sizes. Business AI adoption remained near 20% between December 2025 and May 2026.
Companies employing at least 250 people recorded the highest AI adoption rate, at more than 37%. Smaller companies with fewer than 20 employees recorded significantly lower adoption levels.
A 2024 review closely examines this transition toward data-driven decision-making. The findings suggest that businesses are placing greater emphasis on data analysis instead of depending solely on intuition for strategic decisions.
Human Judgment Still Matters Most
AI can provide recommendations in seconds, but it cannot replicate the value of real-world experience. Therefore, human judgment remains a leader’s most valuable asset. AI cannot weigh competing priorities, recognize ethical considerations, and account for emotions and organizational culture.
Leaders must therefore treat AI as a decision-support tool rather than handing over decision-making authority entirely to machines. Strong leadership in an AI-driven workplace requires knowing when to trust AI’s recommendations and when to question them. Ultimately, organizations are likely to perform best when AI’s analytical capabilities are combined with human judgment, accountability, and leadership.
Shobha Diwakar, vice president of ads product and engineering at Snap Inc., says that AI “will expand what you can do, but really doesn’t tell you what is worth doing.” Therefore, it makes the human role even more central.
The thing is, most AI systems operate on preset rules. Although the advent of agentic systems has enabled AI solutions to plan, decide, and act, replicating human judgment remains a distant prospect. The ability to make sound decisions when established rules fall short develops through repeated experience and direct responsibility.
The Importance of AI Literacy Among Executives
Business leaders do not need advanced engineering expertise in AI, but they should understand its practical applications and basic workings.
AI literacy can help executives distinguish between different types of AI applications and understand what each tool can realistically accomplish. Executives with a fundamental understanding of AI can communicate more effectively with their technical teams. They can ask better questions about data quality, model performance, security, costs, and expected business outcomes.
A solid understanding of AI can also help organizations evaluate technology based on its actual business value rather than its popularity.
However, executive AI literacy is still underdeveloped even as companies set ambitious AI goals. Only 21% of C-suite executives consider their peers proficient in AI, according to the data. CIOs and other technology leaders can support executive AI education across four key areas:
- Responsible AI oversight and risk management
- Assessing AI investments according to their potential business impact
- Preparing employees for AI-driven changes
- Identifying new business opportunities and competitive advantages
Frequently Asked Questions
How can small businesses start using AI for decision-making?
Small businesses can begin with specific, low-risk applications rather than adopting AI across every department at once. Useful starting points include sales forecasting, customer segmentation, expense analysis, and demand prediction. A small pilot can help business owners evaluate accuracy, costs, employee adoption, and measurable results before expanding AI use.
What types of business decisions are best suited for AI?
AI is particularly useful for decisions involving large datasets, recurring patterns, forecasting, and comparisons between multiple variables. Examples include inventory planning, sales forecasting, customer segmentation, pricing analysis, and resource allocation. Decisions involving ethics, employee relationships, company values, or unusual circumstances still require substantial human involvement and contextual understanding.
What does it cost a business to implement AI?
AI implementation costs vary considerably depending on the technology, company size, data infrastructure, and level of customization required. Some businesses can start with affordable software that includes built-in AI features, while larger organizations may invest in customized systems. Businesses should evaluate expected returns, implementation expenses, training requirements, and ongoing maintenance.
Key Facts for AI Adoption and Leadership
| A McKinsey global survey found that organizations worldwide are now using AI in some capacity | Nearly 90% |
| Organizations have moved AI beyond limited pilot programs and standalone applications | Around one-third |
| Overall business AI use remained around this level from December 2025 to May 2026 | Nearly 20% |
| AI adoption among businesses with 250+ employees | 37%+ |
| C-suite executives who view their peers as AI-savvy | 21% |
| HBS research finding | AI delivered stronger results for already high-performing entrepreneurs, while weaker performers saw limited or negative effects. |
AI is unlikely to replace business leaders in the near future. Instead, it is transforming the information, speed, and analytical depth that shape their decisions. Successful leaders will bring together AI’s rapid processing capabilities with human experience and sound judgment.
They will develop new capabilities, rethink how decisions are made, and remain responsible for the results. That combination of technology and human judgment, rather than AI alone, will shape effective leadership in the years ahead.