How AI Is Changing Business Management: Benefits, Risks and Use Cases

AI has become ubiquitous faster than it has become profitable. In IBM’s 2025 CEO study, executives reported that while almost two-thirds have implemented AI solutions, only 25% of those initiatives delivered ROI, and only 16% managed to scale these systems across the enterprise.¹ The contrast shows how much work remains to move from isolated experiments to systems that create measurable value.

As AI becomes more deeply connected to business strategy, employers will need professionals who can guide that transition, identifying useful applications and preparing teams to work differently. To answer that need, pathways will continue to open up in operations and product management, as well as risk and enterprise transformation.

This blog post examines how artificial intelligence in business management can strengthen competitive positioning, where measurable returns are emerging and how disciplined oversight protects long-term value.

The Growing Role of AI Management and Its Top Benefits for Business

AI is becoming part of everyday business management. Almost every organization has reported using AI, while almost three-quarters use generative AI in at least one business function.² Executive priorities are shifting accordingly: 68% of CEOs say their companies rely on critical AI tools for collaboration.³

The use of artificial intelligence in business can improve operational efficiency while raising the quality of employees’ work. For example, a recent study of 758 consultants found that those using generative AI completed more tasks at a faster rate and with higher-quality work than their non-AI-using peers, but only when the assignments matched the technology’s capabilities.⁴

AI can also strengthen forecasting and customer experience. AI-powered forecasting tools can reduce errors by up to 50%, a staggering amount that can potentially save up to 65% of sales that would have otherwise been lost.⁵ Customer service can also see great improvements; at one energy company, they found that email responses to customers were rated almost 20% higher in satisfaction when the finesse of language models was used.⁶

While financial results remain uneven, measurable returns are emerging.

Common AI Applications in Business Management

The role of artificial intelligence in business management is expanding through decision systems that support different functions:

• Operations and supply chains: Businesses can avoid reactive responses by using adaptive models to test and predict inventory-placement choices, production sequences and supplier contingencies
• Human resources: Skills mapping can identify internal mobility opportunities, while workforce-planning tools can reveal where future capacity gaps may emerge
• Marketing: Teams can model audience response and compare creative variations, redirecting spending as evidence accumulates
• Finance and administration: AI can extract obligations from contracts and reconcile high-volume records

The beauty of these systems is that the data can refresh easily once new information arrives.
However, they only create value when the output matches the way you make decisions. For example, a technically accurate forecast isn’t worth much if it arrives after purchasing deadlines or can’t be explained to the person approving the budget. To work well for your team, plan to incorporate employee input on workflow design and a practical route for escalating uncertain cases.

Navigating the Risks of AI Implementation in Business

AI implementation can reshape how a company makes decisions and allocates resources. However, the flip side of that is how easy it is to introduce new vulnerabilities. Responsible adoption begins by understanding where the business is exposed to those risks and how to prevent them.

Data Quality

The quality of the data provided to a model is exponentially more important than the model itself. This is because data quality can determine whether an AI initiative ever reaches full implementation and delivers on its ROI expectations. The risk is real: It’s predicted that 60% of AI projects will get scrapped due to a lack of AI-ready data.⁷

Bad data includes:

• Duplicate customer records
• Outdated product details
• Inconsistent definitions
• Variable formatting

When bad data is used for AI models, you get confident answers built on a faulty foundation. As a result, data must be vetted by teams who know (or can find out) where critical data originated, who can and has altered it and whether, due to those answers, the data is still suitable for the intended use.

Preserving Data Security

Privacy and security create another layer of exposure. Sensitive materials may enter public tools through ordinary employee experimentation, such as when using the free version of AI tools. Additionally, vendors may retain information in ways that conflict with company policy. To reduce that risk, it’s essential to have clear access rules, approved and pre-screened platforms and contractual safeguards in place.

Bias in Data

Bias can enter through historical patterns or incomplete data. Hiring, promotion, lending and pricing decisions warrant careful testing because errors can affect individuals and violate regulatory obligations. Human eyes should always review data, and these reviewers need defined authority and adequate time and resources to make and challenge recommendations.

Potential Workforce Displacement

With the potential for AI to eliminate tasks and reduce demand for certain roles, workforce planning is an important part of enterprise risk assessment. When surveyed, 41% of employers reported they expect to reduce their workforce as AI increasingly automates parts of the job.⁸

Fortunately, displacement is not the only possible outcome. In that same survey, nearly half of employers planned to transition affected workers into other areas of the business. Thoughtful workforce planning can identify which responsibilities are likely to change and what training will prepare them for emerging roles.

Lead the Future of Tech-Driven Business With William & Mary Mason

AI is changing the substance of management careers. Greater responsibility will fall to professionals who can judge an investment, guide adoption and explain the consequences of a technology-enabled decision. Technical curiosity matters alongside financial perspective and an understanding of how people respond to change.

William & Mary’s Raymond A. Mason School of Business online MBA program helps experienced professionals build the broad business judgment required for that work. Students can strengthen their command of strategy and organizational leadership while learning to evaluate complex decisions, such as when and how to implement AI systems across business functions.

Explore the online admissions requirements and contact an admissions outreach advisor today.