How is AI Shaping Business Practices and Decision Making?
Artificial intelligence is only in its infancy in terms of application and implementation in the business world. However, it is already revolutionizing business practices across industries. Companies can enhance efficiency, innovation, and decision-making processes faster than ever through recent applications of large language models, image generators, and data analytics tools. In his research, Felipe Csaszar, professor of strategy, explores how AI transforms how companies make decisions and compete in the marketplace.
In the following Q&A, Csaszar shares his insights on how businesses implement AI into their operations and practices and some of the common challenges they face.
In your research on AI in business, have you found any AI applications that might surprise everyday consumers?
While ChatGPT's record-breaking adoption — reaching 100 million users in just two months — stands out, what really amazes me is how AI has expanded beyond text. Today's multimodal systems can see, hear, speak, and create videos, fundamentally changing how we interact with technology.
What many don't yet realize is AI's transformation of education. A promising application in this area is AI tutors that adapt to individual learning styles, pace, and knowledge gaps. These systems deliver personalized education experiences that sometimes exceed traditional classrooms or human tutoring in specific areas. This hyper-personalization represents a profound shift that will reshape education in the coming years.
How are companies addressing public concerns about AI integration, such as data privacy, algorithmic bias, and other ethical challenges?
Companies are becoming more sophisticated about this. Many are investing in making AI more explainable — ensuring they can understand why an AI made a particular decision. This is especially important for applications in hiring and healthcare.
Organizations are adopting ethical frameworks with clear guidelines for fairness, accountability, and human oversight. These typically include regular AI audits involving diverse teams of ethicists, tech experts, and regulatory specialists.
On the technical side, there's interesting work happening with data anonymization techniques that allow AI models to learn without accessing sensitive personal information. It's a balancing act between innovation and privacy protection, but companies are making real progress.
What are some common AI challenges businesses face when implementing AI?
The biggest headache I see is the talent gap — not just technical specialists but professionals who understand AI capabilities at the business strategy level. At Ross, I'm addressing this by introducing the new MBA elective "AI and Strategy" to bridge this divide.
Then there's the data problem. AI systems need large amounts of high-quality, consistent data, which many organizations struggle to provide from their existing systems.
Integration challenges are also common. Companies face difficulties incorporating AI into existing workflows and IT infrastructure. This isn't just technical — it requires thoughtful change management to address employee concerns and ensure effective human-AI collaboration. Add in the constantly shifting regulatory landscape, and you have a perfect storm of implementation challenges.
Can you share some of your research findings on the intersection of AI and decision-making?
What's really interesting is that our research shows current large language models can generate entrepreneurial strategies and evaluate business plans comparably to human entrepreneurs and professional investors.
We've also found that AI helps overcome our natural cognitive limitations. It spots patterns in massive datasets that humans simply miss. It doesn't just automate our traditional strategy frameworks — it actually makes them better by adding analytical depth and knowledge breadth.
I think the most exciting finding is how AI enables extensive, rapid decision experimentation. You can quickly test many more strategic options before committing resources. That's a huge improvement over traditional approaches, potentially leading to more innovative and effective strategies that might otherwise never be considered.
What insights have you gained from your research that could help firms enhance their innovation, financial, or social performance using AI?
Our research indicates that AI can drive innovation by helping firms explore new product ideas, optimize operations, and enhance customer experiences. However, leveraging AI for innovation requires a culture that values experimentation and adaptability.
On the financial side, AI offers dual benefits: cost efficiencies through process optimization and automation, plus revenue growth through data-driven insights and personalized offerings.
What I find most promising is how AI sharpens market analysis, customer insights, and strategic forecasting. This improves organizational responsiveness — a critical capability in today's fast-changing business environment. I think we've just scratched the surface. We'll be exploring this landscape for years to come, and most business applications of AI are still waiting to be discovered.
