How AI is Shaping Traditional Businesses: Insights and Lessons

In a recent panel moderated by Wan Yuee Low, AI & Digital Trainer at PORTMAN DMC, industry experts Dr. Suresh Kumar and Dr. Suzana discussed the role of AI in business transformation. Below is a Q&A format combined with actionable lessons after each answer to provide practical insights for businesses.

Question: How does AI fit into traditional business models, and what are the main challenges?

Dr. Suresh Kumar: "Data is everywhere—from the moment you wake up to when you go to bed. AI and data have become essential, but the challenge for many businesses is that their data sits in isolation. Departments aren’t sharing information, and as a result, organizations fail to get the intelligence needed to make informed decisions."

Lesson: Integrate data across the organization. For AI to deliver value, businesses must prioritize data sharing across departments. Isolated data cannot generate intelligence. Start by assessing your data flow and removing any barriers to integration.

Question: What should companies understand before integrating AI into their operations?

Dr. Suresh Kumar: "Before diving into machine learning or AI, businesses must first understand the data they possess. Without this, you can't decide whether to apply supervised or unsupervised learning models. Understanding your data is key to building any AI model."

Lesson: Understand your data before deploying AI. Conduct a thorough audit of your data before considering AI solutions. Understand where your data resides, how it can be used, and what business questions you want the AI to answer. Clear data leads to clearer AI models.

Question: What are the biggest challenges when integrating AI into businesses?

Dr. Suzana: "Many businesses think of AI as just a tool or a trend. However, AI is much more—it can transform how we do business and even create new revenue streams. But businesses need to invest time in understanding AI, especially when it comes to ethics and governance."

Lesson: Invest in AI education and governance. AI isn't just a tool; it’s a strategic investment that can reshape your business. Companies should focus on understanding AI’s broader impact, including its ethical and governance aspects, to ensure long-term success.

Question: How can businesses go beyond being AI users and become AI creators?

Dr. Suresh Kumar: "Many companies are just users of AI solutions created by others. What they need to do is shift to becoming creators of AI solutions. This allows them to generate revenue instead of just paying for AI from third parties."

Lesson: Become an AI creator, not just a user. Developing your own AI solutions gives your business a competitive advantage. Invest in talent and resources that allow you to build custom AI models tailored to your specific business needs.

Question: How important is AI governance, and how can companies ensure fairness and transparency?

Dr. Suzana: "AI governance is critical. Without it, AI can become biased, especially if the data it is trained on is flawed. Ensuring transparency in how data is collected and used is essential. AI must be accountable and fair to avoid issues like bias and discrimination."

Lesson: Establish strong AI governance. To build trustworthy AI solutions, ensure that your data is unbiased and transparent. Set up robust governance structures to manage AI use, and continuously monitor for any ethical issues, particularly bias and accountability.

Question: Are we close to creating AI that thinks like humans?

Dr. Suresh Kumar: "We are not there yet. While some companies show impressive capabilities, the reality is that most businesses are still far from using AI at a human level. The fundamentals, like proper data utilization, are still lacking in many organizations."

Lesson: Focus on practical AI implementations. Instead of aiming for human-like AI, focus on building practical, effective AI solutions that solve specific business problems. Start with smaller, realistic goals that improve operational efficiency and decision-making.

Question: What should businesses consider before starting an AI project?

Dr. Suzana: "Before starting, businesses need to assess the mobility of their data and the level of automation they want. They also need to be aware of AI bias risks. If companies don’t address these areas, their AI projects could fail before they even begin."

Lesson: Assess data mobility and bias risks. Before implementing AI, evaluate how accessible your data is across the organization and consider the risks of bias in the data you're using. High data mobility and low bias are key to successful AI adoption.

By integrating AI into traditional business models and following these lessons, companies can navigate the challenges of AI adoption, turning data into insights and innovation into revenue.