Realities of AI Adoption in Enterprises
Yesterday, I had the privilege of representing the Department of Trade and Industry’s Center for AI Research (CAIR) in an information session with enterprises of various sizes, organized with the help of the International Council for Small Business (Philippines). Our mission at CAIR is to partner with enterprises and facilitate innovation through technology, particularly AI. This session provided valuable insights into the challenges and realities of AI adoption in the business world.
One of the key takeaways from our discussion was the identification of significant barriers to AI adoption. These barriers extend beyond mere funding issues. Many enterprises, not just in the Philippines but across the region, struggle to identify the right business questions where AI can be truly beneficial. Additionally, there’s a prevalent lack of quality data necessary for effective predictive analytics. These challenges underscore the complexity of implementing AI solutions in real-world business contexts.
During the session, I felt compelled to emphasize an important point: enterprises should not feel pressured to implement AI systems simply because it’s a trending technology. AI systems are tools, and like any tool, they should only be used when they fit the specific needs and context of the business. This is particularly relevant when we consider that more than 99.50% of Philippine enterprises fall under the MSME category, where basic digitization often remains a more pressing issue than advanced AI implementation.
Interestingly, a recent conversation with one of my best friends, who is also deeply embedded in the AI industry, shed light on another crucial aspect of AI adoption. Her company has worked with many enterprises at different levels of digital maturity, giving her unique insights into the challenges of AI implementation. While much focus has been placed on developing analysts and data scientists, we’re now seeing a scarcity of trained IT personnel and infrastructure experts. This shortage has created a significant bottleneck in system deployment, especially for large enterprises that aren’t digital natives. It’s a reminder that successful AI integration requires a holistic approach to digital transformation, encompassing not just AI expertise but a broad spectrum of IT skills.
As we move forward in promoting AI adoption, it’s crucial to keep these realities in mind. We need to focus on comprehensive digital transformation, not just AI implementation. This means investing in training across the entire spectrum of digital skills, from basic IT to advanced AI. It also requires tailoring our approaches for different enterprise sizes and maturity levels. Collaboration between academia, industry, and government will be key to addressing these challenges comprehensively. I believe these insights are crucial for anyone involved in or considering AI implementation in their organization. They highlight the need for a thoughtful, strategic approach to AI adoption that considers the full landscape of an organization’s digital capabilities and needs.