Ahead of 2025: A CxO Debrief on AI for the Future

With 2025 just around the corner, businesses are under increasing pressure to stay ahead of technological advancements. Fortude recently hosted a CXO Roundtable, where C-suite executives across the ANZ region discussed the future of AI and the latest technologies, highlighting the steps companies need to take today to prepare for the changes ahead. The event featured Fortude’s leadership in conversation with CIOs across a number of industries.

Here are some quick takeaways from the session to help your business strategize and strengthen its AI frameworks as you gear up for 2025 and beyond.

Artificial intelligence is no longer a distant concept; it is already driving efficiencies and transforming operations across industries. As businesses prepare for 2025, those with mature AI strategies are expected to move towards comprehensive automation, integrating AI into end-to-end business processes. This means that tasks such as inventory management and customer service may soon be managed by AI-driven algorithms, with minimal human involvement.

As AI continues to evolve, it will enable predictive capabilities, allowing businesses to anticipate market changes and customer needs with greater accuracy. Moreover, AI’s role in decision-making processes will expand, guiding businesses toward data-driven strategies that enhance competitive advantage.

However, the success of AI initiatives largely depends on the quality of data fueling the algorithms. To ensure that AI acts as a true enabler, organizations must focus on building a robust data foundation.

Incomplete or flawed data can lead to misleading outputs & faulty outcomes. Companies should start by evaluating their data architecture, including how data is collected, stored, and accessed. The data architecture should be adaptable, allowing for scalability and the agility to respond to changing business needs. Developing a comprehensive data strategy covering the entire data lifecycle is crucial for AI success.

To optimize or receive the outcome you’re looking for through AI, ponder these questions:

• What’s my current data architecture?

Look at how your data is being collected, stored and accessed. This is your current data architecture. Make sure your data architecture is scalable and flexible enough to keep up with your aspired growth and changes. Establishing a comprehensive data strategy that covers the end-to-end data process is essential for AI success.

• What’s my data governance framework? What’s my master data management solution?

Your data governance framework outlines policies and standards for data management, ensuring it is secure, accurate and ethically used. Whereas Master Data Mangement (MDM) uses technology and processes to create a unified master data service.

• What is the data required to make the automation, the insights, the activity that I’m looking for from AI?

Work backwards. Think about what your goals are and what data is required to enable your AI tool to support you reach these goals. While identifying the right datasets is essential, so is investing in technology that helps you expedite the process and being proactive in identifying areas where AI can provide the most value.

Navigating AI ethics involves understanding the biases inherent in AI tools and applying mechanisms to manage them. For example, responses from different Large Language Models (LLMs) can vary significantly due to the biases in their training data. Recognizing and managing these biases can help businesses fine-tune AI models to deliver reliable and unbiased outcomes.

Beyond bias management, businesses must adopt a holistic approach to AI governance. This involves not just meeting regulatory requirements but establishing frameworks that address the broader ethical, legal, and operational implications of AI use. Developing a comprehensive governance plan that goes beyond policy compliance and ensures responsible AI deployment is important.