The Top 7 CIO Priorities for Data and AI in 2025

As we step into 2025, the role of Chief Information Officers (CIOs) continues to evolve in tandem with the rapid advancements in technology. Data and Artificial Intelligence (AI) remain at the core of this transformation, driving innovation, efficiency, and competitive advantage. For CIOs, navigating this dynamic landscape requires a focus on key priorities that balance operational excellence with strategic foresight. 

This article outlines the top priorities for CIOs in 2025, offering actionable insights and strategies to lead their organizations into the future.

#1 AI and ML integration

AI and machine learning are revolutionizing business operations, offering opportunities to automate processes and deliver personalized customer experiences.

Key Strategies:

• Identify high-impact AI/ML use cases: Focus on areas such as predictive analytics and process automation to drive significant business value.

• Collaborate with business units: Ensure AI projects align with strategic goals by working closely with various departments.

• Establish ethical guidelines: Develop policies to govern AI deployment and mitigate biases, ensuring responsible use of technology.

Key Takeaway: Strategic implementation of AI and machine learning can transform organizations by enhancing efficiency and innovation. Richard Farrell, Chief Innovation Officer at Netcall, argues, "As organisations continue to embrace AI in 2025, CIOs face increasing demands to deliver secure, efficient, and practical AI applications that genuinely add value. While individual predictions like balancing cybersecurity, streamlining copilots, and embedding AI offer actionable strategies, an integrated platform approach simplifies and accelerates their implementation." 

#2 Aligning AI with business strategy for maximum ROI

AI talent doesn’t come cheap. CIOs pioneering an AI function will be called upon to demonstrate business value early and often. This is why aligning AI initiatives with business strategies is crucial to maximize return on investment.

Key Strategies:

• Anchor AI strategies to business objectives: Ensure AI projects are designed to drive business benefits beyond operational efficiency.

• Combine AI with predictive modeling: Leverage AI alongside other analytical tools to unlock higher-order value creation.

• Focus on revenue and customer engagement: Utilize AI to enhance customer experiences and generate new revenue streams.

Key Takeaway: As AI expert Jepson Taylor points out, many AI projects fail for lack of proper business focus:

There's a long list of failure points. So one of the failure points is, people work on the wrong problems, which is kind of shocking. So you get all the way to the end of the problem, it's being consumed, and then you ask someone, "What is this worth to the business?" And you can hear their eyes blinking. And I get it. AI is the shiny new toy, those cool projects, and a lot of times we're off to the races. But for seasoned CDOs, they always work backwards. They always partner with the business.

Integrating AI with core business strategies leads to greater ROI and business benefits. Modern CIOs know: The best AI strategies are anchored to a business’ core strategic objectives and are focused on driving business benefit beyond operational efficiency.

#3 Leveraging agentic AI for business process automation

Agentic AI is revolutionizing industries by enabling the automation of not only simple, repetitive tasks but also complex, end-to-end processes that were once reliant on human reasoning. This evolution of AI from personal assistants to digital colleagues represents a pivotal transformation. 

These AI agents can now orchestrate enterprise software and execute sophisticated business workflows that go beyond traditional "if/then" logic, offering organizations a competitive edge in efficiency and cost reduction. This shift underscores the pressing need for CIOs to prioritize value-added applications of agentic AI to unlock its full potential and drive meaningful business outcomes.

Key Strategies:

• Identify value-added AI use cases: Focus on processes that can benefit from AI-driven automation.

• Prepare for AI as digital colleagues: Anticipate AI performing end-to-end tasks alongside the traditional workforce.

• Embrace AI for competitive differentiation: Leverage AI technology to gain an edge over competitors.

Key Takeaway: Embracing agentic AI can lead to significant improvements in business process efficiency and effectiveness. Gary Bailey, CIO at Phillips Edison & Co., states, "With AI agents on the horizon, there will be a significant number of business processes that will be a much better fit for AI than we have previously seen."

#4 Cybersecurity and risk management

With the growing reliance on data and AI systems, cybersecurity has never been more critical. Protecting sensitive information and AI infrastructures from cyber threats is a top priority for CIOs.

Key Strategies:

• Implement advanced threat detection tools: Utilize AI-powered solutions to identify vulnerabilities in real time.

• Regularly update security protocols: Conduct penetration testing to ensure robust defenses.

• Educate employees on cybersecurity best practices: Minimize human error through comprehensive training programs.

Key Takeaway: Prioritizing cybersecurity safeguards organizations from costly breaches and maintains stakeholder trust. CIO Richard Farrall points out, "As cyber threats escalate, CIOs, in particular, face intensified pressure to secure operations, while CEOs expect fast-track GenAI innovations." 

#5 Data management and analytics

Effective data management and analytics are essential for turning raw data into actionable insights. CIOs must ensure their organizations leverage data governance frameworks to maximize data value.

Key Strategies:

• Develop a comprehensive data governance policy: Establish clear guidelines for data management across the organization.

• Invest in real-time analytics tools: Enable seamless data integration and timely insights.

• Explore a data product operating model: This new paradigm prioritizes high-value, business-focused data that can be reused by various parts of the business to drive a variety of results. 

Key Takeaway: Strong data management practices allow organizations to make informed decisions and remain competitive. "For AI to deliver safe and reliable results, data teams must classify data properly before feeding it to those hungry LLMs,” points out Ravi Ithal, GVP and CTO of Proofpoint DSPM.

#6 Legacy system modernization

Outdated legacy systems often hinder digital transformation efforts. Modernizing these systems is crucial for supporting advanced data and AI initiatives.

Key Strategies:

• Assess legacy systems: Identify pain points and areas requiring updates.

• Adopt a phased modernization approach: Prioritize critical systems for upgrades.

• Leverage cloud-based platforms: Enhance scalability and flexibility through cloud adoption.

Key Takeaway: Modernized cloud infrastructure facilitates seamless integration of data and AI solutions, enabling digital transformation. “An integrated platform approach simplifies and accelerates implementation,” argues CIO Richard Farrall. 

#7 Cloud services and infrastructure

Cloud adoption is critical for organizations aiming to scale their digital infrastructure and support AI-driven innovation. By enabling flexibility, cost efficiency, and seamless handling of large data volumes, cloud solutions empower businesses to stay competitive. Current trends highlight growing investments in cloud infrastructure, driven by the need to support AI workloads and advanced analytics. 

However, challenges like data silos and latency persist, prompting many to adopt hybrid and multi-cloud strategies for secure, efficient data movement. As businesses navigate this landscape, embracing cloud-based data management allows them to optimize operations, foster innovation, and maintain a competitive edge in the digital era.

Key Strategies:

• Choose hybrid or multi-cloud solutions: Balance flexibility and control by diversifying cloud strategies.

• Optimize cloud expenditure: Regularly review usage to eliminate redundancies and manage costs.

• Ensure data security compliance: Maintain robust security measures across all cloud environments.

Key Takeaway: A robust cloud strategy supports innovation while maintaining cost efficiency and cybersecurity. "Processing AI within the same managed environment as workflows and applications keeps it local, safe, and secure," Farrall points out.  

Conclusion

In 2025, CIOs' priorities will center around leveraging data and AI to drive innovation, efficiency, and growth. By focusing on cybersecurity, data management, AI adoption, legacy modernization, cloud services, talent development, budget control, and digital transformation, CIOs can position their organizations for success in the evolving digital economy.

Curious to learn how a data catalog can help you capitalize on these key priorities? Book a demo with us to see for yourself.