3 CIO strategies to integrate AI and data management for better business results

In 2025, CIOs need to do these three things for better business results: 

1. Merge data and AI management plans 

2. Improve data quality   

3. Prioritize data security and governance to cut risks 

All three of these factors are somewhat intertwined, and we’ll go into more detail below. 

1. Merge data and AI management plans 

AI is becoming a vital part of core operations, improving business results from within. It’s moving from a novelty to a necessary part of data management.  

As such, businesses now see AI’s transformative power and recognize the importance of preparing data proactively. As AI tools become more user-friendly and robust, they boost productivity, allowing companies to effectively use AI’s benefits. 

To ensure AI operates safely and accurately, data teams must diligently classify and organize data before feeding it into AI models. This step is foundational to harnessing AI for reliable and insightful outcomes. 

Looking ahead, the expansion of AI services will empower organizations to manage and utilize data more effectively, yielding deeper insights. With these insights, businesses will be equipped to pose smarter questions, facilitating better decisions and streamlined processes. 

The growing reliance on AI also signals a broader shift towards data-driven models. This transformation not only highlights current tech trends but also demonstrates how companies are nimbly adapting to maintain competitiveness and foster innovation in a rapidly advancing digital landscape.

In this evolving context, integrating cohesive data and AI management plans becomes essential to sustaining growth and innovation. 

To effectively merge data and AI management plans, businesses should consider the following foundational strategies: 

1. Establish unified leadership: appoint leadership roles that oversee both data and AI strategies, such as a chief data and AI officer. This ensures cohesive strategy alignment and prioritization across both domains. 

2. Develop a comprehensive integration framework: create a framework that brings together data management and AI deployment processes. This framework should outline how data flows from acquisition to AI model deployment, ensuring consistency and quality at each stage. 

3. Leverage shared platforms: implement unified platforms that support both data management and AI development. This encourages seamless communication between data engineers and data scientists, fostering collaboration and efficiency. 

4. Create interdisciplinary teams: form teams comprising data engineers, data scientists, and AI specialists who work together to design and implement integrated strategies. This ensures all perspectives are considered in decision-making processes. 

5. Standardize processes and tools: use standardized tools and processes for data preparation, model training, and deployment. This helps in reducing complexity and ensures that data and AI initiatives are aligned and scalable. 

6. Ensure clear communication channels: develop robust communication channels that facilitate regular updates and feedback loops between data and AI teams. This enhances transparency and allows for quick adjustments to strategies. 

7. Invest in training and development: provide training programs that cover both data management fundamentals and AI technologies. This dual proficiency empowers teams to tackle challenges from both angles effectively. 

8. Embed ethics and compliance: integrate ethical considerations and compliance protocols throughout the data and AI lifecycle. This ensures responsible use of AI and adherence to data protection regulations. 

9. Adopt flexible planning: encourage an agile approach to strategy development. Flexibility allows for quick pivots in response to new AI advancements or data management challenges. 

10. Foster innovation through pilots: launch pilot projects that test new methodologies for integrating data and AI functions. Use these projects to learn and refine processes before scaling them organization-wide. 

By following these steps, organizations can successfully align their data and AI strategies, creating a synergistic environment that supports innovation and strategic business objectives. 

2. Improve data quality   

Enhancing the quality and organization of data is a critical step toward improving the accuracy of AI systems and boosting business outcomes. CIOs looking to make a compelling business case for investing in better data practices should consider pointing to past AI failures that resulted from poor data management as a catalyst for change. 

Many brands face challenges with AI implementation due to messy, incomplete, and biased data, often stemming from inconsistent data collection methods on websites and apps over time. 

As Gartner highlights, “by 2026, 75% of businesses will use GenAI to create synthetic customer data, up from less than 5% in 2023”.

In our view, this trend underscores the growing importance of leveraging advanced technologies to alleviate data quality issues, ensuring that AI models are trained on accurate and comprehensive datasets.

 

In light of these developments, the importance of data quality and governance is paramount for effective AI applications, particularly within highly regulated sectors. Forrester notes that “40% of highly regulated enterprises will combine data and AI governance,” a necessity driven by the complexity of AI governance and the lack of universal standards.  

The EU AI Act came into effect in February 2025, thus, enterprises need to unify their data and AI governance frameworks.

This move towards an integrated, transparent, and ethically responsible approach not only meets compliance demands but also enhances the overall accountability of AI systems. 

In this dynamic environment, a hybrid lakehouse model is projected to gain traction, offering a solution that integrates data warehouses and data lakes with both cloud and on-premises storage.

This model facilitates scalability and secure control, providing a robust infrastructure that supports efficient data management and ensures that AI systems operate on high-quality data. 

AI workflows will grow and scale faster with the help of clear data products. The importance of good data quality and management, combined with business insights, will be even more crucial for AI.  

As the landscape changes, the hybrid lake house model, which merges/combines data warehouses and data lakes with cloud and on-premises data storage for scalability and security, is expected to become more popular. 

To further improve data quality, organizations should consider the following strategies: 

1. Implement advanced data profiling and monitoring tools: utilize AI and machine learning-powered data profiling tools to continuously assess and monitor data quality. These tools can automatically detect anomalies, inconsistencies, and deviations, enabling real-time quality improvements. 

2. Foster a data-driven culture: encourage a culture where data is viewed as a critical asset. This involves training employees at all levels to understand the importance of data quality and how their roles can contribute to maintaining it.  

3. Invest in data stewardship and ownership: clearly define roles and responsibilities regarding data stewardship and ownership. Ensure that every dataset has a designated owner accountable for its quality, consistency, and accessibility. 

4. Develop robust data governance policies: Gartner says that “by 2027, the application of GenAI will accelerate time to value of D&A governance and MDM programs by 40%”. You’ll want to create comprehensive data governance frameworks that outline procedures for data collection, storage, usage, and life-cycle management. These policies should also address compliance with evolving regulatory requirements. 

5. Enhance data integration practices: focus on integrating data from diverse sources using advanced ETL (extract, transform, load) processes that cleanse and harmonize data as it flows into centralized systems. This reduces the chances of data silos and discrepancies. 

6. Leverage blockchain for data integrity: explore the use of blockchain technology to ensure data integrity and transparency. Blockchain’s decentralized nature can provide an unalterable ledger of data transactions, enhancing trust and accuracy. 

7. Engage in continuous data quality improvement: adopt a continuous improvement mindset by regularly reviewing and updating data quality metrics and methodologies. Use feedback loops and data quality dashboards to track performance and guide decision-making. 

By incorporating these strategies, businesses can significantly enhance their data quality, setting a strong foundation for successful digital transformation initiatives in 2025 and beyond. 

3. Prioritize data security and governance  

As the push for responsible AI continues to gain momentum, Gartner anticipates that “by 2026, 50% of governments worldwide will enforce the use of responsible AI through regulations, policies, and the need for data privacy.” In our view, this shift underscores the growing emphasis on ethical AI practices and data protection, necessitating organizations to adopt robust data governance frameworks. 

In response to these regulatory changes, organizations are expected to adapt by implementing standardized approaches to secure their data.

Gartner predicts that “by 2027, organizations faced with AI and data security requirements will standardize policy-based access controls to unlock the value from more than 70% of their data.”  

We believe that this indicates a significant shift towards more rigorous data management practices, enabling businesses to fully harness the potential of their data while ensuring security and compliance. 

As security concerns continue to evolve, Gartner forecasts that “by 2027, 70% of organizations will combine data loss prevention and insider risk management (IRM) disciplines with IAM context to identify suspicious behavior more effectively.”  

We believe that integrating these disciplines provides a proactive approach to managing potential threats, allowing organizations to better safeguard their data assets. 

A key challenge that CIOs must address is the management of “dark data,” which includes hidden and outdated information lurking within emails, spreadsheets, and legacy systems.  

These data repositories often contain sensitive information and can lead to security breaches if not properly managed. AI tools can play a pivotal role in uncovering these hidden data areas, thereby exposing potential security risks. 

By raising awareness of these issues, CIOs can better inform stakeholders about the impact of dark data on business operations in the AI-driven era, emphasizing the need for comprehensive data management strategies that prioritize both security and regulatory compliance. 

What’s more, to prioritize data security, consider implementing the following strategies: 

1. Conduct a comprehensive data audit: regularly perform audits to identify and categorize all data assets, including dark data. This process helps in understanding what data is stored, where, and who has access, allowing for more targeted security measures. 

2. Implement robust access controls: utilize role-based access controls (RBAC) to ensure that only authorized personnel can access sensitive data. This minimizes the risk of data breaches by limiting unnecessary data exposure. 

3. Enhance data encryption practices: ensure that all sensitive data, both in transit and at rest, is encrypted using up-to-date encryption standards. This provides a critical layer of defense against unauthorized access and data breaches. 

4. Adopt zero-trust architecture: implement a zero-trust security model, verifying every request as if it originates from an open network. This approach ensures rigorous identity verification and access permissions at every layer. 

5. Deploy AI-driven security solutions: leverage AI and machine learning models to detect anomalies and potential security threats in real-time. These tools can automatically identify and respond to suspicious activities or breaches. 

6. Maintain regular software and system updates: keep all software and systems updated with the latest security patches to protect against known vulnerabilities and cyber threats. 

7. Educate and train employees: conduct regular training sessions to educate employees about data security best practices, phishing attacks, and the importance of safeguarding sensitive information. 

8. Create a data breach response plan: develop and periodically test a comprehensive response plan for potential data breaches. This ensures rapid and effective action to mitigate damage and protect affected parties. 

9. Engage in continuous risk assessment: regularly assess and reassess security risks using a variety of methods, from penetration testing to vulnerability assessments, to adapt security measures to evolving threats. 

10. Strengthen data governance policies: ensure that data governance policies explicitly focus on security dimensions, outlining protocols for handling, storing, and sharing data securely. 

By prioritizing data security with these measures, organizations can effectively safeguard sensitive information against the backdrop of increasing digital transformation and AI reliance, maintaining trust and compliance while reducing risk.