5 strategies to effectively implement AI and data analytics

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Businesses globally continue adopting AI for a growing set of use cases. The IBM Global AI Adoption Index 2022 found that companies globally were 13% more likely to adopt AI compared with 2021. In Australia alone, 24% had already deployed AI, and another 44% reported having begun exploring its capabilities.

For businesses adopting AI, the emphasis must be on strategically adopting it to ensure that investments yield a high return on investment (ROI) and do not become costly endeavours that fail to add value to the business. Planning becomes essential for organisations that are planning to use AI in conjunction with data analytics. When properly aligned with business strategies, AI can transform data into insightful, actionable information that propels business objectives forward. Without a clear strategic framework, these solutions will struggle to use data.

Previously, we explored 5 questions to consider when planning AI adoption. This blog builds on that foundation with five practical strategies to implement AI and data analytics.

Align initiatives with business goals

When using AI for data analytics, start by aligning your strategy with business aims such as increasing operational efficiency, improving customer satisfaction, or fostering innovation, companies can transform data into actionable insights that propel business growth and enhance competitive advantage.

This strategy will include building collaboration across all departments to merge various perspectives and create cohesive strategies reflecting the organisation’s objectives. Cross-departmental cooperation is essential for setting unified goals for AI applications, ensuring that these technological advancements drive enterprise-wide value and reinforce a data-centric corporate culture.

Improve the quality of your data

Your AI and data analytics strategy depends on data quality. High-quality data ensures that AI systems can generate accurate, reliable insights and support decision-making processes effectively. Low data quality means AI tools become less efficient and produce potentially misleading outputs. 

Before deploying AI solutions, your organisation must establish strong data management practices that promote accuracy, completeness, and reliability in their data sets. Data Discovery and Augmentation processes identify hidden data patterns and improve overall data quality. This preparation enhances the performance of AI applications and ensures that analytics are insightful and reflect true business conditions.

Build strong governance policies

Governance policies AI guide the use and management of data and AI within organisations. Such policies ensure that all data-handling practices are consistent, transparent, and comply with legal and ethical standards. Strong governance policies should encompass data privacy, ethical use of AI, and adherence to regulatory requirements. These components help prevent biases in AI outputs, protect individual privacy, and ensure that all activities align with current laws and guidelines.

Select the right tools

Selecting the right tools encompasses analysing your existing systems and considering new platforms to support your AI and analytics strategy. For example, Data Lakes and Data Warehousing solutions help manage large data sets. A Data Lake acts as a central repository that accommodates a wide array of data types, from structured to unstructured, eliminating data silos and simplifying management. Data Warehousing solutions focus on integrating and querying large volumes of structured data, transforming them into actionable insights that drive strategic decision-making. 

Additionally, integrating Data Cloud capabilities such as Salesforce Data Cloud and Azure Data Cloud can significantly enhance data synergy, providing an infrastructure for advanced analytics and comprehensive insight generation.

Start with small-scale projects

Starting with small-scale projects allows for more controlled experimentation with AI capabilities. They make it easier to track results and assess the effectiveness of different strategies without the complexities and high stakes associated with larger systems. This approach minimises risk by keeping potential failures contained and less costly.

Smaller, targeted projects enable the organisation to build a solid foundation for larger-scale implementations. Successes at this stage lay the groundwork for scaling up, allowing for gradual expansion and more complex integrations of AI technology. This phased approach ensures that each step is grounded in real-world application and success, thereby maximising the overall effectiveness of AI initiatives across the organisation.


An effective AI and data analytics strategy requires strategic planning before execution can begin. Aligning AI initiatives with business goals, ensuring high data quality, enforcing strong governance policies, choosing the right tools, and initiating small-scale projects will help your organisation improve its success rate.

Adhering to these strategies provides a roadmap for businesses undergoing digital transformations, using AI and data analytics as tools for innovation and competitive advantage. This systematic approach minimises risks and the effectiveness of data-driven insights, preparing the ground for more advanced applications and broader implementation in the future.

MakeSense can align your AI and data analytics strategy

Transform your approach to AI and data analytics with MakeSense. Our SenseMakers offer customised support, using advanced tools like Data Lakes, Data Warehousing solutions, and integrated Data Cloud capabilities to tackle your specific business challenges. By leveraging these technologies, we help refine your decision-making processes and unlock predictive insights, ensuring your business keeps up and stands out in the competitive market.

Visit our AI & Data Services page today for a detailed overview of how we can help elevate your organisation’s operational efficiency and strategic success.

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