The Key to Unlocking AI’s Full Potential

2025 is shaping up to be a pivotal year for AI adoption, with businesses across industries accelerating their deployments to gain a competitive edge. According to the 2025 Cloud 9 Tech Trends Report, AI adoption is now the primary driver of IT purchasing decisions, surpassing cybersecurity, cost-cutting initiatives, and customer experience (CX) enhancements.

However, many companies are struggling to scale their AI initiatives beyond pilot projects and realize long-term value. The biggest roadblock? A lack of data readiness. A recent study from MIT and Snowflake found that 78% of businesses lack a “very ready” data foundation to support generative AI. Similarly, research from Capital One revealed that 70% of technical teams spend several hours each day fixing data issues, while only 35% report having a strong data culture due to inconsistent support and education.

AI has the potential to revolutionize productivity and efficiency, but its success hinges on the quality of the data that powers it. Without a solid data foundation, AI initiatives underperform, produce misleading results, and fail to deliver their promised benefits.

• 92% of businesses will increase their AI investments over the next three years.

• More than 60% of organizations report large gaps in AI readiness, particularly in data ecosystems and infrastructure.

• 81% of executives and 96% of their teams are using AI to a moderate or significant extent.

• 95% of organizations have encountered hurdles during AI implementation due to poor data quality and organization.

All AI systems rely on high-quality data to function effectively. Poor-quality data leads to inaccurate, biased, or unreliable AI outputs, limiting the effectiveness of AI-powered solutions.

Data readiness refers to the process of transforming raw data into structured, secure, and accessible intelligence that AI systems can use. It applies to all AI applications, including traditional AI, generative AI, and machine learning models. In the coming years, data readiness will be a critical enabler of the next wave of agentic AI—autonomous AI systems capable of making complex decisions and executing tasks with minimal human intervention.

To fully harness AI’s potential, businesses must invest in building a robust data infrastructure—one that ensures data accuracy, security, accessibility, and interoperability across various systems and teams.

Being data-ready is not just a competitive advantage, in today’s AI-driven world, it’s an absolute necessity.

As businesses increasingly rely on data-driven decision-making, those that optimize their data infrastructure will gain significant advantages over competitors. The ability to combine real-time insights with AI-powered automation will enable businesses to adapt to shifting market dynamics, enhance operational efficiency, and unlock new revenue opportunities.

Here are some key benefits of achieving data readiness:

• Faster, more reliable analytics and decision-making

• Reduced time spent on data wrangling and manual data cleaning

• Cost savings through improved efficiency, reduced waste, and greater automation

• Better collaboration and data-sharing across departments and teams

• Enhanced customer experience (CX) through accurate, personalized interactions

When it comes to AI, data quality matters above all else. Using low-quality data can lead to a range of issues, including:

• Inaccurate insights: Training models with weak data can lead to unreliable outputs. This can lead to poor decision-making and erode end-user trust in AI systems.

• Bias issues: Low-quality or unvetted data can create biases—ranging from algorithmic to historical—leading to unintended and sometimes harmful outcomes.

• Degraded performance: Poor data degrades AI model performance over time and drives up maintenance costs.

• Compliance risks: Using non-compliant data can lead to data privacy violations and legal penalties.

Most IT buyers now understand they need access to high-quality data for AI. Salesforce's State of Data and Analytics Report revealed that 92% of IT and analytics leaders feel that the demand for trustworthy data has never been higher, and 86% agree that AI’s outputs are only as good as its data inputs.

Yet, data quality issues remain widespread. Many companies lack the resources to implement and maintain strong data policies, creating an increase reliance and need for technology advisers/agents to help enterprises identify vulnerabilities and provide suppliers, services, and solutions that improve client outcomes.