As organizations look to turn promising AI pilots into full-production deployments that create real business value, data may be the factor holding them back more than any other.
The problem, of course, is not a lack of data. Organizations have never before created, collected, processed, and stored more information. But too often, this data is siloed, inconsistent, inaccurate, or outdated. When AI tools reach into these flawed data environments, their outputs inherit the same problems. This is especially problematic as organizations look to adopt agentic AI. While a generative AI chatbot may provide an incorrect or incomplete answer based on poor data, an AI agent may take actions that negatively impact the business.
More than two-thirds of high-performing companies say data is the primary obstacle to enabling AI at scale, according to McKinsey. In a 2026 report from Drexel University’s LeBow College of Business, 43% of leaders cite data readiness as the most significant barrier to AI alignment.
More and more, it is becoming clear that AI success at scale is dependent on a strong data foundation. But even leaders who know this struggle to build that foundation in their own organizations without outside help.
Building Your Data Foundation for AI
Enterprises that want to scale AI must tackle three critical data priorities: breaking down silos, establishing effective governance, and creating an architecture that can support increasingly demanding analytics and AI workloads.
Break Down Data Silos: Enterprise data is often spread across SaaS applications, databases, cloud platforms, on-premises systems, individual business units, and even employees’ own documents. While organizations must ensure AI tools can access data across these sources, breaking down silos doesn’t necessarily mean moving everything into a single repository. Instead, organizations need an architecture that lets authorized AI tools consistently discover and use relevant data across environments.
Establish Governance: The only thing worse than AI tools not having enough access to enterprise data is, perhaps, AI tools having too much access to this information. When an organization’s most valuable data is available to AI, it becomes more important than ever for leaders to ensure that information is accurate and protected. Effective data governance establishes policies and standards around ownership, quality, classification, security, privacy, access, and lifecycle management. These controls can help organizations expand AI adoption while letting leaders maintain confidence in the information models and applications use.
Create a Scalable Architecture: Finally, organizations need data architectures that can grow alongside their AI ambitions. For example, a platform initially designed for traditional reporting may not be able to support the volume, variety, and performance demands created by AI at scale. Organizations may need to ingest new data sources, process larger volumes of structured and unstructured information, support real-time analytics, and serve multiple AI applications at once. The goal is to avoid building a foundation around a single use case, as this approach can quickly constrain future growth and flexibility. Instead, organizations need architectures that can evolve with changing workloads and business requirements.
Softchoice + AWS = AI-Ready Data
Modernizing an enterprise data estate is often daunting, particularly when organizations have accumulated years of information across on-premises and cloud environments. Softchoice Data Estate Services can help organizations assess their existing data landscapes and create modernization roadmaps tied to measurable business outcomes.
Softchoice works with customers to aggregate structured and unstructured information across cloud, on-premises, and hybrid environments, using AWS services including AWS S3 Data Lake, AWS Redshift and AWS SageMaker Lakehouse to create modern data foundations for analytics and AI.
By working with Softchoice and AWS, organizations can build governance into their data foundations rather than adding it after the fact. Softchoice Data Governance Services helps organizations develop policies, procedures, and standards to maintain data integrity and security across their environments, using AWS capabilities such as AWS DataZone and AWS Macie.
Once this foundation is in place, organizations can begin putting their information to work across increasingly sophisticated use cases. Softchoice Data Analytics Services, for example, helps organizations identify analytics use cases tied to business goals, develop data models and analytics platforms, and turn information into dashboards and insights using AWS QuickSight.
Join Us to Learn More
On Thursday, October 8 at 1:00 pm ET, AI experts from Softchoice, AWS, and The Futurum Group will join for the AI Starts with Your Data: Building an AI-Ready Foundation on AWS webinar, where they’ll discuss building an AI-ready data foundation on AWS. We’ll dive into some of the most pressing data challenges organizations face in their AI initiatives, along with solutions that help them succeed at scale.
Key takeaways will include:
- Why data modernization is the foundation for AI success, along with common data challenges preventing AI adoption.
- How AWS helps create a scalable, governed data platform, with real-world examples of AI and analytics outcomes.
- A practical roadmap for moving from data strategy to AI execution.
Register here to join us.
