Companies struggle with AI not because of the models, but because their data is siloed. Adopting an 'integration-first' mindset is crucial for creating the unified data foundation AI requires.

Data governance is often seen as a cost center. Reframe it as an enabler of revenue by showing how trusted, standardized data reduces the "idea to insight" cycle. This allows executives to make faster, more confident decisions that drive growth and secure buy-in.

Instead of criticizing the current system, frame a data transformation project as a way to eliminate critical blind spots. Present leadership with specific, unanswerable questions that the new model can solve, linking visibility to tangible outcomes like higher performance and lower acquisition costs.

To avoid an adversarial relationship, actively reposition gatekeeper functions like legal and compliance as essential partners. Their role is to ensure the company's long-term success by keeping it safe. This partnership mentality leads to more creative and collaborative problem-solving.

The unreliability of traditional data sources is breaking down organizational silos. Business leaders are now required to become more technically fluent, asking deep questions about data integrity, while tech teams must translate their work into clear business cases, leading to a convergence of roles.

To prove financial impact and ensure rigor, CIBC's CX team funds a dedicated data scientist who sits within the central enterprise analytics team. This structure gives them access to enterprise data while building credibility for their ROI models with other departments.

Digital trust with partners requires embedding privacy considerations into their entire lifecycle, from onboarding to system access. This proactive approach builds confidence and prevents data breaches within the extended enterprise, rather than treating privacy as a reactive compliance task.

In siloed government environments, pushing for change fails. The effective strategy is to involve agency leaders directly in the process. By presenting data, establishing a common goal (serving the citizen), and giving them a voice in what gets built, they transition from roadblocks to champions.

To bridge cultural and departmental divides, the product team initiated a process of constantly sharing and, crucially, explaining granular user data. This moved conversations away from opinions and localized goals toward a shared, data-informed understanding of the core problems, making it easier to agree on solutions.