Rewiring for Digital Transformation: Embedding Data and Driving Adoption (Part 3)

In the first two parts of this series, we explored digital transformation through a consulting and product management lens, focusing on strategic alignment, talent development, scalable operating models, and building a technology foundation. However, a successful digital transformation is not just about having the right technology—it’s about embedding data at the core of decision-making and ensuring adoption at scale.

Companies that fail in digital transformation often do so because they treat data as an afterthought or underestimate the challenges of adoption. For AI and digital solutions to create real business value, organizations must treat data as a reusable product and drive structured adoption and scaling strategies.

This blog covers the next two critical execution steps:

1. Embedding Data Everywhere – Transforming data into reusable assets, building modern data infrastructure, and implementing federated governance.
2. Unlocking Adoption and Scaling – Ensuring user adoption, embedding organizational change, and structuring scalable AI and digital solutions.

Let’s explore these two pillars from a product management perspective, focusing on execution strategies that turn data into a competitive advantage.

Step 1: Embedding Data Everywhere – Making Data Reusable and Scalable

Turning Data into Reusable Products

The Challenge: Data in most companies is fragmented. The sales team has its own data, the marketing team has a separate set, and operations store data differently. This siloed approach makes it difficult to share insights and scale AI-driven decision-making.

The Solution: Organizations must transition to a data product approach—treating data as a high-quality, ready-to-use asset that multiple teams can leverage. Data products provide a 360-degree view of key business entities (customers, employees, supply chain, etc.) and can be easily accessed through APIs or self-service platforms.

How Product Managers Can Drive This Change

1. Identify High-Value Data Products
2. Standardize Data Accessibility
3. Measure the Success of Data Products

Installing a Strong Data Architecture (“Data Plumbing”)

The Challenge: Data often exists in unstructured formats, across multiple platforms, and lacks governance. AI models fail at scale when data pipelines are inefficient.

The Solution: Investing in modern data infrastructure such as data lakehouses—which combine the best features of data lakes (scalability) and data warehouses (structured querying)—allows for a faster, more unified approach.

How Product Managers Can Drive This Change

1. Audit Existing Data Pipelines
2. Implement Scalable Data Engineering Practices
3. Measure the Impact

Federating Data Governance – JP Morgan Case Study

The Challenge: Managing data at scale without creating a bureaucratic bottleneck is difficult. Traditional centralized governance models slow innovation.

The Solution: Leading organizations implement federated governance, where a central data team defines standards and policies, while individual business units manage their own data products.

Case Study: JP Morgan implemented federated data governance by setting clear policies at the enterprise level while allowing business-specific teams to maintain control over their data pipelines. This reduced approval times for new data use cases by 50%, enabling AI-driven risk management and fraud detection.

Key Takeaways for Product Managers

• Avoid bottlenecks—enable data autonomy within business units.
• Ensure governance compliance without hindering innovation.
• Measure success by tracking compliance adherence and data usage improvements.

Step 2: Unlocking Adoption and Scaling Digital Transformation

Adoption is as Important as Development

The Challenge: Many companies focus too much on building AI and digital solutions but fail to drive adoption. For every $1 spent on development, at least $1 more should be spent on adoption.

The Solution: Adoption starts with building solutions that align with business needs—not just technically sound products. Product managers must embed AI into business workflows and redesign processes accordingly.

How Product Managers Can Drive This Change

1. Involve Business Stakeholders Early
2. Build Adoption Metrics into OKRs

Scaling with “Assetizing”

The Challenge: Expanding AI and digital solutions across multiple teams or geographies often fails due to customization challenges and rework.

The Solution: Companies must focus on “assetizing” solutions—ensuring 60-90% of the AI product is reusable, while only 10-40% needs local adaptation.

How Product Managers Can Drive This Change

1. Build Scalable AI Components
2. Measure Scale Success

Tracking What Matters: The Right KPIs

Successful companies track adoption and scaling rigorously. Product managers should link OKRs to operational KPIs:

• User engagement metrics – % increase in daily AI tool usage.
• Time-to-value – Speed at which AI delivers measurable business impact.
• Business unit ROI – Revenue or cost savings attributed to AI-driven improvements.

Turning Strategy into Scalable Execution

Digital transformation is not just about deploying AI models or building new tech platforms. True success lies in embedding data into decision-making and ensuring that solutions are widely adopted and scaled across the enterprise.

In this series, we started with consulting-led strategy, moved to product management execution for building a tech foundation, and now covered data integration and adoption. The final piece of the puzzle is ensuring continuous evolution and improvement.

Digital transformation is not a one-time initiative—it is an ongoing journey. Companies that embed data everywhere, drive strong adoption, and continuously refine their approach will emerge as true digital leaders.

Where does your organization stand in this transformation journey? Let’s discuss in the comments below.