As enterprises move from generative AI (GenAI) experimentation toward scalable, production-ready capabilities, leaders are being forced to rethink how AI fits into operating models, how it is adopted by employees, and how trust is established across the organization. The shift is no longer about proving technical feasibility. It is about aligning data quality, governance, and everyday usage to support sustainable enterprise-wide impact.

Johnson & Johnson’s (J&J) Global Services organization plays a central role in operational transformation by delivering scalable capabilities across data, digital solutions, and business services to various stakeholders.

In Part 1 of this three-part series, Ajay Anand, SVP of Strategic Solutions and Commercial Services, Global Services at J&J, frames GenAI as a stakeholder-first transformation discipline that only scales when it is grounded in high-quality data, governed content, deep domain understanding, and consistent adoption across the enterprise.

In this second installment, Anand continues the conversation with EY’s Kevin Barboza on how Global Services balances near-term GenAI delivery with longer-term agentic AI strategy, how enterprise adoption is driven through structured change management, and why AI governance, accessibility, and disciplined data management form the foundation of trust as organizations prepare to scale AI responsibly into 2026.

Balancing generative AI delivery

As the market shifts from GenAI experimentation toward agent capabilities that integrate into operational systems, Barboza asks how Global Services avoids getting stuck between execution and exploration. Anand answers that both are required simultaneously.

He positions agentic AI as a meaningful value lever for how Global Services serves J&J’s sectors and functions, while emphasizing that the organization treats the space as early-stage and therefore requires deliberate evaluation.

“Being in the very early stages, Global Services is taking a thoughtful approach to evaluating and decisioning ideas that are appropriate and ready for agentic AI,” says Anand.

The larger aim is not simply the adoption of new tools but a more AI-first-enabled operating model that improves performance and decision quality for stakeholders while still preserving the discipline of experimentation.

Start with people and process  

The operational playbook Anand describes does not begin with shiny capabilities. It begins with understanding where agentic AI truly belongs in end-to-end processes — and sequencing the work so that process design and human impact lead the technological decision.

Anand notes that the team has learned in introducing new emerging digital solutions into processes, it is best to focus on the people and process aspects first and implement the right technology solution to support the improvement needs.

From there, the approach becomes parallel: reimagine the process while experimenting with technology, then connect the outcomes quickly once value is clear. The result, he signals, is targeted experimentation informed by process analysis and business case discipline rather than broad, unfocused pilots.

Why change management is a core capability for enterprise GenAI adoption

Barboza pivots to what he describes as a paradigm shift in the world of work. In that shift, Anand argues, the decisive factor is not just technology maturity but the organization’s ability to bring people along.

“We have learned that change management is vital in GenAI transformation to ensure that our employees adapt to the new ways of working,” he notes. Anand emphasizes that this starts with engaging senior leaders and the executive committee from the beginning to help ensure that there is C-suite-level sponsorship.

Anand describes an adoption model that is strategic, structured, and designed to make the “why” legible, reducing uncertainty, building skills, and sustaining support beyond initial rollout.

Training is treated as a practical lever; a checkbox delivered through scalable formats that meet employees where they are. This includes the implementation of programs such as online courses and webinars to reduce uncertainty and equip teams with the skills that are necessary to adopt GenAI.

He highlights the role of early advocates who help translate change into daily practice by identifying change champions early in the process to advocate for innovation and to guide peers.

He also underscores the value of visible, easy-to-access support structures that make adoption feel safe, especially when new ways of working raise questions, friction, or skepticism.

Further, Anand mentions incorporating open feedback loops such as pulse surveys and one-on-one meetings, enabling real-time adjustments while employees feel a sense of shared ownership.

How data governance, accessibility, and quality build trust in enterprise AI

Barboza notes that adoption often hinges on trust in the outcomes, logic, and what goes into the models. Anand agrees, and anchors trust in a familiar but frequently under-executed set of fundamentals: data quality, accessibility, and governance.

“Ensuring data quality, accessibility, and governance is at the heart of our strategy to enable responsible and effective AI,” he adds.

Moreover, Anand points to early governance decisions as pivotal, citing the formation of the J&J Data Management Council, where he serves as Co-chair, early in the journey with sponsorship at the executive committee level and holistic representation from across J&J as crucial for success. 

With 2026 positioned as a strategic horizon, Anand describes an enterprise-wide commitment that prioritizes data quality and governance as prerequisites for scaling AI responsibly.

Data as a product and culture as multipliers for AI readiness

Anand situates his role as Co-chair of the J&J Data Management Council as a mechanism for aligning Global Services’ transformation goals with enterprise-wide data discipline and culture building.

In this framing, execution becomes a structured approach: accuracy, consistency, accessibility, and reliability, supported by diagnostics, remediation, and sustainable practices that prevent quality from degrading over time.

This ensures that data is accurate, consistent, accessible, and reliable across the organization by deploying a comprehensive data quality framework that is focused on the most critical data domains with diagnostics to identify quality gaps, while remediation efforts and sustainable practices ensure long-term reliability of the data that feeds AI platforms and systems.

For Anand, this approach is what turns governance into operational value: curated data, governed data, operationalized data, built to drive real-world outcomes and enable AI at scale.

In conclusion, Anand shares, “Data capabilities continue to expand as the scope and depth of our data grows, and this contributes to enhanced AI readiness, delivery of deeper insights, and the enablement of smarter and more sustainable decisions.”

Listen to Part 1 here.

CDO Magazine appreciates Ajay Anand for sharing his insights with our global community.