Copyright Fast Company
For decades, engineering work has centered around files—saved, versioned, emailed, duplicated, and archived across networks and desktops. But in most industries, the traditional file is becoming obsolete. Marketing teams now collaborate in real time on cloud-based documents using tools like Google Docs and Figma. Finance teams manage live dashboards in Tableau and NetSuite instead of relying on static spreadsheets. Even legal departments are shifting to contract management tools like Ironclad or DocuSign CLM. Cloud adoption is gaining momentum in the engineering field as well. Shifting to data-first, cloud-native platforms modernizes infrastructure and enables real-time collaboration. It also helps with stronger traceability and AI readiness. As leaders prepare to move away from local file saves, they should plan for technical and organizational challenges such as permission mapping, legacy formats, and integrations. It is also important to address governance as well as compliance and validation upfront. Successful adoption can typically follow in three stages: assess and plan, pilot and migrate, then operationalize and scale. Let’s break down each stage into concrete tasks and common pitfalls. Step 1: Assess and plan goals To start, your team will need to lay solid groundwork in the assessment and planning phase. This is crucial to understand your current status, identify potential risks upfront, and set measurable goals for what successful adoption looks like. Conduct a full inventory—files, databases, repositories, CI/CD pipelines, integrations, and large binaries—you name it. This is helpful in getting a handle on all the engineering assets and workflows. Note who owns and uses each item. Subscribe to the Daily newsletter.Fast Company's trending stories delivered to you every day Privacy Policy | Fast Company Newsletters From there, start outlining success criteria. What are some key performance indicators you want to track and improve? This could include factors like time-to-merge, review cycle time, or deployment frequency. Then, document any governance and compliance requirements and put together a migration risk register that lists likely issues (e.g. broken integrations, unsupported formats, slow performance with large files). Step 2: Pilot and migrate incrementally When it’s time to test the waters, pick pilot teams and projects that truly reflect the typical work and difficulties you’re trying to solve. This could be several teams dealing with large file sizes, with automated pipelines set up, or those requiring a lot of cross-functional reviews and approvals. Be upfront about any limitations, like if there are file types or processes you can’t preserve perfectly in this initial migration, so you won’t have surprises down the line. Once those pilot groups are running in the new system, thoroughly test the whole thing out. It’s key to validate all the integrations. Make sure core pipelines like CI/CD are functioning properly and check performance with those big binary file loads. Run some user acceptance testing and apply simple benchmarks to see how real-world conditions feel. Providing adequate support for the pilot participants is critical to success. Establish comprehensive role-based training materials, quick-start guides, and designated office hours or support channels to promptly address any issues or questions. Your team can treat this as a legitimate pilot before broader rollout. Thoroughly validate all downstream systems and integrations that interact with the pilot teams to prevent disruptions to existing automated processes. Step 3: Operationalize and scale Governance is a top priority when operationalizing cloud migration across the organization. Also key? Enforcing access controls, approval workflows, data retention policies, and audit logging. Prioritize the integration of lifecycle systems to have a single source of truth that supports traceability and decision making. Measuring defined KPIs will guide rollout optimizations, while automating repetitive tasks reduces errors and frees up resources. It’s important to know that change management is an ongoing process; continued training, designated team champions, and open feedback channels drive adoption. Additionally, preparing data for future analytics and AI capabilities avoids substantial rework down the line. advertisement CHOOSE THE RIGHT PLATFORM Zooming out, this shift to cloud platforms impacts the entire product lifecycle: PLM, ALM, and PDM all play a role in keeping product information connected, traceable, and accessible. In a data-first setup these systems act as an integrated platform that links requirements, designs, validation, and releases into a single source of truth, eliminating the silos created by disconnected file stores. Cloud-native PDM moves CAD and product data into a shared, version-controlled workspace that updates in real time, which means fewer errors, faster reviews, and less time spent managing files. When PDM is embedded directly in the CAD system, it becomes seamless for users while still providing version control, access management, and auditability; pairing that with integrated PLM and ALM gives engineering, quality, and compliance teams aligned visibility. Choosing the right platform matters: Pick a solution that fits your workflows, migration risks, and integration needs, especially if you’re distributed or in regulated industries. A data‑first approach cuts silos. It also speeds feedback and makes product data usable across the organization while laying the groundwork for analytics and AI. LEAVE “SAVE AS” BEHIND Engineering leaders have long relied on legacy systems because they worked “well enough.” But “well enough” no longer cuts it in a world that demands resilience, speed, and global collaboration across the supply chain. Moving away from static artifacts towards living, connected product systems allows engineers to spend less time managing files and more time delivering innovation and quality. The end of the file marks the beginning of something bigger—a more connected, agile, and intelligent way of working. As data-first platforms, cloud-native tools, and AI converge, engineering teams will rethink how they build, adapt, and lead in a digital-first world. Dave Katzman is general manager of the Velocity Group.
