Software development teams have spent two decades refining how they work: short feedback loops, continuous integration, shared ownership, and ruthless automation. Content and media teams are now catching up. Agile methods and DevOps thinking are increasingly influencing how organizations plan, produce, review, and distribute digital content — and the tooling is following suit.
Why Content Teams Are Turning to Agile and DevOps Principles
At the core of Agile methodology is a focus on team coordination over rigid process. That same principle applies directly to content production. Bob Gower, an Agile marketing coach and co-author of Radical Alignment, frames it clearly: “As with DevOps and Agile, successful content management in my experience is less about technology and more about workflows and teamwork. The power of a flexible workflow is the ability to reflect and get better continuously. Teams and management need to embrace this, but it can feel a tad chaotic at times.”
The parallel to software delivery is intentional. In DevOps, teams break work into short cycles, automate repetitive steps, and continuously improve based on feedback. Content teams adopting similar practices replace waterfall-style handoffs — one person writes, sends to an editor, who sends to an approver — with parallel, iterative workflows where multiple contributors can work on the same asset simultaneously.
According to Nucleus Research analyst Evelyn McMullen, collaboration tools alone are not enough. AI, machine learning, and metadata will be key for streamlining processes and improving media workflow management. Before investing in any tooling, she recommends that organizations first map their workflows, define the outcomes they need to manage, and build a strategy. “One challenge we see enterprises running into is immediate need for content management without any planning or strategy in place,” she said.
The Baseline: Moving Beyond Email Attachments and Word Documents
The simplest starting point for collaborative content work is a non-destructive, multi-user editor. Tools like Google Docs eliminate the need for version control via email, ensure no edits are lost, and allow multiple contributors to work in parallel. Gower is blunt about the alternative: “I’m shocked at how many editors still send around Microsoft Word docs. It’s so inefficient.”
From there, project-specific tools can automate handoffs and task assignments. The gap between content management maturity and software development maturity remains significant, however. As Dan Stoll, director of technical product marketing at workflow automation provider Nintex, puts it: “Content management normally is a set-and-forget process in that people add content, content goes for review and content gets published.” DevOps, by contrast, is characterized by short activity bursts, shared libraries, and reusable components. Stoll believes content tooling will eventually reach similar automation depth, but organizations will need purpose-built tools in the interim.
Real-World Example: Automating Content Cross-Pollination at Nintex
Nintex has built an internal content platform that demonstrates what automated content pipelines can look like. Sales teams can locate marketing materials and price books based on criteria already configured in the CRM system. A cloud automation service reviews and moves approved content from a central gallery into the sales team’s platform automatically. “This process is completely automated,” Stoll explained, “and allows us to go about publishing and reviewing as normal in the gallery, knowing that our content is being cross-pollinated into other systems to increase visibility.”
AI and Metadata: The Next Layer of Media Workflow Intelligence
AI is beginning to take on meaningful roles in content production workflows. Current applications include:
- Automated metadata generation for video and audio assets, enabling fast search, retrieval, and repurposing
- Content scanning for relevancy, flagging outdated material for revision
- PII redaction — automatically identifying and removing sensitive data such as phone numbers or credit card details before publication
- Language identification, entity extraction, sentiment analysis, and scene detection via low-code AI integrations
Greg Loose, head of media and entertainment at AI technology provider Veritone, sees AI-generated metadata as a practical, near-term win: it allows companies to locate media assets within seconds and quickly share, repurpose, license, and monetize content. “AI is interwoven within other enabling technologies, such as on-demand media asset management platforms, making them and the assets themselves intelligent,” he said.
Case Study: Recovering from a Content Database Disaster at WikiLawn
WikiLawn, an online lawn and garden resource, lost its entire content database due to data corruption. The incident forced a complete overhaul of the organization’s content tools and processes, according to president Dan Bailey. The existing on-premises enterprise content management (ECM) system made searches difficult and slowed imports from the website and social media channels.
The team migrated to Microsoft SharePoint as a combined content hub and management service. The transition — including assessing storage needs, evaluating existing content, and completing the move — took approximately two weeks. The database can now operate in the cloud or on-premises, with fast storage, categorization, and search capabilities.
Bailey remains candid about the limitations: “Content management services are focused more on access right now than seamless iteration, which is one of the main things we need.” His ideal would be an all-in-one platform supporting multiple content types with automatic saving to both online and local branches — effectively a content equivalent of a Git-backed workflow.
Case Study: Iterating on Content Like Code at SEO.co
Nate Nead, CEO of digital marketing firm SEO.co, draws a direct analogy between software development and content production: “Getting to the best version of a piece of content is like iterating to the best version of a piece of code.” The company runs weekly content scrum meetings to assign tasks and track progress, having migrated from spreadsheets and Google Docs to Asana for task management and Slack for communications.
“Being nimble is about having the right process coupled with the right tools,” Nead said. The structured sprint cadence ensures content moves through planning, drafting, review, and publication without accumulating in someone’s inbox.
COVID-19 as an Accelerant for Collaborative Content Tooling
Remote work mandates in 2020 forced organizations that had been evaluating collaborative content tools to deploy them immediately. Michael Segner, director of content and communications at AvePoint — a provider of migration, backup, and governance software for Microsoft 365 — reported a sharp spike in adoption of Microsoft Teams and related tools as organizations sent digital workers home.
Segner’s own team moved away from a waterfall-style linear process — write, email, edit, email, approve — to a channel-based model in Teams. Documents are uploaded to a specific channel where any authorized team member can rewrite and edit in parallel. Once approved, the document is published to a central “Go To Market” SharePoint site for internal or public access. “This way, we have a single authoritative document that our whole company can access and leverage and that is easy to iterate,” Segner noted.
What to Call This: The Naming Problem in Content Workflow Evolution
Software delivery has two widely recognized terms — DevOps and Agile — to describe faster, more collaborative, iterative lifecycles. Content teams lack an equivalent shorthand. Several terms have been proposed by analysts and practitioners:
- New work nucleus (Gartner): A framework in which content collaboration platforms replace enterprise file sync and share (EFSS), workstream collaboration replaces unified communications, and collaborative work management replaces traditional project management tools
- Intelligent information management (Nucleus Research): Extends beyond traditional content services to encompass AI-driven classification and retrieval
- Content automation (Nintex): The use of AI to move content through production and distribution phases
- Intelligent content (Veritone): All content supported by AI through metadata and workflows to drive engagement and revenue
- Content as a service (FileCloud): Enabled by metadata integration, smart classification, powerful search, flexible APIs, and cloud-agnostic open systems
- Cloud content management (Box): Encompasses file organization, sharing, storage, access control, permission management, and collaborative workflow
No single term has achieved consensus, but the underlying direction is consistent: content workflows are moving toward the same principles that define modern software delivery — automation, iteration, shared ownership, and continuous improvement.
Case Study: Agile Content Refresh at Simple Life Insure
Insurance broker Simple Life Insure in San Diego needed to keep policy and underwriting documents current while also refreshing website content to maintain search performance. CEO Ty Stewart opted for SaaS tools rather than custom software. The team’s content workflow uses:
- BuzzSumo — for identifying trending topics and emerging long-tail keywords, and for surfacing existing content that needs to be restructured
- Trello — for managing workflow stages: finding topics, drafting SEO-optimized outlines, writing, reviewing, publishing, and monitoring performance
- Cloud-based project management — to move tasks between team members and track completion
“In our industry, making those timely updates is pivotal since information needs to be accurate, given changing regulations in the finance and insurance world, which happens annually,” Stewart said. He acknowledges the workflow doesn’t perfectly mirror DevOps or Agile practices for code, but adds: “They absolutely work for our content refresh strategies and only require a cloud-based project management tool to keep everything on track.”
Key Takeaways for DevOps and Engineering Teams Supporting Content Workflows
- Start with process, not tooling. Map your workflows and define your outcomes before evaluating platforms. Technology amplifies good process and amplifies bad process equally.
- Eliminate linear handoffs. Waterfall-style sequential editing creates versioning problems and delays. Channel-based or document-based parallel editing removes these bottlenecks.
- Invest in metadata. AI-generated metadata is the foundation for fast asset retrieval, content reuse, and monetization — particularly for video and audio.
- Treat content iteration like code iteration. Weekly scrums, task tracking in project management tools, and defined review stages bring software delivery discipline to content teams.
- Automate cross-system content distribution. Publishing to one system and manually copying to others is the content equivalent of deploying by hand. Automation pipelines that push approved content across systems reduce errors and save time.
- Plan for cloud-native or hybrid storage. On-premises ECM systems create search bottlenecks and import delays. Cloud or hybrid architectures support faster collaboration and disaster recovery.
The convergence of DevOps thinking and content management is still early. The tooling is maturing, the terminology is unsettled, and the automation depth lags well behind what software teams take for granted. But the direction is clear: content workflows that embrace iteration, automation, and shared ownership will outperform those that don’t.
This article is based on reporting originally published at searchcontentmanagement.techtarget.com.