Workfront should be the single place where all work infomation lives, from the first request through delivery, so context doesn't get lost and teams aren't stuck rebuilding it with spreadsheets or emails.
Where the breakdown begins
One challenge I see repeatedly with clients is that valuable information gets lost as work moves through the process. A request starts as an intake form, becomes an issue, gets converted into a project, and suddenly the original business context isn't as easy to access. That's where teams begin creating spreadsheets, email trails, or custom workarounds to fill the gaps.
I worked with one client that wanted to maintain every original request submitted, even after it was converted into a project. Their concern wasn't the project itself, it was preserving the decision-making history behind the work. They needed to understand who requested it, why it was requested, and what information was included when the request first entered the system. To solve that challenge, we had to develop additional automation to maintain the historical records while still supporting the operational workflow.
That's why I often say Workfront needs to become the source of truth. When marketing, creative, operations, and leadership teams are all looking at the same information from intake through delivery, decision-making becomes faster and reporting becomes significantly more reliable. AI can certainly help automate the process, but the real value comes from ensuring the data and business context remain connected throughout the entire lifecycle.
What gets complicated in the actual work
Marketing execution sounds simple until you start looking at how work gets done in reality.
A campaign may begin as one strategic initiative, but from an operational perspective it can quickly become dozens of deliverables spread across channels, and creative production teams. Managing those relationships becomes increasingly complex as organizations scale.
One client I worked with struggled because they had large campaign projects, but the actual work needed to be managed across multiple teams and deliverables. Everyone understood their portion of the work, but there wasn't an efficient way to keep campaign-level information flowing between all the supporting projects. As a result, project managers were spending too much time manually coordinating work instead of driving outcomes.
This is where I see AI making a meaningful impact. Rather than spending hours creating project structures or replicating information across workstreams, teams can leverage AI and automation to establish the framework faster. Instead of asking project managers to become administrative experts, we can allow them to focus on resource planning, prioritization, and delivery.
From a marketing perspective, that's where AI is most valuable, not replacing expertise, but eliminating the operational overhead that slows teams down.
Why workflow visibility matters
One thing I've learned is that not everyone experiences work the same way.
Project managers often think in timelines and dependencies. Designers think in deadlines and available capacity. Executives are focused on milestones and outcomes. When everyone is forced to look at the same view, somebody inevitably loses context.
I had a creative team tell me that they didn't necessarily need more meetings or more status updates. They simply wanted visibility into when upstream work would be completed so they could plan their own schedule. Something as simple as viewing dependencies visually through boards or Gantt charts allowed them to better manage their time and reduce urgent, unplanned rework.
The same principle applies to integrations.
One of the biggest wins I've seen came from connecting Workfront directly with Adobe Creative Cloud. Prior to that, designers were downloading files, uploading revisions, sending screenshots in Teams, collecting feedback in email, and then trying to determine which comments were actually actionable. Once that workflow was connected, the feedback, assets, and approvals existed in the same ecosystem. Designers spent less time managing files and more time creating content.
When organizations improve workflow visibility, they're not just improving project management, they're giving employees back time to focus on higher-value work.
How we figure out what is actually slowing things down
One of my favorite questions to ask clients is: "You know you're three weeks behind. Do you know why?"
Most organizations know they have delays. Far fewer can identify the specific cause of those delays.
I worked with a team that repeatedly experienced bottlenecks in recurring campaigns. Every month, the same work seemed to fall behind schedule. Everyone had a theory, but nobody had evidence. Some believed it was legal review. Others thought it was creative production or stakeholder approvals.
We used reporting and custom logic to systematically eliminate assumptions and identify where delays were actually happening. What I find particularly interesting is how AI can accelerate that process. Instead of spending hours building complex reporting structures, I can leverage AI to help build reporting logic, analyze trends, and surface potential bottlenecks much faster.
For me, AI is becoming a business analyst's accelerator. It helps us move from "we think this is the problem" to "we know this is the problem."
That distinction is what ultimately drives better operational decisions.
Where planning and automation change the process
One of the most successful transformations I've led wasn't focused on execution, it was focused on planning.
The client had a fairly mature request process, but the information needed to actually start work wasn't available until weeks later. Campaigns would enter the system, yet delivery couldn't begin because critical briefing details were still being collected. In some cases, the briefing process itself took nearly two months to complete.
We redesigned that process using Workfront Planning and automation. Rather than collecting information through disconnected conversations and documents, we centralized the briefing process and began automating key business decisions.
For example, if a marketing campaign targeted a specific region, the system could automatically determine which deliverables, tasks, and requirements were needed. Teams no longer had to remember every exception or manually configure projects. The process became repeatable and scalable.
The result wasn't simply operational efficiency. It significantly reduced time-to-market and allowed the marketing team to spend more time on strategy rather than administration. In many ways, that's the promise of AI and automation, not doing the work for us, but helping us get to the work faster.
What it takes to make people actually use it
Technology adoption is rarely a technology problem. It's a people problem.
The most successful clients I've worked with all have one thing in common: leadership actively supports the process. If leaders continue to accept work through email, chat messages, or side conversations, employees will do the same. If leaders reinforce the platform as the place where work happens, adoption follows naturally.
I often encourage clients to think beyond task templates. A template shouldn't just define dates and durations, it should define the entire experience. Dashboards, reports, approvals, permissions, integrations, and user views should all support how each role needs to work.
One of my clients experienced a major shift after implementing approval-based workflows. Instead of searching through emails for feedback or tracking down stakeholders, approvals became part of the process. If something wasn't approved, the work couldn't move forward. It created accountability and encouraged users to engage within the platform.
Ultimately, adoption happens when people see value. That's why I always encourage organizations to start with a pilot or proof of concept. When users can see a process become faster, more transparent, and easier to manage, they stop viewing the platform as another tool and start viewing it as an enabler of their work.
FAQ
Why can't a project just replace the original request once work begins?
Because the context behind the request, who asked for it, why, and what information came with it, is often what stakeholders need later for reporting, audits, or decision-making. Losing that history forces teams to rebuild context manually.
How does AI actually help with campaign operations, if it isn't doing the creative work itself?
AI's biggest impact is on the operational overhead around the work, things like standing up project structures, replicating information across workstreams, and building reporting logic, so project managers can spend their time on resource planning and delivery instead of administration.
What's the fastest way to find out why recurring work keeps falling behind schedule?
Use reporting and custom logic to test each theory against evidence rather than relying on assumptions. This turns "we think this is the problem" into "we know this is the problem," which leads to a targeted fix instead of a guess.
Does better workflow visibility mean more status meetings?
Not necessarily. In practice, teams often need less status reporting once they can see dependencies directly, for example through boards or Gantt charts, because they can plan their own schedules without waiting on updates from others.
What's the biggest factor in whether a team actually adopts a new platform like Workfront?
Leadership behavior. If leaders keep approving work over email or chat, employees will too. Adoption follows when leadership consistently reinforces the platform as the place where work happens, and when a pilot or proof of concept lets users see the value firsthand.