Pilot projects are when AI solutions audition for a starring role in an organization’s future performance. When pilots don’t go well, it likely means AI and the people using it were given the wrong stage directions.
Actors in a play don’t just speak their lines, for example. They’re told exactly where to move, where to stop and hit their mark, what props to pick up, and how to interact with other performers.
Those stage directions not only help the actors but also all the backstage crew members who adjust the set, sound, and lighting to enhance what the audience sees.
In business, those stage directions are the workflows that map out how critical processes get executed every day. Establishing the right AI workflow and your pilot project has a much better shot at moving into full production.
Your AI pilot inherited a workflow built for a different era
Enterprises are accelerating AI pilot projects even as they grapple with the technology’s disruption of search. According to WordPress VIP’s Future of the Web report, for example, 72% of business leaders say publishing speed and freshness still matter for AI-driven discovery.
Third-party research reports show 43% of AI projects remain stuck in the pilot stage, even though fully integrated AI companies are four times more likely to experience revenue growth than those whose pilots stalled.
The barrier to success isn’t surprising given that the elements of most enterprise workflows, particularly marketing operations, were designed long before generative or agentic AI entered the picture:
- Approval chains: Until recently, a single copywriter might have written a draft that gets reviewed and signed off by brand owners, legal departments, and sometimes senior executives. If AI helps produce more content more quickly than an entire team of copywriters, it overwhelms those stakeholders and creates an approval bottleneck.
- Data sources: Marketers inform their campaigns using data spread across a CRM, a CDP, a marketing automation suite, and even old-fashioned spreadsheets. These platforms have historically been updated manually by people on a periodic (but not always consistent) basis. That opens the door to potential fragmentation or siloed data, making it harder for AI to work effectively.
- Incentives: A demand gen marketer gets rewarded when they spit out more campaigns that capture leads, just as content operations have often been judged based on volume and engagement. AI offers the opportunity to place greater emphasis on business outcomes such as revenue or the quality of business decisions. You need to help staff understand their role in driving these areas and recognize them accordingly.
How should handoffs change within an AI workflow?
Eighty-five percent of enterprise leaders say AI content published without human review erodes brand trust, according to WordPress VIP’s Future of the Web report. That means even if agents can help draft, optimize, and schedule content, the moment of truth comes when other sets of eyes give it a thorough read.
AI can make that job harder if it’s only used to create more content. The better approach is to create an AI workflow in which agents address the most common errors and brand violations and escalate those that require human analysis. These could be content governed by industry regulations, content that references competitors, or questionable use of proprietary data.
This becomes easier if you bring your ideal customer profile (ICP) and messaging documents into a single source of truth, rather than asking AI agents and people to scour multiple systems. From there, make sure everyone understands where AI is involved in the new workflow, who should check AI outputs, and what to do based on what they see.
What is exception management in AI content operations?
Exception management in AI content operations means you’ve designed a workflow to take into account any actions or outputs that deviate from what’s expected. As humans, we tend to create workflows based on an ideal scenario where everything functions perfectly, but AI pilots perform better when employees are prepared to spot anomalies and intervene.
AI might be fine with turning technical content from a knowledge base into a customer-facing FAQ page or generating metadata for a blog post. Once you’re up and running AI at scale, though, content assets could wind up making claims the brand can’t verify or doesn’t comply with voice and tone guidelines.
These exceptions can be designed as business rules so that employees get alerted about content that’s high-risk, potentially violates a policy, or includes missing or conflicting data
What KPIs should be used to guide AI workflows in enterprise content marketing?
AI ROI is best assessed using key performance indicators (KPIs) that capture how content contributes to an organization’s biggest goals. These could include lowering customer acquisition costs (CAC) through content that attracts more organic leads. It could also mean increased customer lifetime value (LTV) to nurturing relationships through content that encourages repeat spending or higher-ticket purchases.
Correlating content to those KPIs requires having deep visibility into the performance of every asset. Impressions only tell you part of the story. You’ll gain richer analysis by tracking a piece of content’s engaged time, recirculation rate, and how your referrers are changing.
Monitoring these areas should happen from the moment an AI pilot program launches. When you have an analytics platform like Parse.ly as part of your CMS, this becomes much more straightforward.
Expand AI automation based on the most repeatable workflows
AI works best when it functions like a new operating system rather than a set of tools. As your first pilots move into production, begin developing a roadmap based on how frequently a task needs to be done, the degree of manual effort, and whether you’ve already put in business rules and exceptions that tie back to human team members.
Some good candidates in many enterprise marketing functions include:
- Developing briefs that are given to content creators and editors to produce marketing assets.
- Content library reviews to identify assets that need updating or refreshing.
- Content library reviews to identify assets that require localization, translation, or accessibility updates.
- Content repurposing, such as turning a proprietary research report into blog posts, social posts, and webinar decks.
- Internal link and taxonomy updates for blog posts, product pages, and support documents.
Why your CMS can help safeguard AI ROI
Embedding AI within the heart of a marketing function doesn’t require bolting on a point solution. When you invest in an open, intelligent CMS, you get the ability to leverage AI innovations within workflow capabilities for reviews, approvals, and auditing that are already deployed.
The right CMS not only aligns with the business rules you’ll develop or tweak as part of an AI pilot, but enforces them to bolster governance. This is not only critical for regulatory compliance, but also for ensuring basic AI explainability and auditability.
The basic rule of thumb is simple: If an AI pilot doesn’t seem ready for takeoff, it’s time to take a step back and begin rebuilding key workflows from the ground up.
