Opal’s Gem adds an AI copilot for brand-aligned marketing planning
Opal’s new copilot focuses on using brand context and historical campaign data to reduce manual alignment work, from recurring workflow setup to faster performance interpretation.
Opal has rolled out Gem, an AI copilot built into its marketing planning platform, aimed at helping teams align campaign execution and performance interpretation with brand strategy.
The product is positioned to reduce time spent on alignment work such as explaining results in meetings, chasing data across systems, and recreating repeatable campaign workflows.
Table of contents
Jump to each section:
- What Gem is designed to do inside Opal
- Why Opal frames misalignment as an “alignment tax”
- Data, privacy, and the “private environment” approach
- Operational use cases for campaign teams
- What this signals about AI in marketing measurement
What Gem is designed to do inside Opal
Gem is positioned as a conversational copilot that draws on a brand’s historical information, context, and guidelines so that campaign questions and answers are grounded in how the organization plans and executes marketing.
Because it is built on Opal’s existing platform, the promise is not just content generation. It is faster retrieval and interpretation of campaign-relevant context that would otherwise require manual searching, spreadsheet work, or repeated explanations across teams.
Gem launched April 14 and is intended to help synchronize execution with intent by tying campaign activity and campaign data back to brand strategy.
Why Opal frames misalignment as an "alignment tax"
Opal’s CEO George Huff describes ongoing coordination overhead as an “alignment tax,” describing it as time lost to fire drills, meetings, and repeated efforts to get teams aligned on what performance means and what should happen next.
This framing matters because it treats inefficiency as a measurable operational cost, not just a soft collaboration issue. In practice, the “tax” shows up when teams cannot quickly connect campaign outputs to strategy, or when performance narratives depend on manual data gathering and interpretation.
Opal also cites an expectation that machine-generated collateral could grow five-fold by 2030. If content volume rises faster than measurement and governance, teams can end up producing more outputs while becoming less confident about what is working.
Data, privacy, and the "private environment" approach
Gem operates in a private environment within Azure, with Opal indicating that customer data is not used to train underlying models.
For marketers, this is a practical design choice, not just a technical one. Many organizations want AI assistance on internal calendars, performance context, and brand guidelines, but they also need controls that prevent sensitive data from being repurposed for model training.
This approach also supports use cases that depend on long histories of campaign data. Huff notes that some clients have campaign data going back more than a decade, and that built-in context can outperform workflows where users must remember to paste details into each prompt.
Operational use cases for campaign teams
Gem’s value proposition is tied to repeatable planning and reporting tasks that consume time across marketing orgs:
- Turning existing assets, guidelines, and historical context into quicker answers via a conversational interface
- Reducing manual work to assemble board-, investor-, or ownership-ready presentations, based on the experience described by Associated Luxury Hotels International’s VP of marketing Meghan Hanna
- Recreating recurring workflows, such as generating a monthly newsletter calendar from an existing template rather than rebuilding each step manually
- Replicating frameworks from successful campaigns by referencing what already exists in the platform
Opal’s argument is that while general-purpose tools like ChatGPT or Claude can handle some tasks, the advantage here comes from being integrated with the planning system where campaign calendars, assets, and performance context already live.
What this signals about AI in marketing measurement
Gem is being introduced in a climate where marketing leaders are under pressure to prove ROI and where skepticism can rise when measurement is weak. Gartner found that 84% of companies are stuck in a “doom loop” tied to underfunding measurement tools, leading to unclear impact, more skepticism about marketing’s value, and tighter budgets.
In that context, copilots that connect planning artifacts, campaign history, and measurement narratives are less about replacing marketers and more about reducing the time and friction involved in explaining performance and deciding next steps.
The key question for teams evaluating tools like Gem will be whether the AI meaningfully improves decision velocity and confidence, or whether it mainly speeds up documentation while leaving underlying measurement gaps unchanged.
