Expanding Jasper’s Forward-Deployed Model for Enterprise Marketing

Enterprise marketing teams have access to powerful AI. The hard work is connecting it to the product knowledge, brand standards, systems, and decisions that determine how marketing gets done inside their organization.

At Jasper, we believe the greatest gains for marketing organizations will come from redesigning that work around what AI can do now rather than bolting it on to existing systems. To do so requires engineering expertise, marketing judgment, and technology that puts a company’s knowledge to work.

That is why we are expanding Jasper’s forward-deployed model and increasing the number of engineers and marketing experts who work alongside our customer teams. Together, they identify where AI can do the highest value work that is aligned with their marketing goals. Using Jasper they build and integrate workflows that combine company knowledge, purpose-built agents, and human oversight.

Why forward-deployed?

By now, enterprise marketing teams aren’t short of AI tools or token spend. What they’re missing are two things: a consistent definition of good work, and a way to connect that work to business outcomes.

Few marketing teams have a written, agreed standard for what good work looks like. It usually lives in the heads of a few experienced people, varies from reviewer to reviewer, and changes with the deadline. Without that definition there is no way to know whether AI is producing good work or just producing more of it.

Without a clear link between daily tasks and strategic business outcomes, organizations cannot determine which AI investments are actually worth doing. 

Most employees focus AI on isolated, front-of-mind tasks like building campaign plans or reviewing briefs, completely missing opportunities for team-wide workflow transformation. Even when teams successfully redesign a workflow, they remain blind to how those changes impact the bottom line.

We're growing our forward-deployed model to close those gaps.

Our engineers and marketers show customers what's possible, bring best practice from working with hundreds of enterprise marketing organizations, and work alongside them to move AI beyond being a tool that improves individual productivity to a system that drives measurable business outcomes.

What do our engineers and marketers do?

Engineers can build workflows, but they’re probably not the best person to tell you whether your marketing is any good. That’s why we pair them with forward-deployed marketers who have run campaigns, product launches and localization programs themselves. 

Our marketers do more than review output. They help decide which work is worth redesigning, define what good looks like, and work out where AI can act and where human judgment is needed. Our engineers build the systems, integrations and automations that turn those decisions into a workflow that runs inside a customer’s existing tools, with their governance built in.

In our experience, that pairing is the strongest indicator that an engagement will show up in a customer’s P&L, rather than just help with productivity.

How do we know the work is good?

The first thing our marketers do with a customer is sit down with the people who hold the deepest brand knowledge to understand what good looks like for the work the team has to deliver and identify where AI can add value. What makes a product page strong? What makes a localized campaign on-brand in a market the central team has never worked in? What does compliance actually require, as opposed to what people assume it requires? Those conversations turn a standard that lived in a few people's heads into one that is written down and agreed.

Our engineers then encode that definition into repeatable evaluations: a set of tests the workflow runs against whenever something changes, whether that's the model, the knowledge it draws on or the systems it's connected to. That lets us answer the question that matters before anything reaches production. Can the model produce work that meets this standard, not once but repeatedly and reliably?

The same process maps where the model is capable and where it isn't. That map decides where AI can act on its own, where a person reviews, and where AI shouldn't be used yet. In enterprise marketing, the downside of one off-brand claim or missed compliance step can outweigh the upside of a thousand good outputs. We design for that asymmetry up front rather than discover it in production.

This is the step most teams skip, and it's where much of the value comes from. A workflow without a definition of good is automation. A workflow tested against one is something the business can rely on.

How our engagement works

1. Define the workflow, the outcome, and what good looks like. Identify the work to redesign, trace it from the task to the job it serves to the outcome it should move, establish a baseline, and agree on how success will be measured.
   
   
2. Build, test and validate. Bring the customer’s company knowledge into Jasper IQ, connect the required systems, and test the workflow against the evaluations and governance requirements agreed in the first step.
    
3. Operationalize and expand. Upskill the customer team, measure performance, and use the results to guide improvements and decide which workflows to focus on next.

At the end of each engagement our aim is to enable the customer team to adapt, extend and further optimize the workflows we’ve co-created, rather than being dependent on our experts.

What does this look like in practice? 

We're already seeing the impact of this approach across a range of enterprise marketing challenges, from global content operations to AI search.

One global enterprise needed to launch content in more languages, faster, without loosening a strict compliance process. While general-purpose AI tools could translate the text, the customer needed translations that met the same quality bar as its original content, with accurate terminology and consistent brand messaging throughout the approval process.

Our team worked with the customer to define the brand and quality standards behind a successful translation, then mapped how launches moved from locked content to live pages. From there, we built a workflow on Jasper that carried the customer's product knowledge and standards into every language, with compliance review integrated throughout the process rather than left until the end.

The result:

• Reduced average translation timelines from 22 to 13 days.
• Launched content in nine languages in just 11 calendar days, including compliance review.
• Projected to save approximately $300,000 annually.

A global bank was being outranked in AI-generated answers on a commercially important topic in a key market. Monitoring data showed the problem but not what to do about it. Our team helped prioritize the page with the most commercial upside, combined the visibility data with local market knowledge, turned it into specific changes the bank’s stakeholders could review and approve, and measured what moved. Within two weeks:

• Increased citations across leading LLMs by 114%.
• Moved its offering from seventh to top three on Perplexity.
• Achieved 77% brand presence in measured AI-generated answers.

The bank now has a repeatable method it is applying to other pages and markets.

In both cases, the technology was necessary but not sufficient. Our forward-deployed experts made the difference by understanding the work, defining what good looked like, deciding what to change and building it into how the team operates.

The technology underneath

Our forward-deployed teams build on two existing Jasper capabilities: 

• Jasper IQ, our proprietary intelligence layer, that turns a company’s knowledge (brand standards, product information, customer insight, when human judgment is needed) into context and guardrails for AI.
• Purpose-built agents use that intelligence to carry out defined areas of marketing work, with governance and human oversight built in.

We've continued investing in this technology over the past year, introducing GEO Hub, GEO Agent, Jasper MCP, and Product IQ. During that same period, we've nearly doubled our enterprise annual recurring revenue, reflecting growing demand for what we're building with our customers.

Why this matters now

More than 900 enterprises, including 20% of the Fortune 500, use Jasper. The ones getting the most value from AI are those who have rebuilt the systems that run their marketing organization around AI.

The measure of AI adoption is not how many people have access to AI tools, how many tokens they consume, or how much they produce. It’s what it returns to the business. Our forward-deployed model exists to connect the tokens an enterprise spends to the outcomes it needs, and we’re expanding it because that’s where our customers are seeing results.

Learn more about how Jasper’s forward-deployed experts build AI with you.‍