A year ago, marketers we still evaluating whether artificial intelligence (AI) belonged in a B2B marketers workflow. Today, marketers apply AI as broadly across data, content, and creativity as any industry.
The State of AI in Marketing 2026 from Jasper explores how AI has transformed from experimentation to scale over the last 12 months, and what is separating beginner experimentation from advanced operational maturity. Jasper CMO Loreal Lynch says the reports offers a roadmap to marketeering leaders aiming to scale their AI efforts without sacrificing quality or strategic integrity.
In our interview with Lynch, we dive deep into the practical steps required to build a sophisticated AI strategy, including how to transition teams from ad-hoc usage to advanced maturity levels, the critical importance of moving beyond vanity metrics to prove legitimate ROI, and the strategies for leveraging AI to enhance personalization while streamlining complex workflows.
Demand Gen Report (DGR): Loreal, great to talk with you again. Jasper’s latest report states “As AI adoption accelerates, expectations for returns rise with it.” What steps can marketers take to transition from beginner to intermediate or advanced AI maturity levels?
Loreal Lynch: Thanks for having me— what we heard clearly from the 1,400 marketers surveyed is that maturity, not access, is the differentiator. Beginner teams experiment with AI in pockets, while more advanced teams embed it into core operations like content production, campaign execution, and optimization.
Practically, that transition starts by treating content as a system, not a series of one-off outputs. High-performing teams design repeatable pipelines that turn a single strategic idea into a full ecosystem of assets across channels, regions, languages, with governance built in from the start. They assign clear ownership, measure ROI from the outset, and let AI continuously test, learn, and refine.
DGR: How can B2B marketers demonstrate the value of AI investments to leadership by showcasing measurable returns?
Lynch: As AI scales, expectations rise. The report shows that only 41% of marketers can confidently prove AI ROI, down from 49% last year, reflecting higher expectations, not weaker performance. To earn leadership confidence, marketers have to move beyond vanity metrics, like time-saved or assets shipped, and connect AI directly to business outcomes like reduced cost per lead, click, or email, lift in campaign conversion or engagement rates, and, over time, increased pipeline or deal velocity. The payoff is real: 60% of teams that can prove AI ROI see 2x returns or better.
Best AI Use Cases
DGR: What are the most effective ways to narrow AI use cases to enable repeatable workflows and greater consistency in B2B campaigns?
Lynch: Rather than applying AI everywhere at once, the most effective teams concentrate on a few high-impact workflows like scaling content, GEO/AEO, or personalization. This approach allows teams to apply brand controls, build systems (not one-off outputs), and orchestrate end-to-end workflows with intention.
DGR: How can B2B marketers evolve ROI measurement beyond productivity to include outcome-based indicators like cost reduction and campaign lift?
Lynch: Productivity is the baseline and outcomes are the win. With 95% of marketers increasing their AI investment in the next 12 months, the focus has to be tying outputs to clear impact like cost savings, engagement lift, and pipeline efficiency. To move from ROI theory to reality, track both leading indicators—like faster launches and conversion gains—and lagging ones like pipeline impact. The top teams define these metrics from day one, link them directly to AI use, and review continuously.
DGR: With 91% of marketing teams now using AI—up from 63% in 2025—marketers are increasingly integrating AI into their strategies. How can B2B marketers leverage AI to enhance creative and production workflows for faster execution and higher content volume?
Lynch: With AI embedded in workflows, a single idea can evolve into a full ecosystem of assets across channels and regions, all without losing quality. This isn’t about replacing strategic thinking; it’s about protecting it. AI takes on the heavy lifting of production, versioning, and adaptation so teams can focus on strategy and innovation. When friction is removed from the system, teams have room to breathe, and content volume becomes a byproduct of strategic thinking rather than a manual tax on the team.
Content Geared Toward Search
DGR: What are the benefits of using domain-specific AI tools for single and multi-asset generation in B2B marketing?
Lynch: High-maturity organizations are 45% more likely to use tools built specifically for marketing. Domain-specific AI is designed for how marketing actually operates, with brand, audience, governance, and channel best practices embedded from the start. At a time when legal, compliance, and brand review processes are the primary barriers to scaling AI, that built-in context matters. When AI understands brand standards, audience nuance, and approval requirements upfront, teams move faster without increasing risk.
DGR: How can B2B marketers optimize content for both AI-driven and traditional search to improve discoverability and engagement?
Lynch: AI plays a direct role by helping teams operationalize search optimization at scale. Today, 1 in 3 marketers use AI for SEO and AI search optimization, and 40% expect to hire an AI search specialist in the next 12 months. Practically, that means using AI to automate intent mapping and keyword clustering, structure content so answers are clear and extractable, and continuously refresh assets based on performance signals. When AI is used as part of an always-on optimization loop, discoverability becomes a repeatable system rather than a reactive effort.
DGR: What role does AI play in improving personalization for B2B customer experiences?
Lynch: AI transforms B2B personalization from static segmentation to dynamic, data-driven engagement. It goes beyond generic messaging to tailor content by role, industry, buying stage, and real-time behavior. By continuously learning from performance and engagement data, AI adapts messaging to what is actually resonating, not just what was planned. The result is relevance at scale. Buyers receive content aligned to their priorities and context, trust builds faster, and deals progress with less friction.
Enterprise Lessons
DGR: How can AI-driven personalization strategies be applied to improve B2B customer engagement and satisfaction?
Lynch: Nearly half of marketers plan to increase their use of AI for personalization in the next 12 months. Applying AI-driven personalization starts with activating richer datasets—think first-party, behavioral, and intent—to deliver messages that actually matter. The power comes from matching content to each buyer’s real needs, every step of the way.
DGR: What lessons can B2B marketers learn from enterprise-level AI adoption to scale their own AI initiatives effectively?
Lynch: Enterprise marketers approach AI as infrastructure. A third of $10B+ companies put 20% or more of marketing spend into AI, with 79% seeing at least 2x ROI. The lesson for all B2B marketers is that scale follows intention, not speed. Long-term investment, relentless measurement, and a systems mindset are how you make AI a true accelerator. When people, process, and technology are aligned around a long-term vision, AI becomes a compounding advantage.
