Today’s MarTech Top Voice: Jonathan Moran, Head of MarTech Solutions Marketing at SAS.
For the recent issue of the MarTech Insights Top Voice Interview Series, we introduce Jonathan Moran, Global Marketing Lead at SAS for the marketing solutions portfolio, specifically the SAS Customer Intelligence 360 platform, which is the one that everybody talks about in his entire field of work.
SAS is a global leader in advanced analytics and AI, and customers worldwide rely on SAS to transform customer journeys using data-driven approaches when faced with new challenges or when seeking the most effective solutions.
His knowledge spans the marketing technology and customer analytics industry for over 20 years.
Here is a sneak peek of the fascinating interview. Jonathan reveals how the Agentic AI is taking over marketing, the whole scene specifically starting from content creation and eventually to autonomous decision-making, also the struggles of bloated MarTech stacks, and quantum computing is likely to become the next source of marketing intelligence.
Let’s dive in…
MarTech Insights (MTI): Hi Jonathan, welcome to the MarTech Insights Top Voice Interview Series! To begin, please share a bit about your role at SAS and the career journey that led you to this position.
Jonathan: At SAS, I am responsible for marketing activities associated with one of SAS’s many lines of business – that being Martech.
We market and sell a product by the name of SAS Customer Intelligence 360.
I have spent my entire career, over 20 years in the Martech space, working for a variety of companies, large and small. I’ve been lucky enough to do not just marketing, but consulting, pre-sales, technical support, and other roles as well.
MTI: What is the most contemporary definition of “MarTech solutions?” How do you define this at SAS and for customers?
Jonathan: The broad definition of Martech is any piece of technology software that enables better marketing. For us, we focus on customer engagement specifically and use AI and analytics to power intelligent engagement between a brand and its customers.
MTI: In your recent study titled “Marketing and AI: Navigating New Depths,” you have highlighted a widening gap between “Adopters” and “Observers” of Agentic AI. What do you see as the single biggest mindset or structural difference separating these two groups?
Jonathan: I think there are a variety of cultural and organizational differences that create the gap between adopters and observers. For me, I think it comes down to organizations that are agile, open to some risk-taking, and have the resources to embrace and adopt emerging technologies. If the organization is willing to undertake some risk to innovate rapidly and in an agile fashion – and has the resources to do so – the payoff in terms of AI output can be significant.
Surprisingly, the largest number of adopters were from larger organizations versus smaller ones. This tells us that you don’t necessarily have to be a small organization to be agile.
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MTI: The link between Agentic AI and quantum computing is fascinating. Why do you think adopters are already building quantum into their roadmaps, and how might the two converge in marketing?
Jonathan: I think that adopters know that quantum computing is going to be required to handle the workloads of the future, especially when organizations have thousands of agents operating simultaneously. Quantum will be able to handle some of the large-scale, high-volume, complex computing jobs that may be required as AI advances.
MTI: Looking at the state of martech stacks in 2025, many organizations are struggling with bloated tools, poor integration, and underutilized platforms. What do you see as the biggest challenges and opportunities for martech stacks in 2026? And how should CMOs partner with martech experts to streamline, modernize, and solve these problems proactively?
Jonathan: Integration and tech bloat are surely too common problems for marketing organizations. As marketing evolves, I think there are opportunities for martech vendors to streamline their stacks, make them more composable, and improve integration capabilities. CMOs and their teams have a tough job in sorting through the martech vendor landscape to find which solutions work best for them. One piece of advice that I often give is to start with the end goal, meaning which high-priority use cases are mission-critical, and what tools do you need to address those use cases? Working backward from there allows organizations to audit what they need and don’t need in their stacks.
MTI: The SAS “AI for Marketers” framework emphasizes the importance of “getting AI infrastructure and knowledge in place — forging a path from GenAI to agentic.” What are the most common infrastructure or architectural gaps you see in marketing organizations trying to make that transition, and how should they prioritize bridging them?
Jonathan: The most common gaps revolve around people, process, and technologies. First, there is a need for tech workers who are highly skilled in developing and deploying agentic technologies. Not many organizations have those people employed, but they do have people who could do the job with some education and enablement. So, there is a learning gap that exists from a point of view. Second, from a process perspective, data has to be high-quality and accessible to progress to agentic.
On top of that, processes must exist for agentic systems to access and query that data to provide contextually relevant, real-time responses to both internal and external systems. These processes can be difficult to set up initially. Finally, technology ecosystems must be able to handle not only the most basic agentic use cases, such as using chatbots, but they must also be ready to embrace future agentic use cases, such as autonomous customer journey design, orchestration, and optimization.
MTI: Marketers often get stuck at the “pilot” stage with AI. What practical steps can leaders take to move from experimentation to true operationalization?
Jonathan: I think developing an agreed-upon plan of action within an organization before use case deployment is best. This means creating a contract of sorts that states “if we can complete this AI use case, with this success rate, and this percentage of data quality/compliance, etc,” then we can move that use case from development to production – given all of these approvals and sign-offs. Testing and learning in development before deployment is critical – along with maintaining a “human-in-the-loop approach”.
Many cases get stuck in pilot due to a lack of confidence in their ultimate success, with many AI detractors claiming that the use case deployment could ultimately cause more harm than benefit.
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MTI: You mention that Agentic AI goes beyond content generation to autonomous decision-making and real-time optimization. Can you share an example of Agentic AI in action within marketing today?
Jonathan: Sure, here is a simple example:
- Agents can understand who visits web properties and their digital behaviors.
- Agents can pass this information to marketing and buyer teams.
- These teams then employ additional agents to act (create customer journeys).
- A human in the loop informs and approves the action.
- An agent (or a human) can then contact the customers, book a meeting, and work towards a conversion event.
MTI: The study shows that 75% of early adopters are using AI to its full potential. What kinds of ROI are they actually seeing — is it cost savings, pipeline growth, or customer experience improvements?
Jonathan: We didn’t ask them to specifically detail that in the research, but most commonly it’s cost savings and process efficiencies (which translate to cost savings). It’s more rarely CX improvements this early in the AI game, as most customers still prefer speaking/engaging with humans.
MTI: For organizations still hesitant, what’s the most compelling “quick win” use case that can demonstrate Agentic AI’s business value in under 6 months?
Jonathan: This is very dependent on the organization and industry, but automating a low-level tactical task (such as supplying information, documentation, etc.) with a combination of generative and agentic AI – via digital channels in order to reduce cost to serve- is a very common quick win use case we see.
MTI: Five years from now, do you see Agentic AI being a must-have core system for marketers, like CRM or marketing automation is today?
Jonathan: Yes, without a doubt.
MTI: If you could give one piece of advice to marketers worried about being left behind in the Agentic AI era, what would it be?
Jonathan:
- For marketers individually, it’s not too late to start educating yourself and trialing products and solutions of interest to you.
- For organizations, data governance is key (once that is in place) – start experimenting with AI of all types.
Check out job roles for AI engineers – and how roles such as these might benefit your organization.
MTI: Tag a leader in the industry you would like to recommend for the “MarTech Top Voice Interview Series”:
Jonathan: Pawan Verma on AI Decisioning.
Recommended MarTech Insights: MarTech Top Voice Interview with Terry Flaherty, VP, Principal Analyst at Forrester
For media inquiries, you can write to our MarTech Newsroom at sudipto@intentamplify.com
About Jonathan
Jon is responsible for global marketing activities for Smarketing solutions, including SAS Customer Intelligence 360.
With over 20 years of marketing technology and customer analytics industry experience, Jon has had the opportunity to not only architect, develop, demonstrate, and implement analytical marketing software solutions, but also to work with Fortune 500 customers across industries, helping them solve unique business challenges.
Jon graduated from North Carolina State University with an undergraduate double major in Marketing and Spanish Languages and Literatures. Mr. Moran also holds an MBA from North Carolina State University with a concentration in Technology Commercialization.
About SAS
SAS is a global leader in data analytics, artificial intelligence, and customer intelligence solutions, helping organizations across sectors transform data into trusted decisions. With decades of innovation and deep industry expertise, SAS delivers powerful, scalable analytics that drive performance, personalization, and profitable growth for enterprises worldwide.
