Agentic AI adopters anticipate near-term marketing impact from quantum computing
If you’re already using agentic AI in your organization, you know it’s a tool that can act, learn, and make decisions autonomously, often faster and more reliably than any human analyst. The next step is about adding computational power that keeps up. That’s where quantum computing becomes critical.
Think about running hundreds or thousands of autonomous agents simultaneously, managing audiences, testing interactions, generating customer journeys in real time, and adapting models on the fly. Today’s classical computing infrastructure is already stretched. Quantum processing, with its unique ability to handle multiple states of information at once, unlocks throughput previously out of reach. This isn’t speculative. It’s happening now.
According to new research by SAS and Coleman Parkes, 31% of marketers already using agentic AI expect quantum computing to impact their operations within two years. Another 6% say they’re already seeing it. These numbers matter because they come from decision-makers who’ve moved past theory and into practical application.
The reasoning is simple: high-speed, high-volume marketing tasks, like personalization, predictive analytics, and multi-step decision-making at scale, will require something more than incremental CPU upgrades. They’ll need quantum-level firepower.
Jonathan Moran, Head of Martech Solutions Marketing at SAS, said it clearly. To manage hundreds or thousands of agents working alongside your teams, you’ll need what quantum brings to the table: a lift in speed and scope that’s not possible using only traditional infrastructure. If you’re not already thinking about how quantum fits into your tech agenda, now’s the time to start.
AI maturity enhances understanding and proactive preparation for quantum computing
There’s a clear pattern here. The more advanced your AI capabilities, the more likely you are to understand where things are headed. And right now, that means quantum computing.
In companies that have already adopted agentic AI, 49% of marketers report that they understand quantum computing well. That’s compared to just 16% across the broader marketing community. That gap tells you everything. It’s about building systems that are ready to ride the next wave instead of trying to catch up later.
Also worth noting: half of the organizations using agentic AI have already built quantum computing into their digital or innovation roadmaps. That means these teams aren’t just talking about quantum, they’re planning for it, budgeting for it, and engineering towards it.
This is where being an early mover helps. You’re not reacting to the market, you’re helping shape it. AI maturity isn’t just about better models or faster response times. It’s about organizational readiness. And that includes preparing your infrastructure, training your teams, and updating your strategy to factor in the computational environment of tomorrow.
Executives who treat quantum like a future speculation will miss the moment. Those who understand how it integrates with existing AI systems and long-term goals are already ahead. Investing in AI today builds the operational fluency that you’ll need to make quantum viable in your business tomorrow.
Perception of quantum adoption timelines varies by stage of AI integration
Not every organization sees quantum computing the same way. What’s more interesting is how much that perception shifts depending on your current level of AI implementation.
If your company is already running agentic AI, you’re likely looking at quantum computing as a logical next step, instead of a theoretical concept locked five years in the future. You’re already building systems that automate decision-making, manage massive datasets, and respond faster than traditional workflows could allow. For teams in this space, computational bottlenecks are real, and persistent. That’s why these organizations see quantum as something they want to integrate now, not later.
The contrast shows up when you look at organizations still in the planning or exploratory stages. They tend to place quantum adoption further out on the horizon. These delays reflect a difference in strategic urgency, less about the technology itself and more about mindset and readiness.
If you’re a C-level executive, pay attention to how your internal roadmap compares with where the market is moving. Organizations lagging on AI adoption will have longer lead times before they can evaluate emerging technologies like quantum in any meaningful way. The signal is clear: Proactive adoption generates greater visibility and control over your tech trajectory, while hesitation reduces your ability to capitalize on emerging computational capabilities.
The companies pushing hardest on agentic AI today are the same ones that will lead tomorrow’s quantum-enabled marketing operations. They’re aligning internal architecture, team capabilities, and strategic priorities to match what they already know is next.
Distinct industry applications highlight quantum’s Sector-Specific value
Quantum computing isn’t going to land the same way in every sector. The use cases are specific. Marketing leaders in different industries identify the opportunities that match how their systems currently handle data, customers, and outcomes.
In banking, quantum’s value centers on advanced predictive analytics, 80% of respondents in that sector say that’s where they see the near-term opportunity. It makes sense. Banks process massive volumes of constantly changing data and are under pressure to act fast with precision. They want actionable models that can forecast outcomes and adapt instantly. Quantum shortens the time from data to decision.
Insurance firms are aiming at something different. Here, 69% say real-time customer journey simulation is the priority. It shows where the industry wants to go: from reactive claims and support systems to real-time guidance that helps customers and insurers take smarter steps based on the end-to-end interaction.
In life sciences, 67% of professionals identify hyper-personalization at scale as their primary use case. Personalized healthcare, diagnostics, pharma marketing, this space has unique complexity and huge stakes. Quantum supports analysis that has to work across variables you can’t easily compute using only traditional systems.
The public sector is also watching closely. Their focus is on synthetic data generation (29%) and dynamic pricing (27%)—likely related to modeling policy outcomes, optimizing services, and simulating environments in highly regulated spaces where data scarcity is often a problem.
Bottom line, different industries are building different expectations around quantum computing, and each of them is valid in their context. The key is aligning your organizational objectives with the quantum capabilities most relevant to your sector. That allows you to make targeted technology bets. It ensures that you’re moving with intent and precision as new computing capabilities enter your market.
Small and Medium-Sized businesses (SMBs) show interest in quantum-driven synthetic data generation
Quantum computing isn’t limited to the top end of the market. While many assume it’s only relevant to large enterprises, new data makes it clear that small and medium-sized businesses are also looking for ways to extract value. And they’re doing it by focusing on a specific area, synthetic data generation.
Among SMB respondents in the SAS and Coleman Parkes study, 20% believe quantum’s real benefit lies in its ability to generate high-quality synthetic data. For comparison, that number drops to 11% among enterprise respondents. This suggests that smaller players see quantum not as a far-off experiment, but as a solution to immediate, pressing needs, particularly around access to usable data without the overhead of traditional collection and storage methods.
Why synthetic data? Because it allows smaller organizations to run simulations, test models, and refine customer strategies in environments where real-world data is limited, sensitive, or too expensive to gather. Quantum computing enhances this by increasing the volume, accuracy, and speed at which synthetic datasets can be created and applied.
For executives in the SMB space, the message is straightforward. You don’t need to wait for large enterprise adoption to push forward. There’s already a unique use case on the table that aligns with your scale and your priorities. And the playing field is more level than most assume. The question isn’t whether quantum is coming, it’s whether your organization is prepared to leverage it toward specific, high-impact goals.
Proactive integration of quantum capabilities reflects a shift from passive observation to strategic planning
Agentic AI adopters aren’t just watching quantum from the sidelines. They’re building for it. Half of the organizations already using agentic AI say they’ve integrated quantum computing into their digital or innovation roadmaps. That number matters. It tells you where strategy is actually heading, beyond the hype.
This isn’t a reaction to market buzz. It’s long-range planning shaped by current infrastructure needs. Agentic AI systems are already creating significant computational pressure, generating real-time models, continuous automation, and large-scale decisioning. Quantum isn’t viewed as a replacement but as a complementary system that can handle the load ahead.
What’s shifting is the posture. Organizations are moving from passively monitoring quantum developments to actively designing for their future integration. They’re embedding flexibility into their technology stacks and prioritizing cross-functional expertise to ensure the skills and hardware are in place by the time quantum capabilities become commercially viable.
If you’re in the C-suite, this matters. You’re not being asked to bet on a hunch. You’re being told that the groundwork for quantum is already being laid in companies that are serious about staying ahead. Proactive integration means you’re not waiting for the technology to catch up, you’re ensuring your business is ready when it does. That’s not just strategic foresight. That’s disciplined execution aligned with inevitable change.
Key highlights
- Agentic AI drives early quantum adoption: Marketers already using agentic AI are anticipating quantum’s role within two years, with 6% saying it’s already impacting operations. Leaders should assess how scalable agentic systems may soon exceed classical computing limits, prompting the need to explore quantum.
- AI maturity predicts quantum readiness: Companies with advanced AI systems show nearly 3x higher understanding of quantum computing and are 3x more likely to include it in innovation strategies. Executives should invest in AI depth not just for current gains but to accelerate adoption of next-gen tech like quantum.
- Adoption timelines reflect AI integration level: Organizations effectively using agentic AI are preparing for quantum integration now, while those still planning AI adoption expect longer timelines. Decision-makers should recognize that delay in adopting AI slows readiness for broader tech shifts.
- Quantum’s value depends on industry context: Quantum applications differ by sector, predictive analytics in banking, journey simulation in insurance, hyper-personalization in life sciences, and synthetic data in the public sector. Leaders should map quantum integration to high-impact use cases specific to their industry.
- SMBs see real value in synthetic data: 20% of SMBs view synthetic data as a high-potential quantum application, outpacing large enterprises. Executives at smaller firms should consider quantum in focused, high-ROI areas like data generation where it levels the playing field.
- Strategic planning replaces passive observation: 50% of agentic AI firms have already built quantum into their roadmaps, showing a shift from curiosity to execution. Leaders should follow this lead by aligning infrastructure and capabilities now to enable quantum adoption as it scales.
