Agentic AI adopters foresee rapid quantum computing impact on marketing
Marketers at the leading edge, those already using agentic AI, aren’t speculating about quantum computing. They’re planning for it. About 31% of them expect real impact from quantum within the next two years. 6% say it’s already happening. These are organizations investing in large-scale autonomy and processing capabilities that stretch beyond what classical systems can do.
What’s important here is the timing. A lot of leaders make the mistake of waiting until a technology is “proven” before acting. But the path to competitive edge isn’t built on following trends after the fact. The companies already integrating agentic AI are staking their future on deep tech, specifically, tech that scales human cognition and decision-making through automation. That takes serious compute power.
Quantum isn’t hypothetical anymore. It’s quickly becoming the next logical step in marketing infrastructure when we’re talking about processing trillions of customer touchpoints, running models simultaneously in real time, or generating fully autonomous campaigns across channels. These systems won’t wait for manual input, they’ll calculate, optimize, and evolve faster than any human team.
So if your business is planning to remain relevant in the next five years, it’s time to see quantum not as an abstract idea, but as core to the operating model. For most, that starts with understanding where your AI maturity is today. If you’re still in pilot mode, you’re already behind.
Adopting agentic AI drives stronger understanding of quantum
Most marketers don’t fully understand quantum computing, and that’s understandable. It’s still early. Only 16% say they get it well. But that number jumps to 49% among marketers already working with agentic AI. So what’s going on here?
There’s a clear pattern: when you deploy advanced AI systems, systems that operate autonomously, with minimal human prompts, you quickly run into the limits of classical computing. That naturally leads you to learn what’s next. And what’s next is quantum.
Quantum computing doesn’t work the way normal computers do. Instead of binary bits, 1s and 0s, quantum uses qbits, which can hold multiple states at once. This opens up exponential compute possibilities. In short, if you’re generating thousands of AI agents making decisions in parallel, you need a platform that can handle that load. That’s quantum.
Executives need to understand something key: quantum opportunity flows from AI maturity. If your teams are experimenting with AI chatbots or using it to write marketing emails, you’re not there yet. But once you’re running real-time decision engines, autonomous customer journeys, and predictive systems that learn and update on their own, you’ll need quantum to keep up. The curve is steep, and early movers are already climbing it.
Early adopters are proactively integrating quantum computing into their long-term digital roadmaps
Half of the companies already using agentic AI have embedded quantum computing into their long-term digital or innovation roadmaps. That’s 50%. These aren’t companies wondering if quantum will matter. They’ve already decided it will, and they’re aligning internal infrastructure now to accommodate it.
That decision reflects a reality most executive teams are just beginning to grasp: future marketing systems won’t just be faster versions of today’s platforms. They’ll be autonomous, adaptive, and continuous. Marketing decisions won’t be scheduled, they’ll happen in real time. Consumer behavior won’t be tracked, it’ll be predicted and responded to in microseconds. You can’t achieve that scale of intelligence without architectures that go far beyond conventional computing.
Leaders at these early-stage companies are acting on a two-layer insight: first, AI is reaching a point where it can’t be supported at scale without new computational foundations. Second, quantum delivers that next layer.
If quantum isn’t in your roadmap yet, it means your strategy isn’t aligned with next-gen computing. And if that’s the case, it’s only a matter of time before operational bottlenecks become visible, and costly.
Industry-Specific trends indicate diverse applications of quantum computing in marketing
Quantum computing won’t deliver a one-size-fits-all outcome, different industries are already pointing toward different use cases. That’s critical to understand because it means the value of quantum will depend largely on applying it with precision in your own vertical.
In banking, 80% of leaders see advanced predictive analytics as the major opportunity. That tracks, risk modelling, fraud detection, personalization at scale, all of that benefits directly from stronger computational power. In insurance, 69% are pursuing real-time customer journey simulation. They want to drive intelligent decisions around high-volume, high-variability customer interactions.
Life sciences is another vertical moving fast. 67% are targeting hyper-personalisation at scale, this is about understanding individual patient and consumer needs, then using that data to create more accurate interactions, interventions, and product offerings.
Other sectors are experimenting, too. Public sector organizations, often slower to move, are above average in interest for synthetic data generation (29%) and dynamic pricing (27%). SMBs are showing forward-leaning behavior compared to their larger counterparts: 20% see value in using quantum to generate synthetic data, more than the 11% rate from larger enterprises.
For C-suite leaders, the takeaway here is straightforward: benchmark against your sector. Understand where quantum fits specific to your use cases. Tactical implementation doesn’t start broad, it starts with focus. Once the first use case works, scaling becomes possible. The market’s already heading in that direction. Don’t lag behind.
Perceptions of quantum computing’s timeline vary by stage of AI adoption
Not every company is moving at the same pace. The gap in quantum expectations between AI adopters and non-adopters is wide, and it’s not shrinking. Among those already using agentic AI, nearly one in three expect quantum computing to impact their marketing operations within two years. Compare that with planners, those looking to integrate agentic AI within the next year, and observers, who expect to take up to two years. Both these latter groups are predicting longer timeframes for quantum relevance.
This tells us something important: familiarity with AI raises urgency around quantum. Adopters are closer to the limitations of existing systems. When you operate AI that runs autonomously, optimizes on-the-fly, and responds to live customer signals, you realize fast that computational ceilings get hit quickly. That’s why adopters see quantum as a near-term necessity, not a distant concept.
At the C-suite level, this matters for strategic alignment. If your tech roadmap assumes quantum is a decade away, but your product or marketing team is aiming to run fully autonomous systems within three years, you have a misalignment. Business leaders should ensure strategic planning accounts for the speed at which AI adoption accelerates quantum relevance.
Companies that align those timelines now, rather than reactively later, will be positioned to lead their industries while others scramble to catch up.
Key highlights
- Agentic AI signals early quantum disruption: Marketers already using agentic AI anticipate quantum computing will shape marketing within two years, with some reporting current impact. Leaders should prepare now for quantum-driven systems to stay ahead of data and speed demands.
- AI maturity drives quantum readiness: Companies experienced with agentic AI show significantly higher understanding of quantum computing. Executives should identify gaps in AI maturity to accelerate organizational readiness for near-future technologies.
- Quantum is already in strategic roadmaps: Half of agentic AI adopters have included quantum computing in future-facing plans. Leadership teams should align digital infrastructure planning with the evolving needs of large-scale AI and quantum acceleration.
- Use cases are industry-specific and expanding: Sectors like banking, insurance, and life sciences are identifying tailored quantum applications, from predictive analytics to hyper-personalisation. Leaders must focus quantum investments where their industry sees clearest operational gain.
- Adoption stage affects timelines and urgency: Quantum’s relevance is seen as near-term by AI adopters, but as long-term by planners and observers. Executives should evaluate their organization’s AI stage to avoid underestimating the pace of technological convergence.
- Quantum will enable scalable autonomy: Jonathan Moran of SAS underscores that agentic AI’s full potential depends on quantum-level compute power. Decision-makers should begin assessing quantum-readiness across systems, partners, and internal capabilities.
