Do AI-driven peak sending volumes affect email deliverability?
Matthew Whittaker
Co-founder & CTO, Suped
Published 2 Jul 2025
Updated 18 Aug 2025
7 min read
The rise of artificial intelligence in email marketing promises unprecedented levels of personalization and efficiency. Marketers are increasingly turning to AI to predict optimal sending times, aiming to deliver emails precisely when recipients are most likely to open and engage. The core idea is that by aligning sends with individual user behavior, engagement rates will naturally climb, leading to better campaign performance and ultimately, a higher return on investment.
However, this optimization introduces a critical question: how do these AI-driven peak sending volumes affect email deliverability? While the goal is to enhance engagement, the very nature of concentrating sends into narrow, optimal windows can lead to significant hourly volume spikes. These sudden surges in email traffic could potentially trigger spam filters and negatively impact sender reputation, creating a paradox where an attempt to optimize engagement inadvertently harms the ability to reach the inbox.
It is essential to understand the potential repercussions of such sending patterns and to implement strategies that harness the power of AI without compromising the fundamental principles of good email deliverability. The balance between hyper-personalization and maintaining a healthy sending reputation is delicate, requiring a nuanced approach to AI implementation.
How AI optimizes email sending
AI's role in email marketing extends beyond simple automation. It leverages vast datasets to identify patterns in recipient behavior, predicting the optimal delivery time for individual users. This can be based on when a user typically opens emails, clicks on links, or even the type of content they interact with most. The idea is to catch the recipient when they are most attentive, increasing the chances of the email being seen and acted upon. This level of insight aims to transform generic campaigns into highly targeted communications.
Many AI-powered platforms offer features that automatically adjust sending schedules based on these predictions. While this sounds incredibly beneficial for engagement and conversions, it means that instead of sending out large batches of emails at a fixed time, the system will distribute them throughout the day, often resulting in peak sending periods for specific segments or even individual recipients. This dynamic approach challenges traditional volume management strategies.
However, it is crucial to recognize that AI send-time optimization is not a universal solution for underlying deliverability problems. If an email program already struggles with email deliverability issues, AI might not be able to magically fix them. It can optimize for engagement within the current deliverability constraints, but it won't resolve issues like poor list hygiene or a compromised sender reputation. Think of it as enhancing an already healthy email program, rather than repairing a broken one.
Volume fluctuations and sender reputation
Internet Service Providers (ISPs) and mailbox providers (like Google and Yahoo) are highly sensitive to sending patterns, particularly sudden or inconsistent volume fluctuations. They employ sophisticated algorithms to detect unusual behavior that could indicate spamming. A sudden surge in email volume, even if driven by AI to hit optimal send times, can be a red flag. ISPs may interpret such spikes as suspicious activity, especially if the sending domain or IP is not accustomed to such volumes.
This leads to a direct impact on sender reputation. If an AI system causes an email program to jump from sending 10,000 emails per hour to 100,000 emails per hour during peak engagement times, without proper volume management, ISPs might throttle sends, divert emails to the spam folder, or even temporarily blacklist the sending IP (or blocklist). This is especially true for new or dedicated IPs that haven't established a consistent sending history.
The key challenge lies in the unpredictable nature of these AI-driven peaks. While AI can identify patterns, it may not inherently consider the established reputation or warming process of the sending infrastructure. A gradual ramp-up of sending volume, known as IP warm-up, is a foundational best practice for building trust with ISPs. AI-driven sending that bypasses this gradual increase can inadvertently undo weeks or months of reputation building. For a deeper dive, consider how email volume impacts IP reputation.
Strategies for managing AI-optimized sending
While AI offers powerful optimization capabilities, careful management is essential to prevent negative deliverability impacts. Here are some strategies:
Implement throttling: Ensure your AI-driven sending system incorporates rate limiting or throttling. This means emails are released in controlled waves, avoiding massive bursts at the top of the hour or within a very narrow window. Aim for a gradual, consistent flow rather than sharp peaks.
Set reasonable windows: Instead of pinpointing an exact second for delivery, allow the AI to operate within a broader window, perhaps 30 minutes or an hour. This provides flexibility and reduces the likelihood of extreme hourly spikes.
Prioritize list hygiene: AI can't fix a bad list. Regularly clean your email list to remove inactive users, invalid addresses, and spam traps. A healthy list is the foundation of good deliverability, regardless of AI optimization.
Monitor engagement closely: While AI aims to improve engagement, constantly monitor your key performance indicators (KPIs) like open rates, click-through rates, bounce rates, and complaint rates. Any sudden dips or increases in negative metrics could signal a deliverability issue exacerbated by volume spikes.
It is also beneficial to leverage tools that provide insights into your sending patterns and reputation. Understanding your domain and IP reputation through resources like Google Postmaster Tools can help you preemptively identify issues. Regular analysis of DMARC reports also provides crucial feedback on authentication failures and potential spoofing attempts, which can indirectly impact deliverability by eroding trust.
AI-driven sending: Risks vs. benefits
AI can analyze vast amounts of data to predict optimal send times for individual recipients, leading to higher engagement rates and better campaign performance. It automates complex decision-making, allowing marketers to focus on content and strategy.
Potential risks
Volume spikes: Concentrating sends during peak hours can create unnatural sending patterns.
Sender reputation impact: ISPs may view sudden volume changes as suspicious, affecting inbox placement.
Over-optimization: Relying solely on open times can be misleading as open tracking is becoming less reliable.
Balancing innovation and deliverability
The implementation of AI-driven sending optimization requires a thoughtful approach. While the technology can undoubtedly provide valuable insights and improve engagement metrics, it cannot operate in isolation from fundamental email deliverability best practices. The risk of unintended negative consequences, such as damage to sender reputation or increased spam filtering, is real if volume spikes are not managed proactively.
Therefore, the answer to whether AI-driven peak sending volumes affect deliverability is a resounding yes, they can. However, this impact can be positive or negative depending on how carefully the AI is configured and monitored. Focusing on consistent, gradual volume increases and maintaining a clean, engaged list remains paramount. AI should be viewed as a powerful enhancement to an already robust deliverability strategy, not a replacement for it. Continuous testing and monitoring of your email performance metrics are crucial to ensure that AI is working to your advantage and not inadvertently hindering your reach.
Views from the trenches
Best practices
Ensure your AI-driven email platform supports intelligent throttling to prevent sudden, large volume spikes.
Always maintain a consistently clean email list, as even AI cannot overcome issues stemming from poor data quality.
Regularly monitor your domain and IP reputation using postmaster tools to identify any adverse trends.
Test AI optimization on smaller, engaged segments before applying it to your entire subscriber base.
Common pitfalls
Over-reliance on AI without understanding its underlying mechanics or potential impact on sending patterns.
Ignoring the importance of IP warm-up when introducing new sending volumes, even if AI is optimizing times.
Training AI models solely on open times, as this metric can be unreliable due to privacy features.
Assuming AI will solve existing deliverability problems without addressing foundational issues.
Expert tips
Use AI for content personalization and subject line optimization in addition to send time for holistic improvement.
Integrate AI insights with your broader deliverability strategy, including authentication and feedback loops.
Consider AI's impact on your overall sending schedule across all campaigns, not just individual sends.
Focus on incremental improvements and measure the true ROI of AI features carefully.
Expert view
Expert from Email Geeks says that while he wouldn’t worry too much about traffic spiking, it's essential to avoid automations sending everything at once, especially at the top of the hour. He suggested that send time optimization giving smaller than a 30-minute window might not be reliable.
2024-08-06 - Email Geeks
Expert view
Expert from Email Geeks suggests that training AI models solely on open times may not yield the desired results, implying that other engagement metrics might be more indicative of success.
Google and 
Yahoo) are highly sensitive to sending patterns, particularly sudden or inconsistent volume fluctuations. They employ sophisticated algorithms to detect unusual behavior that could indicate spamming. A sudden surge in email volume, even if driven by AI to hit optimal send times, can be a red flag. ISPs may interpret such spikes as suspicious activity, especially if the sending domain or IP is not accustomed to such volumes.
