How to Evaluate AI Tools in Email Workflows

Most teams evaluate AI tools based on what they produce. A tool may generate decent copy or ideas, but that does not mean it improves the system. The real question is whether it removes friction from an existing workflow. If it does not reduce time, errors, or decision effort in a specific step, it will not hold up in daily use.

The starting point is to define the exact problem. Teams that test AI without a clear bottleneck usually end up with tools that sit unused. The more effective approach is to pick one constraint, like writing speed, segmentation, reporting delays, or testing capacity, and check if the tool improves that step. This keeps evaluation grounded in actual work instead of features.

Integration matters too. Tools that require manual exports, copy-paste steps, or separate logins tend to drop off after initial use. Tools that connect directly to ESPs, CDPs, or data warehouses are easier to maintain because they operate on live data. Evaluation should include how data enters the tool, how outputs are used, and whether it adds extra steps to the workflow.

There is also a difference between tools that generate content and tools that make decisions. Copy generation tools are easy to test because the results are visible immediately. Tools that handle send-time optimization, segmentation, or journey logic take longer to evaluate because their impact shows up over time. Many teams focus on the first category because it is easier to measure, even though the second category often has a larger effect on performance.

Another common mistake is choosing tools based on the number of features. Broad tools often look attractive but are rarely used fully. Narrow tools that solve one problem well tend to perform better because they are easier to adopt and repeat. The goal is consistent use in one workflow, not occasional use across many.

Measurement is often weak. Time saved and content volume are easy to track, but do not show business impact. A better approach is to test AI inside real campaigns and compare results against a control. This could include changes in click rates, conversion rates, or retention metrics. Without this, it is hard to tell if the tool is improving outcomes or just speeding up output.

A simple evaluation loop works better than long upfront comparisons. Pick one use case, test a tool in a live workflow, measure results, and then decide whether to keep or drop it. This avoids adding tools that do not deliver.

AI tools work best when they are treated as part of the system, not separate from it. Their value depends on how well they fit into existing processes, how clean the data is, and whether they improve results over time. Tools that do not meet these conditions usually get abandoned, even if they perform well in isolation.

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Crunch is a global fitness brand operating hundreds of gyms worldwide that relies heavily on email for customer communication, including onboarding, alerts, and ongoing engagement. As email volume increased, the company began facing issues with deliverability and domain security. Messages were not consistently reaching inboxes, and the risk of domain spoofing created exposure to phishing attempts that could impact both users and brand trust.

The initial problem was tied to authentication gaps and weak visibility into how their domain was being used. Without properly configured SPF, DKIM, and DMARC policies, mailbox providers had limited signals to verify legitimate emails. At the same time, the lack of enforcement made it easier for attackers to spoof the domain, sending fraudulent emails that appeared to come from Crunch.

To address this, Crunch implemented DMARC with enforcement policies. This required aligning all legitimate sending sources, including internal systems and third-party tools, under a consistent authentication framework. The process involved identifying every service sending emails on behalf of the domain, configuring authentication records correctly, and moving from monitoring mode to stricter enforcement levels over time.

As enforcement tightened, unauthorized sources attempting to send emails from the domain were blocked. This reduced the risk of spoofing and improved trust signals with inbox providers. At the same time, legitimate emails benefited from stronger authentication, which contributed to better inbox placement and more consistent delivery.

Visibility also improved. DMARC reporting provided data on who was sending emails using the domain and whether those messages passed authentication checks. This allowed the team to identify misconfigured services, remove unused senders, and maintain tighter control over their email infrastructure.

The impact was twofold. Deliverability improved as mailbox providers received clearer authentication signals, leading to more emails reaching the inbox instead of spam folders. At the same time, the enforcement of DMARC policies reduced the likelihood of phishing attempts using the Crunch domain, lowering risk for both the company and its users.

The case shows that deliverability and security are closely linked. Authentication is not just a compliance step but a system requirement for reliable email performance. By treating domain configuration as an operational priority and enforcing strict policies, Crunch was able to stabilize inbox placement while reducing exposure to spoofing.

Google’s AI Overviews are starting to appear more frequently in shopping-related queries, changing how users evaluate products before clicking through. Instead of directing users to product pages or category listings, these summaries aggregate information such as product features, comparisons, and recommendations directly in the search interface. This reduces the need to visit individual sites and shifts early decision-making into the search layer itself.

As product discovery moves closer to the search interface, influence shifts toward the sources that shape these summaries. Companies that structure their information clearly and maintain strong third-party signals are more likely to remain visible in this new layer.

Most of the changes outlined here point in the same direction. Control is shifting away from individual outputs and toward systems. Teams that focus on fixing bottlenecks, cleaning data, and tightening integrations will see steady gains over time. Teams that focus on surface-level optimizations without addressing these foundations will struggle to sustain results, even if short-term metrics look good. The work that compounds is not always visible, but it is what determines whether channels remain reliable and scalable.

See you next week.

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