Marketing leaders can no longer ignore the AI hype. The reality is that your competition is adopting AI at incredible rates, and they’re getting results — 96% said AI met or exceeded their ROI expectations in our recent survey of marketing professionals. To keep pace with them, you need to do the same.
In a sea of software solutions all claiming “game-changing” AI capabilities, the question becomes, how do you determine which tool is right for your team? It’s difficult to narrow down the criteria — obviously, much of the selection process will depend on your company’s unique needs and objectives.
In this post, we’ll dive into one key area you should keep in mind as you evaluate software vendors: the explainability of the solution’s AI. Because if you can’t trust how your AI thinks, why should your team or your customers? Choosing a platform with explainable AI will help you maintain compliance with tightening data privacy regulations and avoid costly boondoggles that can set back your marketing team.
Keep reading to learn what explainable AI is and the top 4 reasons you should invest in an explainable AI solution.
Let’s start with some definitions — explainable AI refers to artificial intelligence systems whose decision-making processes are transparent and interpretable to humans. Explainable AI tools provide insight into how and why they reached a particular decision, so users can easily verify the quality of the outputs.
Black box AI, on the other hand, refers to artificial intelligence systems whose internal workings are opaque to human observers. These systems take inputs and produce outputs without providing visibility into how decisions are made. This opacity can result from complex architectures (like deep neural networks), proprietary algorithms, or the sheer volume of parameters involved.
Black box AI poses challenges for regulatory compliance, particularly when it comes to handling sensitive customer data. Because the internal decision-making process is opaque, it becomes difficult to verify how data is processed, stored, or shared. This lack of transparency raises concerns about whether sensitive information, such as personal or financial data, is being handled in compliance with privacy laws like GDPR or HIPAA. Additionally, organizations cannot easily determine if data is being inadvertently shared with third parties, either through the model's design or via integrations.
Another issue with black box AI is its potential lack of accuracy and the difficulty in identifying and correcting errors during training and deployment. Since the decision-making process is not transparent, it is challenging to diagnose why the AI arrives at certain conclusions or to determine if it is making errors in its calculations. Without the ability to inspect and understand these processes, improving the system becomes a trial-and-error endeavor, which is both time-consuming and inefficient.
We’ve covered what explainable AI is and how it differs from black box AI — now, let’s dive into the reasons leading marketers should use it in their organizations.
When AI models provide insights into how they arrive at their conclusions, marketers can validate the accuracy of outputs, such as customer segmentation, personalized recommendations, or campaign performance predictions. This transparency allows teams to trust the AI's decisions and refine strategies based on reliable data.
Explainable AI also empowers marketing teams to address potential errors or biases in the model's outputs, which is critical for maintaining customer trust and brand reputation. By understanding the decision-making process, marketers can identify and correct issues, such as over-reliance on irrelevant data points or unintended biases in targeting. This not only improves the accuracy of the AI over time but also ensures that marketing efforts are ethical and inclusive.
Explainable AI provides visibility into how decisions are made, enabling teams to quickly identify errors, inefficiencies, or biases in the model's outputs. This ability to debug and refine the model in real time reduces the time spent troubleshooting, allowing teams to focus on strategic improvements and execution.
In addition, you can train explainable AI more quickly to meet specific industry or business standards. By understanding the factors influencing the AI's decisions, marketing teams can provide more targeted feedback during the training process, ensuring the model aligns with organizational goals and industry-specific nuances. As a result, marketing teams can deploy AI faster, respond to industry shifts more effectively, and achieve better outcomes with less effort.
When teams can see and understand how AI arrives at its decisions, they’re more likely to trust its outputs and integrate it into their workflows. For example, if a marketing team can trace how an AI model identifies high-potential leads or predicts campaign performance, they can verify that the reasoning is sound. This transparency demystifies the technology, reducing skepticism and resistance to change. As a result, they are more willing to embrace AI as a tool that enhances their capabilities rather than viewing it as an unreliable "black box."
Additionally, when an AI output gets called into question, the user can do more than just shrug their shoulders. They can trace back the logic behind the decision, giving them a leg to stand on in conversations with leadership. This fosters a culture of collaboration and confidence, which is essential for widespread adoption. When team members understand the logic behind AI-driven recommendations, they can provide more informed feedback, suggest improvements, and use the technology more effectively.
Some black box AI platforms list in their contracts that they keep data locked within the user’s ecosystem and do not use it for training purposes. You can rest assured that these solutions are compliant with data privacy regulations. However, you may want to stay away from black box vendors that use murkier language about how they store data and train models.
Explainable AI solutions, on the other hand, give CMOs complete visibility into how customer data is processed, stored, and utilized within the system. This transparency allows them to verify that sensitive customer information is being handled in accordance with stringent data privacy laws like HIPAA, GDPR, and CCPA, rather than taking the vendor’s word for it. This, in turn, reduces the risk of costly fines, legal disputes, and reputational damage.
Additionally, explainable AI provides assurance that data is not being inadvertently shared with third parties as part of the AI's decision-making process, which is critical for maintaining customer trust and meeting regulatory obligations.
CMOs from leading brands use Invoca’s Signal AI Studio to take their campaigns to the next level. This solution helps marketers tap into an often-overlooked data source — phone conversations.
Signal AI Studio is fully explainable, so you can understand how the AI arrives at its conclusions. Its explainability also allows you to verify that the outputs are accurate and free from biases. Because Signal AI Studio empowers you to train your own custom models, you can capture whatever data is most important for your company or industry. The training requires no developer assistance — the intuitive UI speeds you through the process in a few clicks.
In addition, the solution provides accuracy scores, so you get complete visibility into how your AI signals are performing — both during training and after deployment. Instead of relying on guesswork, users can see predicted accuracy scores directly in the UI, giving them immediate confidence in their data before putting it to work in optimizing campaigns.
Below are some common data points that marketers capture from phone calls with Signal AI:
Teams can then use these insights to fuel higher-converting campaigns. For example, you can retarget phone leads who didn’t convert with ads for the products they mentioned over the phone. Or, you can suppress callers who bought over the phone from seeing future ads to conserve your budget. You can also track which Google Ads keywords drive the most high-quality phone leads and double down on them to boost ROI.
Want to learn more about this feature? Check out our blog post, 6 Ways Marketers Can Use Signal AI Studio to Make Their ROAS and Reputation Soar.
Check out these resources for more information on Invoca’s explainable AI solutions:
