What We Learned About AI while Building Experiture

As we worked to build and refine Experiture’s AI-driven customer engagement platform, we’ve learned a lot about how artificial intelligence is transforming marketing—and where it still has room to grow. While AI offers tremendous benefits, there are significant hurdles and lessons we’ve gathered along the way, reflecting broader trends in the marketing industry.

1. AI Adoption is Growing, but Comfort Levels Vary

AI is making significant inroads in marketing departments, with about 65% of marketers regularly using AI tools for various tasks, such as content creation, customer segmentation, and predictive analytics ​(Influencer Marketing Hub, Lumen5)

However, only 30% of marketers feel truly comfortable with AI, pointing to a gap between the potential of these tools and their ease of use. This has been mirrored in our own journey with Experiture, where we’ve had to focus heavily on usability to ensure that even marketers without a deep technical background can leverage the full power of our platform.

2. AI’s Impact on Revenue and Performance

AI’s ability to enhance marketing performance is undeniable. Many organizations using AI report meaningful revenue increases, with 5% or more boosts from applications like personalized marketing and predictive analytics ​(McKinsey & Company)

At Experiture, we’ve seen firsthand how AI can help companies target the right customers more effectively and optimize their campaigns, resulting in better ROI. AI-driven personalization allows for deeper customer engagement, leading to higher conversion rates across channels.

3. Budget and Technical Challenges are Still Major Barriers

Despite AI’s potential, financial and technical barriers continue to impede widespread adoption. Around 34% of marketers cite budget constraints, while 25% report difficulties using AI tools due to a lack of technical expertise ​(Influencer Marketing Hub).

This aligns with what we’ve seen at Experiture—many of our clients have limited budgets and small marketing teams, making it crucial for our platform to be affordable and user-friendly. Additionally, we’ve learned the importance of offering robust customer support and training to help bridge the AI knowledge gap.

4. Training and Upskilling Are Essential

AI is only as effective as the people using it. To tackle the skills gap, 27% of organizations are now offering AI-specific training to their marketing teams​ ​(Influencer Marketing Hub).

At Experiture, we’ve invested in creating educational resources, tutorials, and best-practice guides to help our clients maximize the use of our AI-powered features. Upskilling teams not only helps companies get more out of AI but also builds internal confidence in using these advanced tools.

5. AI for Content Production Faces Mixed Results

One of the biggest revelations we’ve encountered—and one echoed across the industry—is that while AI is effective for content generation, its use has actually declined slightly in 2024. About 35.1% of marketers are using AI for content production, down from 44% the previous year​ (Influencer Marketing Hub).

This may be due to growing challenges around ensuring that AI-generated content maintains authenticity and aligns with brand voice. We’ve taken this into account at Experiture, focusing on how our AI-driven content tools can assist in scaling personalized, data-driven content without losing the human touch.

6. AI Ethics and Accuracy Remain Major Concerns

As much as AI offers in terms of efficiency, there are still risks associated with its use, including biases in data and inaccurate outputs. These concerns are widely shared, with 44% of companies experiencing negative consequences from AI inaccuracies​. (McKinsey & Company)

At Experiture, we’ve prioritized transparency and control, ensuring that users have the ability to oversee and tweak AI-driven decisions. Our platform is designed to give marketers a full understanding of how AI makes recommendations and offers them the ability to intervene when needed.

7. Predictive Analytics is a Key Growth Area

Predictive analytics is seen as one of the most promising areas of AI for marketers, with 18% of professionals anticipating improvements in this field in 2024 ​(Lumen5).

This mirrors what we’ve focused on at Experiture—leveraging AI to predict customer behaviors and optimize marketing strategies in real-time. Our predictive analytics tools help businesses understand customer journeys and anticipate future needs, allowing for more proactive and personalized communication.

8. Technical Issues with AI Tools are Common

Technical challenges continue to plague marketers trying to adopt AI. Nearly 70% report issues ranging from data compatibility problems to difficulty integrating AI into their existing systems​ (Influencer Marketing Hub).

We’ve encountered similar challenges while building Experiture, particularly when integrating our platform with a wide range of third-party tools. This has driven us to simplify integrations and focus on building flexible, user-friendly connectors that require minimal technical expertise.

9. Ethical Concerns are Slowing Down AI Adoption

Despite AI’s potential, there are some significant concerns — such as those around bias, privacy, and accuracy. These concerns raise serious questions about how AI is implemented and its potential negative consequences.  In fact, more than 30% of marketers express reservations about AI ethics, which sometimes prevents them from fully embracing the technology​ (Lumen5). Here is how:

• Bias in AI Outputs: AI models can perpetuate or even amplify biases present in the data they are trained on, leading to unfair or discriminatory outcomes. This is especially troubling in marketing, where customer targeting or segmentation based on biased data could result in unequal access to services or misleading messages​.One of the most well-known examples of a marketing fail due to AI bias occurred with Amazon’s AI-powered recruiting tool. In 2014, Amazon developed an AI system to streamline the process of reviewing resumes. The goal was to create an algorithm that would select the top talent from thousands of applications. However, after using the tool for several years, Amazon discovered that the AI had developed a bias against female candidates.The system was trained on resumes submitted to Amazon over the previous ten years, most of which came from male candidates due to the male-dominated tech industry. As a result, the AI began penalizing resumes that included the word “women’s,” such as in “women’s chess club captain,” and favored male-oriented language. This bias in the data set led the algorithm to recommend male candidates disproportionately, effectively discriminating against women.Amazon scrapped the project after discovering the bias, highlighting how AI can inadvertently reinforce existing biases if it is trained on unbalanced or skewed data​.

  This case serves as a cautionary tale for companies looking to implement AI in hiring, marketing, and other sensitive areas where biased data can lead to significant ethical and reputational issues.

• Inaccuracy and Misinformation: Generative AI tools, while powerful, often produce content that is factually incorrect or misleading. This can have severe repercussions, especially in industries like finance or healthcare, where inaccurate content could misinform customers ​(McKinsey & Company, Lumen5)The risk of inaccuracy is recognized by nearly half of businesses using AI, making it a critical issue in AI-driven marketing campaigns.
• Privacy and Data Security: AI’s reliance on large datasets has sparked concerns about data privacy and compliance with regulations such as GDPR. Marketers must ensure that they use customer data responsibly, and many organizations are still grappling with how to balance AI’s power with the need for strong data protection measures ​(Lumen5).

These ethical concerns have led some organizations to slow their AI adoption, despite its potential benefits. The ongoing debate centers around how to mitigate these risks while leveraging AI’s capabilities responsibly.

10. Balancing AI Automation with Human Creativity

The key to success with AI in marketing is finding the right balance between automation and creativity. While AI can significantly speed up processes and deliver data-driven insights, human oversight is critical to ensure that the content resonates on an emotional level and stays true to brand values. This is something we’ve emphasized at Experiture, where our platform is designed to enhance, rather than replace, the creativity of marketing teams.

Conclusion

Building AI-powered solutions like Experiture has been both a rewarding and challenging journey. The lessons we’ve learned reflect broader trends in the industry: AI offers incredible opportunities for marketers to improve performance and efficiency, but only if companies can overcome the technical, ethical, and financial barriers that stand in the way. With the right balance of education, strategic implementation, and human oversight, AI is poised to be a game-changer for marketing in the years ahead.

Curious how Experiture can help your business? Request a demo today and see how our AI-powered platform can transform your marketing efforts!