MarTech Cube Interview with Taylor Mahoney, Global Vice President, Solutions Consulting at Persado

Hi Taylor, Could you start by sharing a bit about your professional journey and your current role at Persado?
I started my professional journey at Banana Republic where I managed marketing creative development across projects. In that role, I developed a strong foundation for both the creative and operational sides of marketing.

Then four years later, I joined Persado. Initially, I worked directly with our customers, helping them drive maximum value with our solutions. Through that experience, I gained a deep understanding of our product through the lens of the customer, and that sparked my interest in transitioning to the product side of the business.

In my current role at Persado, I lead our Solutions Consulting teams, where we partner with prospects to understand their business challenges, identify areas of opportunity, and develop solutions that are both practical to implement and impactful in driving value. It’s an exciting role because it’s positioned at the intersection of our product innovation and the evolving needs of our customers. We’re constantly engaging in conversations about what’s happening in their industries while staying closely connected to the broader martech and generative AI space where Persado operates.

Throughout your career, you’ve solved complex challenges by combining strategic insight with a customer-first approach. How do you see AI playing a role in solving the most pressing challenges faced by organizations today?
Today’s organizations and leaders face myriad challenges as teams are tasked to accomplish more and drive greater revenue with fewer resources. Many are turning to AI for automation and efficiency gains to improve operations and performance— however, the benefits of AI can go far beyond efficiency.

In marketing, personalization has always been king. Through personalization tactics, brands can deliver better customer experiences, but also drive more engagement and conversion. Still, it’s historically been a challenge for companies to rapidly personalize marketing content that is tailored to specific audiences.

Now with AI, digital marketers and product managers can both create or optimize marketing copy in real-time, and at scale, to deliver the words and phrases that will best resonate with your customers and motivate them to engage with your brand. Not only does this help companies increase revenue, but it also creates a CX that encourages brand loyalty. For example, brands are using the Persado Motivation AI platform to generate emotion-informed marketing content that breaks through the noise to engage different segments.

Take Gen Z. Brands want to understand how to effectively engage with this generation and gain their loyalty, especially given the group’s growing spending power (reaching over $360 billion). Since Gen Z has its own unique set of values that drive decision making, brands need to adjust their marketing content to resonate— authentically. With Persado, retail, travel, and financial brands can speak Gen Z’s language, embrace their digital savviness, and align with their values without sacrificing their own brand identities. The enterprises that leverage proven, purpose-built AI will quickly and effectively differentiate themselves and outpace those who don’t.

How do you envision AI improving team collaboration and operational efficiency and as it continues to evolve, what are some key areas where tech teams and marketers can work together to drive greater performance?
AI enables us to have more comprehensible data at our fingertips and make easier decisions with greater speed. It won’t replace humans but it will challenge us to think in new ways, work more efficiently and ideally make smarter decisions.

To unlock the value of AI, marketers and tech teams need to work together and it’s important to make sure members of both departments are involved starting with the early stages of adoption.

Tech teams can bring new capabilities to marketers to improve their campaigns. To ensure the new capabilities drive efficiency and deliver value, however, marketers must be open to new approaches and have some understanding of how the technology works. Cross-functional collaboration across other departments, product teams, and business leads will also be vital. To drive the greatest possible results, remember to educate and review AI use across departments.

• Educate – Educate your teams when preparing for AI by sharing your exploration and insights and then comparing and contrasting what your colleagues are currently familiar with. This will help your teams identify areas they may need to hone in on to improve.
• Review – Once areas for improvement are determined, explore the benefits that AI can bring as you look to make adjustments that will propel your organization forward. Reviewing these areas helps you pinpoint strengths and how AI can drive even further success and outcomes in those areas.

What are some challenges companies face in maintaining compliance with AI tools and how can they ensure compliance using purpose-built AI agents?
There are two major challenges businesses are facing with compliance.

• The pace – Across the globe, AI regulation is quickly evolving, making it hard for many companies to keep up. Global organizations also face the obstacle of ensuring they are compliant with a diverse patchwork of regulations.
• Fragmentation – Businesses often lack visibility and communication across departments when it comes to AI use and as a result, achieving compliance becomes a very resource-intensive process. This lack of visibility creates a fragmented approach to AI implementation that slows down review cycles and makes it difficult to ensure both brand and legal compliance. It also prevents businesses from getting the most value out of their AI solutions.

At Persado, we designed our AI platform to help companies remain compliant with brand and regulatory guidelines. This is particularly important for financial services institutions that need to ensure copy is aligned with best practices and regulations. The platform ingests compliance and brand guidelines directly into Persado’s model. Our built-in tools allow for easy compliance and brand review, feedback, and approval before deployment.

In 2025, I expect more purpose-built AI agents will emerge to help companies address compliance issues by identifying problems and notifying human teams in real-time so they can course correct. As organizations grapple with increasingly stringent regulations, solutions designed to address compliance challenges will be crucial. This increased use of AI, however, will not replace the role of people in the process. AI will add value by augmenting tasks, but humans still need to be involved to provide oversight and maintain brand authenticity.

Persado’s Motivation AI has been a game-changer in crafting emotion-informed content for marketing engagement; could you explain how banks and other organizations are leveraging this technology?
Financial services companies engage with millions of customers every day through digital channels, with the goal of motivating each customer or prospect to act. Persado Motivation AI helps industry-leading card issuers and banks like Chase, Ally, and SoFi achieve that goal by creating on-brand, personalized, and compliant marketing content that drives higher customer engagement, and thus performance.

Persado has the only large language model (LLM) trained with statistically valid customer data from 150M+ U.S. customers: surpassing 1.4M messages from customer communications and topping 330B estimated consumer impressions, fueling its ability to generate personalized communications at scale. Content generated by our Motivation AI outperforms human-only content by more than 43%, on average.

Banks and card issuers can improve the customer journey, upsell products, and enhance financial marketing with Motivation AI. This leads to increased customer acquisition, engagement, cross-sell, and all-too-critical retention. To date, our platform has collectively driven more than $2.5 billion in additional incremental revenue for large financial services organizations in the past five years alone.

How do you see AI-driven solutions aligning with marketing strategies to create more personalized, effective campaigns?
AI has been a game changer for marketing teams to generate personalized, engaging content at scale. Persado Motivation AI, for example, helps brands deliver highly relevant and emotion-driven content to customers in real time so marketing messages better align with the words and phrases that are most likely to resonate with each customer, across channels, and at each point in their journey.

Imagine a marketing campaign that’s targeted to Gen Z. As a generation of digital natives, Gen Z can sniff out inauthenticity, so brands must ensure their marketing content is authentic. With AI, your brand can personalize content to align with this important segment’s values—such as inclusivity, social consciousness, and community—while keeping the brand identity intact. For example, in banking, a phrase that draws on inclusivity can look like, “Investing — for absolutely everyone.”

In your experience, how is the relationship between tech/data teams and marketing evolving?
Many businesses today have very strong data science and marketing teams but they tend to be siloed. AI solutions for content generation can help create a single source of truth for martech stacks by ingesting guidelines and continuously learning, which helps ensure that all marketing content is on-brand, relevant, accurate, and compliant. This = benefits for marketing, IT, and data teams.

Collaboration requires buy-in from data and marketing teams who must start to get familiar with functions outside of their specialty. I do see marketers increasingly taking on a more technical role that requires education and training, particularly with AI use. But in general, marketing teams need to be brought in earlier to ensure the smooth adoption and fine-tuning of AI tools.

What are the biggest opportunities AI presents to marketing leaders today, and what risks should they be aware of as they integrate AI into their campaigns?
As marketers look to leverage AI in their campaigns, they need to be aware of the differences between generic and specialized AI tools. While generic AI capabilities, such content generation embedded into broader enterprise suites, can be faster to adopt, they often do not deliver the most value and pose risks. For example, many AI tools refer solely to open-source models, which can lead to content that misses the mark (such as “hallucinations.”). This embedded functionality is also designed for productivity alone, missing out on performance and compliance benefits.

On the other hand, although purpose-built, full-stack tools (grounded in LLM, ML, and application layers) may take a little longer to implement, they provide functionality tailored to the unique needs of marketers and have been proven in-market for some time. These tools have accumulated a deep, specialized knowledge base and ingest legal and brand guidelines that speed content approvals and reduce risk.

Most important, marketers shouldn’t be investing in AI tools that aren’t driving measurable impact—from increased clicks to conversions to loyalty. In marketing, this will be especially important as teams face potential budget cuts and turn to AI to both streamline processes and fuel better performance.

Data is at the heart of AI’s ability to drive personalized experiences. How do you see the role of data evolving in the coming years, especially as AI becomes more advanced?
Data is vital to AI development and making business decisions, however, data is only valuable to enterprises if it’s accurate, accessible, and relevant.

As companies race to adopt AI, where and how that AI is trained is becoming a major hurdle. Experts are predicting the internet will soon be too small to be an effective source for training AI and regulations are starting to focus on limiting web scraping, leaving fewer options for developers.

Some enterprises are turning to synthetic data as a solution, but it often introduces noise and may not always align with real-world quality. Instead, I expect the future of AI development to rely on community-based data collection. This will allow organizations to ensure greater accuracy and governance.