This article originates from Siara’s talk at the Revenue Marketing Summit in San Francisco, 2023. 

Catch up on this presentation, and others, using our OnDemand service. For more exclusive content, visit your membership dashboard.

AI (artificial intelligence) today isn’t just a tool – it’s a revolutionary force reshaping how we connect with and understand our customers. 

I'm Siara Nazir, former Head of Growth Marketing at Autodesk, current Founder & CEO of Inovient.io, and Advisor at Silverside AI. Throughout my career, I've embraced the challenges and opportunities presented by AI, leveraging it to refine marketing practices and drive business growth. 

In this article, I'll share insights and practical strategies for integrating AI into your marketing efforts, ensuring your business is well-positioned to harness the full potential of this transformative technology.

The unseen power of AI in marketing

As I delve into the topic of unlocking the power of AI for marketing, I can't help but draw an analogy to a familiar experience many of us might relate to. 

Imagine being in Las Vegas, sitting in front of slot machines, pulling the lever, and winning every single time. While this wasn't exactly my luck in Vegas, where I lost more often than not, it's an apt metaphor for the potential AI holds in transforming our experiences and expectations.

AI has become a pervasive force, almost like an unspoken promise of greatness for every market and enterprise. Its impact extends far beyond the obvious, influencing even the minutest aspects of our daily lives. 

For instance, consider the hotels in Japan that are operated by robots. These aren't just any robots – they're sophisticated machines that learn your preferences, from the type of pillows you like to your preferred room amenities. This interaction is just a part of a massive $75 trillion IoT market, where devices like your Aura Ring or Apple iWatch contribute data that AI synthesizes and analyzes.

We've been interacting with AI for longer than most of us realize. Simple AI applications like chatbots have been around for a while, but the true revolution in AI for marketing began with display campaigns and look-alike modeling. This technique involved seeking out impression inventory that was successfully converting, thus allowing campaigns to become more targeted and efficient over time.

I remember when companies started investing more in display advertising. Initially, campaigns were broad, but as they gathered more data on successful impressions, AI algorithms began to focus on acquiring more of that high-converting inventory. 

This shift wasn't without its challenges. Pausing a campaign for testing meant that when you resumed, the AI had to relearn patterns, a precursor to the sophisticated AI-driven marketing strategies we see today.

The transformative impact of AI on the global economy and workforce

As we look towards the future, the impact of AI on the global economy is set to be monumental. By 2030, it's projected that the US economy will gain an additional $15 trillion, thanks to AI. This is not just an American phenomenon; nearly a quarter, around 24-25%, of China's GDP will be attributed to AI services, products, and technology. 

The United States follows closely, with AI contributing to approximately 14-15% of its GDP. These figures underscore the massive influence AI is set to have on the world's economic landscape.

However, with great transformation comes great challenges, especially in the realm of employment and automation. We’re on the brink of a new industrial era, reminiscent of the seismic shift brought about by the advent of computers in the 1980s. 

Predictions indicate that up to 30% of jobs could be automated, echoing the significant changes seen during the computer revolution.

This prospect might seem daunting, but I believe it also opens up tremendous opportunities for skill enhancement and career evolution. In marketing, for instance, the rise of AI enables us to offload much of the manual, tedious work to automated systems, freeing us to focus on strategic, high-value tasks. This shift isn't just about efficiency; it's about enabling the kind of thought leadership and innovative strategy that can revolutionize industries.

The corporate world is acutely aware of the importance of AI. In 2021, AI was a topic in 40% of investor calls, and one can only imagine that this percentage has increased since then. There's immense pressure on enterprises, whether they're established companies or startups, to understand and integrate AI into their operations. 

Investors and boards are particularly keen on this, as they recognize the transformative potential of AI in every sector.

The integration of AI in corporate strategy

In a remarkable study conducted by Accenture, thousands of executives who had integrated artificial intelligence into the very fabric of their companies were interviewed. The findings were nothing short of groundbreaking. 

They revealed that AI was adopted 26% faster than digital transformation milestones. This statistic is a clear indicator that, even as we continue to learn about AI, it is rapidly becoming an integral part of our professional lives – on our desktops, in our virtual meetings, and beyond.

This swift adoption of AI is particularly noteworthy given that many of us are still navigating the complexities of digital transformation. Numerous legacy companies are still in the midst of this process, but AI's emergence is poised to eclipse these efforts. 

The reason is simple: Digital transformation largely revolves around data and processes, both of which AI can utilize and automate, thereby streamlining and enhancing these transformations.

Accenture's study further categorized companies into four distinct groups based on their stage of AI adoption:

  • Achievers: These are organizations that have fully embraced AI. They've integrated machine learning and similar technologies into their processes and products. For them, AI is not just a tool but an essential part of their operational and project management strategies.
  • Builders: Builders are on their path toward AI integration. Comparable to those in the digital transformation phase, they have a roadmap and are actively working towards implementing AI, often in the initial stages.
  • Innovators: These companies have identified a unique spark with AI. They are now in the process of pivoting their business models to leverage AI technologies more effectively.
  • Experimenters: This group, likely encompassing many of us, is still exploring the potential of AI. Experimenters are actively learning and testing AI applications in marketing and other fields, aiming to revolutionize their strategies and accelerate the achievement of business goals.

Imagine being asked by a C-suite executive, the head of your company, or an investor about your AI strategy. How would you respond? What specific actions would you take in the next 30, 60, or 90 days? This question isn't just hypothetical; it's a crucial one that demands a clear understanding of AI's impact and opportunities for your department and job function.

The term AI is often used loosely, but in reality, it's a complex and multifaceted discipline. When discussing AI, it's essential to be specific. Are we talking about natural language processing (NLP), voice recognition, or machine learning? And if it's machine learning, is it supervised or unsupervised? 

Understanding the nuances of these technologies is vital, not just to appease experts in the field, but to effectively translate business requirements into technical specifications.

While traditional AI focuses on pattern recognition and predictive outcomes, generative AI represents a significant leap forward. It resembles human thought processes, understanding the relationships between topics and concepts, and conceptualizing ideas that go beyond hard data. This is where I see a massive opportunity for revenue marketing to scale personalization, engagement, and other key marketing objectives.

Generative AI is distinct in its ability to create thoughts that aren't solely based on singular data points but are more abstract and holistic. Tools like Midjourney exemplify the power of generative AI, harnessing context from various sources to produce innovative and unforeseen ideas. 

This aspect of AI is what I believe will revolutionize marketing, taking us beyond traditional strategies and into a realm of deeper, more meaningful engagement with customers.

The evolving landscape of SEO in the age of AI

Recently, I've been exploring Google's Bard Beta, and it's fascinating to see the rapid changes occurring in real-time. The introduction of an interactive section on the search results page, which displays answers to typed phrases or keywords, is reshaping how we view search engine results. 

Initially, this section was hidden and required manual expansion, but now it's more prominent. What's striking is how this feature pushes traditional paid and SEO listings below the fold, a significant shift considering these are Google's main revenue sources.

The evolving nature of Google's search interface, including the addition of a singular SEO ad accessible via an arrow, raises critical questions for marketers. Who will secure these coveted ad spots? How will this affect the monetization of what was once free traffic? These developments necessitate a reevaluation of where we allocate our marketing budgets.

Mark Wiens, a travel blogger I frequently follow, offers an insightful example of how SEO is transforming. The traditional focus on singular keywords and search engine algorithms is giving way to the complexities of large language models (LLMs). LLMs don’t operate on single keywords; they understand and relate content in a much more interconnected way.

In the context of a blog like Mark Wiens', you can see how content interconnectivity becomes crucial. His posts about global travels link to content about other destinations he's visited, demonstrating the importance of inter-relational content. 

Future SEO won’t just be about churning out pages filled with high-traffic keywords; it’ll be about teaching LLMs the connections between different pieces of content on your site and building bridges within your content ecosystem. This approach is key to adapting to the new SEO landscape and ensuring your business stands out.

Bridging AI with marketing expertise

My career in marketing spans a vast array of channels, from offline television, print, and radio commercials to online marketing. There isn’t a marketing channel I haven’t managed, leading me to my current deep involvement in AI.

Over the last five years, I've focused on AI’s impact on marketing, developing proof of concepts, and now advising companies and startups on navigating the disruptive changes brought by AI.

I work closely with organizations, CMOs, and startups, providing strategic advisory and consulting services. My role is to guide them in understanding the potential disruptions AI can bring and assist in product road mapping for a future shaped by AI.

At the core of my approach to AI in marketing is problem-solving. While at Autodesk, I noticed a peculiar challenge: One of our lower-priced products had a prolonged 68-day purchase cycle. Customers were bouncing between products, unsure of their needs, despite initially coming in for a specific 3D animation software. It was clear there was friction that needed addressing.

Traditional chatbots, though available, were still quite linear in their approach. To overcome this, I turned to open-source software and developed a relationship between Google keywords and our site content, recognizing that keywords represent customer intent. 

This led to the creation of a unique chatbot that mimicked Instagram questions to boost engagement.

This AI-driven approach yielded remarkable results. We reduced the 68-day purchase cycle to just 14 days, quadrupled revenue, and saw a 109% increase in page engagement. Impressively, these tests were conducted on non-branded keyword terms, which are notoriously challenging due to the lack of brand loyalty and clarity in customer intent.

This success story demonstrates the efficacy of natural language processing in addressing complex marketing challenges. If NLP can significantly impact marketing outcomes in the most difficult scenarios, it holds immense potential for all kinds of marketing mediums.

Mastering the AI-driven marketing funnel and navigating potential risks

AI and machine learning offer a plethora of technologies designed to enhance each stage of the marketing funnel. From lead generation tools like Node.js, which excels in segmenting customers, to engagement platforms like Game On that use gamification to solidify brand presence. Then there's Conversica for lead scoring, guiding potential customers down the funnel toward sales. However, it's important to note that these examples barely scratch the surface of what's available.

Selecting the right AI tool is not straightforward, given the vast array of options available for each funnel stage. This decision is critical in effectively harnessing AI for marketing success.

While AI presents vast opportunities, there are also risks to consider. A recent incident involved Samsung's proprietary code being uploaded to ChatGPT by an employee, highlighting the vulnerabilities in AI models that learn from pooled information. A similar situation occurred at Amazon, raising important concerns about data security and privacy.

5 key considerations for AI implementation

  • Clearly define the purpose: Articulate the specific gaps AI technology will fill. Whether it's boosting revenue, enhancing engagement, or improving customer segmentation, clarity is crucial for organizational support.
  • Data consolidation challenges: At Autodesk, I faced the common challenge of disparate data sources. Effective AI implementation requires integrating various data types, from customer intent to financial metrics.
  • Tech stack integration: Assess whether your current technology stack is cohesive and whether different parts communicate effectively.
  • Legal and privacy concerns: These aspects can often halt projects, so ensuring compliance and support from legal and privacy teams is essential.
  • The buy vs. build dilemma: This is a constant struggle. My experience with building a chatbot at Autodesk showed the limitations of scaling with limited resources. Weighing the pros and cons of purchasing a solution versus building it in-house is crucial.

When choosing a vendor, it’s vital to:

  • Clarify your business objectives.
  • Demand evidence to substantiate vendors' claims.
  • Understand their data sets and systems, and how they will integrate your customer data.
  • Investigate their data handling practices, including retention and deletion policies.

The journey to integrating AI in marketing is filled with exciting opportunities and challenges. The key to success lies in strategic decision-making, understanding the technology, and being mindful of its implications.