I’m excited to share with you this special 100th edition of my #ShoryuWill newsletter, where I dive deep into how generative AI (or gen AI) can reshape marketing strategies and ultimately drive success for private sector professionals, managers, and business owners right here in Australia. Today, I want to take you on a journey through the exciting and sometimes challenging world of AI in marketing.
Difference Between Generative AI (Gen AI) and Analytical AI
In short:
Use analytical AI when you need reliable predictions or classifications from existing data.
Use generative AI when you need fresh, creative content or personalised communication — keeping in mind the need for review if accuracy matters.
Why Gen AI Is a Game-Changer
Generative AI has been creating quite a stir in the business world. Unlike traditional AI, which mostly focuses on analysing data and making predictions based on structured information, gen AI is all about creating content. This means it can help craft everything from product descriptions and social media posts to blog articles and personalised customer messages. According to Salesforce’s “State of Marketing” report, a whopping 96% of global marketers are either using or planning to use AI in some capacity, but only 32% have fully implemented it into their operations. This shows that while the interest is massive, the journey to full integration is still very much a work in progress.
I find this incredibly relevant to us in Australia, where businesses are continually looking for ways to stand out in a competitive market. With the right approach, gen AI can help boost our efficiency, improve customer engagement, and even drive better sales outcomes. But as with any technology, there are risks and trade-offs to consider.
#ShoryuWill Newsletter #100 By William Zhang
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The Three Key Decisions in Integrating Gen AI
From my research and the insights shared by industry experts, there are three critical decisions we need to make when adopting gen AI in our marketing efforts:
Do we use gen AI or stick with traditional analytical AI? Traditional AI is fantastic for analysing structured data – think spreadsheets full of numbers that help predict what a customer might buy next. Gen AI, however, goes a step further by generating new content. For instance, while analytical AI might predict that a customer is likely to buy a certain product, gen AI can craft a personalised marketing message to encourage that purchase.
Do we need custom inputs or will general inputs do? This decision boils down to whether we use broadly sourced data (like publicly available information) or if we need to train our AI with proprietary, firm-specific data. Custom inputs can help create highly tailored content that fits our brand perfectly, but they often come with higher costs and greater risks regarding data privacy.
How much human oversight is necessary? Some applications of gen AI might work well if the content goes straight to the customer, especially if the stakes are low (like summarising online reviews). However, in cases where accuracy is critical – for example, creating content for legally binding promotions – it’s wise to have human agents review and edit the output to avoid any costly mistakes.
Each of these decisions comes with its own set of benefits and challenges, and the balance we strike will depend on our specific business needs, risk tolerance, and available resources.
A Simple Framework to Guide Our Decisions
I’ve found it incredibly helpful to think of these decisions in terms of a simple framework, which divides our choices into four quadrants:
Quadrant 1: General Input, No Review This is the fastest and most cost-effective option, but it comes with higher risks of inaccuracies and privacy issues. An example would be using gen AI to summarise online reviews without any human intervention. This approach might be suitable for internal use, where the stakes aren’t too high.
Quadrant 2: General Input, With Human Review Here, we still use broadly sourced data, but every piece of content is reviewed by a human before it goes out. This adds an extra layer of security and accuracy, though it does slow down the process. This quadrant could work well for generating social media posts or blog articles that require a personal touch and high accuracy.
Quadrant 3: Custom Input, No Review In this scenario, we invest in creating a custom data set that fits our brand’s needs, but we let the AI output go directly to the end user. This approach can speed things up and reduce privacy risks, but it might not always be as accurate as needed. Think of it as an in-store product locator that uses firm-specific data without needing a human to double-check the output.
Quadrant 4: Custom Input, With Human Review This is the most cautious – and typically the most expensive – approach. It involves both using tailored data and having a human review the content before it reaches the customer. This method is best for scenarios where precision is non-negotiable, such as creating an official product description for a high-stakes campaign.
Understanding these quadrants can help us make more informed choices about where to invest our time and money when incorporating gen AI into our marketing efforts.
Let’s talk facts and figures to give you a clearer picture of why these decisions matter:
Conversion Rates: Vanguard, for example, has used gen AI to increase LinkedIn ad conversion rates by 15%. This is a clear indication that when used correctly, AI-generated content can drive significant improvements in engagement and sales.
Customer Service Efficiency: Unilever has seen its customer service agents reduce their time-to-respond by 90% thanks to gen AI tools. This means that customers get the help they need much faster, which can lead to increased customer satisfaction and loyalty.
Market Adoption: Despite the high potential benefits, only about one-third of marketers have fully integrated gen AI into their operations. This gap shows that while many businesses are aware of the technology, the journey to full adoption is still underway. For us in the Australian market, this represents both a challenge and an opportunity – those who get it right could gain a significant competitive edge.
The Value This Newsletter Brings to You
In this edition, I want to offer clear direction and actionable insights to help you navigate these decisions. Here are some key values and takeaways that I believe will be particularly beneficial:
Clarity and Simplicity: I’ve broken down complex concepts into simple, digestible pieces. Whether you’re a seasoned manager or a small business owner, you can quickly grasp the essentials of how gen AI works and why it matters.
Data-Driven Insights: I’ve backed up our discussion with factual data and real-world examples. These aren’t just theoretical ideas – they’re based on what successful companies are already doing. This makes it easier for you to justify your investment in AI technologies to stakeholders.
Risk Management: Understanding the trade-offs between speed, cost, accuracy, and privacy is crucial. I’ve outlined a clear framework to help you decide which quadrant best suits your needs, so you can minimise risks while maximising benefits.
Future-Proofing Your Business: The marketing landscape is evolving rapidly. By embracing gen AI now, you’re not only keeping up with trends – you’re positioning your business to thrive in the future. I encourage you to see this as a long-term investment in efficiency, innovation, and customer satisfaction.
Personalised Approach: Every business is unique. The decisions you make about whether to use general or custom inputs, and how much human oversight is required, will depend on your specific goals and risk tolerance. I’m here to help guide you through these choices, sharing my own experiences and learnings along the way.
Final Thoughts and Next Steps
In wrapping up this edition, I want to leave you with a few practical steps that you can start implementing right away:
Assess Your Needs: Take a close look at your current marketing operations. Identify areas where AI could make a real difference – be it in content creation, customer service, or data analysis.
Choose Your Quadrant: Based on your needs and risk tolerance, decide which quadrant of our framework aligns best with your goals. Remember, there’s no one-size-fits-all solution; it’s about finding the right balance for your business.
Pilot and Evaluate: Start with a pilot project to see how gen AI performs in your specific context. Monitor the results carefully, paying close attention to conversion rates, customer feedback, and overall efficiency.
Invest in Training: Whether it’s upskilling your team or partnering with experts in AI, ensure that you’re prepared to manage this new technology effectively. Continuous learning and adaptation are key to staying ahead.
Stay Informed: The field of AI is evolving at a rapid pace. I encourage you to keep up with the latest trends and developments, so you’re always ready to leverage new opportunities as they arise.
By taking these steps, you’ll be well on your way to integrating gen AI into your marketing strategy in a way that is both effective and sustainable.
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About Me: I'm William Zhang—an engineer, creator, and business strategist with a deep passion for AI technology and digital innovation. As a business owner in engineering consulting, I also focus on helping others with personal development, financial awareness, startup coaching, business strategy, AI implementation, and building effective teams and partnerships. I believe strong relationships and the advancement of technology can create a better future, and I'm excited to share my insights with you.
