Let’s be real: the world of marketing is already complicated. Agile marketing isn’t easy to implement or manage on an ongoing basis, and the promise of helping teams prioritize work – and say no to things – remains elusive. But today, marketing processes are evolving even faster, with generative AI becoming the main enabler.
How to combine agile marketing and generative AI? What will this powerful duo bring you? This article explores the concept of “Agile + Generative” and its potential impact on the careers of businesses and marketers over the coming decade.
While experts emphasize that agile marketing is more about prioritization and focus than faster and better execution, in my experience the two go hand in hand. Generative AI is poised to accelerate priority work, particularly in the areas of content management and SEO.
This article does not discuss prioritization strategies. Instead, we’ll explore the impact, effectiveness, and gains that generative AI can bring to an agile marketing team. Buckle up as we navigate this new frontier of marketing innovation.
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What will “Agile + generative AI” bring?
Although all white-collar workers are worried about the impact on their careers (and rightly so), the dynamic is too big to ignore. 2024 has plunged us into the “trough of disillusionment” with generative AI. There will eventually be a new level of maturity in the coming years, with many case studies to provide proof of results. Some of these case studies already exist, while the “how to’s” are commonplace:
As these articles highlight, it’s critical for marketers (including agile teams) to evolve quickly and integrate generative AI skills, processes, and tools.
Agile Marketing Improvements
Looking in the rearview mirror, companies that demonstrate process excellence and advanced use of technology consistently achieve better results. That’s what tons of research conducted by management consultants over the past 20 years reveal. Generative AI will do the same, especially when it comes to search and content, costing marketers a lot of time and money. He also interacts with a ton of prospects and customers.
Leaders, leaders, and stakeholders need to ask themselves a few key questions: What happens when your competitors can produce content with three times the productivity? What if the content and media you produce cost three times less? What if it was three times more targeted on the most important characters? Will this have an impact on revenue? Will this help sellers close more deals? What about competitive advantage and brand perception? What happens when you combine Agile marketing with generative AI? Does one plus one equal three? And doesn’t this also affect SEO and conversion rates?
Generative AI can “supercharge” (someone call the fashion police) agile marketing by providing:
- Faster delivery and time to market.
- More iterative adaptation.
- Improved content quality.
- Better conversion rates across personas, segments, channels, industries and products.
- Increased revenue with reduced costs.
“Agile + Generative AI” key metrics can have an impact
This evolution of generative AI makes marketing content development more productive. It also reduces customer acquisition costs (CAC), which is any CMO’s golden ticket to “doing more with less.” Savings can be reallocated to other areas if content represents 30% of your marketing budget. The quality of your content will increase if you do it right.
The main question remains: can we trust generative AI to provide quotes and quote sources that meet journalistic and credible standards? Not completely.
To support lower CAC, conversion rates can increase across all channels, all based on hyper-personalization. Better marketing productivity should lead to more revenue and growth, right? If not, are you doing things the right way? Real-world examples have shown that marketing productivity can be increased threefold for many processes and even 10x for others.
Again, these are not hand-wringing claims – as of 2024 – the market has already proven them. It’s just not widely known and common yet. Generative AI is still very new. Only innovators achieve this and perhaps some of the early adopters.
Innovation requires more resources than basic operational improvements. But a “wait and see” attitude means you’ll be starting from behind. Laggards will have a problem, like when Netflix crushed Blockbuster with “normal” AI.
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Rapid design and micro-language models: the key to “agile and generative AI”
A basic understanding of generative AI is necessary, particularly as it relates to rapid design and microlanguage models. Conceptually, a microlanguage template looks like the attachments you upload with your prompt.
Rapid design or rapid engineering
Over the past couple of years, rapid engineering has become “in” with engineers and coders. The media world made sure everyone knew that this was the most in-demand job. What exactly is it?
To use an analogy, web design differs from website engineering with JavaScript, HTML, and CSS. Along the same lines, rapid design is different from rapid engineering. In both cases, design and engineering (or coding) can overlap. Prompt engineers will write “code” in Python and English to resolve issues. However, businessmen “write English prompts” and give requirements to engineers when code is needed.
Regardless, prompt design should be treated like code because words are important in a prompt. Even a small change to a prompt (written in English) can create drastically different results. This is especially true if you want to reuse prompts in different circumstances while avoiding engineering costs.
Writing a Simple Prompt Design Versus a Complex Design
Typing a few questions or sentences is a very basic prompt. Anyone can do it. However, a prompt can be three, five, or even 10 pages of written words, or even more.
Again, this doesn’t necessarily have to be created or implemented by engineers. “Normal humans” can learn to write complex prompts. They just need a technical mindset and the ability to write well. With simple prompts, productivity increases significantly. We have all played with ChatGPT.
However, more sophisticated prompts can significantly improve results. For complex prompts, version control and file management become crucial because the wording functions like code. Additionally, reusing prompts is essential for productivity; you don’t want to write a new prompt from scratch every time you need to generate output.
What is a microlanguage model?
Paste this into a prompt to find out:
“You are a world-class CMO and agile marketing leader at an award-winning SaaS software company. You are an expert in achieving agile marketing results and an expert in designing generative AI prompts. You know how to lead agile marketing teams to achieve goals in these key areas: faster delivery, improved quality, better adaptation, improved communication, increased revenue and faster time to market. Your goal is to answer questions about generative AI and agile marketing:
1. What is a microlanguage model?
2. How is a microlanguage template similar to the attachments you submit in a prompt?
3. How are quick content attachments used in content marketing?
4. What are the top 10 considerations when using attachments for content production?
5. How can attachments help improve the quality and speed of content?
6. How can well-designed prompts and attachments impact the key marketing metrics that matter to a CMO?
This is the end of the prompt
The bottom line is this: use attachments. Although some of the results are not useful, much of them are. The results will make you think! For the agile mindset, start now with micro-language templates (high-quality attachments).
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