Brian Alvey is a CTO, not a professional chef, but he’s serving up innovations that are helping satisfy enterprise organizations with a big appetite for AI.

Speaking on an episode of the AI Why podcast, Brian talked about how WordPress VIP’s open and intelligent approach to enterprise content management gives large businesses strong governance, security, and flexibility. The latter is important when you need to tailor AI to specific business needs.

The podcast described Brian and WordPress VIP as offering “AI-powered ‘editorial recipes’ (that) turn hours of manual headline testing and cross-linking into minutes.”

Recipes are actually a great way to think about AI adoption. It’s a concept that shouldn’t be limited to traditional editorial operations in the publishing industry, though. Enterprise marketers could benefit from some AI recipes too.

Why enterprise marketers need AI recipes

The recipe concept represents a way to standardize AI adoption in enterprise marketing. Today, it’s inconsistent: only 37% of marketing teams report having a clear AI strategy from leadership, and 52% say they don’t own their own data strategy. That means there’s a lot of siloed, ad hoc experimentation, potentially duplicating or wasting effort among team members.

Instead of looking for a magic AI formula (which produces formulaic or generic results), a recipe allows for some customization. When you’re preparing a meal, for example, a recipe guides the process, but you can adjust ingredients to change the way it looks, smells, and tastes.

In the same way, AI marketing recipes could be developed based on predefined, repeatable workflows that use AI to execute specific marketing tasks. The ingredients would include prompts, rules, data inputs, and feedback loops that could be reused but also refined over time to optimize content production.

How to develop an enterprise AI marketing recipe

Traditional recipes are developed through extensive experimentation as chefs see how well ingredients complement each other and how they respond to chopping, stirring, or heating. AI requires a similar spirit of trial and error, but marketers should bring together common elements so employees aren’t reinventing the wheel every time. These include:

• Trigger: AI should always serve a specific purpose. For example, AI could help streamline or accelerate content production for an upcoming marketing campaign, or be aimed at boosting productivity by augmenting workflows in an always-overworked content team.
• Inputs: AI only works as well as what you use to fuel it. First-party data sources, like audience segments, are a great start, while performance data from past campaigns can provide a more holistic context. SEO keyword research is also still important, while any proprietary data (like a customer survey or report) can help boost visibility for answer engine optimization (AEO).
• Prompt framework: AI tools need to be told what to do as specifically as possible. Thinking of these as recipe ingredients is helpful because it’s easy for someone to come up with a prompt or tailored instructions for a task, only to forget them.
• Variation engine: When it’s used well, AI not only completes a task but opens up possibilities to improve the results. Instead of using a generative AI tool to come up with a title idea, why not try a dozen you could test? You could also use it to test variations in calls to action (CTAs) or the timing of an email blast or social post.
• Decision layer: Based on your objectives and marketing KPIs, select or develop the rules or models you’ll use to rank or evaluate AI outputs. This will tell you where you need to tweak your recipe or whether some of the learnings could be applied to other recipes you develop.
• Activation: Some AI capabilities are already core features within existing platforms. WordPress VIP’s headline testing tool, content recommendations (via Parse.ly), and JetPack AI are all good examples. In other cases, you’ll need to consider what kind of integration you’ll need to connect AI tools to your marketing automation tool, CRM, or ad platform.
• Feedback loop: Even if you’re following a recipe, you need to keep a close watch on whether AI is working as it should. Identify what kind of performance data you can easily gather and share with other stakeholders, including those outside of marketing, to talk about your progress and plans for improvement.  

An AI marketing recipe example

Here’s an example of how an enterprise marketer could create an AI recipe for optimizing titles for a blog post:

• Brand voice and tone guidelines
• Click-through data and SEO performance data based on previous reports
• An analytics platform with A/B testing capabilities
• A CMS that includes generative AI features for content production and optimization  

1. Generate 10 headline variations for your blog post or other content marketing asset based on brand voice and SEO targets
2. Score them using historical CTR data
3. Push top variants into A/B testing tools
4. Feed results back into future generations

What you learn from the results may require you to change or add ingredients, such as an additional data set. You may also have to add steps, such as reviewing metrics like engaged time and recirculation rate. But this kind of recipe is a way to establish clarity across the marketing team on what’s required to use AI in a way that delivers insight as well as results.

High-impact AI recipes for enterprise marketing

Your AI marketing recipes will institutionalize high-performing tactics across teams, particularly helpful in enterprises with teams spread across regions or operating in multisite environments. They also reduce reliance on individual expertise or tribal knowledge, scale your efforts, and let you build in the appropriate governance.

Ready to begin making AI marketing recipes of your own? These are some potential areas to begin:

1. CTA personalization across customer segments

Marketing content can inform or educate, but most often the goal is to convert prospects into paying customers. That makes CTAs one of the most important elements in any asset you produce.

2. Campaign timing and distribution recipes

Reaching your target audience at the moment of need makes all the difference in conversions and, ultimately, revenue. That’s why AI recipes that help determine optimal send times for social posts, email blasts, or text messages can boost campaign efficiency.

For your ingredients, you can use not only historical engagement data but also external signals, like seasonality in the B2C space or industry trends that influence the purchase cycle in B2B. From there, see how your recipe could allow you to adjust scheduling dynamically by channels like email, social media, and your website, and maximize the number of A/B tests you can run.

3. Internal linking and content recommendations

Enriching content with internal links is essential for keeping visitors on your site and encouraging deeper exploration, but it can feel like grunt work. The goal should be to use AI for automatically identifying linking opportunities across large content libraries, which is often the case when you’re working in an enterprise operating multiple websites.

Any AI marketing recipes you develop here should aim to improve SEO and engaged time without having to do manual audits. It can also contribute to better AEO, encouraging large language models (LLMs) to scrape and cite your content in AI-powered summaries and overviews.

From a testing perspective, prioritize finding repeatable workflows that help you link based on authority, relevance, and conversion potential.

4. Content repurposing pipelines

One high-performing ebook or research report could spawn many additional assets to nurture a prospect relationship. However, it’s usually up to individuals to figure out how best to turn longform content into a blog post, email, social media post series, or landing page.

An AI marketing recipe would help you not only get this work done but to ensure brand messaging is consistently maintained while tailoring how it is used to deliver an omnichannel digital experience. You’ll know your recipe is working if you’re reducing production time while increasing reach.  

Measuring success with AI marketing recipes (beyond output volume)

Many marketing teams initially turned to generative AI to create more content. Now they’re realizing it can be equally valuable, or even more valuable, in helping them achieve their business goals.

Just as a cook tests their recipe before serving a meal to guests, your initial use of AI should be evaluated based on:

• Time-to-publish reduction: How long does it take to write a blog post, an email blast, or the results of a customer survey? The answer can make or break whether your brand sounds relevant or outdated. AI should balance both speed and quality.
• Conversion rate improvements: A bump in click-through rates (CTR) is an obvious win, but in some cases, you want to see an increase in form fills to capture leads passed to the sales team.
• Consistency across campaigns, sites, and regions: Whether you’re operating in one country or 100, your content should still feel like it speaks with a single, authoritative voice. Use AI to avoid weird disconnects in multisite environments, or changes in tone from one campaign to another.
• Incremental lift from continuous optimization loops: The rush to embrace AI has sometimes suggested companies should expect overnight success. In practice, you’re using and tweaking marketing recipes to get better with each asset, campaign, and initiative based on the analysis you conduct over time.

Operationalizing recipes in the martech stack

Even the best recipes don’t get you very far without the right kitchen equipment. The stove, for instance, is a critical multipurpose appliance that lets you roast, bake, saute, or fry food at a variety of temperatures using pots, pans, and other accessories.

AI needs something similar: an enterprise-grade CMS that lets you connect AI to marketing automation and decisioning systems, APIs, orchestration layers, and security/governance capabilities that allow marketing teams to experiment and innovate with confidence.

Having rapid authoring tools and built-in workflows for reviews and approvals minimizes the work required to keep humans in the loop as AI usage becomes common and supports AI explainability and auditability.

With a solid CMS in place, you can put your recipes to work and feast on all the value AI has to offer.