5 Things You Can Be Doing Today to Prepare For AI Marketing
It has been an exciting few months for content strategy and marketing.?I do not believe that a single marketer has not been tracking the rapid rise of generative AI.?For marketing (and maybe for everyone else as well) AI represents a change that is equal to digital, radio and TV, or the printing press.?The amount of noise around the subject is deafening.?Every vendor is racing to integrate generative AI into their products.?New companies are being formed every day.?
It isn’t without complexity and risk.?Brand safety, copyright risk, and ethical AI concerns are real, especially early on.?Content review and approval processes will become more critical.?Skill sets for the team are different and need to be developed (how many ”prompt engineers” are currently on staff…and what the heck is a prompt engineer? ??).?Workflows will shift and take on unique challenges around metadata management.?Budgeting will become less predictable and more complex (Do you have AI credit budgets as a line item for your experiences?).?The volume of content that needs to be stored will increase by orders of magnitude and content discovery and “findability” will become incredibly difficult.
The real question is, what can your organization do TODAY to prepare your organization to harness the benefits of AI over the next five years?
1. Develop an Actionable AI Policy And Integrate It into Your Content Operations
Many enterprises are already drafting “AI Use Policies” that outline the corporate stance on leveraging generative AI and guidelines and approvals required.?This is a great first step.?However, whether you know it or not, AI is likely already getting used on the fringes of your experience creation operations.?In addition to drafting a policy, organizations need to thoughtfully weave AI governance into their operational workflows.?
Tag content that was AI Influenced.?Later if there are copyright issues or policy changes, you can quickly identify liability and exposure and take corrective action.?Likewise, as models and prompts evolve, you may wish to revisit content to increase quality.
Ensure that appropriate human review and approval is in place and documented.?
Risk to brand safety is higher with AI generated content and ensuring that it gets appropriate human review is critical.?Additionally, because the types of mistakes, issues, and risks are different with AI generated content than human generated, reviewers needs the appropriate heads up and potentially a different set of review prompts or steps.?Because the legal and regulatory environment for AI is rapidly evolving, operationalizing AI content workflows also give legal and compliance teams an effective hook to rapidly respond to court decisions, legislation, and regulation on a jurisdictional basis.
Be sure that you learn from these early work and can replicate the rough steps used for creation so that you can later modify or extend the work to other projects.?This includes capturing the systems used, prompts, models, and tuning parameters.?As AI based models and products evolve in sophistication and capability and your own body of best practices grows, it is important to document the inputs to generative AI systems so you don’t have to start from scratch.?Because generative AI is highly API based, it now unlocks the opportunity to bulk optimize content based on performance, improved technology, or evolving internal expertise.
2. Adapt your Content Storage, Retention, and Discovery Process
Content storage systems are a relatively stable part of marketing operations infrastructure.?However, in a generative AI world, content creation will increase by a minimum of two to three orders of magnitude.?Content creators might push five, ten, or even fifty versions of a piece of creative through a review process.?This highlights the need for more disciplined work in process storage strategies and infrastructure.?For each system where content comes to rest, ensure that you have a clear understanding of the cost implications of data volume and the scalability limits of the infrastructure (e.g. DAM ingestion rates, cloud storage fees, local NAS volume size limits).
Implement or revisit retention policies at each “at rest” state of your content creation workflow to check for leakages in content retention.?Do the same for core content repositories, CDNs, and DAM systems for ”finished goods” content.?Lastly audit your channel delivery systems like CMS, social, marketing automation/email to understand their data retention options and look for areas where you can collapse down to single systems of records.
Finally, review the discoverability of your content.?For most organizations, it is hard to find the content you are looking for today.?When that increase by a factor of a hundred or more, this becomes much worse.?Ensure that search capabilities of your content storage systems are performant at the potential future scale.?Revisit your asset tagging taxonomies for discoverability.?Learn to leverage AI auto tagging, semantic search, and summarization technologies to let AI help solve the discoverability challenges that AI creates.
To prepare for omnichannel personalization, be sure that content is tagged with all the dimensions that you will ultimately personalize such as markets, segments, personas, products, brands, etc.?By doing this now, you will be able to generate AI based content plans that will fill in all the content holes you have for upcoming multichannel experience launches.
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3. Build an Intentional AI Training Strategy and Get the Infrastructure in Place to Support It.
When you hire a new creative, they examine your brand and start to get a feel for your brand voice.?Over time, they get better and better and creating truly great “on brand” content.?While that happens through a combination of osmosis and onboarding for creatives, for AI content generation, you need an intentional training strategy.
You will get better and better and mastering the prompts and tuning parameters of generative AI tools.?Because of the number of content creators will increase dramatically, the cost of acquiring this mastery needs to be leveraged, or you’ll waste time as you scale your early AI pilots and you will experience uneven quality.?By integrating a prompt management strategy with a system of record, discoverability, and tool integration you can help uplevel everyone’s effectiveness.?As your experts find prompts that define your market segments or personas well, document them in a way that they can be easily leveraged in tooling.?For personalization, leverage these high quality prompts to generate content renditions for segments and channels.?Even when the original content wasn’t AI generated, this is a great lower risk early productivity gain that organizations can use as they adopt AI.
Increase your content quality tracking game.?Large Language Models are trained against a staggering amount of data.?This is what underlies their uncanny ability to produce high quality results.?Unlike many other AI model types, you do not need to dump mountains of data into them to get the results you need.?What you do need, is smaller amounts of data that represent your intended outcome.?If you can flag content that performed well in a specific context you can fine tune your generative AI models to create truly impactful content.?Creating Content ROI strategies today so that you can begin to build your training sets.?As you start to produce AI generated content, these feedback loops will allow you to make rapid progress in marketing efficacy.
Just as with prompts, as you build these model feedback loops, you need to create a model management strategy.?This can be done early on and evolved over time.?Deciding what aspects of your performance to train a model on is a program that marketing operations or a data science COE can collaborate.?As the stable of high quality models increases, integrating model selection or recommendation into your content creation workflows can allow for powerful scaling of content performance and consistency as the team and project size grows.
4. Start Adapting your Brand Guidelines for an AI World
Prompt management, AI powered content creation workflows, training data and models can be intimidating.?One of the easiest ways to make early progress with scaling generative AI is by integrating it into your brand guidelines.
Add prompt snippets for used platforms that gets the colors and fonts used in image generation correct.?Add definitions for segments or brand voice.?As the very early work uncovers things that work to create acceptable content, these high level items become a lightweight “prompt management” infrastructure that can help partners, the media, and other constituencies get better branded content outcomes through their own generative AI projects.
5. Get AI Costs In Your Budget Planning Process And Identify Pilot Projects
Adoption of AI Marketing is introduces some complexity as well as excitement to the budget process.?By getting a head start on the changes while it represents a small percentage of your marketing activities and spend, you can get the process kinks worked out and be prepared for your next year’s budgeting process.
Generative AI systems generally charge based on tokens and number of passes taken at generating content (e.g. number of image prompts).?This injects a lot of uncertainty into total costs of content.?Tokens are arbitrary sized blocks of texts (a couple thousand characters).?That makes it hard to determine how many tokens will be required for the ultimate deliverable.?Further, generative AI prompt crafting is iterative.?It make to a few passes to get to the desired outcome.?Finally, because generative AI is frictionless, it allows creators to generate a large number of variations for teams to review and select from.?Budgets have to account for the variability of generative AI’s usage based pricing.
Pilot project selection is a target rich environment.?There are a few rules of thumb that are successful.?Text is generally easier than image, video, or other content types.?While AI may run autonomously while the rest of the marketing organization is golfing in the future, for early tests, embracing a “marketer as editor” strategy works well.?Projects like adapting existing high performing content for other markets or segments is a high return use case.
Content marketing has always been governed by laws of content scarcity.?You take smart bets in your content calendar to cover as many of your priorities as you can afford.?In the future, AI Marketing creates a world of content abundance.?The bottlenecks for experience creation shift to other scarce resources like posting frequency, media placement costs, and similar.?This takes some effort to adjust and means that processes, systems, and templates need to be revisited.
Generative AI is an exciting development.?While it seems alien and magic, there are clear things enterprises can do today to prepare for scale.?I would love to understand what your marketing organization is doing to prepare. Please drop me a LinkedIn InMail or a comment.