5 Easy Ways to Use AI for Content Management Right Now
- AI streamlines content management, offering benefits like SEO-optimized product descriptions and personalized recommendations.
- To leverage AI, understand your content challenges, choose the right tools, and ensure they complement your existing systems.
- Invest in software systems like PIM and DAM first to create a solid foundation for AI tools to deliver their full potential.
In this Article
Nine months ago, a Reddit user posed this question in a managers’ community: “Anyone using AI for enterprise content management?” The user wanted suggestions to help build a content management system using artificial intelligence (AI).
AI has been massive news since ChatGPT’s launch on November 30, 2022, so one expected hundreds of suggestions. However, the post has only one comment, and it doesn’t discuss the role of AI in content management.
Could this suggest limited knowledge of AI applications in content management? Do content managers realize how much their jobs could be more productive with proper integration of AI in their workflows?
Chances are that you are mulling over these questions too and wish for clarity on whether AI is valuable to your work. As you will see, AI is a game-changer. And there is more than one way to implement it in content management.
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Understanding AI in Content Management
What is AI?
Artificial intelligence (AI) describes the scenario where machines, especially computer systems, simulate human intelligence processes. These processes include learning (acquiring information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction.
This technology has been around for some time, but it went mainstream with the rise of robots on manufacturing floors and chatbots on websites.
How AI works
AI is everywhere, and chances are you’ve likely been using it in several ways without noticing. For example, your smartphone’s facial recognition system uses AI to identify and authenticate users; when you search online, AI algorithms rank and display relevant results, streaming services recommend shows or music based on your viewing history, and AI suggests words as you type, making texting more efficient.
Years of advances in creating and fine-tuning AI algorithms have made the technology look easy, but this appearance could be deceiving. Let’s get a rough idea of how AI works by examining chatbots.
Understanding AI through chatbots
Chatbots are AI-powered tools that simulate human conversation. They interact with users in a natural, human-like way, answering queries, providing information, and guiding users through various processes.
Here’s how AI works in a chatbot scenario:
- User input: The process begins when a user types or speaks a query or command. The chatbot receives this input.
- Processing: The chatbot uses Natural Language Processing (NLP), a subset of AI, to understand the user’s input. NLP enables the chatbot to interpret the meaning of the input, even if it’s not phrased exactly as programmed.
- Response generation: Once the chatbot understands the user’s input, it uses machine learning (ML) algorithms to generate an appropriate response. These algorithms are trained on large datasets of conversations, enabling the chatbot to learn how humans respond in similar situations.
- Learning and improvement: Over time, the chatbot continues learning from interacting with users. It uses this feedback to improve its responses and become more accurate and helpful.
AI in content management
One of the key findings in a recent HubSpot report is that AI unlocks growth. According to the analysis, AI boosts effectiveness and productivity – in fact, the report established that the average employee saves two and a half hours a day using the technology.
Specifically, HubSpot found that most business professionals use AI to automate manual tasks. This way, they can spend more time on more impactful tasks. If part of your job description includes content management, you’ll already understand the significance of this. Nonetheless, let’s explore further.
Content management involves creating, organizing, distributing, and storing digital content. This could be in the form of text, images, videos, or any other multimedia format.
For industrial brands, content management might entail creating product descriptions, blog posts about industry trends, instructional videos, and social media updates. Then, another significant chunk of time and resources is spent on organizing and distributing this content across various channels, such as the company’s website, email newsletters, and social media platforms.
One thing to note is that content management can be time-consuming and involves several manual processes. However, with the help of AI, these processes can be automated and optimized to improve their efficiency and effectiveness.
The question arises: how can content managers leverage the effectiveness and efficiency of AI in their jobs? Here are five easy, practical applications.
5 Easy Ways You Can Put AI Into Action as a Content Manager
1. Generating SEO-optimized product descriptions
Creating product descriptions is one of the most time-consuming tasks for content managers, especially in eCommerce. Each product requires a unique, engaging, and SEO-optimized description to rank well on search engines and attract potential customers.
The task gets more exacting when dealing with hundreds of products. A typical industrial brand has a leading product and several variants, and each must be described uniquely to convince the buyer to purchase.
Although tools like product information management software (PIM) solutions provide templates for product descriptions, you’ll need some tweaking for each product to stand out. The point is that writing high-quality and unique product descriptions is time-consuming and energy-sapping.
This is where the text-generating capabilities of AI can be a game-changer. For example, consider a power tools manufacturer that has just acquired a PIM platform and wants to write optimized and engaging product copy at scale.
The company can choose AI text-generating tools like Jasper or Copy.ai to do the job. These tools are trained on massive datasets of existing product descriptions, including those ranking high in search engine results pages (SERPs). This allows them to understand the critical components of compelling descriptions, such as:
- Keywords: AI can identify and incorporate relevant keywords related to specific products and their functionalities. For example, a description for a cordless drill might include keywords like “brushless motor,” “variable speed,” and “maximum torque.”
- Clear and concise language: When properly prompted, AI tools can generate easy-to-understand and navigate descriptions.
- Benefit-driven content: AI can highlight each product’s key benefits and value propositions, addressing customer pain points and showcasing how the product can solve their needs.
- Call to action: Effective descriptions often include a clear call to action, encouraging users to learn more, add the product to their cart, or purchase.
Are AI writers better than human copywriters?
Well, it’s complicated. Right now AI tools prevail over human copywriters in some circumstances because they can instantly produce hundreds or even thousands of on-brand product descriptions tailored for search engines. This saves content teams significant time while giving every product a better chance to rank in search results for relevant customer queries.
So, if the power tools manufacturer wants a description for a drill and they chose a human copywriter, the result is likely to be something like:
18V Cordless Drill: This drill is powerful and versatile, perfect for various drilling tasks.
However, AI can produce something more comprehensive and higher quality in terms of optimization for search engines. For example, the simplistic human-generated copy can be transformed into:
Dominate any DIY project with the [Brand Name] 18V Cordless Drill. Featuring a powerful brushless motor and variable speed settings, this drill tackles tough materials with ease. Enjoy effortless control and precision if you’re drilling into concrete, wood, or metal. Experience the difference – add the [Brand Name] 18V Cordless Drill to your cart today!
This AI-generated description incorporates relevant keywords (“18V Cordless Drill”, “brushless motor,” “variable speed”), highlights benefits (“powerful,” “versatile,” “tackles tough materials,” “effortless control”), and includes a clear call to action (“add to your cart today”).
The result shows clear benefits of using AI to generate product information, including:
- Increased efficiency
- Improved SEO potential
- Clear and concise descriptions focusing on benefits can improve engagement and conversion rates
Nonetheless, keep in mind that AI-generated content may not always be accurate. Therefore, have human eyes review and edit the copy before publishing.
Note
When using AI for content creation, audit models for bias. Generated content should avoid perpetuating harmful stereotypes or representations. Monitor AI output for accuracy and diversity.
2. Image creation
Images are an integral part of product content. They play a crucial role in that they provide instant information, help customers better understand the showcased products, and help to create emotional connections. Images can also help to up-level your SEO game – when adequately tagged with relevant keywords, they can help improve your product’s visibility on search engines.
Do you still need convincing about the significance of images in eCommerce? See what reputable statistics show:
- eMarketer revealed that most customers (62%) would choose a product if they could see photos first
- Think with Google found that 50% of shoppers relied on images to decide what to buy
- Splashlight established that sellers with the highest-quality product images attracted the majority of buyers, so we could go on
However, creating high-quality and captivating visual content and also being able to stay on-brand can be challenging. On the one hand, you can lack the technical skills within the organization, or, on the other hand, the current tech stack may not offer an optimized platform to make this work.
Some digital asset management (DAM) tools like Catsy DAM provide workarounds, especially to the latter challenge. For instance, Catsy DAM integrates with Canva and Adobe Photoshop for enhanced productivity. In short, Catsy DAM simplifies asset creation (through third-party tools) and management, optimizing efficiency and accelerating your go-to-market strategy.
However, let’s assume you don’t have access to Catsy DAM’s collaboration with Canva and Photoshop. In that case, AI’s image-creation capabilities are invaluable.
DALL-E is one of the most talked-about image generators in the market right now. It is far more popular than the competition, mainly because it was the first tool of its kind to offer advanced image-generating capabilities. Similar tools include Stability AI’s DreamStudio, Visme, WOMBO’s Dream, AI Generator by Getty Images, and more.
So, if the power tools manufacturer would like to exploit AI’s image creation capabilities on top of text generation, the organization has the following use cases to explore:
- Generating realistic settings: AI image generators can take your existing product photos (your brand assets) and place them seamlessly into new backgrounds and scenarios. For example, imagine a power drill shown on a rugged construction site or a saw displayed in a workshop with realistic lighting and textures.
- Rapid prototyping: The brand can experiment with various backgrounds and compositions to find the most visually compelling images for marketing campaigns.
- Scaling visual content: AI makes producing an extensive library of product images for different channels and use cases faster and more cost-effective.
Again, as with all AI-generated content, it’s crucial to understand that AI-generated images are not foolproof. While they can be remarkably realistic, they may still require human intervention for final edits and quality assurance. Additionally, ensure your brand assets are used appropriately and comply with copyright and trademark regulations.
3. Metadata enrichment
Metadata is the information that describes and contextualizes your product content. The details make finding, using, and managing product information and digital assets easier. Metadata enrichment involves enhancing the details to improve your content’s discoverability and usability.
Typically, metadata enrichment isn’t challenging when using a PIM and DAM solution. For example, Catsy PIM allows users to create, control, and manage metadata schemas, systems, and models. This feature is essential for digital-facing brands because properly managed metadata enhances an online store’s discoverability, organization, and presentation. In other words, Catsy is the ideal candidate when shopping for an E-commerce PIM.
Metadata management also applies to digital assets. Brands must provide content information, like product images, to enhance searchability and categorization. Again, Catsy excels in this regard.
The platform has a pre-integrated DAM solution to automate the metadata management process, among other features. The DAM streamlines the addition of alt-text and metatags to product photos, using a built-in AI image recognition tool to automatically add metatags to images upon upload into Catsy’s centralized library.
This feature makes Catsy one of the best PIM software and DAM software combined systems in the market, especially for businesses looking for a comprehensive product content management solution.
However, there is no denying that PIM and DAM do not entirely do away with manual processes.
You can leverage AI tools to analyze your content and generate relevant metadata. These tools use machine learning algorithms to understand the content’s context, identify key themes and entities, and generate appropriate metadata tags.
For instance, if you have a video demonstrating how to use a power drill, an AI tool could analyze the video, identify the power drill as the primary entity, understand the demonstration context, and then generate metadata tags like “power drill,” “product demonstration,” “DIY,” and “home improvement.” This can significantly improve the video’s discoverability on your website and other platforms.
Moreover, these AI tools can enrich your metadata with search-engine-optimized keywords, improving your content’s visibility during searches. They can also ensure consistency in your metadata, enhancing your content’s organization and usability.
Consistency and organization are also PIM and DAM solutions’ most significant selling points. So, combining these features with AI metadata enrichment capabilities creates the most robust product content management system.
4. Personalized product recommendations
Content management is broad – it cuts across the creation, storage, delivery, access, and optimization of product information and associated assets. One of the strategies for successful content management is enhancing customer experience through personalization.
Personalization may sound appropriate for business-to-customer (B2C) contexts, but it also applies to business-to-business (B2B) scenarios.
For instance, B2B buyers, like B2C consumers, expect vendors to make relevant suggestions that fit their organizations’ needs. If an organization’s representative engages with your content, chances are high that they will choose your products. Personalization is a robust tactic you can use to engage buyers.
One approach to personalization is using insights from customer behavior to recommend the most relevant products. This can happen when a buyer visits your online store or digital catalog. However, generic “You may also like” suggestions often miss the mark. Neither does manually curating personalized product recommendations for each buyer make practical sense.
AI has made considerable strides in this regard, with tools already in the market for optimizing product recommendations. For example, Nosto (leveraging the experience.AI™ neural core) can analyze buyers’ behavior and preferences and recommend products they’re likely interested in. This tool uses machine learning algorithms to understand the buyer’s browsing history, search parameters, and other interactions with your brand and then predict their future behavior.
For instance, suppose a representative from a construction company is researching heavy-duty rotary hammers on your website, and they’ve added one model to the cart. In this context, the AI tool’s recommendations might focus on:
- Complementary tools: Suggest compatible chisel bits, dust extraction systems, or carrying cases designed for that specific hammer model.
- Bulk orders: If the cart quantity is low, subtly encourage larger purchases (“Contractors often order 5+ for their crew…”)
- Service plans: Offer extended warranties or maintenance packages relevant to that type of power tool.
- Related consumables: Recommend long-life drill bits that work well with the selected tool.
The fact that this level of personalization happens in real-time considerably up-levels your content management game. The ultra-personalization makes customers more likely to find and purchase suggested products.
5. Content gap identification
Content gaps are the areas where your existing content doesn’t adequately address questions or topics that matter to your users. They represent the difference between your current content and the content you need to meet your audience’s needs and achieve your business goals.
As such, you must conduct regular content gap analysis to identify and evaluate these missing pieces. This is the surest way of aligning your content with the various stages of your target audience’s buyer’s journey.
But with hundreds (or thousands) of products and constant new releases, it’s challenging to manually pinpoint content gaps across websites, campaigns, and channels. AI content analytics and optimization tools like Optimizely can identify missing or underperforming content across a brand’s digital properties.
Optimizely may uncover that a key product line drives search interest and traffic but has little optimized content focused on customer pain points and usage-based keywords. The tool surfaces these kinds of content gaps through data analysis – enabling your content team to see exactly where they need more content investment to drive greater value.
For instance, if you’re not covering topics like “power tool maintenance” or “power tool safety” on your blog, the tool could identify these as content gaps. It could also suggest formats like how-to guides, video tutorials, or infographics that your audience might prefer.
Moreover, AI-powered content optimization tools can suggest SEO keywords that you should target to fill the content gaps. This can significantly improve your content’s visibility on search engines, driving more traffic and sales.
Key takeaways
The pace of AI innovation is rapidly transforming content management, providing new opportunities to work smarter and serve customers better. But, while the possibilities are exciting, implementing AI-enabled content management solutions still requires careful evaluation and planning.
The key is to build a solid foundation first – with systems like PIM, DAM, and CMS – and then identify targeted needs where AI can drive tangible impact. Some examples include translating content at scale, generating optimized product descriptions, and uncovering content gaps. Then, choose tools that solve your most pressing bottlenecks.
Keep in mind not all AI software lives up to the hype. So, vet solutions thoroughly and start with limited pilot projects. Focus on augmenting rather than replacing your talented human team’s efforts – the goal should be effective human-machine collaboration.
The point is:
Approached thoughtfully, AI can significantly uplift your brand’s content management strategy. But rushing into it without the proper infrastructure and strategy risks limited results.
Laying a robust groundwork and evaluating needs is the right way to do it because you’ll adopt the most appropriate AI solutions. This way, you can create, manage, and distribute content that rises above competition.
Common AI tools include content generators, metadata taggers, image creators, analytics, personalization engines, and more.
Nope. AI augments and assists human creators but cannot fully replace creative thinking.
Content managers do not require specialized skills to leverage AI. They only need to understand their content goals and workflow needs. In any case, most AI solution vendors have teams that assist with technical integration.
Understand your content management challenges, evaluate how AI can address them, and choose tools that align with your needs, goals, and existing systems.
A robust foundation, such as PIM and DAM solutions, ensures your content is organized, accessible, and optimized across various platforms. This foundation is crucial for extracting the full potential of AI-powered content management tools.
