Exploring Generative AI in Content Management: Opportunities & Challenges
- Emerging revolution. Generative AI in content management is reshaping how CMS, DAM and PIM systems operate, enabling targeted and personalized content at scale.
- Operational efficiency. Use of generative AI drastically reduces time to market, fuels ideation processes and scales product information, giving businesses a competitive edge.
- Cautious adoption. While generative AI offers transformative capabilities, it also raises concerns around data security, quality and ethical implications that enterprises need to navigate.
You won’t be hard-pressed to find generative AI integrations into content management systems software. Vendors tend to latch on to a technology craze or two, and ChatGPT’s debut in November 2022 certainly caused a tech craze in the last 11 months.
But what’s the true larger picture when it comes to generative AI in content management? Who will be the use-case winners in the world of content management (powered by content management systems, or CMS), rich-media assets and digital asset management (DAM) and product information management (PIM) — and why?
“Gen-AI is becoming embedded as a capability within CMS, DAM and PIM systems enabling enterprises to create new content for digital and print channels,” Forrester Principal Analyst Chuck Gahun told CMSWire in a recent catchup. “Through its ability to create net new copy, generate product information and also support creative expression of existing brand assets, it can help enterprise content teams scale personalized content creation and management efforts. This enables businesses to create more targeted messages, increasing the accuracy and fidelity of product information across their digital experiences, ultimately increasing customer, and employee, satisfaction.”
Gahun served as one of the authors of an August Forrester report on the topic — ”Generative AI: What It Means For Content Management” — that found generative AI is not just an innovative tool but a revolutionizer in the realm of digital experiences.
Its prowess extends to:
- Accelerating personalized text creation
- Fueling ideation in rich-media creative processes
- Scaling product information syndication to digital storefronts
These capabilities are not mere enhancements but transformative competencies that can redefine how content is managed and delivered, according to Forrester authors.
And, naturally, as with any new technology integrated into the enterprise, there are well-documented cautions with AI. Kristy Lynne Andreadakis, chief marketing officer for 1Social Buzz, lays out those cautions nicely in an August LinkedIn post:
- Quality concerns
- Ethical considerations
- Job displacement
- Dependence on data
- Security risks
What else did we discover about generative AI in content management in our catchup with Forrester?
Related Article: The AI and Content Marketing Paradox: Empowering and Threatening the Future
Time-Based Competition as a Business Case
Forrester finds the business case for generative AI is rooted in time-based competition principles. By making value-delivery systems more efficient and quicker, enterprises stand to gain a competitive market advantage while enhancing variety and responsiveness to customers.
The implications are far-reaching: increased employee productivity, consistent brand representation in the market and reduced time to market, all converging to outpace competitors in customer acquisition, growth and geographical expansion.
“Most CMS vendors have incorporated or are in the process of incorporating capabilities to generate copy within their systems,” Gahun said. “Some CMS vendors go as far as providing capabilities to generate new copy to target specific audience segments in a specific tone and voice.”
Related Article: How to Pick the Right Flavor of Generative AI
Enhanced Productivity in Content Pipelines
The year 2023 has witnessed a surge in AI investments, with 62% of global business and technology professionals acknowledging significant increases in their AI budgets over the next 12 months, according to Forrester. This trend is a clear indicator of the growing reliance on AI to boost productivity in content pipelines, including text, rich-media assets and product information.
“Gen-AI can support creative teams by producing a wide array of creative concepts for further fine-tuning and ideation,” Gahun said. “Previously achieved through brainstorming and ideation sessions by creatives, this accelerates creative teams’ ability to generate new concepts for further review and fine-tuning. The benefits include reduced creative agency spend and wider/creative array of ideas. The challenges are that brand assets must be reviewed carefully to ensure they are being represented correctly and in the right context.”
Related Article: Staying Human While Using Generative AI Tools for Content Marketing
Real-World Impact and Efficiency
An example from the report highlights how fonQ’s content specialists can now onboard 100 products a week using ChatGPT, a significant increase from the previous 30 to 35 products. This example not only demonstrates enhanced efficiency but also the real-world impact of generative AI in content management.
Another case points out how Orange Logic’s DAM users are saving approximately 3,000 labor hours annually, showcasing the tangible benefits of integrating AI capabilities.
How mature is the experimentation with generative AI in content management? Are there any notable success stories or cautionary tales?
According to Gahun, organizations have already leaned heavily into experimentation with generative AI capabilities and embedded features within software suites. Despite this acceleration of experimentation, he added, each of the three content pipelines — content, rich-media assets and product information are moving at different velocities.
Specifically, according to Gahun:
- Text generation is the furthest along and at late stages of experimentation. The key use cases being new copy creation and copy translation for personalized experiences for customer acquisition and growth in new geographies.
- Product information management is at more of an intermediate stage. This focuses on scale, time to market and diversifying supplier base. As an example, Forrester connected with a retailer in Germany that has about 100,000 products sourced from over 106 suppliers. A team of two was managing the product data and “romance copy” in their PIM. Now with machine generated and validated data, they are able to scale faster by having more products available from more suppliers, in more geographies.
- Image creation, especially when brand assets are involved, seem to be in the earliest stages. The key use cases here being brand assets being leveraged within new settings (having AI create the landscape, not the actual brand asset). One global services provider leader shared with Forrester that it helps free up human talent, so they can work on more innovative things.
Related Article: Retail Reinvented: The Path to Consistent and Personalized Customer Experiences
Scaling Product Information Syndication to Digital Storefronts
Generative AI helps product onboarding processes. It can map product fields from suppliers to the target data model in the PIM by providing logical recommendations for teams to review. Then, it maps and subsequently loads information at scale, according to Gahun.
“Previously a manual task that was highly prone to errors, it can be automated to deliver higher accuracy in product information syndication,” he added. “Additionally, it can generate product ‘romance copy,’ translate the copy into multiple languages, and flag when product data attributes are missing. All these enhanced capabilities enable ecommerce businesses to increase the quantity of products available for syndication and increasing geographical reach for top-line business growth.”
Related Article: Why Marketers Should Stop Worrying and Embrace AI in Content Marketing
Skill Sets Needed for Generative AI in Content Management
What kind of skill set is required for businesses to leverage generative AI effectively? Is there a learning curve, and how can businesses prepare their teams?
Although generative AI copy generation is quite seamless, product information and creative teams are being very deliberate, Gahun said, about the generated content and imagery by transitioning copy creators to copy reviewers and aggregators within their organization.
“So, while net-new skill sets are not required, new roles are being established to review and validate the generated copy, information and imagery, before dissemination in market,” he said. “One global organization we interviewed has hired an offshore third party to review and validate generated content.”
Related Article: 6 Ways Generative AI Is Changing Content Management
Privacy and Data Security Concerns, Risk Management in Generative AI in Content Management
Amid the enthusiasm for AI’s capabilities, Forrester researchers issue caution over data security and privacy. One approach is using isolated environments with specific prompts to prevent learning from customer enterprise data, ensuring that data privacy and security are not compromised.
As organizations navigate from experimentation to implementation, understanding and managing the associated risks is crucial.
“It is very important for organizations to understand their business context,” Gahun said as a caution to brands who roll out generative AI in content management. “As an example, we interviewed a consumer food company who told us how 99.7% accuracy in a food recipe is not sufficient when consumer safety from allergens is on the line. On the other end of the spectrum, a footwear manufacturer told us about how they are using generative AI in their PIM to translate product descriptions and expand into new geographies aggressively. For the first, consumer safety is paramount, while the second is focused on business growth. Know your business context.”