--- title: "You Have AI Options—Use Them Wisely" id: "25717" type: "post" slug: "you-have-ai-options-use-them-wisely" published_at: "2025-11-18T12:48:40+00:00" modified_at: "2025-11-18T12:40:50+00:00" url: "https://review.content-science.com/you-have-ai-options-use-them-wisely/" markdown_url: "https://review.content-science.com/you-have-ai-options-use-them-wisely.md" excerpt: "Artificial intelligence isn’t a monolith. It’s more like a toolbox—and the tools you pick or ignore can determine whether your content strategy and operations thrive or falter. In our firm’s long-running research into content operations, we’ve found that the biggest..." taxonomy_category: - "Article" - "Featured" - "Uncategorized" taxonomy_subject: - "Artificial Intelligence" --- [Artificial intelligence](https://review.content-science.com/what-is-artificial-intelligence/) isn’t a monolith. It’s more like a toolbox—and the tools you pick or ignore can determine whether your content strategy and operations thrive or falter. In our firm’s long-running research into [content operations](https://review.content-science.com/what-is-content-operations/) , we’ve found that the biggest gains don’t come simply from “doing [AI](https://review.content-science.com/the-ai-strategy-gap-what-it-is-how-to-start-closing-it/) .” They come from choosing the *right* AI, in the *right* place, within the *right* operational framework. We see too many organizations take a “copycat” approach where they jump into generative AI because a competitor or an organization they admire is doing it. If you’re a [leader or executive](https://review.content-science.com/why-content-c-suite-issue-ai/) in product, marketing, [customer experience](https://review.content-science.com/what-is-customer-experience/) , or [communications](https://review.content-science.com/reimagining-marketing-and-communications-for-rollins-school-of-public-health-at-emory/) , it’s time to move your organization beyond AI imitation into AI strategy, where you make thoughtful choices. To jump start your thinking, I’m sharing five sets of AI options to consider. Related: [Full Report – What Makes Content Operations Successful in the Age of AI?](https://content-science.com/content-operations-study/) ## AI Option 1: Content Creation or Other Use Cases? Because it’s easy for all of us as individuals to use gen AI like ChatGPT and Claude to create content, it’s tempting to think that’s what an enterprise should do. But our latest study of [content operations](https://review.content-science.com/what-are-content-credentials/) and AI finds that enterprises operating at the [highest levels of content ops maturity](https://review.content-science.com/what-is-the-state-of-content-operations-in-2025/) are much less likely to focus first on the content creation use case. And they’re having much more success with scaling AI. For instance, in our recent [panel discussion about content operations and AI](https://review.content-science.com/content-operations-ai-insights-leaders/) , Senior Director Laura Barnes of Red Hat shared that her team tackled the use case of enhancing metadata first. AI freed her team from the tedium of updating and maintaining metadata so they could focus on more strategic and rewarding work. Yes, generative AI (for drafting, rewriting, image-creation, summarization) has its place. But it’s also *risky* when your processes, governance, and measurement aren’t ready. Consider AI use cases where the risk is lower and value more immediate such as - [Personalization](https://review.content-science.com/how-atlassian-builds-trust-through-change-with-structured-release-notes/) and distribution (e.g., delivering tailored messages or flows) - [Content intelligence](https://review.content-science.com/the-benefits-of-content-intelligence-in-the-age-of-ai/) and optimization (e.g., tagging, metadata enrichment, audit automation) - [Governance](https://review.content-science.com/what-is-content-governance/) (ensuring brand voice, compliance, [quality](https://review.content-science.com/what-is-content-quality/) ) - Operational efficiency ([workflow](https://review.content-science.com/what-is-content-workflow/) automation, routing, version control) Related: [6 Areas of GenAI Risk for Enterprises](https://review.content-science.com/6-areas-of-generative-ai-risk-for-enterprises/) ## AI Option 2: Build or Buy? Once you’re committed to using AI, you face a classic question: Do you build your own models and [systems](https://review.content-science.com/a-content-systems-framework-excerpt-from-the-content-advantage-edition-3/) internally, or do you buy existing tools/platforms? Each path has trade-offs. **Sample build (proprietary model) trade-offs:** - Potential for competitive advantage and customization (you tailor models to your domain, voice, data) - Higher initial cost: infrastructure, talent, integration - Longer time to value **Sample buy (existing tools/platforms) trade-offs:** - Much faster to deploy - Lower up-front investment - Possibly less tailored to your content situation or brand voice And it’s very possible to use both in a smart way. Buy commercially available platforms to get moving, then build components that differentiate (e.g., domain-specific fine-tuning, proprietary datasets, custom workflows). Related: [Mastering Content Complexity: The Secret to Enterprise AI Success](https://review.content-science.com/mastering-content-complexity-the-secret-to-enterprise-ai-success/) ## AI Option 3: Deep Learning (Generative) AI vs. Machine Learning AI This choice is often overlooked, but it’s foundational, as I discuss in [The Content Advantage](https://content-science.com/publications/content-advantage/) . Let’s take a close look at these options. ### **Deep Learning / Generative AI** Generative AI (e.g., LLMs, diffusion models) is powerful, creative, and flexible. But it also carries inherent unpredictability. For instance, deep learning models - Can produce variations each time they run (stochastic outputs). - May hallucinate facts or misinterpret context. - Struggle with certain structured tasks like precise numerical reasoning and math. - Are harder to audit and govern. - Introduce additional risk in regulated or high-stakes environments. ### **Machine Learning (ML)** Machine learning approaches—such as classification, clustering, regression, recommendation systems, or rules-based hybrids—offer: - Higher predictability and reproducibility. - Lower risk, especially where accuracy must be consistent. - Stronger data traceability and interpretability. - Excellent performance in structured tasks (e.g., scoring, routing, detecting anomalies). Forward-thinking organizations treat generative AI as one powerful tool among many. And I think the best systems emerging combine them. Machine learning governs decisions and structure, while deep learning enhances creativity, translation, or natural-language interpretation. Related: [Handling Content in AI-Driven Digital Transformation (Webinar Recording)](https://review.content-science.com/handling-content-in-ai-driven-digital-transformation/) ## AI Option 4: Large Language Models or Small Language Models? If you choose deep learning AI, then you’ll face another choice. Do you go big with large, general-purpose LLMs (large language models) like GPT, or do you opt for smaller, more specialized SLMs (small language models)? Again, factor in the trade-offs. **Sample LLM trade-offs:** - Powerful and flexible - Handle wide variety of tasks out of the box - Often expensive (compute, licensing) and harder to control in terms of data security, bias, domain specificity **Sample SLM trade-offs:** - Less flexible across diverse tasks - Easier to fine-tune for niche or domain-specific needs - Lower resource cost - Easier to align with your brand voice, data privacy, workflow constraints Organizations that understand when they need broad flexibility and when they need focused or specialized accuracy will make solid choices here. Related: [Small Language Models: Big Potential for Enterprise Content and AI](https://review.content-science.com/small-language-models-big-potential-for-enterprise-content-and-ai/) ## AI Option 5: Productivity or Transformation ROI? Finally, consider your approach to ROI from AI carefully because many organizations are struggling to see returns. Is your goal short-term productivity (faster drafting, editing, publishing), long-term [transformation](https://review.content-science.com/5-signs-your-digital-transformation-problem-is-really-a-content-problem/) (new offerings, deeply personalized experience, business model change), or a combination? If you focus on productivity, the benefits might show up quickly, such as less time spent, fewer bottlenecks, and faster time to market. But those benefits might not add up to a return that exceeds your investment, and you’ll quickly hit a ceiling. If you aim higher, using AI to fuel business transformation such as reimagining customer experiences, enabling personalization at scale, or creating entirely new offerings and capabilities, then the ROI can be exponential. Productivity is like tuning your engine. Transformation is like building a whole new vehicle equipped to go where you haven’t been before. You might want the engine tune-up now, but if you don’t eventually plan for a new vehicle, you’ll be stuck in yesterday’s race. And regardless of the option you choose, assessing ROI will be nearly impossible if you don’t have a system to measure [content effectiveness](https://review.content-science.com/what-is-content-effectiveness/) and impact before and after deploying AI. Related: [What Is Content Intelligence?](https://review.content-science.com/what-is-content-intelligence/) ## Closing Thoughts Your enterprise relies on content, the substance of digital products, services, communications, and customer experiences. So when you make choices about AI, you’re not just selecting a tool—you’re making a strategic statement about how you view content. Is your choice ad hoc, tactical, or transformative? If your organization tries to “just use AI” without regard for why or how, you risk deepening chaos or missing out on ROI. But if you deliberately choose your path, AI stops being a risky experiment and becomes a force multiplier. ## The Author Colleen Jones is the president of [Content Science](http://content-science.com) and a recognized expert on content, AI, and digital transformation, advising top organizations around the world such as Dell, Thompson Reuters, and The Home Depot. The author of the top-rated book The Content Advantage (third edition)*,*Colleen hosts conversations with senior leaders on the Content Powers AI podcast. A passionate entrepreneur, Colleen has led Content Science to conduct the world’s largest ongoing study of content operations and AI, develop the patented ContentWRX evaluation system, and architect the AI superagent ContentIQ. She served as head of content at Mailchimp during its transformation into a marketing platform and eventual $12 billion acquisition by Intuit. Colleen also led customer experience transformation at AT&T and the CDC. Sharing insights based on her experience and her firm’s research, she is a frequent contributor to business and technology publications and podcasts as well as a top LinkedIn Learning instructor. Follow Colleen on [LinkedIn](https://www.linkedin.com/in/leenjones/) . ## What to Check Out Next - [Types of AI Infographic](https://review.content-science.com/types-ai-infographic/) - [Are AI Agents Useless Failures or the Key to AI's Future?](https://review.content-science.com/are-ai-agents-failures-or-the-future/) - [The AI Strategy Gap: What It Is + How to Start Closing It](https://review.content-science.com/the-ai-strategy-gap-what-it-is-how-to-start-closing-it/) **This article is about**[Artificial Intelligence](https://review.content-science.com/subject/artificial-intelligence/) Related Topics:- [Content Technology](https://review.content-science.com/subject/content-technology/) ## Comments **COMMENT GUIDELINES**We invite you to share your perspective in a constructive way. 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