The crime scene: Where AI meets reality
Let’s be brutally honest: The AI revolution’s promising soundtrack got stuck on repeat in endless IT meetings. Conference speakers say technical writers’ jobs are safe. But the real threat might be different: What if AI doesn’t intend to replace you? What if it’s aiming to eliminate your tools instead?
Picture this: You’ve spent years mastering your CCMS, building sophisticated content strategies, and perfecting modular workflows. Then AI walks in and says, “Thanks, but I’ll just work with Word documents.”
That’s not science fiction. That’s the scenario keeping CCMS product managers awake at night.
But here’s where it gets interesting and why you need to pay attention whether you’re a technical writer, content strategist, or CCMS administrator: Our hard-earned knowledge about software and automation misleads us when it comes to AI. Understanding this fundamental shift is your key to survival. So, let’s start investigating our case.
Profiling the suspect: AI’s schizophrenic skill set
Think like a detective. To predict where AI will strike next, we need to understand its behavioral patterns. And those patterns are frankly bizarre.
The Tic-Tac-Toe paradox
Back in August 2023, Nicolas Carlini’s “GPT-4 Capability Forecasting Challenge” revealed some of AI’s most puzzling traits through a simple experiment. This experiment was meant to highlight what the model could and couldn’t do back then.
Among other things, Carlini presented ChatGPT-4 with two seemingly related tasks: Firstly, to identify the best next move in a simple tic-tac-toe grid, a puzzle any elementary school child can solve in seconds. And secondly, to write a complete JavaScript webpage that enables perfect tic-tac-toe gameplay against a computer opponent, including user interface, game logic, and AI strategy.
The result? AI nailed the complex programming challenge but stumbled over the elementary game strategy. Every schoolchild understands tic-tac-toe logic, yet few adults could code a flawless implementation.
While today’s language models, especially newer “reasoners,” can usually solve such tasks, these types of experiments reveal some persisting and surprising weaknesses of LLMs.
This is AI’s “jagged frontier” – capabilities that spike and plummet unpredictably. Things that seem equally complicated to humans can fall either within AI’s comfort zone or be completely beyond its capabilities. This makes dealing with AI particularly tricky for humans who are used to a different skill progression. It’s something we all need to grapple with.
The Mollick method: Your survival training
Ethan Mollick, author of the New York Times bestseller “Co-Intelligence,” offers brutal but necessary advice: Spend ten hours seriously wrestling with AI. Not generating recipes or poems – but actually trying to accomplish your real work with the assistance of AI.
Here are his four survival principles:
1. Become an experiment maniac
Run dozens of daily AI tests instead of waiting for your IT department's quarterly AI initiative. They have meetings and protocols; you have immediate problems to solve.
Start small but start immediately. Each experiment builds pattern recognition. The goal isn't perfection – it's developing judgment about when and how to deploy AI effectively.
2. Stay in the loop
Mollick's original argument centers on keeping humans in the loop for AI quality assurance. By now, this should be common sense in technical communication. So, I reinterpreted this recommendation:
When your company launches AI projects affecting technical documentation, resist the temptation to spectate from the sidelines. You’re the only one who truly understands your daily workflow. The gray area between “definitely works” and “definitely broken” is where real learning begins. These insights can't be learned secondhand. This is where you develop experience navigating the jagged frontier’s shifting boundaries.
3. Talk human, not machine
Generative AI is trained on human conversations. This makes interacting with it very different from other computer software. Describe your requirements as if talking to a colleague, not issuing commands to software. Using specific examples rather than abstract rules can tweak your prompts from failure to success.
4. Never stop testing
It is important to note: Today’s AI is the worst AI you’ll ever use. Inch by inch, AI engineers push the jagged frontier outward and expand AI’s comfort zone. That way, Carlini’s “impossible” challenges from 2023 have become routine for modern AI systems, demonstrating how rapidly capabilities evolve.
The killer’s profile: AI’s fatal characteristics
Experimenting and working with AI exposes the following key traits:
Master wordsmith, terrible calculator
AI transforms text brilliantly while preserving meaning. Need to convert the phrasing pattern from “You must lift the cover” to “Lift the cover” across 500 pages? AI handles this transformation flawlessly.
But here’s the critical difference from traditional software: AI is fundamentally non-deterministic. When you ask a calculator for an answer, it delivers the same correct result today, tomorrow, and next year. Try seven-digit multiplication in ChatGPT – now results become interesting, and different every time.
The perfect impostor
AI’s word acrobatics extend beyond simple text transformation. It identifies gaps in incomplete plans and supplies missing elements.
However, this strength has a dark side: AI fills knowledge gaps with confident fiction. It does this so eloquently that your normal “something’s fishy” instincts fail completely. When AI gets it wrong, it sounds totally right.
The code genius with blind spots
AI codes impressively across multiple languages. It writes clean, logical programs that work perfectly… until they don't. Experienced programmers instinctively spot edge cases, while AI misses them. Your program looks solid but fails when it comes across unexpected data. In addition, AI regularly creates code with serious security flaws.
The always-available expert (sort of)
AI functions like a knowledgeable telephone joker who is on call 24/7. When no domain expert is available, AI often provides valuable guidance. But when real experts are accessible, they typically deliver superior results.
AI handles massive documents easily. But give it a rule book? It gets creative with the details. It’s not a database that can retrieve and follow hundreds of constraints simultaneously.
The logic faker
Here’s generative AI’s dirty secret: It doesn’t actually reason. It performs statistical analysis on text. A classic example illustrates this limitation: AI might not know “Who is Mary Lee Pfeiffer the mother of?” but instantly answers “Who is Tom Cruise’s mother?” (Mary Lee Pfeiffer). This famous example is now part of modern AI’s training data. But the underlying problem subsists. Try “Maria Hölzel” and the Austrian pop singer “Falco” as an alternative example.
While bidirectional information retrieval is standard for knowledge graphs, LLMs use impressive pattern matching that creates the illusion of understanding. But you can add logic-based reasoning to the mix when combining knowledge graphs and LLMs.
Intuition instead of explanation
We love crime shows where the old detective gazes into the distance, shakes his head, and says: “Something doesn’t add up.” Against all odds, the detective unmasks the real culprit through intuition rather than logical deduction. In reality, professional environments demand explanations. We want to understand how results were reached, especially for liability-critical technical documentation.
But an LLM is a black box. Nobody understands how LLMs actually work.
The CCMS murder plot
We now know our suspect’s profile, but what’s its plan for eliminating CCMS systems? Could AI execute the perfect crime?
Currently, technical writers spend more time managing content than creating it. This counterintuitive reality makes sense. Modularization and systematic reuse dramatically reduce long-term costs for creation, maintenance, updates, and translation. This efficiency drives the entire CCMS value proposition. But what if AI flips this fundamental equation?
Consider this scenario: AI content creation and machine translation would improve in such a way that AI drives translation and creation costs close to zero, fundamentally altering the cost-benefit analysis that justifies the CCMS’ return on investment.
If content becomes effortless to generate, AI could bypass the CCMS entirely by returning to classic document-level workflows that predate the CCMS era:
- Create main document → Human review
- Generate variants by copying and modifying → Human review
- Repeat for thousands of variants → Continuous human review
Even for companies managing massive variant libraries – some maintain thousands of variations of a single manual – this document-level approach becomes feasible when AI handles the mechanical work of copying, modifying, and maintaining consistency across versions.
The perfect murder weapon: Change management
However, AI’s real threat to CCMS emerges during systematic updates across multiple document variants. Consider changes that involve factual situations rather than simple text replacements. For example, imagine regulatory requirements force machine control software to work differently in North America than they do in Europe because safety standards diverge. Now you need to locate all information relating to this software and the North American target market and understand contextual relationships rather than just finding and replacing text strings. As a human, you would struggle with thousands of variants, but AI could theoretically handle this workload tirelessly across all documents.
Even in this scenario, AI doesn’t need a CCMS. Does this mean “game over” for component content management systems? If the new technology doesn’t need traditional infrastructure, why maintain complex systems designed for human limitations?
But here’s where the murder plot encounters an insurmountable obstacle.
Why the CCMS survives: The black box defense
The previous scenario assumes perfect AI execution. But generative AI isn’t error-free. In safety- and liability-relevant environments like technical documentation, human verification remains essential even in optimistic future scenarios. The question becomes: How do you efficiently verify AI’s work without turning the human reviewer into a hamster spinning endlessly in a wheel?
Checking what AI changed is relatively straightforward. But identifying what AI deliberately ignored becomes impossible. Because AI is a black box. Nobody understands how it makes decisions. When questioned about its choices, AI provides eloquent post-rationalization rather than revealing actual decision logic. It’s like asking someone to explain their Black Friday impulse purchases. You’ll get some reasons, but not the real motive.
This creates a verification nightmare: You can see what changed, but you can’t systematically identify what should have changed but didn’t. In document-level workflows, this forces you to review everything rather than focusing on areas of actual risk.
The CCMS’ bulletproof vest: Explicit logic
CCMS modularization makes variant creation rules transparent and auditable. Through sophisticated logic systems built into modern CCMS platforms, you can determine exactly which content modules are affected by any given change. This transforms verification from exhaustive review into targeted quality assurance.
The CCMS approach works because it externalizes and documents the decision-making process that AI keeps hidden. When, in our example, regulatory requirements change for software for North American markets, a well-designed CCMS can identify every content module tagged for that region and software version. You know precisely which sections need review and which remain unaffected.
This systematic approach enables the four-eyes principle exactly where it is needed, rather than applying it universally. Content managers can focus verification efforts on high-risk changes while maintaining confidence in unchanged sections.
Plot twist: Partnership, not murder
And this is where our crime story takes an unexpected turn: Rather than eliminating CCMS systems, AI can be expected to transform them into something far more powerful.
What might this look like? Think autonomous driving for content creation.
The car isn't replaced – it's enhanced. You specify your destination and let AI navigate while the CCMS provides infrastructure and structured pathways. This collaboration minimizes review efforts because the CCMS maintains a logical structure while AI handles content generation within proven frameworks.
This requires entirely rethinking the CCMS’ architecture. AI won't interact through mouse clicks. That would be like building a robot to steer a steering wheel. Instead, next-generation CCMS platforms offer API-driven architectures designed for AI collaboration.
The verdict: Evolution, not extinction
CCMS technology faces transformation, not termination. Though AI excels at content manipulation and generation, its black-box nature creates a verification gap that only explicit logic and auditable change management can solve.
Structured content management therefore remains the most efficient approach for scalable technical documentation – whether human-generated, AI-assisted, or hybrid workflows that combine both approaches strategically.
Recommendations for CCMS professionals
For CCMS professionals, the survival strategy focuses on strategic adaptation:
- Experiment relentlessly with AI to understand where it adds genuine value.
- Stay involved in organizational AI initiatives to ensure practical workflow knowledge informs implementation decisions.
- Stop perfecting tasks that will be automated and start learning the skills that won’t be. Focus on process orchestration, quality assurance, and escalation management rather than content creation mechanics.
- Understand quality standards and legal frameworks. As AI scales content output, you’ll need sophisticated filtering systems to identify which changes require human review. You need to understand quality standards and legal frameworks to route only critical or questionable content modifications to human verification, preventing review bottlenecks while staying compliant.
- Prepare to become a technical documentation project manager. Your role is evolving from content creator to process enabler: ensuring all resources are available, managing AI-human workflow integration, and handling exceptions when automation fails.
The bottom line: AI isn’t killing your CCMS. It’s forcing it to evolve. And this will transform you from a content creator into a process enabler. The question isn’t whether you and your CCMS will survive this revolution. It’s whether you’ll lead the evolution toward AI-driven systematic content management.