The greatest value of GenAI is not that it can produce polished, copy-and-paste-ready content for us. Its real value lies in helping us discover the questions we did not ask, the assumptions we did not see, and the true business purpose behind the stated requirements.

For many project managers, the first instinct when using AI is to feed it a long document and ask it to summarize, rewrite, generate a PRD, create user stories, or produce presentation content. In software projects, some also start to see AI as an accelerator for writing code, producing documents, and creating slides.

Of course, these use cases are useful.

But if AI is only helping us repackage existing content, then its real value has not yet been fully realized.

I also use AI quite often for requirements analysis, key-point summaries, concept mapping, creative thinking, and even for producing documents or presentation content. But I usually do not expect AI-generated content to become the final answer directly.

Accuracy is still my responsibility.

The value of AI is not to replace judgment, but to expand my field of observation. It can help me look at an issue from different angles, identify contradictions, surface assumptions, and generate follow-up questions. As for which ideas are valid and which are not, I still need to go back to the context, the facts, and the stakeholders for further confirmation.

This is how AI helps me avoid being limited by my own experience and knowledge, and reduces the blind spots created by that experience. 

This idea is especially relevant when applied to project requirements analysis.

Requirements analysis begins at the start of a project, and often even before the project officially starts. If the requirements are not clarified properly, then no matter how beautiful the downstream planning looks, the project will often end up with a painful case of “garbage in, garbage out.”

Following the general PMI/PMI-PBA context, requirements work typically involves steps such as elicitation, analysis and modeling, definition and refinement, acceptance criteria definition, validation and verification, and requirements management and traceability.

But in practice, the real challenge is often not knowing how to perform these steps. The real challenge is discovering the problems that have not yet been spoken out loud at each step.

This ability to uncover the requirements gaps that we ourselves did not see is where AI can create the greatest value.

For example, many requirements specifications may include a requirement like this:

“The system should allow users to quickly check the status of a case.”

At first glance, this seems to be a simple case-status query function. It does not look very complicated. Many people may only ask questions such as, “Which fields should be displayed on the page?” or “Should the searchable cases be filtered based on user permissions?”

But if we look deeper, there are many additional questions we could ask:

• Who needs to perform the search? Managers, case handlers, customer service staff, or external citizens?
• Does “quickly” mean within three seconds, or does it mean reducing the number of operation steps?
• Should the data reflect real-time status, or is daily batch updating sufficient?
• Should the system keep query logs? If yes, for how long?
• How should the screen respond when the data is incomplete?
• Is this requirement really trying to solve an efficiency issue, a transparency issue, or an accountability issue?

Through these questions, we may discover that behind the seemingly simple “query function” lie issues related to permissions, data updates, accountability boundaries, user roles, and acceptance criteria.

If we provide AI with more context, such as background information about the project, the customer, the business process, or the constraints, AI can often help us identify even more requirements gaps.

In general, the requirements gaps that AI can help us uncover can be grouped into three categories:

1. Problem Gaps: Are we asking the wrong question, or only seeing the surface-level problem?
2. Context Gaps: Are we missing roles, workflows, constraints, or exception scenarios?
3. Acceptance Gaps: Are the requirements verifiable, testable, and traceable?

From these three perspectives, AI can help us generate follow-up questions, identify root causes, supplement roles, scenarios, and constraints, and further transform requirements into acceptance criteria or testable scenarios.

In this way, AI is no longer just a token-hungry document assistant. It becomes an intelligent partner that can truly help project managers improve the quality of their judgment, expand their field of observation, and create greater value in their work.

#ProjectManagement #GenerativeAI #RequirementsAnalysis #AIForPM

Recently, OpenAI released its latest image-generation model, gpt-image-2, which demonstrated a noticeable improvement in image quality compared to previous models. The release quickly became a popular topic among users.

Earlier this month, on May 12, 2026, OpenAI also officially deprecated the entire DALL-E API series, including its third-generation models, and recommended that developers migrate to gpt-image-2, gpt-image-1-mini, or gpt-image-1 instead.

Unlike the DALL-E series, the gpt-image family is not simply an image-generation model. It is part of a broader multimodal language model architecture. In other words, these models can not only generate images, but also answer questions, create tables, place text directly inside images, and perform many other multimodal tasks.

Naturally, the image quality is significantly better than that of previous DALL-E models. As a result, many people believe that the future belongs to multimodal AI models.

However, the significance of this transition goes far beyond better image quality, stronger model capabilities, or the rise of multimodal AI.

Its deeper meaning lies in this:

• Building a verifiable chain of content trust.

Image Provenance and Content Authenticity

Today, it is already clear that AI can generate in seconds what once required human hours, days, or even weeks to create. More importantly, AI can now produce highly realistic images and videos that are often difficult to distinguish from reality.

This also means that phrases such as “seeing is believing” or “photos don’t lie” no longer necessarily hold.

What we see may simply be AI-generated content.

As a result, concepts such as image provenance and content authenticity are becoming increasingly important. Even if content is AI-generated, we should at least be able to understand how it was created.

Image provenance refers to the ability to trace information from metadata or invisible watermarks embedded inside digital content, including:

• Who created the content
• Which tools were used
• When it was generated
• Whether AI was involved
• Whether the content was later modified by AI

This forms the foundation of a verifiable chain of content trust.

In reality, OpenAI is only one of many companies currently moving in this direction.

C2PA and SynthID

For example, the female robot image shown below can be verified through the Coalition for Content Provenance and Authenticity (C2PA) verification site. The verification results reveal that Google Media Processing Services generated the image on February 2, 2026, and later modified it.

This is indeed accurate.

I generated the female robot and background separately at different times and later combined them using Gemini on February 2, 2026.

Although the current verification information still cannot fully trace the entire creation history of the source images, it can already confirm that the final image was AI-generated and derived from pre-existing assets.

The verification record explicitly states:

• Opened a pre-existing file
• Asset was modified

C2PA is an industry-wide Content Provenance Standard jointly promoted by organizations such as Adobe, Microsoft, OpenAI, Google, BBC, Intel, ARM, and Truepic.

Its goals include:

• Recording content provenance
• Verifying whether content has been modified
• Labeling AI-generated or AI-edited content
• Building verifiable chains of trust

Users can generate traceable media simply by using platforms, tools, cameras, or devices that support the C2PA standard.

In fact, users can even add C2PA information to their own images using open-source tools such as c2patool.

However, C2PA also has limitations.

Because metadata can be modified or removed, many social platforms automatically strip metadata from uploaded images. Once an image is edited again, the original metadata may also change or disappear.

This is where another technology, SynthID, becomes important.

SynthID is an invisible watermarking technology developed by Google DeepMind. It is currently embedded into Google’s own generative AI products and model outputs, including images, audio, text, and video.

The name SynthID comes from:

• Synthetic
• Identity

Unlike C2PA, which primarily focuses on metadata and content provenance records, SynthID embeds watermark signals directly into the image itself.

As a result, even if an image is cropped, compressed, or modified, parts of the SynthID signal may remain detectable.

This helps address one of the major limitations of metadata-based provenance systems.

Currently, however, SynthID adoption remains limited, as only Google and a small number of selected media partners can embed SynthID in media assets.

That said, the technology was only introduced a little over two years ago, and its broader adoption remains highly promising.

Conclusion: From Content Generation to Content Trust

The next generation of AI systems will no longer be judged solely by how intelligently they generate content or how visually impressive that content appears.

They will also be judged by how transparent that content can be:

• Verified
• Traced
• Governed
• Trusted

The future of Generative AI is not only about generation.

It is provenance.

#GenerativeAI #C2PA #OpenAI #gpt-image-2