Your Name is On It. Not the AIs

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Every tradeshow I attend right now has the same conversation happening in it.

Not on the main stage. Not in the keynote. Not at registration.

In the hallway, over coffee, between sessions, where people actually say what they are thinking. AI adoption is ingrained in every company leader and growth plan. The excitement is real. The momentum is real. The budgets are moving.

And almost nobody in those conversations is talking about what happens when something goes wrong.

Not theoretically wrong. Legally wrong. Reputationally wrong. The kind of wrong that arrives eighteen months after the tool was adopted, when a client flags a proposal that reproduces language that belongs to someone else, or a government agency identifies a technical claim that the AI fabricated and your organization certified as accurate.

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In a market that rewards visible motion, to sit with the question long enough to feel its full weight. The ramifications can be felt on what “might” happen.

That question is this. When your organization publishes AI-generated content, and something goes wrong, who is accountable?

Not the tool. Not the vendor. Not the model that generated the output.

You.

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Decision

In February 2025, a federal court issued a ruling that every organization using AI tools needs to understand before they publish another word of AI-generated content.

Thomson Reuters sued Ross Intelligence in Delaware, alleging that Ross had infringed Thomson Reuters’ copyrights in its Westlaw headnotes by using them to train a competing AI-powered legal research tool. The court granted partial summary judgment in favor of Thomson Reuters, finding that the Westlaw headnotes were original and protected by copyright, and that Ross Intelligence’s use of them was not fair use.

The detail that matters most for your organization is not the verdict. It is how it happened.

Ross Intelligence did not set out to steal anything. They contracted a vendor to create training data, and that vendor used Westlaw content in the process. Ross never directly handled the infringing material. It did not matter. The court’s finding landed on them regardless.

If a generative AI output infringes a copyright in an existing work, both the AI user and the AI company could potentially be liable. The user might be directly liable for prompting the AI to generate infringing content.

That means your organization. The one whose name is on the proposal, the published content, the client deliverable. Not the tool. Not the vendor.

You.

The number of infringement cases filed against AI companies in 2025 more than doubled the total from 2024, growing from around 30 cases to over 70. This legal landscape is not stabilizing. It is accelerating. And every organization that has not built a governance structure around its AI output is accumulating exposure with every piece of content it publishes, whether leadership knows it or not.

Check out some of my prior posts here:

You Do Not Have One AI Problem, You Have Two

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Chaos to Clarity

Proposals

If your organization uses AI to write proposals, the risk profile is significantly more serious than most leadership teams have been told.

In 2025, federal contractors relied on large language models to help prepare a significant number of bid protests, monetary appeals, and related procurement filings. At least twenty public decisions that year showed the hallmarks of what tribunals are now calling generative AI misuse, defined as the intentional or negligent use of an AI resulting in inaccuracies that waste public and private resources.

Here is the number that should stop every proposal leader cold.

Large language models generate likely but false statements and citations approximately 30% of the time.

Not occasionally. Not in edge cases. 30%. That is a structural reliability problem sitting inside a tool that proposal teams across the country are using right now to write technical claims, describe past performance, and articulate capabilities their organizations will be contractually obligated to deliver.

Contractors often treat AI as a competitive advantage in proposal development, but proposal teams frequently struggle to describe AI-assisted work in a way that is technically accurate and legally defensible. Overbroad claims may score well at submission.

They become devastating eighteen months into performance, when the government expects delivery of capabilities that were never actually feasible.

The downstream exposure from that gap is not theoretical. The False Claims Act creates liability for organizations that submit false or misleading claims to the federal government. An AI-generated proposal that fabricates a past performance reference, overstates a technical capability, or reproduces proprietary language from a competitor’s methodology does not create a problem at submission. It creates a problem when performance fails to match what was promised, and someone starts asking why.

For a government contractor, False Claims Act exposure is not a compliance footnote. It is an existential threat that no efficiency gain from AI adoption justifies ignoring.

Gap

The organizations getting hurt by AI output liability are not reckless ones. They are organizations that adopted AI tools for legitimate reasons, saw real efficiency gains, and then delegated the review process without ever defining what the standard actually was.

That delegation is the vulnerability.

Because the accountability for what your organization publishes does not travel with the task when you hand it to someone else. It stays at the leadership level. A CEO whose team publishes AI-generated content that infringes on someone’s intellectual property does not get to point at the team that published it. They get to explain to a judge, a regulator, or a client why their organization did not have the governance structure to prevent it.

Most leadership teams have never formally answered the question that sits at the center of that exposure. Who in this organization is specifically accountable for reviewing AI-generated content before it carries our name into the world? Not generally responsible. Specifically accountable. With a defined scope, a defined standard, and the explicit authority to stop something from shipping when it does not meet that standard.

If that accountability lives somewhere in a job description but has never been operationalized into an actual checkpoint with teeth, you do not have an AI governance structure. You have an assumption dressed up as one. And assumptions do not hold up in a courtroom, a contract dispute, or a client conversation where trust is already on the line.

Practice

Owning your AI output is not an argument against using it. The efficiency gains are real. The competitive value is real. The organizations that figure out how to use AI well while managing its risks will have a genuine advantage over the ones that either avoid it entirely or adopt it without guardrails.

Owning your AI output means one human being with genuine accountability reads every significant piece of AI-generated content before it ships under your organization’s name. Not a junior reviewer checking for spelling. Someone who understands what the content is claiming, where those claims could be challenged, and what the downstream consequences of a wrong claim look like in your specific market.

It means having a documented process for verifying that AI-generated proposals do not reproduce proprietary language, fabricate performance data, or make technical claims that cannot be substantiated when delivery begins. The model generating that content has no mechanism for making those distinctions. Someone on your team has to.

It means defining in writing who is accountable for what ships, by content type, by channel, by business unit, so that when the accountability conversation arrives, your organization can demonstrate it had a structure in place and that structure was followed.

That is not bureaucracy. That is the minimum standard of accountability that your name on the content already demands, whether you have built the structure or not.

This week, before another AI-generated proposal, piece of content, or client-facing document leaves your organization, answer one question that only you can answer at the leadership level.

If something your AI generated was found tomorrow to contain a fabricated claim, reproduce proprietary content, or create legal exposure for your organization, could you name right now the specific person who was accountable for reviewing it before it shipped, what standard they were reviewing it against, and what authority they had to stop it? And who ultimately owns it within your organization?

If any part of that answer is unclear, you have the same vulnerability that has already cost other organizations contracts, relationships, and legal fees they never planned for.

The AI that generated your content will never be in the room when those consequences arrive.

You will.

And your name is already on everything it produced.

See you next week.

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