What does it Cost to GAIN it all?
You’ve probably seen it: obscure facts shared in a presentation, podcast, or a citation quoted in legislation that links to an article that doesn’t even exist. These are hallucinations, produced by generative AI.
Hallucinations can discredit a study or undermine consumer confidence. Their effect can reach far beyond those of the Make America Healthy Again Report — they’re eroding trust across client interactions, pitch decks, and marketing campaigns. For clarity, OpenAI’s ChatGPT defines hallucinations as: “Generated information that sounds plausible but is false, misleading, or fabricated. This can include incorrect facts, made-up names or citations, or misrepresented data.”
In short, generative AI isn’t thinking — it’s predicting. Its outputs are based on data it’s trained on like books, websites, and news articles. The systems use predictive analytics to fill in the blanks with what it expects will seem like a plausible response, not what’s verifiably true.
When a hallucinated prediction slips into your marketing funnel, whether it’s a stat in a keynote, a quote in a blog, or an insight in a client proposal — you risk more than the embarrassment of a typo. You risk trust with your audience.
AI Regulation and Effective Use
Consumers, clients, and strategic partners are becoming more informed about how AI works — and where its limits lie. As regulators push for AI accountability (see: the EU AI Act and the recently rescinded U.S. Executive Order 14110), businesses that lack internal governance may find themselves exposed — both legally and reputationally.
An effective, transparent policy can build confidence with your audience, clients, and strategic partners alike. While there’s much to lose through hapless use of generative AI, it's also important to ask: What do we have to GAIN?
The GAIN Framework
Our GAIN Framework was developed to help teams evaluate and guide how generative AI is being used. The framework communicates appropriate use of generative AI models based on the intended outcome. By creating a shared language of process and procedure between content creators, strategists, and executives -- everyone remains on the same page as to when AI is an asset and when it becomes a liability.
Gut Check
Use AI sparingly. This applies to thought leadership articles, specialty blogs, and any content intended to establish expertise. If you intend to copyright the content, AI should be used minimally or not at all.
Align
Use AI to align your ideas — for example, to generate outlines, prompts, or starting points. The core thought leadership still comes from the human. This stage is often helpful for social media planning or brainstorming.
Intern
Many people are using AI like a digital intern. They are using prompting to generate the bulk of the work product and then inserting human-led edits and insights afterward. This is appropriate for internal documentation or any content unlikely to require copyright protection or personal attribution.
Navigate
AI handles the bulk of the work, with a human in the loop for quality assurance. This use is ideal for checklists, technical instructions, and internal processes where hallucination risk is low.
GAIN FRAMEWORK
| AI Role | Human Role | Best For… | |
|---|---|---|---|
| Gut Check | Minimal Support | Full authorship | Thought leadership, expert articles |
| Assist | Brainstorming, Outlines and Content Ideation | Strategic direction, thought leadership, the meat on the bones | Campaigns, social media captions, creative planning |
| Intern | Majority of content | Editor, Strategic Impact | Internal documents, recaps, SEO focused blogs w/ minimum thought leadership |
| Navigate | Complete | Oversight + QA | Checklists, technical instructions, SOP’s |
This framework supports marketing departments and executive teams by setting boundaries and clear expectations around use of AI-generated content.
How do you take GAIN from theory to practice?
- Establish the framework internally. When we use GAIN, you may find that you need more or fewer levels of AI involvement. The key is to establish an internal team-based guidance that your team understands and can clearly adhere to.
- Train teams to recognize hallucinations. That includes not just spotting false facts, but also flagging tone mismatches, logic gaps, and unsupported claims. Train teams to ask generative AI to cite the source — then go and check the source for validity and relevance. AI is a tool, not a replacement for research best practices.
- Fact-check everything before it goes out. This is non-negotiable. Treat AI copy like a first draft from an intern or a junior strategist: useful, but not ready for client eyes. AI has access to a lot of data, but little to no nuance. The human element can be the difference between “That was bomb” and “That was BOMB!” (Did you read that in two different voices? Shout out to the ’90s.)
- Maintain an internal database of trusted sources. Ensure that your teams (and your AI prompts) are pulling from vetted, aligned, and up-to-date datasets. Having basic guidelines around what is considered a credible source can save you the headache of having to explain a lack of due diligence in the future.
A single hallucinated insight in a pitch deck can damage your reputation with a board. A flawed stat in a report can undermine your expertise in-market. But when you operationalize governance, train teams, and use AI with intentionality, you do more than protect your brand — you build trust. And in a market where confidence is currency, that’s something you can’t afford to lose.
So, ask your team:
What do we have to GAIN?
