Why a Polished AI Deck Still Needs a Persuasive Meeting

In a recent LinkedIn post, Betsy Tong discusses why polished, AI-generated presentations do not necessarily win approval. This article reports on Tong’s LinkedIn post and examines her argument: once presentation materials are easy to produce, understanding the people in the room becomes central to persuading them.

“When anyone can polish slides, understanding what gets people to say yes is the skill.”

When a good idea still gets a no

Tong opens with a familiar workplace frustration: colleagues may like an idea and still decline to support it. She sketches different reasons decision-makers might hesitate. Manuel cannot take on more work; Jon is reluctant to commit; Jill is unsure what she needs; Cheryl does not want to risk another cleanup; and Tom has already heard similar proposals.

In Tong’s account, those reactions point to a gap that better slides alone cannot address. A presentation can make a proposal clearer, but it may not answer what the proposal means for each person—or what risks they believe they are being asked to accept.

Seven conversation techniques Tong highlights

Tong draws on advice she attributes to former FBI and CIA professionals, presenting seven ways to explore hesitation rather than simply push for agreement. The suggestions include inviting a low-stakes “no,” treating a stated lack of priority as a sign that something else may need to happen first, and asking what saying yes would cost the other person.

“You see a promise. They see more work for them”

That contrast captures Tong’s emphasis on perspective-taking. According to Tong, a proposal that looks beneficial to its champion may appear to colleagues as extra workload or personal risk. The post recommends asking questions to uncover those concerns, including “Which part would be hardest for your team?”

Responding to what is left unsaid

Tong also urges presenters to notice changes in the room. If discussion turns quiet or someone reacts to a timeline, she suggests asking about the shift rather than ignoring it. Another recommendation is to name a possible concern and let the other person confirm or correct the interpretation.

“That hasn’t been tested. Here’s how we’ll find out.”

As Tong notes, this example makes honesty about uncertainty part of the conversation. Her post presents composure and clear next steps as more useful than claiming certainty that the evidence does not support.

The meeting remains the work

Tong’s closing point is that AI can help assemble a storyline, sharpen headlines, create charts, and draft speaker notes, but it cannot replace judgment about how people will respond. Her argument shifts attention from slide production to the live exchange: listening for resistance, asking what is behind it, and addressing the cost or concern a colleague sees.

The post ultimately frames persuasion as a human task that begins where the deck ends. Tong’s seven lessons offer prompts for a more attentive discussion, while leaving the presenter to understand the specific people and circumstances in the room.