Five AI in PR Lessons We’re Taking into 2026

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Key Points

• The 'Man vs Machine' series showcased human experts outperforming AI in high-stakes events, emphasizing that human intuition fills AI blind spots in volatile situations.
• AI models often converge on consensus picks, but this can lead to shared errors due to common inputs, highlighting the importance of diverse data sources.
• Effective collaboration between humans and AI, leveraging each's strengths, emerged as the best strategy for decision-making in PR.

Five AI in PR Lessons We’re Taking into 2026

Over the past year, Précis AI‘s ‘Man vs Machine’ series put a human expert’s picks head to head with multiple AI models across March Madness, MLB World Series, the US Open, and the Kentucky Derby. The goal wasn’t to crown a permanent champion, but to see where human judgement and algorithmic logic converge, collide, or diverge – and what that teaches communicators about using AI with intent.

Human intuition fills AI blind spots

In volatile, high‑stakes moments, people can read signals that don’t cleanly appear in AI model inputs.

Where we saw this:

In the MLB Wild Card recap, our baseball enthusiast went a perfect 4‑for‑4 while every AI missed at least one.

“My edge has set me apart. While the AI tools went with safe, popular choices, I’ve leaned on bold insights from that hard‑to‑define “feel” you get from watching baseball for years.” – Patrick Strelka, PR Tech Expert

The Derby installment echoed this: chaos in a 20‑horse field and race‑day temperament routinely defy tidy modeling, rewarding experienced handicappers who blend numbers with nuance. In fact, Fuscus relied on crunching numbers by devoting hours to reviewing race charts and leveraging his experience from off-track industry conversations.

AI Models herd around consensus favorites

We repeatedly saw AIs converge on the same pick.

In the Derby showdown, all six models chose Journalism; at the US Open, most AIs backed Jannik Sinner, with a lone model backing Carlos Alcaraz. Consensus can be a strong signal — until the upset comes.

“Despite my human eye for top tennis talent, I ultimately finished in third place. And while some AIs struggled with matchup predictions and misreading the draw, it was Gemini that made enough correct picks to outshine the rest.” – Josh Kram, PR Tech Expert

Shared inputs create shared errors

When different models make the same mistake, it often traces back to common data or framing. Early MLB posts documented bracket hallucinations and stale context (e.g., penciling in teams that weren’t actually in), and the Wild Card recap flagged that when “every model trips on the same matchup,” it’s a telltale input blind spot.

“When I asked the models to generate their postseason picks, it wasn’t exactly a great day regarding AI hallucinations. […] ChatGPT refused to accept late-season reality and still had the Mets included.” – Patrick Strelka

The win often comes from human + AI collaboration

The most reliable pattern was division of labor: let the machine scan mountains of data and surface probabilities; let humans interpret momentum and nuance. Our NCAA tournament write‑ups reinforce the performance gains from effective human – AI pairing as the ‘sweet spot’.

“While AI excels in processing data and identifying patterns, it lacks the capacity for empathy, finesse in the face of uncertainty, and the flexible problem-solving skills that come naturally to humans. The sweet spot may be in human and AI collaboration.” – Mark Hazlin, PR senior leader

There’s no permanent ‘winner’

Results swung by sport and by moment. Stephen A. Smith outperformed six AIs in March Madness; in MLB, the human led early after the Wild Card round; and in the US Open recap, an AI edged the field. The point isn’t to pick a side – it’s to know where each excels and deploy accordingly.

Three things PR leaders should know for 2026

With AI in PR, there are three key insights communications leaders can take away from our Man vs Machine series:

1. Pair AI consensus with human context. Use LLMs to establish the baseline view, then layer stakeholder mood, cultural currents, and timing – especially for launches and issues where conditions change fast.
2. Audit inputs to prevent shared mistakes. Treat your data and prompts like you treat your copy: verify sources and recency, rotate inputs, and re‑frame questions so the same blind spots don’t cascade into strategy.
3. Operationalize the hybrid model – and keep benchmarking. Assign AI to monitoring, synthesis, and scenario modeling; keep humans on narrative, judgment, and relationships. Measure outcomes by objective and channel, then adapt as contexts shift.

Stayed tuned for our upcoming 2026 Man vs Machine series!