Can't I just use ChatGPT to analyse my event surveys?

ChatGPT can summarise what respondents said. It cannot benchmark those results against comparable events, apply a consistent methodology that enables year-over-year comparison, or tell you whether your event portfolio is performing above or below industry norms.

Yes. Explori uses AI as a component within a purpose-built event intelligence platform. The distinction is what the AI is working on, structured, benchmarked event data within a governed methodology, not unstructured exports from ad hoc surveys.

What is the hallucination risk with AI event analysis?

General-purpose AI can misinterpret ambiguous responses, confabulate patterns that are not statistically significant, and draw conclusions that sound plausible but are not grounded in robust methodology. For intelligence that informs event investment decisions, this is a credibility risk.

Why can't AI replace an event benchmark database?

Building a benchmark database requires measuring thousands of comparable events consistently over time using the same methodology. General AI has no access to this proprietary dataset. The benchmark is not a feature, it is the product.

Explori vs general-purpose AI tools

Comparison

✦AI tools

Explori uses AI. The question is not AI versus Explori, it is what the AI is working on. General-purpose AI tools working on unstructured survey exports produce summaries. Explori's AI works on structured event data, benchmarked against a global dataset, within a consistent measurement framework.

What each platform is actually for

General-purpose AI tools

AI tools like ChatGPT, Claude, and Gemini can produce useful thematic summaries from open-text survey responses quickly and cheaply.

They reduce the manual effort of reading through verbatim feedback.

For low-stakes internal summaries where a general sense of sentiment is sufficient, they are a reasonable approach.

Explori

Explori is built for the intelligence problem that general AI cannot solve: benchmarking results against 10,000+ events measured since 2012, applying a consistent measurement framework that enables year-over-year comparison, and producing portfolio-level intelligence across an entire event programme.

These capabilities require purpose-built infrastructure, not a general-purpose language model.

Side by side

Capability	General-purpose AI tools	Explori
Benchmark database	None, no external reference point	10,000+ events measured since 2012
Output type	Thematic summary of what respondents said	Benchmarked conclusions about event performance
Year-over-year comparability	Not possible, methodology inconsistent	Built-in consistent measurement framework
Portfolio intelligence	Not possible	Cross-event comparison across the full programme
Hallucination risk	Present, can confabulate from ambiguous data	Controlled, structured data, governed methodology
Executive credibility	Depends on how outputs are presented	Purpose-built for CFO and CMO-level accountability

The gap that matters

AI can summarise what respondents said. It cannot tell you whether what they said is good. A 4.2 satisfaction score summarised by AI is still a 4.2 score with no context. Explori's benchmark database provides the reference point that makes a score meaningful, and that reference point requires a decade of purpose-built measurement across thousands of comparable events. General-purpose AI has no access to that data.

Who should use each

Use General-purpose AI tools if

If you need a quick thematic summary of open-text feedback for internal use, with no requirement to benchmark, compare portfolios, or produce leadership-grade intelligence, AI tools are low-cost and fast.

Use Explori if

If you need to walk into a CFO conversation with evidence that your event portfolio is performing, benchmarked, comparable, and defensible, Explori provides the intelligence infrastructure that general AI cannot.

FAQs

See Explori in action

A 30-minute conversation about your event programme. No pitch, no sales cadence.