AI doesn’t have to be a moonshot to create value. Jay Singh, founder of Casper Studios, shares a modular “singles & doubles” approach that gets real adoption—highlighted by a hedge fund case study that cut Q&A prep by ~80% and slashed synthesis time by ~50%. We dig into trust, integrations, voice-agent surveys, and how to know if your org is truly ready for AI.

Chapters:

00:00 – Intro
00:36 – Jay’s background: LinkedIn → Casper Studios & mission to build AI that gets used
04:42 – Common AI mistakes across startups & enterprises: swinging for the fences too soon
06:17 – The middle ground: move fast without panic; expectation-setting for leaders
08:00 – What changed this year: near-universal use of chatbots & AI coding tools at work
09:47 – Why clients come to Casper: mapping workflows & upskilling with no-code automations
11:12 – Open-ended vs. scoped asks: why buyers now want discovery + guidance
12:38 – Readiness signals: execs using AI daily & clean(ish) data/process maps
14:46 – Case study: hedge fund’s corporate access workflow (before state)
16:56 – Why modular beats monolithic: “singles & doubles” that compound
19:07 – Modules 2 & 3: transcript ingestion → buy/sell/hold signals
21:01 – Guardrails & observability: “show your work,” Langfuse/Wayfound feedback loops
23:09 – Results: 80% faster Q&A prep; 50% faster analysis (and compounding gains)
24:14 – Speed to production: discovery in 2–4 weeks; first usable module in ~2–4 more
25:26 – Integrations matter: IR docs, 10-Qs, transcripts, SharePoint/Google, vendor APIs
27:41 – Why moonshots stall + the shipping-container analogy (reimagine around the tech)
32:44 – One question to ask your team tomorrow (psychological safety for AI usage)
34:09 – First win idea: voice-agent “employee survey 2.0” that surfaces automation targets
36:12 – Who should reach out: growth-stage, Fortune 100, hedge funds & PE + portcos
37:05 – Wrap

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