Deck 17 · AI Semantic Conversations (Search-to-Sales)

OMX Innovation · Deck 17 · AI Semantic Conversations (Search-to-Sales)

Ask Lens, get the answer.

A semantic conversation layer that lets every OMX staff member ask data questions in plain English — "show me last week's margin vs last year" — and get a real answer with the underlying data. Internal first; external (Ask Max for customers) once the muscle is built.

01 / 06

Why now

The wedge.

OMX has the data in Snowflake (PPSS sources, Lens dbt models, F_AGG_SALES_PERFORMANCE_*, F_CUST_TARGET_*) — but only a handful of analysts know how to ask it
Anthropic / OpenAI now produce production-grade text-to-SQL and semantic-layer translation
Lens is the visualisation layer — the conversation layer sits ON TOP of Lens, not replacing it
Same engine, two markets: internal (analysts, reps, managers) first; external (customers via Ask Max) once the patterns are proven
"Search-to-sales" — internal: search-for-answer; external: search-for-product-then-buy

02 / 06

What this covers

What's in scope.

01

The opportunity

most OMX staff can't ask the data; the data team is the bottleneck

02

Semantic layer foundations

dbt model definitions become the "vocabulary" the AI uses

03

Internal use case sweep

sales/AM/analyst/planner/ExCo use cases by frequency + value

04

QBR pre-fill

biggest sales-side win; links to Deck 18 (Company Research)

05

Architecture

Claude + Snowflake semantic layer + Lens widgets

06

Governance

what AI can and can't answer; citation; PII/sensitive data fence

03 / 06

The problem

What's broken.

01

Data-team bottleneck

every reasonable question requires a ticket to the analyst pool

02

Reps don't ask the data

too slow to be useful in a sales conversation

03

QBR prep takes days

manually pulling the same view per account, per quarter

04

Tribal knowledge

only a few people know which dbt model has the right field

05

Lens has answers, but the question hasn't been asked yet

dashboards answer what was anticipated; conversations cover the unexpected

04 / 06

The benefits

The value story.

Lever

Mechanism

Sizing

Analyst capacity reclaim

Self-serve answers for the long-tail questions

Reclaim 30-50% of analyst time on routine queries

Sales-rep response time

Real-time answers in customer conversations

Higher win rate; faster deal cycle

QBR prep speed

Pre-filled QBR packs from conversation

Memory: QBR prep currently days per account; could be <1hr

Decision quality

Faster + broader access to data = better decisions

Strategic

Phase 2 platform reuse

External Ask Max reuses internal infra

Lower marginal cost for Deck 01 expansion

05 / 06

The ask + roadmap

What we need.

Now

Cover

analyst at laptop typing a question; semantic answer + chart resolves

P2

Problem vector grid (4)

: Analyst-bottleneck / Reps-can't-ask / QBR-prep-cost / Tribal-knowledge

P3

Use case sweep

6-9 example questions with the answer surface

P4

Phase 1 vs Phase 2 — same engine

visual showing internal use today, external Ask Max tomorrow

P5

Architecture pipeline

User question → Claude semantic layer (dbt-aware) → Snowflake query → Lens widget + citation

Audience

Primary: Chief Digital Officer + Head of Analytics + Sales Director. Secondary: ExCo (they're heavy users of the QBR pre-fill use case). Tertiary: IT / Data team — they're co-build participants.

References

Memory: Lens (BI tool replacing Sisense) — visualisation layer this deck sits above
Memory: Lens design language v1 — pattern for the answer-surface UX
Memory: OMX dbt models at lens/Current/libraries/dbt/models/presented/ — F_AGG_SALES_PERFORMANCE_*, F_CUST_TARGET_*, D_CALENDAR
Memory: PR-013 fact-checking gate — citation is non-negotiable
Memory: FDL REVIEW_OMX_dbt v2.0 — semantic-layer definitions live in dbt

06 / 06