Sixteen candidate AI use cases discovered, scored, and classified through the Foundry Atlas methodology, with five Tier 1 pilot blueprints. The deliverable, the rationale, and the methodology — exactly as you'd receive it.
A consumer electronics brand with 200 outlets across 12 cities, 35 field reps, and a function under pressure to absorb growth without expanding headcount. Here's the shape of what the Atlas produced.
The Function Inventory is built using the Operational Surface Method — six operational surfaces where AI applies, examined systematically. The inventory below was completed in pre-work and validated in Day 1 morning.
Day 1 afternoon and Day 2 morning. Every score has a one-line rationale captured in the room. The Function Owner ruled tiebreaker on six contested ratings, all surfaced below.
| # | Use case | Impact | Data Ready | Speed | Rationale (combined) |
|---|---|---|---|---|---|
| 01 | Visit reporting | Yes | Immediate | Foundation for every downstream field intelligence use case; reps already capture rough notes; visible in week 1 of deployment. | |
| 02 | Sample stock requests | Yes | Immediate | Volume small, time saved per request small; quick to deploy but low standalone value. | |
| 03 | Demo unit fault tickets | Yes | Short | Cuts demo-unit downtime which directly affects sales; needs ticketing system integration. | |
| 04 | Lead prioritisation | Yes | Immediate | Direct link to rep productivity and outlet coverage; outlet data, recency, sell-through all available; rep adoption needed but tech is ready. | |
| 05 | SKU push selection per outlet | Partial | Short | Could lift sell-through 8-12% in mid-tier outlets; outlet profile data exists but not consolidated; 6-8 weeks of data prep needed. | |
| 06 | Outlet escalation routing | Yes | Short | Saves manager triage time; routing rules need definition with each downstream team. | |
| 07 | WhatsApp follow-ups | Yes | Immediate | Better outlet relationships, indirect on sell-through; reps can use immediately. | |
| 08 | Regional weekly summary | Yes | Immediate | Saves 8-10 hours/week across 8 regional managers; RM review still needed but draft is automatic. | |
| 09 | RSP recommendation support | Partial | Short | Improves close rates, but RSPs are not directly employed by brand; SKU data yes, customer-need data unstructured. | |
| 10 | Regional performance narrative | Yes | Immediate | Saves head-of-function 2-3 hours/week, enables faster leadership response; highest-leverage time saving in function. | |
| 11 | Outlet anomaly detection | Yes | Immediate | Catches sell-through drops 7-14 days earlier than current process; weekly outlet data structured; high-confidence early signal. | |
| 12 | Share-of-shelf measurement | Yes | Immediate | Strategic value high, tactical sell-through impact moderate; photos taken on every visit. | |
| 13 | Planogram compliance | Yes | Immediate | First-ever scaled compliance data; correlates with sell-through directly; rep behaviour change minimal — they already photograph. | |
| 14 | Visit-to-CRM data flow | Partial | Long | Improves data quality network-wide but is infrastructure not insight; 12+ weeks of integration work. | |
| 15 | Mystery shopper replacement | Partial | Short | Could replace ₹12L/year audit spend with continuous coverage; need rep photo discipline at scale; 8 weeks to validate against current audit baseline. | |
| 16 | RSP training assessment | Partial | Long | RSP quality matters but training is run by retail partners not brand; multiple stakeholders, slow rollout. |
The tier classification rules from the methodology, applied mechanically. Most cases were uncontested — the rule fires, the tier is assigned. The remaining debates are surfaced in the boundary calls above.
For every Tier 1 use case the sprint produced a one-page blueprint with the same eight fields. Drafted in pairs in Day 2 morning, refined with the room in Day 2 afternoon, owners named before the readout.
| Metric | Baseline | Target (Day 90) |
|---|---|---|
| % of weekly visits with usable structured record | 70% | 95% |
| Manager time spent following up on incomplete records (hrs/week) | 6 | 1 |
| Regional manager rating of report quality (1-5) | 2.8 | 4.2 |
| Metric | Baseline | Target (Day 90) |
|---|---|---|
| Lead-to-meaningful-contact rate (% of weekly leads contacted) | 60% | 85% |
| Average days since last contact (across rep portfolio) | 9.2 | 5.5 |
| Brand-promoted lead conversion rate | 14% | 22% |
| Metric | Baseline | Target (Day 90) |
|---|---|---|
| Time spent writing narrative weekly | 180 min | 20 min |
| Narrative completeness against leadership ask (1-5) | 3.5 | 4.5 |
| Action items raised per narrative | 2.1 avg | 4.0 avg |
| Metric | Baseline | Target (Day 90) |
|---|---|---|
| Average days from sell-through drop to manager noticing | 11 days | 3 days |
| % of flagged anomalies that were genuine | n/a | 75%+ |
| Anomaly-driven actions taken per week | 1-2 (informal) | 4-5 (structured) |
| Metric | Baseline | Target (Day 90) |
|---|---|---|
| % of outlets with current compliance data | <15% (audit-based) | 95% (visit-based) |
| Average compliance score (network) | unknown | benchmark established |
| Sell-through correlation with compliance | not measured | causal hypothesis tested |
The Atlas closed at 1700 on Day 2 with the Function Owner committing to all five Tier 1 pilots in front of a senior leadership observer. The discipline that separates a sprint that produced a document from one that produced action.
A 30-minute introductory call to discuss your strategic question and whether the Atlas is the right next step. No pitch deck.