AI product portfolio

Ten AI products designed for Arvind Fashions — scored by business impact, build effort and time horizon.

Products in portfolio

5

Concept to pilot

Avg impact score

86/1002 quick wins

Impact high, effort low

In pilot

2

Running with live data

Shippable in 90 days

4

Horizon: Now

Portfolio

Select a product to see the full brief

Stage mix

5
  • Pilot2
  • Prototype1
  • Concept2

Stream split

Quick wins vs core transformation

  • Quick win40%
  • Core build60%

Horizon split

  • Now80%
  • Next20%
  • Later0%

Portfolio health

86%

Average impact score

40%

Share that are quick wins

Impact vs effort matrix

Tap a dot to open that product brief

Quick winsBig bets
Impact ↑Effort →

Technology building blocks

Most reused across the portfolio

1
1
1
1
1
1
1
1
FitCross-brandReturn-reasonEarly-signalAllocationMarkdownPropensityInventory

Cross-Brand Fit Twin

Prototype · Now · Customer-facing

One fit profile per customer that travels across U.S. Polo Assn., Arrow, Tommy Hilfiger and Calvin Klein, so the size is right the first time and the return never happens.

Who uses it

Shoppers across all brands, ecommerce, planning

Metric it moves

Return rate

Target outcome

Size-related returns down 20-30% across all four brands

Stream

Core build

Why Arvind wins

Only the portfolio owner can normalise fit across four brands and every channel

Fit embeddingsCross-brand size graphReturn-reason feedback loop
Open full dashboard →
Business impact91/100
Build effort54/100
Delivered by the Aiera stack on Arvind Fashions data — 5 geographies, 5 languages, grounded on 3 product sets.

Rollout sequence

How the ten land across the year

Now

4
  • Cross-Brand Fit Twin
  • Drop & Allocation Intelligence
  • Store Clienteling OS
  • Generative Campaign Studio

Next

1
  • Trend-to-Tech-Pack Engine

Later

0