EY Consumer Goods global

Asahi Europe & International

Real-time Analytics · Sales Automation

Asahi's acquisitions left it with different processes, customized SAP systems and data silos across markets, while revenue growth teams were still working in spreadsheets and could not analyze promotions fast enough to respond to volatility, changing consumer behavior and complex campaigns.

Read the original story Explore all use cases

19 breweries across nine markets in eight European countries and the US

adopted across five European markets by 2025

daily use of the tool is growing 60% year-on-year

within 24 hours produce a full list of analysis and recommendations

Reported impact

Business outcome

Asahi gained faster and more consistent promotion analysis, broader visibility into trade performance and a scalable platform it could roll out market by market, helping sales teams make quicker decisions, negotiate more effectively and support growth with better evidence.

Solution

Asahi Europe & International partnered with EY and Microsoft to modernize trade promotion optimization through an AI-enabled tool built around revenue growth management data. EY teams co-designed a managed-services model and created three connected intelligent capabilities: descriptive analytics to reveal sales and promotion drivers, prescriptive logic to generate recommendations and sales plans, and predictive models to forecast the revenue impact of sales activity. Built on EY's revenue growth management framework and Microsoft's cloud-based Azure platform, the solution combines point-of-sale data with weather patterns, trading restrictions, regional differences and other promotion signals, while integrating with Asahi's customized SAP environments. EY consumer commercial analytics capabilities and a low-code voucher application extended the workflow so sales, distributor and finance teams could act on insights more quickly and consistently across markets.

Data flow

Revenue growth management data, point-of-sale signals, weather patterns, trading restrictions and regional promotion differences flow into the AI-enabled TPO tool on Azure. The system analyzes drivers, generates recommendations and forecasts promotion impact, while the linked voucher application sends digital artifacts through retailers, distributors and finance for faster execution and audit handling.

Solution architecture

5 components · 3 layers

Compute
- Microsoft Azure Provides the cloud machine learning foundation that powers the promotion optimization workload.
- EY consumer commercial analytics platform Adds commercial analytics and multi-layered modeling to evaluate promotion performance and spending effectiveness.
Serving
- AI-enabled TPO tool Combines descriptive, prescriptive and predictive intelligence to analyze promotions and recommend actions.
- low-code/no-code application Digitizes paper vouchers so field teams, distributors and finance can move promotion workflows electronically.
Orchestration
- customized SAP environments Provide the enterprise process and data context the optimization tools integrate with across markets.

Architecture clues

The Microsoft-powered voucher workflow sends digital vouchers to retailers, distributors and finance.
The analytics stack combines point-of-sale data with weather patterns, trading restrictions and regional differences.
The solution integrates with Asahi's customized SAP environments rather than replacing them.
The tool uses descriptive, prescriptive and predictive capabilities on top of EY's revenue growth management framework.

Evidence from the source

"Built on EY's RGM framework and powered by the machine learning capabilities of Microsoft's cloud-based Azure platform, the tool is tailored to support each of the brewer's multiple markets and easily integrates with customized SAP environments."

"By 2025, the AI optimizer tool had been adopted across five European markets, providing a platform for a market-leading position."

"Together with the Asahi team, EY teams co-designed an artificial intelligence (AI)-enabled TPO tool, with three connected intelligent capabilities: descriptive, prescriptive, and predictive."

"Using automation lets us look at all promotions, not just the biggest ones."

"Within 24 hours produce a full list of analysis and recommendations we could take to the customer."

Transparency Evidence & methodology 95/100 reference value · 85% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value95/100

Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth25/25
Rewards named components, data flow, integrations, and architecture detail.
Outcome specificity20/20
Measures whether the reported benefit is concrete rather than a generic claim.
Quantified impact25/25
2 metrics describe measurable impact.
Deployment scale5/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence85%

Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability25/25
12 of 12 key fields link to a named source.
Direct evidence20/20
5 direct source quotes retained.
Date provenance0/10
The publication date has no documented provenance.
Source transparency10/10
1 source reference document publisher, URL, authority, retrieval, and supported fields.
Implementation specificity10/10
Rewards source-backed components, data flow, quantified impact, and a specific outcome.

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

EY
How AI-led transformation helped a major brewer grow | EY - Global

First-party
https://www.ey.com/en_gl/alliances/asahi-ai-transformation

Source type
Vendor-published customer story
Supports
- Customer
- Industry
- Region
- Company size
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- Business outcome
- Metrics
- Architecture
- evidence quotes

Date evidence

First archived Apr 10, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
Open date source

Similar use cases

See all like this

Google Cloud Archived · Aug 5, 2026 Horizon Media Commercial and Professional Services United States While these behemoth agencies are often slowed down by complex layers of approval, Horizon Media turns its independence into a major advantage. The agency relies heavily on its core superpowers: structural agility,… Achieving 20% capacity gains and a IT Operations AutomationReal-time Analytics Gemini · Gemini platform · Looker

Google Cloud Modified · Jul 29, 2026 RIA Catalyst Financial Services United States To build a data infrastructure capable of supporting advanced machine learning calculations without expanding operational overhead, RIA Catalyst migrated its user interface layer to a decoupled Next.js web application… Reclaimed 20 engineering hours per month by automating storage ingestion pipelines Real-time AnalyticsSales Automation AlloyDB · AlloyDB Omni · BigQuery

Bain Archived · Apr 29, 2026 First Abu Dhabi Bank Financial Services United Arab Emirates First Abu Dhabi Bank needed a faster way for relationship managers and advisers to spot and validate sustainable finance opportunities because manually scanning news, market updates, and sustainability data was slow and… $300M+ potential revenue gain by 2030 Real-time AnalyticsSales Automation AI agent · Sustainable Finance Market Pulse · sustainability intelligence platform