Enterprise efficiencies with
Nir Manor・Transformational Retail
15 April 2025
AI Evolution
AI waves
Agentic AI Enables to manage and control reality.
Generative AI Create and interact with reality.
Traditional AI Allowed analysis and predictions.
AI fundamentals
Essential attributes of AI Agents
- Continuously modifies plans in response to evolving circumstances to fulfill processes efficiently.
Adaptable Planning
Autonomy
- Performs objective-oriented tasks with limited human supervision.
Context Understanding
Reasoning
Interprets and responds to natural language and other modalities.
Situation-aware decision processes, discretionary choices & compromise considerations.
Action Enabled
- Authorized to implement actions through integration with online platforms providing competencies.
10 core GenAI Use Cases for Retail/CPGs in 2025
- Redefining and accelerating product development
- Optimize operations
- Products descriptions and visuals
- Democratize data access and analysis
- Agentic AI for various enterprise tasks
- Demand Forecast 3.0
- Retail planning optimization
- Improve Customer Experience
- Personalization at scale
- New research trends
- Strengthen working teams
Examples
Enterprise automation: AI-Agents examples
- Compliance & Risk Agent
- Monitors and ensures compliance with industry regulations and internal policies across all systems and workflows.
- Integration Coordinator Agent
- Manages and streamlines integrations across various enterprise systems like ERP, CRM, SaaS, cloud, on-premise, and legacy platforms.
- Sales Pipeline Agent
- Manages the sales pipeline by analyzing leads, predicting conversions, and automating follow-ups.
- HR & Workforce Agent
- Automates HR tasks like recruitment, onboarding, payroll processing, time-off management, and performance evaluations.
- Financial Controller Agent
- Automates financial tasks such as budgeting, forecasting, reporting, and revenue recognition by integrating data from multiple financial systems.
Challenges
AI Agents will be everywhere, but the challenges are:
- The challenge is to seamlessly connect to the enterprise backend, which mostly has legacy and/or on-prem systems.
- AI agents to act as per corporate policy related to the subject in case.
- Complicated integrations to ERP, billing systems, and proprietary databases pose significant hurdles for AI agents.
Innovation
AI-Powered Financial Operations
Unlocking new possibilities
AI Agent orchestration – Use Cases
- Automate data flows, validation, enrichment across multiple SaaS & internal databases.
- Data Enrichment & Automation
- Procure-to-Pay (P2P)
- Order-to-Cash (O2C)
- Automation of procurement workflows, connecting ERP (NetSuite, SAP, Priority) to procurement platforms (ZIP, Coupa).
- Orchestrate end-to-end sales-to-finance process between CRM (Salesforce, HubSpot) and ERP systems.
- Customer Support
- Smart Data Mapping
- Automation for Zendesk, ServiceNow, Freshdesk to ensure proactive CS.
- Helping SaaS companies integrate with ERP/CRM systems of their clients.
Challenges
Procurement & cash management challenges
- More purchasing autonomy by numerous departments
- Countless vendors
- Overspending (mainly before issuing a PO)
- Cross-team approvals and risk reviews
- Unclear ordering process and law orchestration
AI Applications
Procure-to-pay (P2P) – Use Case
- Automation of Connecting ERP (NetSuite, SAP, Priority) to procurement platforms workflows (ZIP, Coupa).
| Automation of procurement workflows | Connecting ERP (NetSuite, SAP, Priority) to procurement platforms(ZIP, Coupa). |
|---|---|
| Vendor performance analysis | AI Agents analyze historical vendor performance, pricing trends,and order urgency to recommend or automatically select the best supplier for each order. |
| Invoice discrepancy resolution | Flags discrepancies when invoices don't match purchase orders or delivery receipts,classifies the issue,and resolves it or escalates to procurement. |
| Inventory shortage prediction | Predicts shortages based on sales trends,supplier lead times,and seasonal fluctuations,triggering POs before shortages occur. |
Case Study
Walmart Case Study – P2P
AI deployment in Walmart deployed an AI negotiation agent in its procurement Procure-to-Pay process, transforming how supplier contracts are managed.
Challenges in About 20% of suppliers were on standard, non-negotiated procurement contracts, and most processes were manual, slow, and prone to mistakes.
Achievements and The AI negotiator closed deals with 68% of suppliers, savings achieving 3% cost savings and extending payment terms by 35 days, leading to millions in savings.
Order-to-Cash (O2C) - Use Case
- Contract Scanning for Compliance
- AI scans new customer contracts to identify missing details, inconsistencies, or compliance risks before they are approved.
- Automatic Payment Term Adjustments
- AI Agents analyze past payment behavior, market conditions, and risk scores to auto-adjust payment terms before a new contract is signed.
- Self-Learning Invoice Matching
- AI Agents match invoices to contracts and deliveries, using NLP-based document processing, auto-fixing common discrepancies.
- Predictive Payment Follow-Ups
- AI predicts late payments based on customer behavior and adjusts outreach strategies dynamically.
Case Study
Siemens Case Study – O2C
AI in cash collection Pain points before AI
Siemens uses AI for collection of cash from thousands of clients worldwide.
- High write-offs rate, delayed payments causing cash flow problems, and heavy manual workload on financial teams.
AI-Driven automation Results achieved
- Dynamic risk assessment, reconciliation of documents, and personalized payment follow-ups have been automated.
- 20% reduction in delayed payments, 10% reduction in write-offs, improved cash flow predictability, 25% reduction in manual work.
Comparison
AI vs. rule-based automation
What differentiates them?
Dynamic decision-making
- AI adjusts credit risk assessments in real-time. It responds to changing payment behaviors before problems occur.
- Rule-based systems rely on static thresholds. They can't adapt to evolving situations.
Natural Language Processing (NLP)
- AI extracts key points from unstructured text. It catches discrepancies in contracts that algorithms miss.
Predictive analytics
- AI predicts payment behavior and prioritizes accordingly.
- Personalizes collection strategies for each customer.
AI in E-Commerce
Product Description - Use Case
Automated Content Generation
- Automatically generate, optimize, and personalize product descriptions, images, and category content across e-commerce platforms and digital catalogs.
Natural Language Generation (NLG)
- Based on product specs, AI creates SEO-optimized and customer-tailored descriptions in multiple languages.
- Continuously test variants of titles and descriptions to improve conversion rates.
- Automatically generate lifestyle images or product mockups using product metadata.
Visual AI Tools
A/B Testing
Omnichannel Adaptation
- Tailors messaging for specific channels (e.g., mobile, desktop, social media) using customer behavior insights.
- Less manual workload, shorter T2M for new products, enhanced customer engagement, CVRs, ensured consistency and personalization across all digital touchpoints.
Case Study
The Home Depot Case Study – Product Descriptions
Challenge Managing content for over 2 million SKUs. Ensuring consistency, SEO performance, and personalization at scale.
AI Use Create and optimize product descriptions, images, titles, and long-form content. Tailored content to customer personas using behavioral insights. A/B tested variants to maximize conversion rates.
Results Achieved Significant reduction in manual work. Increased conversion rates on product pages. Improved organic search visibility. Faster product onboarding with reduced manual workload. Consistent, personalized messaging across channels.
Challenges
Customer Service challenges
- Manual and hard to scale
- Long SLAs that reduce CSAT
- Bad reviews that damage brand reputation
- Hard to train with high employee churn
AI for Customer Service
NLP/LLM, integrations, personalized process
Hey Jane,
I'm sorry to hear the size of the boots from order #24345 did not work out for your husband. I've gone ahead and started the exchange process for new Legend | Black boots in a size 9. Please see the Happy Returns shipping label here.
… and please send happy birthday to your husband!
Getting info from multiple integrations
Taking actions using APIs
Exchange Legend Boots size 8.5 9
Issue a return label
CSAT
AI Agent’s CSAT vs. Human Agent’s
“
You can’t stop the waves, but you can learn to surf
Jon Kabat-Zinn
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