Conjunto de herramientas AI para CIO y CDO de marcas minoristas
Welcome to the definitive AI Toolkit designed for CIOs and CDOs of retail brands. This guide is your go-to resource for navigating AI adoption across sales, distribution, and business operations—whether you’re part of a rapidly scaling organization or an established enterprise.
01. AI Options: Corporate Centralised AI & SaaS Embedded AI
Retail brands have two primary AI approaches. Understanding their differences is critical for selecting the right balance for your business needs.
| Criteria | Corporate Centralised AI | SaaS Embedded AI |
| Training Data Sources | Multiple systems like ERP, SFA, DMS, HRMS, E-Commerce, etc. | Complete-in-itself platform (e.g., Sales, Service, Procurement) |
| Tiempo de despliegue | 12–24 months; requires complex integrations and data lake setup | Weeks to months; often pre-trained and ready-to-use |
| Use Case Examples | Enterprise-wide forecasting, resource allocation planning | Order recommendations, procurement risk prediction, employee engagement |
| IT Complexity | High – needs data lakes, data pipelines, robust APIs, governance, integration layers | Low – minimal integration required |
| Scalability | Broad scope across functions and geographies, but constrained by IT complexity | Quicker scalability within specific workflows and functions |
| Investment Level | High upfront costs with long-term strategic benefits | Lower cost with immediate business impact and ongoing vendor-led innovation |
02. AI Adoption Strategies
2.1 Strategies for Smaller & Mid-Sized Organisations
These businesses typically lack the resources to build a centralised AI setup. Instead, they should aim for minimal overhead with maximum impact.
| Challenge | Solution | Detailed Approach |
| Limited Budget | Adopt ready-to-deploy SaaS AI | Use subscription-based unified platforms to lower Capex |
| Data Fragmentation | Use integration-friendly tools | Standardize inputs across systems; avoid full-scale data lake |
| IT Resource Constraints | Prioritize embedded AI | Choose pre-trained, plug-and-play tools |
| Tool Sprawl | Consolidate systems | Select platforms scalable enough to cover a full function (sales, procurement, HR); avoid over-consolidating on horizontal tools |
Note: Avoiding a full data lake and choosing point-to-point integrations creates vendor decoupling risks—but helps sidestep significant infra costs.
2.2 Strategies for Large & Mature Enterprises
For large enterprises, a hybrid AI strategy is recommended: use Embedded AI for quick wins and Centralised AI for long-term, cross-functional insights.
| Key Factor | Recommended Strategy | Detailed Approach | Expected Outcome |
| Data Management | Leverage existing infra for Embedded AI; selectively integrate | Use unified platforms + high-impact centralized analytics | Rapid deployment; scalable as use cases grow |
| Vendor Risk Management | Ensure easy system decoupling | Choose best systems per function | Greater flexibility, reduced vendor lock-in |
| Scalability | Hybrid approach for scalability in both function and enterprise-wide levels | Start with embedded, grow into centralized | Immediate ROI; long-term innovation |
| Enterprise Architecture | Use open, modular systems | Avoid full-scale data lake unless necessary | Agile infrastructure for future growth |
| Configurability | Prefer industry-specific, enterprise-configurable tools | AI should allow toggle, retraining, and custom flows | More alignment with unique needs |
Quote
“We have evolved from building everything in-house to only building custom use cases. For the rest, we buy best-of-breed enterprise platforms and integrate via a data orchestration layer.”
- Nischai Nevrekar, Chief, Digital Initiatives
2.3 Build vs. Buy Decision
The build vs. buy decision is especially critical in the AI era due to the pace of technological change.
Three Phases of Building In-House
| Metric | Construcción propia AI | Buy SaaS Embedded AI |
| Development Cost | High—dedicated AI team & infra needed | Lower—subscription-based, vendor support |
| Time to Deploy | 12–24 months (uncertain outcomes) | Weeks to months—evaluate before buying |
| Maintenance & Upgrades | Ongoing, resource-intensive | Vendor-managed, predictable cycles |
| Realización del ROI | Slow, uncertain | Fast, measurable with clear metrics |
03. Justifying the AI Investment: ROI & Business Impact
A clear, structured framework is vital to justify and secure AI investments.
AI ROI Calculation Framework
| Cost Component | Investment Details | Potential ROI Impact |
| Infrastructure Setup | Cloud services, data lakes, AI platforms | Improved data readiness; scalable insights |
| Software Licensing | Subscription fees for AI tools | Revenue uplift via optimized sales & pricing |
| Operational Costs | Training, vendor fees, support | Savings from automation and lower manual work |
| Implementation Time | 3–12 months | Faster time-to-market; better competitive edge |
ROI Formula
ROI = (Aumento de ingresos + Ahorro de costes) / AI Inversión
Example
Revenue = $1000
RTM Cost = $100
AI Uplift = 5% revenue + 10% cost reduction
AI Inversión = $10
ROI = ($50 + $10) / $10 = 6X
04. Building an AI-Ready Team for Retail and Consumer Goods
Your team determines the success of your AI initiatives. Focus on integration, strategy, and measurement—not just development.
Best Practices
- Prioritize mature, pre-trained AI solutions over custom development.
- Equip your team to evaluate AI vendors.
- Build capabilities for impact measurement—every leader will demand it.
- AI security can be in-house or outsourced.
Essential Roles & Responsibilities
| Role | Key Responsibilities | Focus Areas |
| AI Strategy | Develop AI roadmap; align with goals | Strategy, ROI, vision |
| AI Vendor Management | Evaluate/select vendors; ensure fit | Vendor selection, contracts, performance |
| AI Data Analysis | Measure outcomes; track KPIs | Data quality, continuous improvement |
| IT AI Integration | Seamlessly embed AI into systems | Systems integration, support, governance |
05. About BeatRoute & Our Approach to AI
BeatRoute is a goal-driven SaaS platform serving 200+ enterprise brands in 20+ countries. Our platform is scalable, configurable, and designed to deliver measurable sales impact across retail sales and distribution networks.
What Sets BeatRoute Apart
- Goal-Driven AI: Ready-to-use AI with easy integration options for centralized intelligence.
- Highly Configurable: Adapt to your processes and workflows.
- Continuous Learning: AI improves as it learns from your data.
Sales Uplift from BeatRoute’s Goal-Driven AI
| Use Case | Uplift | Modules & Workflows | Measurement Parameters |
| Sales Team Enablement | 12.6% | KPI Scorecard, Operational AI (Help Me Plan, Task Recommendations), Gamification, Conversational AI | Monthly sales pre/post, lines sold, new retailer ratio, gamification leaderboard movement |
| Customer Engagement | 5.2% | Milestone visibility, Spin & Win, Communication campaigns, Order Nudges, Customer Gamification | Sales pre/post, lines sold, scheme budget use, incremental orders |
| Operational AI | 4.3% | Order Recommendation AI, Help Me Plan, Task Recommendations, Customer Insights | Monthly sales, productive visit count, average order value |
| Conversational AI & Analytics | 5.3% | CuesBot, Configurable Dashboards, Competitor Insights | Suggested vs. actual actions, manager actions pre/post, sales from same set of customers |
Ready to Build a Long Term AI Stack?
Book a free demo with BeatRoute to see our AI in action, and learn how you can incorporate our AI platform in your tech stack.
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