# Rob Want

Rob Want shares practical perspectives on using AI and digital tools in retail and consumer-goods operations, including customer experience, supply chains, and factory transformation.

- Profile: https://b2binfluencer.marketing/creators/rob-want/
- LinkedIn: https://www.linkedin.com/in/robwant
- LinkedIn followers: 2,532
- Topics: artificial-intelligence, supply-chain, supply-chain-resilience, enterprise-ai-adoption
- Based on 17 dated posts, latest 2026-06-23

## Summary

Rob Want is most useful for practical AI and digital-tool perspectives on retail and consumer-goods operations decisions.

- Best for: A CPG manufacturer planning a scoped digital-twin, factory-data, or supply-chain AI pilot.
- Not for: Brands seeking large-reach sponsored content or audited client case studies with named results.
- Check first: Confirm audience demographics, post performance, and partnership terms, since posts publish no media kit or rates.

### Takeaways

- AI value comes from focus, not scale, and data quality matters more than model.
- Retailers should own customer relationship and conversation data, not cede shopping journey to third-party AI apps.
- Factory transformation is an operating-model and data problem; digital twins belong in capital project.

## Main ideas

- **Good selling is consultative, and AI should scale it rather than replace it**: Rob Want's retail philosophy comes from a family computer and office-supply store in the 1980s and 1990s. The owner learned each customer's hobbies and background, asked what the machine was for in the short and long term, then made a recommendation. That approach produced loyalty and repeat business. Want argues that in-person service cannot scale beyond one store, so retailers should extend it with conversational AI: assistants for frontline workers, who already use generative AI in daily life and might otherwise turn to unsanctioned tools, and agents on the retailer's own digital channels, grounded in its data.
- **Retailers should own the customer relationship and conversation data, not cede the shopping journey to third-party AI apps**: Shopping journeys increasingly begin in generative AI apps such as ChatGPT, Copilot, and Gemini, while personalization expectations are shifting toward natural-language and multimodal interaction and visual search. Want warns that if a third-party AI app makes the journey easier, loyal customers who start on the brand's channel will move there. Running conversational shopping on your own site is framed as a commercial control decision: the retailer keeps the customer relationship and conversation data, meets expectations it has already set, and grounds answers in its unique knowledge.
- **AI value comes from focus, not scale, and data quality matters more than the model**: In a recap of a Shoptalk 2026 supply-chain session, Want highlights four themes: AI adoption succeeds when it starts from a clearly scoped business problem; clean and accessible enterprise data is the primary constraint; retail and supply chain lead adoption because returns show up directly in fulfillment, demand planning, and operations; and non-technical leaders can now build and test agents themselves. The practical recommendation is to start with the decision that needs improving, apply AI selectively, and connect enterprise data to surface insights.
- **Factory transformation is an operating-model and data problem, and digital twins belong in the capital project**: Across several posts, Want argues that CPG manufacturers should model and simulate plants before building or rebuilding them, replacing post-installation line testing with virtual validation. Claimed benefits include lower capital spending through standardized designs, faster modular plant setup, simulation before deployment, and optimized layout, flow, and throughput. Two companion posts widen the argument: one gives improvement ranges and lays out a five-level data-maturity path from lean basics to flexible, fully autonomous plants aligned to ISA-95; another says the biggest barrier is not technology but the operating model and fragmented data, and describes unifying engineering, operations, quality, and maintenance behind one view with recommended actions.
- **Supply-chain risk concentrates in specific materials and short contract cycles, and agentic AI can forecast and model it**: Want argues that plastic and packaging dependencies expose consumer packaged goods products to short-term cost increases and downstream price increases, noting that supplier contracts and inventory cycles can run only two to eight weeks. The proposed response is agentic AI in three roles: identifying real-time conflict updates and related market data and tying them to forecasting models for the products a company makes; suggesting mitigation strategies that worked in similar past crises; and modelling likely scenarios.

## Work to read

- [The Original Retail Agent & Timeliness Principles](https://www.linkedin.com/posts/robwant_the-original-retail-agent-timeliness-principles-activity-7437108792674111488-kj2O)
- [Shoptalk 2026 Serendipity - AI in Supply Chain](https://www.linkedin.com/posts/robwant_shoptalk-2026-serendipity-ai-in-supply-activity-7444770776563593216-k0LF)
- [Unlock the next wave of CPG manufacturing value with Digital Twins](https://www.linkedin.com/posts/robwant_cpg-manufacturing-digitaltransformation-activity-7459945216414797824-Trxo)
- [Build your next factory BEFORE you build it](https://www.linkedin.com/posts/robwant_digitaltwin-manufacturinginnovation-cpg-activity-7462853407226089472-wovu)
- [The future factory is UNIFIED and not siloed](https://www.linkedin.com/posts/robwant_futureofmanufacturing-cpg-aitransformation-activity-7463215783821754369-NQHb)
- [EVERY Consumer Packaged Goods product is at risk...](https://www.linkedin.com/posts/robwant_every-consumer-packaged-goods-product-is-activity-7451987601370189824-vCdh)
- [Personalization and customer experience expectations continue to evolve](https://www.linkedin.com/posts/robwant_personalization-and-customer-experience-expectations-activity-7442608983942553600--peD)

## Brand relationships

- **Avanade** (disclosed_partnership)

## Partnerships

Rob Want's posts do not describe a partnership or sponsorship offer: no media kit, rate card, sponsored-format description or partnership inquiry route appears. They promote an Avanade presence and AI lounge demo tour at NRF 2026 (booth #4A) and recap Shoptalk 2026 sessions on personalization, customer experience, and supply-chain AI. Those are conference appearances and product commentary tied to a company's event presence, not commercial terms a brand could book. For a CPG consulting engagement, a project conversation would begin directly rather than through a published sponsorship package. Ask directly about available partnership formats, pricing, deliverables and contract terms.

## Alternatives

- Santi Adavani: Choose Santi Adavani when the buyer’s decision turns on digital-twin taxonomy — which level of twin maturity to fund and why. The posts lay out a level-by-level model, starting with L1 descriptive twins as the 'blueprint phase': asset modeling of every component and subsystem, physics-based engineering design for vehicle dynamics and battery performance, and immersive VR showrooms for remote collaboration. The factory post explains what a twin ingests and how it is used, naming live data from PLCs, MES systems, IoT sensors and inventory databases. It lists use cases including production scheduling, bottleneck identification, layout optimization, predictive maintenance and workforce planning, and adds adoption context: 86% of manufacturing executives see practical applications and 44% are already implementing them. For a CPG manufacturer that accepts digital twins as the direction and needs help deciding scope — design-phase modeling versus live-floor scheduling versus maintenance prediction — this offers a more granular starting point than Rob Want’s roadmap-level framing.
- Mark Bidgood: Choose Mark Bidgood when the buyer’s main blocker is data definitions and governance rather than a specific pilot. The posts diagnose a direct failure mode: consumer goods companies deploy AI while still disagreeing internally about what 'product,' 'customer' and 'margin' mean, with marketing, supply chain, finance and retail partners each using different hierarchies. Mark Bidgood argues that layering AI onto that inconsistency produces trade spend optimized against the wrong signals, forecasts that are 'confidently wrong,' untrustworthy retail media ROI and agents that technically work but miss business intent. The recommended sequence is explicit: create unified definitions across customer, product, promotion and margin; connect retail, supply chain, ERP, CRM and trade systems in one governed architecture; give AI context rather than raw data; and build a trusted operational layer before deploying autonomous agents. A second post takes the argument to the boardroom, framing trusted data as a governance concern and noting that connected systems alone will not settle meaning. For a CPG company whose transformation has stalled on conflicting master data, or whose finance and supply chain teams report different numbers, this is a sharper, more diagnosis-oriented fit than a factory-roadmap conversation.
- Dani Goldman: Choose Dani Goldman when the buyer wants an industry-level orientation before narrowing to a project — a current read on which CPG pressures are shifting and how data, AI, supply chain and customer experience connect. The reviewed trend post covers four shifts: AI moving from experimentation and dashboards into demand forecasting, supply-chain optimization, personalization and product innovation; consumers expecting hyper-personalized experiences and smarter promotions; supply-chain resilience becoming a competitive advantage as over half of CPG executives report rising challenges, from labor shortages to geopolitical disruption; and sustainability tied directly to business performance through traceability expectations from consumers, retailers and regulators. The second post supplies the architecture vocabulary behind those shifts: the 'context layer,' built on retrieval-augmented generation, knowledge graphs and data platforms, grounds a model in a specific business through governance and access controls. It also argues that agentic AI needs continuous structured context to execute workflows such as demand planning. For a CPG team that needs a shared frame across commercial and supply-chain stakeholders before committing to an advisor, this is broader and more current than a single-decision scoping session.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/