by Yassine Ben Mansour | October 22, 2025
Retail supply chains are moving from scheduled plans to always-on, self-optimizing systems. AI and intelligent automation are now embedded across forecasting, pre-season planning, replenishment, and in-season allocation—turning siloed processes into a responsive network that senses, decides, and acts continuously. For retailers running unified commerce on modern ERP/OMS foundations like Jesta I.S.’s Vision Suite, the outcome is faster decisions, leaner inventories, greater resilience, and stronger margins—without piling on complexity. To see how this connects to omnichannel outcomes, read how ERP bridges online and in-store.
Why AI + Automation Will Soon Become Non-Negotiable
Demand spikes, supply shocks, labor constraints, and rising customer expectations have exposed the limits of batch planning and manual exception handling. AI addresses these limits by pairing predictive capabilities (what’s likely to happen) with prescriptive capabilities (what to do next) and by making decisions explicit, auditable, and measurable. Retail leaders are increasing AI budgets and scope as shown in recent investment coverage, while frontline use is expanding through agent-style assistants and operations interviews. Board and finance teams are prioritizing modernization too, as highlighted in C-suite trend summaries.
We align with a semi-autonomous, human-in-the-loop approach: software drafts and executes within guardrails; people approve exceptions. Guidance on digital workers and decision intelligence supports this path as comfort and governance mature.
In practice, the ERP is the orchestration layer for intelligent automation: forecast signals feed buys and replenishment; assortment and size strategies flow into allocation; in-season learning tunes rules; and KPIs refresh in near real time. With this model, supply chains don’t just react faster—they compound improvements week after week. For context on execution bottlenecks, see common warehouse challenges.
Where Intelligent Automation Pays Off First (Aligned to Jesta Offerings)
1) Forecasting, Pre-Season Planning & Assortment (live and expanding)
Modern models blend POS history with promotions, seasonality, weather, events, and product attributes to produce store/SKU-level forecasts. The value lands when those signals drive pre-season buys, assortment breadth/depth, and size strategies directly inside Vision—so planners work from proposals instead of spreadsheets. Phase 1 focuses on demand forecasting and pre-season planning optimization (including MIP-based buy plans and distribution schedules) to raise forecast accuracy and GMROI while reducing stockouts and deadstock. This direction reflects current retail planning commentary.
We’re incorporating feedback that size planning is rising in importance, store-level modeling should reflect customer personas (not just A/B/C grading), and self-learning is critical—including visual/AI-assisted product attribution to speed setup and improve clustering.
What this changes for you
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Bulk PO proposals and assortment recommendations are generated in-policy (MOQs, lead times, budgets).
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Persona- and attribute-based clustering improves local fit.
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Planners approve in context; Vision pushes changes forward to execution—audited end-to-end.
2) Replenishment & In-Season Allocation (live and expanding)
Automation doesn’t end at the plan. Vision introduces autonomous replenishment that adjusts to short-term signals (sales velocity, on-hand/on-order, service levels) and generates store/DC-specific reorder proposals. Where policy allows, replenishment can be configurably automated; otherwise it routes for review. In-season, the system monitors exceptions (e.g., unexpected lifts) and proposes re-allocation or buy-forward within configured guardrails.
What this changes for you
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Fewer CSV round-trips, more click-to-apply proposals.
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Clear owners/SLAs for exceptions; fewer surprises for OTIF and availability.
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WOS and stockout rates improve as the loop tightens.
3) Sizing & Pack Optimization (on roadmap, prioritized)
Retailers and analysts consistently flag size planning as a differentiator—especially in apparel/footwear. Jesta’s roadmap brings size-pack logic that adapts to fulfillment realities (e.g., smaller pre-packs to reduce labor in breaking cases) with store-persona context to lower returns and markdowns.
4) Pricing & Markdown Optimization (Phase 2)
Dynamic pricing/markdown management is part of Vision Phase 2. Our approach keeps merchants in control with policy guardrails (floors/ceilings, competitive sets, promo rules) and auditable publishing to channels. Broader market commentary on AI in pricing and ops is summarized in technology trend rundowns.
Note on scope: We do not position generic document-processing/BPO or robotics orchestration as Jesta services. Our focus is ERP-anchored merchandising and supply optimization—forecasting, pre-season planning, replenishment/allocation, and (roadmap) sizing, pricing, and AI-assisted product attribution—delivered within Vision Suite. External validation of our retail tech footprint can be seen in recent recognition.
Foundations That Separate Leaders from Laggards
Unified, trustworthy data. AI value depends on high-quality product, supplier, inventory, order, and pricing data with consistent IDs. Vision Suite provides this single source of truth so models consume clean, current signals.
Near-real-time instrumentation. Streaming events across suppliers, DCs, and stores shrink the latency from “problem occurs” to “action executed,” aligning to board-level priorities.
Simulation & digital twins (pragmatic use). Scenario “what-ifs” for demand, capacity, and lead times help teams pre-bake playbooks; we’re incorporating these in what-if simulators for planners as Vision scales.
Human-in-the-loop governance. AI should recommend; the business decides. Vision encodes guardrails (service levels, vendor caps, substitution rules), role-based approvals, and monitoring for model drift—consistent with the shift to agent-style operations.
Low-/no-code adoption. We’re packaging models for forecasting, replenishment, and planning so business users configure while IT governs—accelerating value while preserving control.
What’s Emerging (2025–2027): Focused Signals for Your Roadmap
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Agentic AI in merchandising flows. Expect more semi-autonomous tasks (e.g., creating buy transfers under thresholds) with humans approving exceptions—supported by decision-intelligence briefs.
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AI-assisted product attribution. Visual and textual models speed attribute assignment—fuel for better clustering, forecasting, and size strategies.
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Persona-driven store modeling. Moving beyond static grades to customer-persona mixes for assortment, size curves, and in-season moves.
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Pricing & markdown services. Controlled, auditable optimization that respects retail policy and channel constraints (Phase 2).
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Consumer behavior shifts. More shoppers are using AI to compare prices and find deals, as reflected in seasonal behavior snapshots.
A 90-Day Playbook to Operationalize AI on Jesta
1) Select two no-regret automations tied to P&L.
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Store/SKU forecasting → pre-season buy plan + initial allocations to move WOS and availability.
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Autonomous replenishment with human-in-the-loop exceptions.
2) Wire the signals.
Enable near-real-time events for POS, inventory, orders, shipments, and exceptions; set SLOs (e.g., hourly refresh for replenishment; sub-hour alerts for top sellers).
3) Put decisions where work happens.
Surface proposals and exceptions inside Vision Suite (merchandising, allocation, replenishment). No CSV exports or email chains.
4) Close the loop with KPIs.
Attach owners and targets to stockout rate, forecast accuracy, WOS, OTIF, markdown %, and pick lines/hour; review weekly and tune thresholds.
5) Scale with governance.
Codify price floors/ceilings (for Phase 2), service-level targets, vendor caps, and exception SLAs; stand up model monitoring and periodic business reviews.
Tangible Outcomes You Can Expect
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Higher availability with less inventory. Smarter buys and replenishment reduce lost sales and carrying costs simultaneously.
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Throughput up, errors down. AI-guided tasks raise planning productivity and compress in-season response times.
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Fewer disruptions, faster recovery. Exception sensing and persona-aware modeling cut the impact of delays and mis-fits in assortment/size.
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Audit-ready decisions. Every recommendation carries context, policy checks, and approvals in Vision.
When AI operates through your ERP/OMS as the single operational backbone, intelligent automation becomes a performance engine: better forecasts → smarter buys → leaner replenishment → faster in-season moves → fewer exceptions → tighter margins. This is the future we’re enabling with Jesta I.S.—pragmatic, governed, and measurable from day one.
FAQs
1) Do we need a big data science team to benefit from AI on Jesta?
No. Jesta partners with you to establish a rich data pipeline—migrating, transforming, and enriching data for our ensemble models. This baseline unlocks AI-ready access for forecasting, pre-season optimization, and autonomous replenishment and sets the foundation for natural-language analytics across enterprise data.
2) Which KPIs should we track first to prove value?
Focus on availability and efficiency: stockout rate, WOS, forecast accuracy, OTIF, markdown %, and pick lines/hour. Tie each initiative to one or two KPIs with explicit targets and owners.
3) How does “agentic AI” show up in Vision Suite?
Within bounded policies, agents draft buys or replenishment transfers, flag exceptions, and propose actions. Humans review and approve; everything is logged and governed—aligned with operating-model shifts. Additionally, Ask Jane enables natural-language queries, reporting, and analysis across enterprise data.
4) Where should we start if we have limited bandwidth?
Pick one demand-side (store/SKU forecasting → buy plan) and one in-season (autonomous replenishment) automation. Both deliver measurable wins within a quarter and set the stage for size optimization and pricing in Phase 2.
