Digital Wave Technology CEO Shares Stage with Microsoft at Groceryshop 2025 to Spotlight Agentic AI

Digital Wave Technology CEO to Share Stage With Microsoft at Groceryshop 2025, Highlighting Innovations in Agentic AI

Groceryshop 2025 will put agentic AI front and center, with Digital Wave Technology's CEO sharing the stage with Microsoft and others. For operations leaders, this is a signal: AI is moving from demos to day-to-day workflows. The promise isn't hype-it's fewer stockouts, tighter labor, cleaner data, and faster decisions.

Why operations leaders should care

Agentic AI goes beyond chat. These systems accept a goal, plan the steps, call your tools, and execute with oversight. Think of it as an extra set of disciplined hands across merchandising, supply chain, and store ops-without adding headcount.

High-impact use cases to prioritize

• Demand planning with multi-signal inputs: Blend POS, weather, promo, and local events to lower MAPE and reduce stockouts and shrink.
• Autonomous replenishment suggestions: The agent proposes orders by store/SKU, flags anomalies, and routes exceptions for human approval.
• Price and promo ops: Scenario testing for lift, cannibalization, and margin; auto-generate tags, audits, and post-event readouts.
• Store task orchestration: Create, assign, and sequence tasks; push to handhelds; verify completion with photos and timestamps.
• Vendor and DC exception triage: Monitor ASN/EDI variances, create cases, and chase resolution before they hit shelves.
• New item setup and content syndication: Pull specs from vendor docs, validate attributes, enrich content, and publish to channels.
• Customer service deflection: Resolve "where's my order," substitutions, and returns with policy-aware actions.

Questions to ask vendors on stage or in meetings

• What systems can your agent call today (ERP, WMS, OMS, TMS, planogram tools)? How is tool access authorized?
• What guardrails prevent bad actions? Is there human-in-the-loop for high-risk steps?
• Latency under load? What's the 95th percentile response time for complex workflows?
• How do you handle PII and store-level data? Data retention and isolation model?
• What's logged for audit (prompts, tool calls, decisions)? Can we export logs to our SIEM?
• TCO over 12 months (tokens, hosting, orchestration, integration, change management)?
• Proof of value: baseline KPIs, win metrics, and time to measurable impact.

Guardrails you should set before scaling

• Human approval gates for orders, price changes, and any customer-facing action above a set threshold.
• PII redaction at ingestion and strict role-based access to sensitive data.
• Audit trails of prompts, plans, tool calls, and outcomes-immutable and searchable.
• Fallbacks to safe defaults when data is missing or confidence is low.
• Test sandboxes seeded with synthetic orders, stores, and SKUs before touching production.
• Map controls to an established framework like the NIST AI Risk Management Framework.

90-day rollout plan (practical and lean)

• Week 1-2: List 10 repetitive workflows; score by effort vs. impact. Pick 2 pilots.
• Week 3-4: Pull clean samples (orders, SKUs, store attributes). Define success metrics and guardrails.
• Week 5-6: Wire the agent to read-only data and simulate decisions. Compare against human outcomes.
• Week 7-8: Add tool calls with approval gates (e.g., create draft orders, not final posts).
• Week 9-10: Train front-line users on review flows. Collect friction and failure modes.
• Week 11-12: Expand SKU/store coverage. Report KPI deltas and decide to scale or iterate.

Metrics that show real impact

• OOS rate by store/SKU
• Forecast MAPE and bias
• Shrink and sell-through on promoted items
• Labor hours per 1,000 units picked or stocked
• Ticket deflection rate and average handle time
• Promo compliance and price accuracy
• Time-to-publish for new item setup

Stack considerations before you sign

• Data layer: Clean product, store, and event data with lineage. Low-quality inputs produce noisy actions.
• Retrieval: Vector search or rules for policies, planograms, SOPs, and vendor terms.
• Tool orchestration: Safe interfaces to ERP/WMS/OMS, with policy checks and rate limits.
• Observability: Monitoring for cost, latency, failure rates, and drift; alerts routed to ops.
• Hosting: Clarity on cloud region, tenancy, and egress fees. Consider existing footprints like Azure AI services.

What this means for your team

You don't need a moonshot. Start with one pilot that removes tedious work and proves value in weeks, not quarters. Keep the human in control, log everything, and scale what works.

If you want structured training for operations teams evaluating AI agents, see our curated paths by role at Complete AI Training.