Socradata  ·  Pillar 01  ·  Enterprise Transformation
Pillar 01 Production-grade CIO · COO · VP Supply Chain

Operational AI for Enterprise Systems.

We help mature enterprises convert decades of ERP, WMS, and supply chain investment into AI-driven operational decisions — without pilot theater, without rip-and-replace, and without governance debt. Run the business, modernized.

Start a 14-day diagnostic See engagement architecture
−38%
Median forecast error reduction across deployed inventory models, audited at the 90-day mark.
+19%
Picks per hour after slotting and picking-path optimization in 3PL warehouse networks.
−22%
Safety stock reduction at constant or improved fill rate, across LATAM retail and consumer goods.
$50M+
Average prevented inventory write-offs and operational efficiency gain per program.
01 — The buyer

We sell to operators, not to AI tourists.

Enterprise Transformation buyers are accountable for operational KPIs — inventory turns, OTIF, fill rates, working capital, labor productivity — not for AI experimentation. They have an installed base of Infor, SAP, Oracle, or Microsoft Dynamics, and a growing patience deficit for proof-of-concepts that never graduate.

Who buys

The operating leadership

CIO, CTO, COO, CFO, VP Operations, VP Supply Chain, Head of Digital, Head of Data, business-unit GMs. The people whose quarterly numbers move when the model gets it right — and who lose sleep when it gets it wrong.

Where they sit

Industries we serve

Manufacturing, supply chain and logistics, retail and consumer goods, energy and utilities, financial services, telecommunications, agribusiness. Sectors where a forecast error has a direct profit-and-loss consequence inside the same quarter.

How they buy

The buying mode

Budget-anchored, KPI-led, procurement-governed, ROI-focused, risk-averse. Decisions are written into operating committee minutes, not announced on stage. References are checked. Auditors are present.

The pains we walk into

Legacy systems running below their potential. Productivity stagnation despite years of digital transformation budget. A talent gap between data engineering and operations. Data fragmentation across business units. Regulatory exposure that grows faster than the controls library. And — most consistently — the production gap between AI pilots and real operations.

02 — Sub-domains

Five workflows. One operating system on top of your enterprise stack.

Each sub-domain is a workflow inside the enterprise system of record — not a horizontal "AI use case" floating above it. The unit of work is an operator decision, dated and measured.

Domain 01 Inventory & demand

Predictive inventory and demand optimization.

Hierarchical forecasting with explicit confidence intervals at SKU, store, and channel level. Replenishment recommendations that respect lead-time variability, supplier reliability, and working-capital constraints. Write-off prevention as the leading KPI, not a downstream metric.

Built on the MLOps maturity model, audited against operator override logs, and tuned to the planning calendar your business actually runs.

From data lake to live decision
Domain 02 Warehouse & logistics

Warehouse and logistics intelligence.

Slotting optimization, picking-path generation, labor forecasting, and anomaly detection on WMS event streams. Operator dashboards calibrated to the picking shift, not to the boardroom.

Embedded in Infor WMS, Manhattan, Blue Yonder
Domain 03 Control tower

End-to-end supply chain control towers.

Cross-platform visibility across supply, logistics, and warehousing. Exception triage routed to the operator who owns the decision, with confidence and feature drivers visible by default.

Socradata for Infor Nexus
Domain 04 Supplier & procurement

Supplier and procurement intelligence.

Supplier-network risk scoring, scenario simulation, and policy-aware procurement recommendations. Designed for the chief procurement officer who has to defend her decisions to audit and to the operating committee on the same day.

Provenance-aware where regulation requires it
Domain 05 AI copilots

AI copilots embedded in operator workflows.

Natural-language queries against ERP and WMS, exception triage assistants, and agentic workflow execution under explicit human-in-the-loop checkpoints. Confidence, drivers, and override paths surfaced at every triggered action.

Agentic execution with audit trail
03 — Capability stack

The horizontal stack, tuned to enterprise intensity.

Four capabilities flow across all three Socradata pillars, applied with different weight in each. In Enterprise Transformation, AI and digital transformation lead at full intensity; blockchain is selective and earns its place only where provenance and traceability are auditable requirements.

Capability Intensity Applied here as
AI / ML / LLMs / Agents Dominant Forecasting with confidence, anomaly detection with feature attribution, decision support, embedded operator copilots, and agentic workflow execution under documented human-in-the-loop checkpoints. Built on the MLOps maturity model and the NIST AI Risk Management Framework.
Digital transformation Central Operating-model redesign, KPI architecture, change management, governance forum design. Productionization is a discipline here, not a slogan: the artifact ships when the rollback path ships, never before.
Data & analytics Foundational Instrumentation, decision intelligence, KPI baselining, observability. Data quality and lineage are non-negotiable preconditions, not afterthoughts. Productized assets — the IP library — are harvested from this layer.
Blockchain & DLT Selective Earns its place only where provenance, traceability, or programmable settlement are auditable requirements — food, pharma, automotive, regulated logistics. Refused where it is solution-in-search-of-problem.
04 — Engagement architecture

Four engagement formats. One graduation logic.

Each format is fixed in scope, priced, and outcome-anchored. The graduation logic is explicit: the diagnostic is a stepping stone to a sprint, the sprint is a stepping stone to a program, the program is a stepping stone to advisory. We refuse the consulting habit of thick decks at the front and quiet handoffs at the back.

Stage 01 · Diagnose

The Diagnostic

14 days · fixed fee

Two weeks of structured interviews, document review, and a model inventory. Output: a written diagnosis with risk classes, capability gaps, and a candidate KPI map. Read the institution before the technology.

Deliverable · Written diagnosis
Stage 02 · Sprint

Pilot Sprint

6–12 weeks · fixed fee

One workflow, one operator decision, one model in production by week eight. Built with the client's engineering and risk teams, with model card, evaluation suite, and rollback procedure shipped alongside the artifact.

Deliverable · Live decision in workflow
Stage 03 · Program

Operational AI Program

3–12 months · time & materials

Multi-workflow rollout across distribution centers, plants, or business units. Embedded delivery with the client's teams. Governance forum, model inventory, and the institutionalization runbook are co-built — not retrofitted.

Deliverable · Owned capability
Stage 04 · Advise

Executive Advisory

Quarterly retainer

Board-level and operating-committee advisory on AI policy, model risk, and the next twelve months of operational AI investment. Closed-door working sessions, written briefs, and standing access to the principal.

Deliverable · Standing access & written briefs
05 — Differentiation

What we are not.

A clean way to read a market is to see who else is selling adjacent answers. Socradata Enterprise Transformation is positioned against four kinds of incumbents — and the positioning is deliberate.

Adjacent player What they bring Where Socradata leads
Big-4 advisoryProcess, governance, scale Process maturity assessments, transformation programs, and large delivery footprints. Strong on procurement readiness and audit comfort. AI depth and operational credibility. We embed in the WMS and the data lake on day one, not in month nine.
AI boutiquesModeling, research Strong modeling craft and research orientation. Often elegant solutions, sometimes elegant only on a notebook. Governance, integration discipline, and a documented productionization path. The model ships with its rollback procedure.
System integratorsERP rollout, integration Deep platform certifications, integration patterns, and project-management infrastructure built around the platform vendor's roadmap. AI-first orientation, not platform-feature-driven. We extend the platform with intelligence; we do not rebrand its release notes.
Strategy housesFrameworks, decks Frameworks, market views, and a brand that signs procurement papers easily. Strong narrative work. Production, not slides. The deliverable is a model in a workflow, with KPIs measured at the 90-day mark.
06 — KPIs that matter

KPIs before APIs.

Every Enterprise Transformation engagement opens with the measurement design and closes with audited outcomes. Baselines are documented before model selection. Targets are signed off by the operating committee. The gap between pilot success and production success is measured here, in writing.

Forecast quality
Forecast error

SKU- and channel-level forecast error reduction, audited at the 90-day mark against a frozen baseline. Measured with mean absolute percentage error and bias decomposition.

Working capital
Inventory write-off prevention

Quarterly write-off avoidance and working capital release, with full attribution back to model recommendations and operator overrides.

Operations
Labor productivity & OTIF

Picks per hour, mis-pick rate, on-time-in-full, and exception mean-time-to-resolution — measured at the operator console and the cross-DC roll-up.

Reliability
Model uptime & drift

Production model uptime, drift detection signal-to-noise, and the time between a drift alert and a documented retraining decision.

Governance
Audit-trail completeness

Percentage of triggered model actions with complete audit trail (input, prediction, confidence, driver, operator decision, outcome). The non-negotiable governance KPI.

Time-to-value
Time-to-production

Days from kickoff to first model live in workflow with a documented rollback procedure. Our 90-day production target is what separates this pillar from POC theater.

07 — What this pillar refuses

A short list of engagements we will not take.

Saying no is part of the discipline. Enterprise Transformation has a clean refusal list — and we publish it because every refusal protects the credibility of the engagements we do accept.

We do not engage on:

  • Pilot-only engagements with no graduation path to production.
  • Generic "AI strategy" decks unanchored in a system of record.
  • Marketing AI engagements masquerading as operational programs.
  • HR or talent AI deployments without a governed evaluation harness.
  • Blockchain projects that do not solve a real custody, provenance, or programmability problem.
  • Vendor-rebadging mandates dressed as neutral consulting advice.
Enterprise Transformation is the firm's revenue engine and the empirical foundation that makes Applied Innovation and Smart Cities credible. It must be the most disciplined of the three.
Direct line · CABA, GMT−3

Start with the diagnosis. Then decide whether to build.

Two weeks. One written diagnosis. No deck theater. If the answer at the end of the fortnight is that you do not need us, we will say so in writing.

Reach the principal
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