Fix Fragmented Data Before AI Can Improve Pharma SC

A lot of pharma leaders are buying AI while quietly accepting a basic constraint: their supply chain information is scattered across partners, PDFs, email threads, portals, spreadsheets, and “temporary” data extracts that become permanent. The result isn’t just slower decisions. It’s AI that can’t be trusted, because it’s reasoning over partial, stale, or contradictory data.

The LogiPharma 2025 AI & Tech Report numbers quoted in the source article make the point sharply: only 40% of organizations say they have balanced human–AI workflows, 32% say siloed or low-quality data is a core obstacle, and just 8% report strong human oversight. That mix is risky in any industry. In life sciences, it’s unacceptable, because the downstream cost of a bad decision is measured in patient impact, recall scope, and regulatory exposure.

This post is part of our “AI in Supply Chain & Procurement” series, where we get practical about what actually drives AI outcomes: forecasting accuracy, supplier performance, risk sensing, and compliance. Here’s the stance I’ll take: pharma doesn’t have an “AI problem.” It has an information integration problem. Fix that, and agentic AI becomes useful fast.

Fragmented information is the hidden risk in pharma supply chains

Fragmented information is the #1 reason “AI pilots” fail to become production workflows. Not because models are weak, but because the environment around them is built for humans to interpret, not for systems to act on.

Pharma supply chains are uniquely prone to fragmentation because they’re inherently multi-enterprise:

• Contract manufacturers, 3PLs, wholesalers, and specialty distributors each run different systems
• Serialization, cold chain, quality events, deviations, and recalls all generate separate records
• Many handoffs still rely on documents (COAs, temperature logs, change notices) rather than structured data

When information is fragmented, three things happen:

1. Visibility becomes “reporting,” not operational awareness. You can look backward and explain what happened. You can’t reliably act in the moment.
2. Decision latency becomes the norm. Teams spend days reconciling status, not hours solving issues.
3. AI outputs become brittle. The model might be right statistically, but it’s wrong operationally because it’s missing context.

Here’s the blunt reality: an AI agent with incomplete context behaves like an eager intern with half the file. It will still produce answers. That’s the danger.

What “information readiness” actually means (not a buzzword)

Information readiness isn’t a single data lake project. It’s a set of capabilities that make supply chain data usable by both people and machines, continuously.

An information-ready pharma supply chain has:

• Full digital capture of events (not just documents attached after the fact)
• Embedded metadata (lot, serial, site, lane, excursion thresholds, quality status, trading partner IDs)
• Shared definitions (what counts as “released,” “quarantined,” “excursion,” “on time,” “handoff complete”)
• Real-time access patterns designed for action, not monthly reporting

If your system requires a human to interpret what a field means, translate partner formats, or chase missing values, it’s not information-ready.

Why AI “hallucinations” often start with your architecture, not the model

Most organizations blame the model when the real failure is upstream. Agentic AI (systems that don’t just analyze, but also trigger workflows) raises the bar even higher.

The source article calls out common architectural anti-patterns that create hallucination-like behavior in enterprise settings:

• Data exists in silos
• Information isn’t fully digitalized
• Metadata is added after the fact
• Reasoning is layered on top rather than built into the operating system of workflows

That last point matters. If your “reasoning” is a prompt taped onto a chatbot, you’ll get fragile results. If reasoning is built into the process layer (rules, tolerances, escalation logic, approvals, audit trails), you get repeatability.

The myth: “AI will clean up our data later”

I’ve seen teams postpone integration work because they assume modern AI can compensate. It can’t—not in the ways that matter for regulated supply chains.

AI can help with:

• Document extraction (turning COAs or PDFs into structured fields)
• Supplier email classification and routing
• Anomaly detection on temperature or lane performance

But AI cannot reliably:

• Guess missing chain-of-custody events
• Infer the true “release status” if quality data is delayed or inconsistent
• Determine which of three partner systems is the source of truth during a disruption

If you want AI for demand forecasting, supplier management, and risk mitigation, you need fewer “interpretive” steps and more native, structured events.

Build a supply chain “information fabric” that agentic AI can use

A pharma information fabric is a shared operating layer that standardizes key supply chain events across enterprises. It doesn’t replace every ERP, WMS, or QMS. It makes them interoperable.

Think of it as a set of connected capabilities:

• A shared data model for core entities (product, lot, serial, shipment, location, partner, quality status)
• Standard event types (pack, ship, receive, store, temperature excursion, quarantine, release, return)
• Real-time orchestration and exception handling
• Auditability by design

This is where AI in supply chain & procurement becomes practical. Once the fabric exists, you can deploy agents that do real work—without “mystery context.”

A pragmatic blueprint: start with 5 event streams that drive 80% of value

If you try to unify everything, you’ll stall. Start with the event streams that repeatedly cause financial loss or patient risk:

1. Order and promise events (commit dates, partials, substitutions)
2. Shipment and handoff events (tender, pickup, departure, arrival, POD)
3. Inventory status events (available, allocated, quarantined, released)
4. Quality and deviation events (hold reasons, investigations, disposition dates)
5. Cold chain telemetry summaries (not every raw sensor ping—validated summaries tied to shipments)

Once these are standardized, AI can do high-confidence work like:

• Predicting late deliveries earlier (before the miss)
• Recommending safe substitutions or reallocations based on release status
• Triggering supplier corrective actions when lanes drift out of tolerance

Where procurement teams should focus first

Procurement often gets pulled into AI discussions late, as a “tool buyer.” That’s backwards.

In pharma, procurement is one of the biggest sources of fragmentation because supplier data lives in:

• Qualification systems
• Quality agreements and change controls
• Scorecards maintained manually
• Risk data scattered across third-party platforms

A near-term, high-ROI target is to create a single supplier identity and performance record that connects:

• Quality performance (deviations, right-first-time, audit findings)
• Delivery performance (OTIF by lane, variability, lead-time adherence)
• Commercial terms (incoterms, penalties, expedite rules)
• Sustainability/compliance requests (response SLAs, completeness)

Once that’s in place, AI can support supplier management with concrete outcomes: earlier risk signals, fewer firefights, and smarter negotiation positions based on operational truth.

Compliance gets easier when reasoning is built in, not bolted on

Agentic AI can strengthen compliance when every decision is transparent and auditable. The source article notes that regulatory uncertainty worries 58% of companies—but well-designed systems reduce uncertainty because they make process behavior observable.

The key is to avoid black-box decisions. In regulated environments, “the model said so” is not a control.

What “auditable agentic workflows” look like in practice

If you want AI-enabled orchestration without audit nightmares, design for these elements:

• Human-in-the-loop gates for high-impact decisions (release holds, recalls, substitutions)
• Decision logs that capture: inputs used, confidence, policy/rule references, approver identity
• Policy-as-code for tolerances (temperature excursion thresholds, lane risk rules, supplier status)
• Exception playbooks embedded into workflows (what to do when telemetry is missing; when to stop ship)

This is how you scale AI while keeping quality and regulatory teams on your side.

Use cases that get real once data stops being fragmented

Predictive use cases stay under-adopted because the data foundation is weak. The source notes scenario planning sits at 14% adoption, and demand forecasting at 25% adoption. Those numbers track with what I see: teams attempt forecasting, but can’t keep master data, substitutions, and true demand signals aligned.

Here are three practical “agentic” use cases that become feasible when the information fabric is in place.

1) Demand forecasting that incorporates constraints (not just history)

Better pharma demand forecasting isn’t only about time series. It’s about constraints: release timing, allocations, cold chain capacity, and supplier variability.

With connected data, AI can:

• Adjust forecasts based on known supply constraints (quarantine inventory, expected disposition dates)
• Separate true demand shifts from channel noise (backorders, partials, substitutions)
• Improve forecast accuracy in peak periods (year-end budgeting, seasonal therapies, public health volatility)

2) Recall execution measured in minutes, not days

A recall is an information test. If you need humans to stitch together lot movement, you’ll lose time.

With standardized lot/serial events across partners, an agent can:

• Identify affected inventory locations
• Generate partner-specific notification tasks
• Track acknowledgments and removal confirmations
• Produce an auditable timeline for regulators

Even if you keep humans in final control, AI reduces the manual tracing load massively.

3) Supplier risk sensing that’s operational, not theoretical

Most “supplier risk” programs rely too heavily on static scores. The better signal is operational drift:

• Lead times creeping up
• Temperature excursion rate rising by lane
• Documentation completeness declining
• Quality investigations taking longer to close

Once those signals are connected, AI can flag risk early and recommend actions (alternate sites, inventory buffers, expedited qualification).

A 90-day plan to move from AI pilots to supply chain outcomes

If you want AI ROI, treat integration as the product—not the plumbing. Here’s a realistic 90-day sequence I’d bet on.

Days 0–30: Pick one workflow and define “truth”

• Choose a workflow that hurts: cold chain exceptions, backorder triage, or supplier OTIF
• Define the canonical entities and event definitions
• Identify the minimum metadata required to make decisions safely

Days 31–60: Instrument the handoffs and close the gaps

• Automate event capture at handoffs (ship, receive, release, hold)
• Build validation rules (missing fields, conflicting statuses)
• Create exception queues for humans to resolve—fast

Days 61–90: Deploy an agent that acts, with guardrails

• Start with agentic actions that are reversible: notifications, ticket creation, escalation
• Add human approval for high-impact decisions
• Measure outcomes weekly (time-to-detect, time-to-resolve, % exceptions auto-triaged)

One metric to prioritize: decision latency. If it drops, everything else improves: service levels, cost, and compliance posture.

The better way to approach AI in pharma supply chains

Most companies get this wrong: they treat AI as a layer you add after the fact. Pharma needs the opposite order. First, make information complete, real-time, and interoperable. Then put AI agents on top of it.

If you’re building toward AI in supply chain & procurement—demand forecasting, supplier management, and risk mitigation—your next investment shouldn’t be another isolated model. It should be the shared information fabric that models depend on.

The question to leave on your team’s whiteboard is simple: If an AI agent made a decision right now, could you prove exactly what it knew—and what it didn’t? If the answer is no, you already know where the work starts.