Frictionless Enterprise AI for Faster Support Decisions

Most enterprise AI projects don’t fail because the models are “bad.” They fail because the workflow is. If your agents have to open three systems, submit a ticket for access, wait for indexing, and then still copy/paste answers into the CRM, adoption stalls—quietly at first, then all at once.

That’s why platform updates like Squirro’s latest Long-Term Support release matter for customer service and contact centers—especially for supply chain and procurement organizations where the “answer” is usually buried in contracts, PDFs, emails, shipping documents, and exception reports. When customers ask “Where is my order?” or “Why did pricing change?” the best response isn’t a generic apology. It’s a precise, defensible explanation backed by the right document.

Squirro’s release highlights three themes that, frankly, separate AI pilots from AI that gets used every day: ad-hoc document reasoning, multimodal understanding, and governed prompt standards. Here’s how those ideas translate into practical contact center outcomes—and how to apply them in your own operation.

The real barrier to AI adoption in contact centers: friction

Friction is anything that forces people to change how they work before they get value. In customer service, friction compounds fast because agents already operate under handle-time pressure, QA scrutiny, and constant context switching.

In supply chain and procurement-related support, that friction usually shows up in predictable places:

• The “document gap”: the truth lives in a PDF contract addendum, a scanned bill of lading, a supplier scorecard, or an email thread—not in a neatly structured knowledge base.
• The “indexing delay”: traditional enterprise search and knowledge systems require ingestion and indexing, so the moment someone needs an answer “right now,” the system can’t help.
• The “context loss” problem: agents ask one question, get an answer, then ask a follow-up—and the tool forgets what they’re doing.
• The “governance headache”: security, permissions, and audit requirements slow rollouts to a crawl in regulated or risk-sensitive environments.

Squirro’s release focuses on removing these blockers with capabilities that look simple on the surface (file upload, better chat memory, prompt governance), but are directly tied to daily agent behavior.

Why this matters more in supply chain & procurement support

In the AI in Supply Chain & Procurement series, we keep coming back to one reality: customer experience increasingly depends on operational truth. When inventory is constrained, carriers miss scans, or suppliers ship late, customers don’t want “we’re looking into it.” They want a timeline, accountability, and options.

That’s why AI for customer service in operational environments needs to do more than answer FAQs. It must:

• read messy documents,
• reconcile exceptions,
• explain decisions,
• and stay auditable.

What “frictionless” AI looks like: three capabilities that change agent workflows

Frictionless enterprise AI isn’t about flashy demos—it’s about shortening the path from question to verified answer. Squirro’s update emphasizes three workflow-level improvements that map cleanly to contact center needs.

1) Direct file uploads: ad-hoc document analysis without waiting

Direct file upload turns AI into an “instant analyst” for one-off issues. Instead of forcing teams to pre-load everything into a repository and build a taxonomy up front, the agent can drag a document into chat and ask targeted questions.

In customer service tied to procurement and supply chain, this fits common scenarios:

• A customer disputes a fee: the agent uploads the contract schedule and asks for the clause defining the surcharge.
• A supplier claims compliance: the team uploads the certificate and asks whether the date range and standards match requirements.
• A shipment exception escalates: the supervisor uploads the carrier exception report and asks for the pattern across lanes.

This is the difference between “AI for knowledge management” and AI for operational decision support.

Operational note: If you want adoption, design for the first week. Direct upload features let you start with the messy real world immediately—then decide what’s worth formalizing into structured knowledge.

2) Deep context memory: follow-up questions without losing the thread

Context retention is what makes AI feel usable in a real interaction. Supply chain and procurement inquiries are rarely single-shot questions. They’re threaded investigations:

• “What does the contract say?”
• “Does that clause apply to this region?”
• “What changed in the latest renewal?”
• “Draft a customer-facing explanation and an internal note for audit.”

Squirro describes a “Chat with Item” approach that retains deep memory context for long-form documents (including a substantial page window plus a summary). In practice, this supports a more natural agent workflow: investigate → verify → communicate, without re-explaining the scenario every turn.

My take: context memory is the quiet feature that reduces average handle time and improves QA scores—because the AI can stay consistent across the interaction.

3) Multimodal image reasoning: charts, tables, scans, and screenshots

Contact centers don’t just deal with text. They deal with screenshots from customers, scanned customs forms, images of damaged goods, and PDFs where the “table” is actually an image.

Multimodal reasoning matters because it helps AI interpret:

• embedded tables (pricing matrices, penalty schedules)
• charts (inventory trends, supplier performance metrics)
• scans with stamps and signatures
• screenshots from portals (order status pages, error messages)

For supply chain support, this is big. Many organizations still rely on scanned documentation for trade, compliance, and logistics—especially across regions and partners.

Governance isn’t optional: prompt libraries and behavioral rules

Most companies underestimate this: AI governance is a product feature, not a policy document. If governance lives only in a PDF, it won’t survive contact center reality.

Squirro’s release calls out two governance-aligned capabilities that map directly to contact center scale:

Enterprise prompt libraries: standardizing quality across teams

A prompt library is how you make “good agents” repeatable. Your best reps already know how to ask the right questions, extract the key clause, and communicate clearly. A prompt library captures that craft.

In customer service and contact centers, a useful prompt library typically includes:

• Order exception summary prompts (structured output: cause, timeline, next actions)
• Contract clause extraction prompts (quote + interpretation + risk flag)
• Supplier dispute response prompts (tone rules + escalation triggers)
• Refund/chargeback documentation prompts (audit-ready notes)

If you’re trying to scale AI across multiple queues (returns, shipping, billing, procurement inquiries), prompt libraries reduce variance and speed up onboarding.

Custom global instructions: “one voice,” safer outputs

Global instructions are how you prevent chaos at scale. They can enforce:

• a consistent tone (professional, plain language)
• safety protocols (don’t fabricate; request missing fields)
• escalation rules (when to involve procurement, legal, or compliance)
• formatting standards (always output a customer message + internal log note)

This isn’t about making agents robotic. It’s about making your AI reliable enough that supervisors trust it.

A useful internal standard: “If the AI can’t cite the document section or source object it used, it should say what it needs next.”

Global operations need global document intelligence (OCR + language handling)

Supply chain and procurement are global by default. Customer service inherits that complexity immediately—different regions, different documentation standards, different languages, and a lot of scanned paperwork.

Squirro’s update highlights OCR support improvements (including support for Simplified Chinese, Traditional Chinese, and Arabic) and better handling of complex compound words (common in Germanic languages). In contact center terms, that translates to:

• fewer “we can’t read that PDF” dead ends
• higher retrieval precision in multilingual knowledge bases
• better accuracy when customers submit scans or photos

A practical KPI to watch here: reduction in “manual translation / manual extraction” time per case.

From chat to agentic workflows: where this is headed in 2026

A lot of teams are planning for agentic AI—systems that can take actions, not just answer questions. But there’s a hard truth: you don’t get safe automation until you have trustworthy reasoning and audit trails.

Squirro positions its release as foundational for agentic workflows: deep context, multimodal understanding, and governed prompts. In a contact center supporting supply chain operations, that points to a near-term evolution:

1. Assist: AI summarizes documents and drafts responses.
2. Recommend: AI proposes next-best actions (refund, reship, escalation).
3. Coordinate: AI opens internal tickets, populates case fields, routes to procurement.
4. Execute (with approval): AI triggers workflows after human sign-off.

If you’re aiming for step 3 or 4, step 1 has to be frictionless—or you’ll never get adoption strong enough to justify automation.

A concrete example: “Where’s my shipment?” done right

Here’s what a frictionless AI-assisted flow looks like for a high-stakes shipment delay:

• Agent uploads the carrier exception report PDF (or selects the relevant item).
• AI reads the embedded tables and extracts the last scan, exception code, and likely cause.
• AI drafts:
  • a customer message (clear timeline + options)
  • an internal note (source fields + recommended escalation)
• Agent approves and sends.

The customer gets clarity. The business gets consistency. QA gets documentation.

How to apply this in your contact center: a 30-day rollout plan

You don’t need a massive transformation program to benefit from these ideas. Here’s a pragmatic way to implement frictionless enterprise AI patterns in a month.

Week 1: Pick two “document-heavy” queues

Choose queues where agents regularly open PDFs or scan attachments, such as:

• billing disputes tied to contract terms
• shipping exceptions
• supplier compliance documentation
• returns with damage evidence

Define success as: time-to-answer, not containment.

Week 2: Create 10 prompts that reflect real work

Build a starter prompt library based on top case types. Make outputs structured:

• customer-facing message (short, clear)
• internal case note (audit-ready)
• missing info checklist

Week 3: Add governance that agents can feel

Implement global instructions that force better behavior:

• cite source object (document name/page/section if available)
• never guess dates or fees
• escalate when confidence is low or policy thresholds are hit

Week 4: Instrument and iterate

Track:

• adoption rate (percent of cases where AI used)
• average handle time for targeted queues
• QA rework rates
• escalation accuracy (fewer wrong transfers)

If adoption is low, assume workflow friction—not “agent resistance.” Fix the steps.

What to do next

Frictionless enterprise AI is quickly becoming the dividing line between “we tested AI” and “AI actually supports the business.” For contact centers embedded in supply chain and procurement realities, the bar is even higher: the AI must read real documents, handle scans and tables, and operate under governance that auditors and security teams can live with.

If you’re planning your 2026 roadmap, I’d push for one decision: treat AI adoption as a workflow design project, not a model selection project. The platforms catching up to that reality—through direct file analysis, multimodal reasoning, and prompt governance—are the ones you can scale.

What’s the highest-friction moment in your support process right now: document lookup, policy interpretation, or writing the final customer message?