Frictionless Enterprise AI for Support & Operations

Most enterprise AI projects don’t fail because the model is “bad.” They fail because the workflow is. If your agents have to leave the system they’re working in, wait for indexing jobs, hunt down the right version of a document, or paste screenshots into a separate tool, adoption stalls fast.

That’s why Squirro’s latest Long-Term Support (LTS) platform release caught my eye. It’s not flashy “AI theater.” It’s a set of practical changes—direct file uploads in chat, multimodal image reasoning, prompt libraries, and deeper long-document memory—that target the real bottleneck: getting AI used daily by the people doing the work.

This post is part of our AI in Supply Chain & Procurement series, but it’s also highly relevant to customer service and contact centers. In 2026 planning season (and with peak returns and post-holiday demand spikes arriving right now), supply chain exceptions, invoice disputes, and “where’s my order?” tickets collide. The same friction that blocks AI adoption in procurement also blocks AI in support.

Why “frictionless AI adoption” is the KPI that matters

Frictionless AI adoption means agents and operators can get reliable answers inside their existing workflow—without extra steps, extra tools, or extra waiting. That’s the difference between a pilot and a program.

In customer service, friction shows up as:

• Knowledge that exists, but can’t be found fast enough during a live interaction
• Long, messy documents (contracts, warranty terms, supplier agreements) that agents won’t open mid-call
• Screenshots, tables, and scanned PDFs that break standard text-based search
• “Prompt chaos,” where every team invents its own prompts and tone—then compliance has to clean it up later

In supply chain and procurement, it looks similar:

• Suppliers sending PDFs and images that don’t parse cleanly
• Expediting decisions based on half-understood tables and exceptions
• Disputes that hinge on a clause buried 60 pages deep in a contract
• Global teams working across languages and scanned documents

The common thread: the AI has to meet people where they work. Squirro’s release is basically a blueprint for that principle.

What Squirro’s release gets right (and why contact centers should care)

The release focuses on workflow-first features that reduce “context switching,” speed up analysis, and improve governance. Those are the exact ingredients that drive adoption in contact centers and operations teams.

Direct file upload in chat: stop waiting for indexing

Squirro’s update adds direct file uploads into chat so users can analyze a document on the spot rather than relying on traditional “index everything first” workflows.

That matters because indexing-first is slow and brittle. It’s also the wrong mental model for how work actually happens:

• A customer escalates a dispute, and the agent receives a PDF attachment.
• A supplier sends a revised ASN or invoice.
• A carrier sends a rate sheet or a claim form.

In those moments, the team doesn’t want a content engineering project. They want an answer.

Practical contact center use case: An agent drags a customer’s warranty PDF into chat and asks:

• “What’s covered for accidental damage?”
• “What exclusions apply to refurbished units?”
• “Draft a response that matches our tone and includes the relevant clause.”

Practical procurement use case: A buyer uploads a supplier contract and asks:

• “What’s the termination notice period?”
• “Is there an inflation escalator? If so, how is it calculated?”
• “Summarize the service credit schedule in a table.”

When direct upload works with permissions and auditing (as Squirro emphasizes), it becomes a safe bridge between ad-hoc work and enterprise controls.

Long-document memory: the difference between “answers” and “resolution”

Squirro introduced a “Chat with Item” agent designed to keep deep context—retaining the first 100 pages plus a summary—so multi-step Q&A doesn’t lose the thread.

This is a bigger deal than it sounds. Many AI chat experiences do fine with short FAQs, but fall apart in the real work: long policy documents, shipping contracts, MSAs, and SOPs.

In customer service, long-document context directly impacts:

• First contact resolution (FCR): agents can finish the task without escalating to a specialist
• Handle time: fewer “please hold” moments and fewer transfers
• Quality assurance: the answer stays grounded in the actual policy text

In supply chain exception management, long-document memory supports:

• Root-cause analysis on recurring issues (e.g., packaging nonconformance)
• Faster claims handling (carrier claims often involve long documentation)
• Faster dispute resolution on terms and SLAs

My take: If your AI can’t stick with a 40-minute investigation, it won’t earn trust. Deep context is how you move from “nice chatbot” to “reliable co-worker.”

Multimodal image reasoning: because operations live in screenshots

Squirro’s multimodal image reasoning aims to interpret charts, tables, and images inside documents, plus improved table rendering in chat.

This matters for contact centers because customers don’t submit clean text. They submit:

• Photos of damaged items
• Screenshots of error messages
• Scanned forms
• Bills with table layouts that break copy/paste

For supply chain and procurement, images are everywhere:

• Packing slips and labels
• Certificates of analysis
• Rate cards and lane tables
• Warehouse photos used in damage and compliance investigations

A practical example that hits both worlds: A customer complains about a late delivery. The support agent needs to parse a carrier exception screenshot and check the service-level clause in the logistics contract. Text-only systems slow this down. Multimodal reasoning plus deep document context speeds it up.

Prompt libraries and global instructions: the governance layer most teams skip

Squirro added an enterprise-grade prompt library and custom user prompt instructions so admins can standardize behavior: persona, tone, safety rules, and consistency.

Most companies get this wrong. They treat prompts like personal notes, then wonder why:

• Agents produce inconsistent answers
• Disclosures go missing
• Escalation language varies wildly by team
• Sensitive data gets handled inconsistently

A prompt library is not about “better prompts.” It’s about repeatable operations.

If you’re running a contact center, you want prompts like:

• “Refund policy response (with required disclaimer)”
• “Order delay apology + next best action + escalation trigger”
• “Billing dispute triage checklist + response template”

If you’re in procurement, you want prompts like:

• “Supplier performance review summary (QBR-ready)”
• “Contract clause comparison table (supplier A vs B)”
• “Risk memo draft (sanctions, country risk, single-source exposure)”

When these are centrally managed, you get speed and control.

Language and OCR improvements: underrated, but decisive

Squirro also highlighted enhanced PDF OCR support for Simplified Chinese, Traditional Chinese, and Arabic, plus better handling of complex word decomposition common in Germanic languages.

For global operations teams, OCR accuracy is not a “nice to have.” It affects:

• Supplier onboarding and compliance checks
• Invoice and packing list reconciliation
• Search relevance inside procurement and logistics repositories
• Customer support for global regions where scanned documents are the norm

If you operate across regions, better OCR is a direct productivity gain.

Where this fits in AI for supply chain & procurement (and why support teams should collaborate)

Supply chain and contact centers are converging around the same problem: exception resolution. Customers call because something deviated from plan—late shipment, wrong item, missing part, invoice mismatch. Procurement and operations teams handle the upstream cause, while support handles the downstream impact.

Here’s the opportunity I wish more leaders would take in 2026 planning:

Treat customer service, supply chain, and procurement knowledge as one connected system—because the customer experience is only as strong as the operational truth behind it.

Platforms that connect structured enterprise knowledge (systems of record) with ad-hoc workflows (the messy reality) are the ones that actually scale.

A simple operating model that works

Answer-first: Let agents and operators ask natural-language questions in chat.

Grounded retrieval: Responses must cite internal knowledge sources and respect permissions.

Workflow outputs: The AI should produce artifacts people use—tables, summaries, drafts, checklists.

Governance: Standard prompts, logging, and controls aren’t optional in regulated environments.

Squirro’s release reads like it’s designed for this model.

A practical rollout plan for “frictionless AI” in contact centers and operations

The fastest way to get real adoption is to start with 3 workflows that already hurt, then measure outcomes weekly. Don’t start with a giant knowledge program.

Step 1: Pick three high-volume, high-friction workflows

Good candidates:

1. Order status + exception explanation (late, damaged, partial)
2. Returns and warranty eligibility (policy-heavy, clause-heavy)
3. Invoice/billing disputes (tables, screenshots, scanned PDFs)

Step 2: Build a prompt library like you’re building SOPs

Your first prompt library should include:

• A “triage” prompt (collect required fields, decide escalation triggers)
• A “resolution” prompt (policy-grounded answer + next step)
• A “documentation” prompt (case notes + disposition codes + summary)

Keep prompts short, versioned, and owned by QA/Compliance—not just enthusiasts.

Step 3: Make multimodal a first-class requirement

If your customers submit images, scanned PDFs, or screenshots, treat multimodal reasoning as table stakes.

A quick internal test I’ve found useful:

• Take 20 real cases from the last month
• Count how many contain non-text evidence (images, tables, scans)
• If it’s over 30%, text-only AI will underperform in production

Step 4: Measure adoption like a product team

Track outcomes that map to both cost and experience:

• Containment rate (what % resolves without escalation)
• FCR (resolved on first interaction)
• AHT impact (but don’t chase AHT at the expense of quality)
• Reopen rate (a quality proxy)
• Time-to-decision for procurement exceptions (approve/deny/expedite)

If those don’t move, you don’t have an AI problem—you have a workflow and governance problem.

What to ask when evaluating platforms like Squirro

The right questions are operational, not technical. Here’s the shortlist I’d use:

1. Can agents upload a file and get a permission-aware answer immediately?
2. Does the system keep long-context memory for policy-heavy investigations?
3. How does multimodal reasoning handle tables, charts, and screenshots?
4. Can we standardize prompts and enforce global instructions by role/team?
5. Are responses auditable enough for regulated workflows and QA reviews?
6. How does it handle global languages and OCR for scanned documents?

If a vendor can’t answer these clearly, you’ll feel it later—in rework and low adoption.

The stance: adoption beats novelty

Squirro’s latest platform release is a reminder that enterprise AI progress often looks boring: fewer clicks, fewer handoffs, fewer “please export to…” steps. That’s exactly what makes it valuable.

For leaders in AI in supply chain & procurement, this is the direction to watch: tools that connect enterprise knowledge to the messy, daily workflows where exceptions happen. For contact center leaders, the message is even simpler: if AI can’t handle attachments, long documents, and screenshots with governance baked in, it won’t scale past a demo.

If you’re planning your 2026 roadmap right now, the question I’d put on the table is: Which customer and supply chain exceptions would you resolve faster if your teams could analyze any document—text or image—inside a governed chat workflow?