Agentic AI in Production: One Pattern for Risk, One for Growth

Case A — ForgeLine Tool Group: Agentic AI for risk and compliance

The company: the North American mechanics tools business of a global tool manufacturer.

The challenge. An earlier digital programme had moved warranty claims online and straight into the ERP, cutting call-centre volume — but every claim still needed a person to review it across several systems: ERP orders, uploaded photos and separate form fields. As volumes grew, that manual review produced multi-minute handling times, inconsistent decisions, and limited ability to spot repeat or fraudulent submissions at scale.

The solution (built on AgilePoint AI & Agentic Orchestration):

• AI image analysis — uploaded claim photos are assessed against thousands of historical cases; the AI classifies each tool by brand, type and defect and judges eligibility for automatic approval.
• Straight-through ERP integration with fraud and duplicate detection agents — IP, address and customer validations run first, then dedicated agents screen for fraud and duplicates; approved and denied decisions flow directly into the ERP for immediate fulfilment or closure, with human-in-the-loop review reserved for exceptions and high-risk claims.
• A unified review dashboard — order details, images and claim reasons consolidated into one interface, eliminating system-switching for customer service representatives.
• Human oversight where it matters — reviewers concentrate exclusively on exceptions, and their decisions feed back as training signal, improving consistency across all high-risk claims.

Reported results (vendor-reported):

• 76% overall reduction in time and labour cost attributable to claim processing
• Manual processing time down 80% — from 5 minutes to 1 minute per claim
• CSR throughput up 400% (five times more claims per reviewer per day)
• Abuser identification up more than 12x versus pre-AI baselines
• Duplicate-claim detection up more than 20x versus manual detection
• 71% of in-scope incoming claims now governed by automated or AI-assisted decisioning
• Upfront cost more than 5x lower, and ongoing cost roughly 2x lower, than a leading AI platform alternative
• Product replacement and shipping savings up 6x

Case B — NorthGrid Industrial: Agentic AI for growth and revenue

The company: a global manufacturer of industrial connectivity and automation products.

The challenge. Inbound sales email volume kept climbing, but triage and routing were manual: inconsistent email structure made every lead harder to classify, routing scaled poorly and followed availability rather than fit, follow-ups were missed, and leadership had no clear view of pipeline health — decisions ran on instinct rather than data.

The solution (IT and Sales Operations working with AgilePoint):

• AI email classification — every inbound email is classified into one of five categories at 89–90% confidence, with the reasoning shown for each decision and human-in-the-loop validation built in.
• Smart routing — leads are auto-assigned by region, category and industry, under a 3-day response SLA and with equitable distribution across the team.
• Automated follow-up orchestration — a 3-week cadence with weekly automated follow-ups, automatic closure of unresponsive leads, and inbox-triggered re-engagement to reduce lead leakage.
• Live pipeline visibility — real-time dashboards cover every lead, including lost and unquoted activity, supported by structured customer interaction forms that feed the workflow directly with a full audit trail.

Reported results — first 60 days (vendor-reported):

• 86% of emails auto-classified with high confidence, needing little or no review
• Lead triage effort reduced 2.5x to 3x
• Inbound volume handled up approximately 2x (2.05x)
• Processing effort down 60% or more, even as volume doubled
• Follow-up consistency up 2x to 3x
• Projected Year-1 ROI of 86%, running ahead of schedule, with the implementation cost recovered within Year 1

Two maturity journeys, one architecture

ForgeLine and NorthGrid reflect two different starting points for enterprise AI — one defensive, one offensive — running on the same governed architecture.

The shared foundation. Both implementations run on the same governed architecture:

AI classification → business rules → orchestration → human oversight → analytics → continuous optimization

ForgeLine proves the architecture works for risk. NorthGrid proves the same architecture works for growth.

Mapping the cases to an enterprise AI maturity model

• Level 1 — AI Assistants: chatbots, copilots, content generation.
• Level 2 — AI-Augmented Workflows: classification, recommendations, human approval.
• Level 3 — Agentic Process Automation: dynamic routing, autonomous follow-ups, workflow orchestration, human-in-the-loop governance. NorthGrid sits largely here.
• Level 4 — Closed-Loop Autonomous Operations: AI monitors, detects anomalies, takes action and learns from outcomes. ForgeLine approaches this level.

Which level is more relevant for most enterprises today? For most organizations, the NorthGrid pattern — Level 3 — is the more achievable next step:

• The data already exists.
• The workflows (sales, service, HR, procurement, operations) already exist.
• ROI can be demonstrated in weeks instead of years.
• Business leaders directly see the productivity and revenue outcomes.

Candidate processes include lead qualification, customer onboarding, medical inquiry management, learning assignment management, training follow-up workflows and HR recruiting triage. Most AI deployments stall at chatbots, content generation and internal productivity pilots; NorthGrid's significance is that it moved beyond experimentation into production-scale process transformation — re-engineering a customer-facing process around AI decisioning without repeated redesign cycles.

Why this matters for TAPAS Advisory's work

The NorthGrid pattern transfers directly: the same classify → route → orchestrate → oversee architecture that triages sales leads can be applied to the workflows TAPAS Advisory designs and supports for commercial and capability functions:

• HCP inquiry triage
• Medical information requests
• Training nomination workflows
• Learning completion follow-up
• Coaching observation routing
• Content approval processes
• Field-force support requests

This is also where Agentic AI meets change readiness. An orchestration layer only performs when the people around it — roles, managers, workflows, measures — are ready to work differently. That readiness is what TAPAS Advisory diagnoses and builds through CHANGEReadyX™, its transformation readiness and adoption system: the technology pattern above, paired with the operating-model and capability work that makes it stick.

The takeaway from both cases is not the specific use case. It is the repeatable enterprise pattern — governed Agentic AI, with human oversight designed in, moving from isolated pilots to production systems that produce measurable business outcomes on the first implementation.

FAQ

What is the difference between Agentic Process Automation and closed-loop autonomous operations?

Agentic Process Automation (Level 3) uses AI agents to classify, route and follow up within a workflow, with humans validating decisions in the loop. Closed-loop autonomous operations (Level 4) go further: the AI monitors the process, detects anomalies, takes action and learns from outcomes, with human oversight concentrated on exceptions and high-risk cases.

Which business functions benefit first from Agentic AI?

The earliest production results come from high-volume, rules-bearing workflows that already exist — sales lead triage, warranty and claims processing, customer onboarding, service requests, HR recruiting triage and learning administration — because the data, the process steps and the success measures are already in place.

What results have production Agentic AI implementations reported?

In the two published cases summarised here (vendor-reported, not independently verified), a warranty-claims implementation reported a 76% reduction in processing time and labour and a 20x improvement in duplicate detection, while a sales lead-triage implementation reported 86% of inbound email auto-classified, triage effort down 2.5x to 3x, and a projected Year-1 ROI of 86%.