Emagia Staff
27 February, 2026
According to Emagia VP of Customer Success and longtime industry practitioner Phyllis Saavedra, the conversation on AI’s potential impact on Order-to-Cash has shifted away from “if” there will be an impact toward something more realistic and practical:
Where does AI deliver measurable impact in collections — and how do we deploy it responsibly?
As B2B receivables environments grow more complex, traditional collections models struggle to scale. Higher invoice volumes, fragmented customer behavior, increasing dispute frequency, and globalized operations have rendered aging-based, reactive approaches insufficient.
As such, Saavedra noted in a recent Emagia MasterClass series session that a strategic and critical shift from questions like “Who is overdue?”
to “Who is likely to pay late, why, and what action will accelerate cash without damaging the relationship?”
This is where predictive and Agentic AI moves from theory to tangible value in the area of collections.
The Evolution of Collections: From Manual Effort to Intelligent Orchestration
Traditional collections rely on episodic human intervention. Collectors review aging reports, send reminders, escalate disputes, and manually prioritize worklists.
AI-driven collections fundamentally change this model, putting a focus on the following:
- Predictive analytics assess delinquency risk and prioritize accounts dynamically.
- Personalized dunning strategies adapt tone, timing, and channel.
- Agentic AI coordinates workflows across collections, credit, pricing, and customer service.
- Disputes are treated as structured workflows — not ad hoc exceptions.
The result is not simply automation. It is continuous orchestration.
That said: studies from researchers including Gartner, Callisto Grand, and Hackett Group found heading into 2026 that most finance leaders still struggle to grasp where realistic starting points are, what can be applied – and or applied quickly – and with realistic expectations/impact.
With that said, below are three practical, real-world use cases that illustrate how AI-powered collections can work within AR operations taken from Saavedra’s recent online MasterClass presentation.
Use Case 1: Intelligent Dispute Detection and Workflow Orchestration
Scenario:
A $120,000 invoice is short paid by $15,000.
In a traditional environment, the short pay might trigger manual investigation, email exchanges, and internal routing delays.
With AI-driven collections:
- The system detects the short pay immediately.
- Based on historical patterns, it identifies the issue as a pricing dispute.
- The case is routed directly to the pricing team — not collections.
- SLA tracking begins automatically.
- Internal reminders are triggered.
- The customer is proactively updated.
- Once resolved, collections efforts resume for the remaining balance.
Strategic impact:
- Faster resolution cycles
- Reduced DSO impact
- Improved customer communication
- Less manual cross-department coordination
Disputes are no longer disruptions. They are well-managed workflows with less manual time spent on the intervention.
Use Case 2: Risk-Based, Next-Best-Action Collections
Scenario: Next-best-action by customer segment
Not all customers require the same treatment. AI enables dynamic segmentation and personalized intervention.
- Low-risk customer:
One reminder sent three days before due date. - High-risk customer:
Earlier reminder at Day-20, request for payment confirmation, scheduled follow-up. - Dispute-prone customer:
AI checks invoice backup documentation before sending any reminder and proactively attaches it.
Strategic impact:
- Reduced friction with dependable payers
- Earlier intervention for high-risk accounts
- Fewer avoidable disputes
- Smarter allocation of collector time
In this framework, collectors move from blanket outreach to precision engagement.
Use Case 3: Intelligent Communication Optimization
Scenario:
A logistics provider has five open invoices with a retailer
Knowing that collections strategies often suffer from over-communication and misaligned messaging, AI-powered solutions can do the following:
- Bundle invoices into a single personalized message.
- Prepare communication that references the retailer’s preferred payment schedule.
- Adjust tone based on prior interactions.
Strategic impact:
- Improved response rates
- Reduced customer fatigue
- Higher collection efficiency
- Better relationship management
Here, AI aligns communication strategy with behavioral intelligence.
What These Use Cases Have in Common
Across all three examples, key principles emerge:
- AI operates continuously — not episodically.
- Human oversight remains in place.
- Decisions are data-driven and explainable.
- Customer experience is protected alongside cash acceleration.
This is not about replacing collections managers.
As highlighted in the MasterClass, AI functions as an “exoskeleton” — enabling teams to manage significantly larger portfolios with dramatically greater precision.
Final Perspective
AI-driven collections is not a future concept — The building blocks are already operational. Saavedra stressed that the organizations leading the way in the O2C space will not be those who automate the most tasks. Rather, it will be those who orchestrate actions intelligently — across systems, teams, and customer touchpoints.
When predictive intelligence informs prioritization, when disputes are resolved faster, and when communication is optimized by design, collections activities are transformed and become a competitive advantage rather than repeated reactionary behavior.
