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5 questions to ask before investing in AI agents for collections

Artificial intelligence (AI) agents aren’t just a futuristic concept; they’re already reshaping how organisations approach collections and recoveries.

From digital assistants that guide customers through sensitive financial conversations, to decision-making tools that optimise repayment strategies, AI is rapidly becoming a must-have technology across the sector.

At Marketplace by Arum, we’re seeing growing interest from our clients, who are eager to understand how AI agents can support their operations. But with so many solutions flooding the market, how do you cut through the noise and find the right fit?

Here are five key questions every buyer should ask before making the leap. 

1. What do we want to achieve?

Before you start speaking to vendors, it’s crucial to step back and define exactly what you want the technology to deliver. Many organisations begin with a vague ambition like “reduce costs” or “improve efficiency,” but this isn’t enough to guide a meaningful procurement process:

• Are you primarily focused on reducing inbound call volumes to free up staff capacity?
• Do you want to enhance your ability to support vulnerable customers through sensitive financial wellbeing conversations?
• Or perhaps your goal is to increase digital collections rates and offer customers more flexibility to self-serve?

By setting clear objectives upfront, you’ll not only narrow down your shortlist but also create the right metrics for measuring success once the solution is deployed. 

Top tip

Start by mapping your current customer journeys and pain points. This will highlight where AI can make the biggest impact and prevent you from chasing shiny features that don’t solve your real problems.

2. What type of AI agent do we need?

Not all AI agents are the same, and understanding the differences is vital:

Conversational agents act as virtual voice or chat assistants that interact directly with customers. They can take payments, set up repayment plans, and even detect signs of vulnerability during conversations. Some are built on fixed decision-tree conversations with tightly controlled, pre-defined logic, while others leverage industry-trained LLMs, knowledge bases, and guardrails to handle more natural, flexible conversations.

Decisioning agents work in the background. They analyse data and behaviour patterns to recommend (or even automatically carry out) the best course of action for each individual customer.

Operational agents are designed to support your internal teams, automating tasks such as summarising calls, drafting notes, or highlighting risks in real time.

Each type solves a different problem. While some organisations may benefit from a combination, most will have one area where the need is most urgent. Choosing the wrong type of agent, or the wrong design approach (rigid decision tree vs adaptive LLM), could mean investing in technology that doesn’t address your real challenges. 

Top tip

Match the type of AI agent to your biggest operational bottleneck. If customer conversations are your pressure point, start with a conversational agent. If decision-making is inconsistent, explore decisioning agents first.

3. How will it fit into our existing systems?

Even the most advanced AI agent will struggle to deliver value if it can’t integrate smoothly with your existing technology stack.

A key question for any vendor is how well their solution connects with collections platforms such as Debt Manager, Tallyman, CACS X, or Control+, as well as your CRM and data sources. Without seamless integration, you risk creating siloed processes and incomplete customer views.

It’s also essential to check whether the solution can meet regulatory requirements and provide the right level of human oversight. Compliance with frameworks such as FCA, Ofcom, and GDPR should never be an afterthought.

Beyond compliance, think about the customer experience: does the AI sound natural, adapt to different tones, and respond empathetically when needed?

Scalability is another major consideration; can the agent handle multiple channels, languages, and high interaction volumes without service degradation? And of course, you’ll want a cost model that’s transparent and predictable as usage grows. 

Top tip

Ask vendors to demonstrate how their solution integrates in real-world conditions, preferably with systems similar to your own. Don’t just take their word for it; insist on a pilot or proof-of-concept before committing.

4. What pitfalls do we need to avoid?

AI projects often fail, not because of the technology itself, but because of how they’re implemented. A common mistake is rushing into deployment without properly testing customer acceptance. If customers find the AI clunky, unhelpful, or impersonal, adoption will stall quickly.

Another pitfall is leaving compliance and QA teams out of the planning process. Without their involvement, you risk running into regulatory roadblocks later.

It’s also tempting to select a vendor based on hype rather than results. The market is full of providers making bold claims, but not all can back them up with evidence.

Finally, many organisations underestimate the importance of continuous monitoring and retraining. AI isn’t a “set and forget” tool, it requires ongoing tuning to maintain accuracy and relevance.

Top tip

Build in a phased rollout with feedback loops. Start small, gather data, and refine the solution before scaling. This reduces risk and helps secure buy-in across the organisation.

5. Who can help us make the right choice?

With so many vendors promising innovation, it can be overwhelming to know who to trust. This is where independent benchmarking and market intelligence become invaluable. At Arum Global, we provide an impartial view of the vendor landscape through our Arum Approved process, which evaluates solutions across three critical dimensions: solution capability, commercial model, and track record of implementation.

This means buyers can go beyond vendor marketing and instead compare solutions side by side, access independent case studies, and request demos with confidence. By leaning on external expertise, you can reduce the time, cost, and uncertainty of procurement while ensuring you choose a partner that genuinely fits your needs. 

Top tip

Don’t go it alone. Use independent resources like Marketplace by Arum, or speak to the team at Arum Global directly, to validate vendor claims and de-risk your investment.

Final thoughts

AI agents offer huge potential to transform collections and recoveries, but success depends on careful selection, thoughtful deployment, and strong governance. By asking the right questions, and using independent resources, you can avoid costly mistakes and unlock real improvements in customer outcomes and operational efficiency.

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Author: Matt Riddall, Senior Director, Arum Global

Matt has been with Arum Global since 2008, starting as a consultant and advancing to Senior Director in 2024. With over 16 years of experience in collections and recoveries technology and operations, Matt brings a wealth of expertise in leveraging data, technology, decision-making, communications, and AI, to drive clients' success. As a leader at Arum Global, he is committed to delivering exceptional consulting and services to clients, ensuring excellence across all areas of the business.

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