Predict Customer Needs Before They Call—With AI

A contact center can do everything “right” and still feel broken. You staff up for peak hours, tune your IVR, update your knowledge base… and customers still wait, repeat themselves, and escalate.

The real problem usually isn’t your agents. It’s timing. Most contact centers are designed to react after a customer is already frustrated enough to reach out.

That’s why the idea behind Operative Intelligence—an Australia- and U.S.-based startup that says it helps contact centers figure out what customers really need faster—is worth paying attention to. The promise isn’t just “a better chatbot.” It’s predictive customer service: spotting intent early, improving automated inquiries, and reducing wait times by routing the right issue to the right resolution path.

This post is part of our AI in Customer Service & Contact Centers series, and I’m going to take a strong stance: the winners in 2026 won’t be the companies with the fanciest bot—they’ll be the ones that prevent contacts in the first place, and handle the remaining ones with ruthless clarity.

Predictive customer service is the fastest path to lower wait times

Answer first: If you want shorter queues, you need fewer “avoidable” contacts—and for the contacts that remain, you need faster identification of what the customer is actually trying to do.

Most queues are inflated by two buckets:

1. Preventable contacts (status checks, known outages, password resets, “where is my order?”)
2. Misrouted or misclassified contacts (the customer chooses the wrong menu option, the chatbot guesses wrong, or the issue is more complex than the front door can detect)

AI that focuses on intent detection and next-best action can reduce both. When a system reliably infers what a customer needs—based on signals you already have—it can:

• Put the right answer in front of them before they reach for the phone
• Steer them into the correct self-service flow (instead of a dead-end FAQ)
• Route them to the best agent group with the right context attached

“Lower wait times” is often treated as a staffing problem. In practice, it’s usually an information and decisioning problem.

Why this is especially relevant in December

Friday, December 19 is when a lot of operations are in “survive the season” mode. Holiday volume is high, staffing is tight, and customers are less patient because they’re trying to fix something quickly—shipping changes, billing issues, travel rebooking, device replacements.

This is exactly when proactive AI in contact centers pays off:

• It reduces inbound spikes by pushing the right update at the right moment
• It prevents repeat contacts (“I already asked this yesterday”) by carrying state across channels
• It shrinks handle time by telling agents what matters before they say hello

What Operative Intelligence signals about where contact center AI is going

Answer first: The interesting part isn’t that a startup raised money—it’s what they’re betting on: AI that identifies needs faster and improves automation quality, not just automation volume.

From the RSS summary: Operative Intelligence aims to help contact centers “figure out what customers want more quickly,” improving automated inquiries and cutting down on wait times. That’s a clear pointer toward a category I’ve seen growing across the market:

• Operative analytics (understanding what’s happening in support operations)
• Intent intelligence (understanding why customers are contacting)
• Decisioning (choosing the best resolution path, not just generating a response)

The contact center industry already has plenty of bots. The hard part has been getting them to:

• Ask fewer, smarter questions
• Avoid looping customers through generic scripts
• Recognize when to escalate—and to whom

A system that improves speed-to-understanding can affect multiple metrics at once:

• Lower average handle time (AHT) because the agent starts with context
• Higher containment because automation stops guessing
• Lower transfers because routing is based on predicted need
• Higher CSAT because customers don’t repeat themselves

A practical definition you can use internally

Here’s a definition I’ve found teams can rally around:

Predictive customer service is the practice of using behavioral, account, and journey signals to anticipate a customer’s intent and deliver the right resolution path before or at the moment of contact.

That’s broader than “chatbots,” and it’s a better north star.

How AI predicts what customers really need (without creeping them out)

Answer first: Good predictive systems use operational signals and journey context—not spooky surveillance—to infer intent and recommend the next action.

Most organizations already have the raw ingredients:

• Recent orders, shipments, delivery scans
• Login failures, password reset triggers
• Subscription changes, failed payments, renewals
• Outage/incident status and affected cohorts
• App events (crashes, feature usage drop-offs)
• Prior cases and contact reasons

When you combine these with channel behavior (chat starts, IVR selections, “help” page paths), you can predict a likely intent with enough confidence to take action.

What “take action” looks like in a contact center

Predictive AI doesn’t have to mean sending aggressive popups. Often the best wins are quiet:

• Dynamic IVR: If the system sees a recent failed payment, it can promote “billing issue” to the top of the menu.
• Smarter chatbot prompts: Instead of “How can I help?”, it starts with “Are you contacting us about your delivery scheduled for tomorrow?”
• Agent assist: The desktop opens with probable intent, relevant policy, and the last three actions the customer attempted.
• Proactive notifications: When a known incident impacts a customer segment, you message them first with status and expected resolution time.

The goal is simple: less interrogation, more resolution.

Guardrails that keep it customer-friendly

Customers don’t hate automation; they hate feeling trapped or misunderstood. The systems that work tend to follow a few rules:

• Be specific, not presumptive: “Looks like your last payment failed—want to update it?” beats “We know you’re having billing problems.”
• Always offer an exit: “Talk to an agent” shouldn’t be hidden behind three flows.
• Don’t over-collect: Use what you already have, and only ask for what you truly need.

A step-by-step playbook to use predictive AI in your contact center

Answer first: Start with one high-volume contact reason, build an intent model and decision flow around it, then expand—measuring containment, transfers, and time-to-resolution.

If you’re trying to generate leads (or you’re the person who will later have to justify budget), you need a plan that maps to real metrics.

Step 1: Identify your top “avoidable” drivers

Pick one or two that meet all three criteria:

• High volume
• Clear resolution paths
• Good existing signals

Examples that are usually strong candidates:

• Order status / delivery changes
• Password resets / account access
• Billing failures / refunds
• Appointment confirmations / rescheduling

Step 2: Map the intent-to-resolution decision tree

Don’t start with prompts. Start with decisions.

A simple structure:

1. Confirm identity (only if needed)
2. Detect intent (from signals + short questions)
3. Choose resolution path (self-service, guided workflow, agent)
4. Capture outcome (resolved, escalated, abandoned)

If your team can’t agree on the decision tree, AI won’t save you. It’ll just automate your confusion.

Step 3: Improve your automated inquiries with “minimum questions” design

Most automated flows ask too much. Use progressive disclosure:

• Ask the smallest question that changes what you do next
• Pre-fill details where possible (order number, account type, plan)
• Summarize what you know and ask for confirmation

A good chatbot question feels like:

“I can help faster if I confirm one thing: is this about order #10482 placed on Dec 12?”

Step 4: Add routing that’s based on predicted need, not org charts

Routing should reflect how work gets solved, not how teams are structured.

A common anti-pattern: “Billing” is one queue, but half those cases are actually cancellations, promo disputes, or fraud checks.

A better pattern: route by resolution capability:

• “Refund eligible—policy A” group
• “Refund exception—supervisor required” group
• “Suspected fraud—special handling” group

Step 5: Measure what matters (and expect trade-offs)

Use a small set of operational metrics and track weekly:

• Containment rate (but only for intents where containment is appropriate)
• Transfer rate (a direct signal of misrouting)
• First contact resolution (FCR)
• Average speed of answer (ASA) and queue time
• Customer effort score (or a simple post-contact “Was this easy?”)

Be honest about trade-offs. For example, pushing containment too hard can reduce CSAT if customers feel blocked.

“People also ask” questions your team should answer now

Answer first: If you can’t answer these, your AI rollout will stall in procurement, compliance, or operations.

Does predictive AI replace agents?

No—and that’s the wrong framing. Predictive AI removes repetitive contacts and gives agents context so they can handle complex issues faster. It shifts labor to higher-value work.

How does predictive AI reduce wait times?

It reduces demand (fewer inbound contacts) and increases throughput (shorter handle time, fewer transfers). Wait time drops when volume-to-capacity drops.

What data do we need to get started?

Start with what you already log: contact reasons, outcomes, order/billing events, incident data, and basic journey events. If your data is fragmented, the first win is often stitching identity and case history across channels.

Where do chatbot and voice AI fit?

They’re the “front door,” but predictive systems make the front door smarter. Instead of a generic bot, you get context-aware chatbots, dynamic IVR, and agent assist that all share the same intent intelligence.

The contact center teams that win will stop chasing “more automation”

Automating more conversations isn’t the goal. Resolving more needs with less effort is the goal—and predictive customer service is how you get there.

Operative Intelligence’s positioning is a reminder that the next wave of contact center AI isn’t about sounding human. It’s about being useful faster: predicting needs, improving automated inquiries, and cutting queue time without burning out your agents.

If you’re planning your 2026 roadmap, here’s a practical next step: pick one high-volume driver (shipping status, billing failure, access issues) and design an intent-to-resolution flow that uses your existing signals. Then measure transfers and time-to-resolution like your budget depends on it—because it does.

Where could your support operation prevent the next 10,000 contacts: before the call, or in the first 30 seconds?