Avoiding Spam Likely: AI Call Strategies

Introduction

If it feels like your SDRs are working twice as hard for half the conversations, you’re not imagining things.

The phone channel is still incredibly powerful, 76% of leaders in one survey said phone sales are effective, but there’s a new gatekeeper between your reps and your prospects: AI. Carriers and spam apps now use machine-learning models to score every call, in real time, and decide whether it rings through… or shows up as that silent killer: 'Spam Likely.'

Hiya’s latest State of the Call report says 80% of unidentified calls now go unanswered, and research cited by Numeracle shows three in four Americans will never answer a call from an unknown number. On top of that, approximately 25% of business numbers are at risk of being mislabeled as spam or scam. numeracle.com If you’re running a high-volume B2B outbound motion and not thinking about this, you’re running blind.

In this guide, we’ll break down how AI-driven call filtering actually works, what 'Spam Likely' is really costing your sales org, and, more importantly, how to use AI call strategies to avoid those flags without neutering your activity. We’ll talk about caller-ID reputation, branded calling, cadences, compliance, and how SalesHive applies all of this in the wild for clients.

Grab a coffee. This is the playbook your outbound team needs for the AI-filtered phone era.

1. The New Reality: AI Is Deciding Whether Your Calls Go Through

How spam analytics engines think

Forget the old-school picture of someone manually curating a blacklist. Today, your outbound calls are judged by:

• Carrier analytics (AT&T, Verizon, T-Mobile, etc.)
• Third‑party spam apps and analytics providers (Hiya, TNS, First Orion, RealCall, and others)

These systems ingest massive volumes of data, Hiya alone analyzed over 262 billion calls for its 2025 report, and use machine learning to predict whether each new call is wanted or unwanted.

They look at signals like:

• Call volume per number, Hundreds of dials per day from a single line is a red flag.
• Call duration, Lots of sub‑10‑second calls resemble robocalls or bad lead lists.
• Answer rates, If almost nobody picks up, the system assumes people don’t want those calls.
• User feedback, Blocks, 'report spam' actions, or complaints all lower your score.
• History of the number, Recycled numbers might come pre-loaded with a bad reputation.

You can be TCPA-compliant and still get crushed if your behavior looks like a scammer’s. That’s why AI call strategy is less about what you say on the phone and more about how you show up in the metadata.

STIR/SHAKEN: Necessary but not sufficient

You’ve probably heard STIR/SHAKEN tossed around in meetings. It’s the industry framework carriers use to authenticate that a call actually originates from the number shown on the display, making it harder to spoof caller ID.

Here’s the catch: STIR/SHAKEN doesn’t guarantee you won’t be labeled spam. Kixie’s 2025 guide calls this out directly: a call can get the highest 'A' attestation (fully authenticated) and still be flagged as spam based purely on your dialing behavior and engagement patterns. kixie.com

Think of STIR/SHAKEN as table stakes. It proves the call is really from you. It doesn’t prove you’re worth answering.

The AI robocall crackdown, and what it means for B2B

In February 2024, the FCC issued a declaratory ruling clarifying that AI-generated voices used in robocalls are considered 'artificial' under the Telephone Consumer Protection Act. Translation: unsolicited robocalls that use AI-generated voices are now explicitly illegal, with potential fines exceeding $23,000 per call.

That ruling was aimed squarely at scammers and political disinformation (like the infamous AI-Biden robocalls), but it changes the risk calculus for everyone:

• You cannot rely on AI voicebots to brute-force your way to more connects.
• You can safely use AI behind the scenes, for targeting, pacing, research, and coaching, as long as the actual voice interacting with the prospect is human and consent rules are followed.

So the winning strategy is obvious:

> Use AI to steer and support the call, not to be the call.

2. What 'Spam Likely' Really Costs Your Sales Org

Your outbound engine is bleeding silently

PhoneBurner’s research shows that about 33% of all outbound calls are flagged as spam or scam each month. That doesn’t mean every flagged call is blocked, but it does mean a big chunk of what your SDRs dial never had a chance.

Layer that on top of Hiya’s finding that 80% of unidentified calls go unanswered, and Numeracle’s data that three in four Americans never answer unknown callers, and you can see the problem: a 'Spam Likely' tag basically zeroes out your connect rate.

Now connect the dots to revenue. Kixie cites an industry report where:

• 81% of businesses say they’ve lost revenue due to incorrect spam flagging
• 15% lost more than $100,000
• 53% reported more than 10 positions affected by layoffs or hiring freezes tied to call-flagging issues kixie.com

This isn’t just a 'telecom issue'. It’s a P&L line item.

A quick pipeline math example

Let’s say you have a 10‑person SDR team:

• Each SDR dials 80 numbers per day from a few shared lines
• That’s 800 dials per day, ~16,000 dials per month
• You historically booked 1 meeting for every 100 dials (1%)

So you expect ~160 meetings per month.

Now assume a third of your calls are quietly flagged as spam and see dramatically worse answer rates. Conservatively, say those calls book meetings at one‑third the normal rate.

• 10,666 'normal' dials @ 1% = ~107 meetings
• 5,333 'spam-labeled' dials @ 0.33% = ~18 meetings
• Total: 125 meetings instead of 160, roughly a 22% hit to bookings

At a $1,500 average meeting value in closed/won revenue, you just lost $52,500 of pipeline per month, or over $600K a year, because the algorithm doesn’t like your call patterns.

And that’s a tame example.

The human side: burnout, churn, and brand damage

The numbers are bad enough, but 'Spam Likely' also kills the human side of your engine:

• SDR morale tanks when they dial for hours and nobody picks up.
• Top reps churn to environments where they can actually hit quota.
• Brand equity erodes when your name and number are associated with spam in people’s phones.

Meanwhile, 82% of customers say they crave more human interaction with businesses, according to research cited by Numeracle. numeracle.com People want real conversations, they just don’t want to risk talking to scammers. Your job is to prove, at a glance, that you’re one of the good guys.

3. AI Call Strategies to Avoid Spam Flags (Without Killing Activity)

Let’s get practical. Here’s how to use AI and human judgment to keep your calls out of the spam bucket.

3.1 Smart volume and cadence management

The first rule of AI-era calling: don’t behave like a robot.

Kixie’s guidance, echoed by many reputation providers, is to keep each number under roughly 100-150 outbound calls per day, spread relatively evenly, and avoid sudden bursts of activity. kixie.com Your AI dialer should enforce this for you.

Tactical moves:

• Cap calls per number per day. Set hard limits in your dialer so a single line never becomes a firehose.
• Spread activity across a number pool. Use multiple registered numbers per team or region, each with healthy but not insane volume.
• Avoid 'burst dialing'. Let AI stagger calls across time zones and hours so your pattern looks organic.
• Let it ring longer. Very short calls with immediate hang‑ups scream robocall; DNC scrubbing and better targeting should reduce instant rejects, and reps should let the phone ring at least 5-6 times when possible. kixie.com

AI excels at this kind of pattern design. Give it guardrails and let it pace your team instead of letting reps 'floor it' because the dashboard says they’re behind on dials.

3.2 AI-powered targeting and list hygiene

Bad data doesn’t just waste time, it poisons your reputation. Calling disconnected numbers, people who’ve never opted in anywhere, or contacts who haven’t engaged with you in years tanks answer rates and drives spam reports.

Use AI on the front end to:

• Score accounts and contacts based on engagement (email opens, site visits, ad clicks) and firmographic fit.
• Identify risky segments, like older, untouched lists that haven’t produced meetings in months.
• Automatically suppress DNC and opt‑out contacts before they ever hit a call queue.
• Predict 'likelihood to pick up' based on historical connect times by persona, industry, and region.

Cleaner targeting improves every signal spam engines care about: more connects, longer calls, fewer angry 'stop calling me' hang‑ups. That’s exactly what you want AI optimizing.

3.3 Reputation monitoring and number management

You can’t fix what you can’t see. In 2025, caller-ID reputation is a core operational metric, not an afterthought.

Best practices:

• Use a monitoring tool to check your numbers across major carriers and apps for spam or nuisance labels.
• Define thresholds where a number is temporarily removed from high-volume campaigns if its reputation dips.
• Centralize number management in RevOps, not scattered across individual teams.
• Document a playbook: when a number is flagged, what gets paused, who investigates, and what remediation steps you take.

Numeracle reports that with proactive number reputation management, 99.8% of numbers they manage stay label‑free across more than 500,000 customer numbers. numeracle.com That kind of protection is what you’re aiming for, whether you use a vendor or build internal processes.

3.4 Branded calling: making your calls recognizable and trusted

If 'unidentified' is poison, branding is the antidote.

Branded caller ID (sometimes called branded calling, branded display, or rich call data) lets you show:

• Your company name
• Sometimes a logo
• Often a short call reason (e.g., 'Demo Follow‑Up' or 'Security Briefing')

Hiya’s data shows that unknown calls get ignored roughly 80% of the time, but their branded calling clients have seen average answer-rate increases of around 80%. One S&P 500 company reported:

• 81% increase in answer rates
• 33% increase in contact rate
• 50% increase in call duration

Other providers show similar results, Caller ID Branded Display cites up to a 56% answer-rate increase, and Regal.io reports branded calls driving average 30% answer rates, about 3x industry averages, with some customers seeing 60% lifts.

Consumer surveys also back this up: TNS found 78% of Americans are more willing to answer when they see the logo and name of a brand they recognize on caller ID.

For B2B outbound, branded calling is huge when you:

1. Warm the account first with email and LinkedIn.
2. Use a clear call reason that ties to that interaction ('Security Webinar Registration', 'Pricing Follow‑Up').
3. Align the number and branding with your primary corporate presence (no weird shell company names).

You’re basically telling both the AI filters and the human, 'This is a legitimate, expected call.'

3.5 Human-first conversations, AI-assisted reps

Remember that 82% of customers want more human interaction, and 76% still see phone sales as effective when done right. numeracle.com Nothing in the AI call-filtering arms race changes that.

What does change is how reps prepare and execute:

Use AI to:

• Surface account research and trigger events in the dialer before each call.
• Generate personalized openers based on recent activity ('I saw you downloaded our SOC 2 checklist yesterday…').
• Suggest objection responses in real time for common pushbacks.
• Auto‑summarize calls and push structured notes into the CRM.

All of that makes your reps sound sharper and more relevant, which leads to longer, more productive conversations, another positive signal to spam engines and a direct booster to meeting rates.

4. Designing an AI-Powered, Spam-Safe Outbound Engine

Let’s zoom out from tactics and look at what a modern, AI-smart cold calling system actually looks like.

4.1 The stack

A typical B2B outbound stack that plays nicely with AI spam filters includes:

• CRM (Salesforce, HubSpot, etc.), system of record.
• Sales engagement platform (Outreach, Salesloft, Apollo, etc.), sequences and multichannel cadences.
• AI-powered dialer/call platform (Kixie, PhoneBurner, RingDNA, etc.), pacing, connect logic, analytics.
• Caller ID reputation/branding tool (Numeracle, Hiya, TNS, Caller ID Reputation, etc.), number monitoring and branded caller ID. numeracle.com
• Data and list-building sources, firmographic and contact data with enrichment.
• Compliance tooling / DNC scrubbing, to keep you out of regulatory trouble.

The magic happens when these aren’t just a pile of tools, but a coordinated system.

4.2 A practical AI-driven workflow

Here’s what a spam-smart workflow can look like:

1. Data in, scored and cleaned

Accounts and contacts are imported into your CRM and scored by AI based on intent, fit, and recent engagement. DNC and opt‑out records are applied automatically.

1. Cadence design

RevOps and sales leadership design multichannel sequences, email, LinkedIn, and calls, with AI helping test subject lines, call timing, and channel order.

1. Call scheduling logic

The AI dialer schedules call steps based on:

• Contact’s local time zone
• Historical best connect times by persona
• Per-number daily and hourly volume caps
• Recent spam-flag events (e.g., backing off numbers with degraded reputation)

1. Real-time call decisioning

As reps click 'start sequence' or go into a power-dial session, the system:

• Prioritizes high-scoring, recently engaged contacts.
• Routes calls through the healthiest numbers.
• Falls back to alternative numbers if primary lines show elevated spam risk.

1. Feedback loop and optimization

Call outcomes, talk time, and answer rates feed back into the AI. Over time, the system learns which:

• Time windows work best
• Industries are more sensitive to unknown numbers
• Cadence steps produce the most meetings

1. Reputation monitoring and remediation

A separate process (often weekly) reviews number health reports. Any lines showing negative labels move into a remediation workflow, contact analytic providers, adjust call patterns, or temporarily reassign that number for low-risk or inbound use.

4.3 Governance: who owns what

To make this work in the real world, you need clear ownership:

• RevOps owns dialing policies, number inventory, reputation monitoring, and vendor coordination.
• Sales leadership owns goals, scripts, and rep behavior (e.g., honoring 'do not call' requests, not playing games with spoofing).
• Marketing owns brand consistency in caller ID names, branded call display, and alignment with campaigns.
• Legal/compliance sets boundaries for consent, TCPA, and AI usage.

If caller reputation is 'nobody’s problem', it will quietly blow up everyone’s quota.

5. Playbooks and Scripts That Work Under AI Scrutiny

Let’s talk about what your reps actually do and say on the phone in an AI-filtered world.

5.1 Pre-call warming: earning recognition before you dial

Given that 87% of people won’t answer if they don’t recognize the number, you’re fighting an uphill battle if your first touch is a cold call from a mystery line.

So build cadences where calls come after some of this:

• A short, relevant cold email.
• A LinkedIn view or connection request.
• A resource send (e.g., playbook, case study) tied to their role.
• A webinar registration or content download.

With AI personalization (like SalesHive’s eMod engine on the email side), you can warm up hundreds or thousands of prospects with messages that actually resonate, then have the dialer prioritize calls right after opens or clicks.

5.2 Trust-building openers that acknowledge reality

Prospects aren’t dumb. They’re bombarded with spam calls and fraud stories. Scripts that pretend otherwise feel tone-deaf.

Give your reps permission to meet that head-on. For example:

> 'Hey Alex, this is Jordan with Acme Security. I know you probably get a ton of random vendor calls, I’m the person behind that email I sent yesterday about your SOC 2 renewal. Did you happen to see it?'

Or:

> 'Hi Priya, it’s Nina from Vertex Analytics. I realize my number might show up as unfamiliar, we work with a few other data teams in fintech and I wanted to quickly run an idea by you. Got 30 seconds?'

The key moves:

• Quickly anchor to something they recognize (email, webinar, mutual customer).
• Be transparent about who you are and why you’re calling.
• Ask for a tiny time commitment instead of pretending it’s purely 'a quick question'.

5.3 Using AI insights in real time (without sounding robotic)

Real-time AI suggestions are great, as long as reps don’t read them like a teleprompter. Train them to use AI as a nudge, not a script.

Practical approach:

• Before the dial: AI surfaces 2-3 bullet points, recent news, tech stack hints, relevant case study.
• During the call: small pop-up suggestions for likely objections ('We’re under a hiring freeze', 'We do this in-house').
• After the call: AI summary and suggested next steps go into the CRM automatically.

Reps stay present in the conversation instead of head-down in note-taking, and prospects get a more tailored, human experience, exactly what both buyers and spam engines like to see.

5.4 Handling objections about cold calls directly

The more hostile the phone environment gets, the more your team has to be comfortable addressing 'Why are you calling me?' without getting flustered.

Some simple frameworks:

• Respect + context + choice

'Totally get that, and I don’t want to be another noise call. The only reason I reached out is because you’re hiring several AE roles and we help teams like yours double connect rates without burning reps out. If now’s not the time, I can send a quick summary for later, what’s best for you?'

• Tie to value they care about

'I get it, unknown numbers are usually bad news. The only reason I still pick up personally is when it’s something time-sensitive. The reason I called instead of just emailing is that this is usually a 3-4 minute conversation about reducing no‑shows in your pipeline. Want the 30‑second version and then you can tell me if it’s relevant?'

If the number already shows as branded and the prospect has seen your emails, this style of honesty goes a long way.

6. How This Applies to Your Sales Team

Let’s bring this down from theory to your actual org chart.

For VPs of Sales and CROs

You should be asking:

• What percentage of our numbers are currently labeled spam or nuisance?
• How many meetings per month are we missing due to low answer rates on specific lines?
• Do we have a formal owner for caller-ID reputation and AI dialing policy?

If nobody can answer those questions with data, that’s your first red flag.

For SDR and BDR leaders

Operationally, your priorities are:

1. Audit & triage (weeks 1-2)
• Run a full inventory of all outbound numbers in use.
• Use a monitoring tool to check for spam labels.
• Immediately cap per-number call volumes and adjust cadences.

1. Stabilize & brand (weeks 3-6)
• Register your numbers with Free Caller Registry and ensure CNAM records are accurate.
• Pilot branded calling on 1-2 key segments (e.g., existing inbound leads, late-stage opps).
• Start reporting connect rate and talk time by number, not just per rep.

1. Optimize & scale (weeks 7-12)
• Turn on AI-driven call-time optimization and lead scoring.
• Refine scripts using AI call analysis (win/loss language, objections that actually matter).
• Retire or reassign persistently low-reputation numbers instead of burning them out.

For RevOps and Sales Operations

You’re the quarterback of this whole thing.

Your checklist:

• Document dialing policies, per-number and per-rep caps, ring time, and time-of-day rules.
• Own the stack integration, CRM ↔ engagement platform ↔ dialer ↔ reputation tools.
• Build dashboards that track:
• Answer rate by number
• Spam-label incidence over time
• Meetings booked per 1,000 dials per line
• Run quarterly reviews with Legal/Compliance on TCPA, AI usage, and consent practices.

For Marketing

Marketing isn’t off the hook here. You control the story that shows up on caller ID and in the prospect’s head before the call.

Key moves:

• Keep brand names consistent across email domains, landing pages, and caller ID.
• Coordinate campaigns with call pushes, when you run a webinar or launch content, weave call steps into follow-up cadences.
• Help write call reasons for branded caller ID that actually mean something ('Q4 Budget Planning: RevOps Audit', not 'Business Call').

When marketing, sales, and RevOps align on caller identity and timing, your odds of escaping the spam bucket go way up.

7. How SalesHive Puts This Into Practice

At SalesHive, we don’t have the luxury of treating 'Spam Likely' as an edge case, if we did, our entire model would fall apart.

We run outsourced SDR programs for 1,500+ clients, across hundreds of campaigns at any given time. That means we’re constantly:

• Managing large pools of numbers across U.S.-based and Philippines-based SDR teams.
• Monitoring caller reputation and adjusting cadences before issues blow up.
• Syncing cold calling with AI-personalized email outreach using tools like our eMod engine.

In practice, that looks like:

• Smart number strategy from day one. We provision and register numbers intentionally, by region, industry, and use case, rather than randomly handing out lines to reps.
• AI-informed sequences. We use engagement data to trigger calls after prospects interact with our emails, content, or ads, so our calls never truly come 'out of the blue.'
• Ongoing health checks. Our ops team monitors for spam labels and answer rate drops, then reroutes volume, adjusts messaging, or kicks off remediation with partners when needed.
• Compliance baked in. TCPA rules, time‑zone logic, and opt‑out handling are built into how we operate, not bolted on later.

Because we see what works across so many programs, we can quickly apply best practices from one client to another, whether that’s a new branded caller ID rollout, a smarter AI-driven call cadence, or just a better script for skeptical CFOs who hate the phone.

If you’d rather skip the 12‑month DIY learning curve and plug into a machine that already knows how to avoid 'Spam Likely' while still booking meetings, that’s literally what we’re here for.

Conclusion + Next Steps

The phone channel isn’t dead, it’s just heavily guarded.

AI on the carrier side is blocking billions of bad calls and, unfortunately, sweeping a lot of legitimate sales calls into the same bucket. In an environment where a quarter of business numbers are at risk of mislabeling, 80% of unidentified calls go unanswered, and 81% of businesses report revenue loss from spam flags, you can’t afford to treat caller reputation as an afterthought. numeracle.com

The playbook is clear:

1. Accept that AI is the new gatekeeper. Plan for it instead of fighting it.
2. Use AI where it’s safe and powerful, targeting, pacing, monitoring, and coaching, while keeping humans on the phone.
3. Stabilize and brand your number pool, register everywhere that matters, and monitor reputation like a core KPI.
4. Align calls with multichannel engagement, so you’re rarely a 'mystery call.'
5. Train reps to build trust in the first 10 seconds, acknowledging that cold calls are under suspicion by default.

Whether you build this engine in‑house or lean on a specialist like SalesHive, the mindset shift is the same: your outbound motion isn’t just competing with other vendors, it’s competing with the spam filters themselves.

Get on the right side of those AI models, and 'Spam Likely' becomes just another obstacle you’ve learned to outrun, while your competitors keep wondering why their dashboards are green and their calendars are empty.