What To Expect From Apollo Next with Tyler Phillips

Most outbound tools rely on static filters like job titles, industries, and company size. But sales teams know that’s not enough. The real challenge is finding the right prospects with real buying intent. That’s where Apollo’s AI platform changes the game.

Tyler Phillips, Principal PM of AI at Apollo.io, explains how their latest AI power-ups transform outbound sales:

Automated research at scale: Apollo’s AI scans and refines prospect data beyond basic filters, helping sales teams surface high-intent leads faster.
AI-first prospecting experience: Instead of manually sifting through thousands of records, reps can use AI to fine-tune targeting in seconds.
Built-in personalization: With Apollo’s sequencing tools, teams can craft hyper-personalized outreach based on AI-powered insights.

Apollo’s all-in-one approach makes advanced targeting accessible, especially for smaller sales teams. Unlike Clay, which offers endless customization but requires technical skills, Apollo prioritizes ease of use and automation.

The result? Smarter prospecting, less manual work, and faster conversions.

Finding Alpha

Outbound sales has a signal-to-noise problem. Traditional filters aren’t enough. The real edge comes from “finding alpha”: uncovering hidden signals that indicate a prospect has a real pain point your product can solve.

Apollo’s AI-powered prospecting helps sales teams do exactly that. Instead of relying on outdated targeting methods, Apollo surfaces micro-signals, subtle indicators that a company or prospect is actively experiencing a problem.

Why This Matters

The spray-and-pray model is dead. Buyers are flooded with AI-generated outreach, making hyper-relevant messaging the only way to stand out.
Alpha beats intent signals. While many tools track intent, true outbound success comes from spotting unique, underutilized signals before they become mainstream.
AI-powered research finds the right prospects faster. Apollo’s AI models analyze vast datasets to surface high-intent leads without the guesswork.

AI That Works for Sales Teams

Unlike technical-heavy platforms like Clay, Apollo prioritizes ease of use:

Smart AI model selection: Instead of forcing reps to choose between OpenAI, Perplexity, or Anthropic models, Apollo auto-selects the best one based on the use case.
Intuitive prompt generation: Reps can describe their needs, and Apollo’s AI structures the best prompt. No advanced configuration is required.
Continuous model upgrades: Apollo is constantly refining its models to improve accuracy and usability.

For sales teams, the message is clear: prospecting success is about finding the right leads.

How Sales Teams Are Using Apollo to Close More Deals

Apollo’s AI power-ups give sales teams an edge in outbound by turning scattered data into targeted, actionable insights. Here’s how companies use it to move beyond basic prospecting and execute smarter sales plays.

Smartling’s Translation Gap Play

Smartling, an AI translation company, used Apollo to identify companies with missing website translations, a problem they could fix. Before AI, this type of research was time-consuming and inconsistent. Now, Apollo automates the entire process:

Extract website translation data: AI scans company websites to detect available language options.
Find translation gaps: It checks if foreign-language pages contain untranslated English text.
Trigger personalized outreach: A sales rep can now send a highly specific email: “Hey [First Name], your Spanish webpage still has English text. We can help translate your content at a fraction of the usual cost.”

This approach doesn’t rely on guesswork. It finds a clear, provable problem. Making it easy for prospects to say yes.

Using AI to Spot Market Gaps

Another high-impact workflow? Competitive analysis at scale. Sales teams use Apollo to:

Track which tools a prospect’s competitors are using.
Identify new strategies their competitors are testing.
Spot gaps where a prospect is falling behind.

This allows sales reps to frame their pitch around urgency: “[Competitor] is already doing [X]. Have you considered how that might impact your business?”

By presenting precise, relevant data, sales teams shift the conversation from generic outreach to strategic insight.

Automating List Cleaning and Deliverability

Many sales teams still manually export, clean, and re-upload prospect lists. A slow and error-prone process. Apollo is addressing this by:

Integrating ZeroBounce for built-in email verification, eliminating the need for separate list cleaning tools.
Improving exports with automatic file naming and search tracking so reps don’t lose track of lists.
Investing in deliverability improvements to help emails land in inboxes, not spam folders.

These updates cut down on busy work and let teams spend more time selling.

Scaling Enrichments and Optimizing Outbound

As sales teams push Apollo’s AI to handle larger lists and more complex workflows, usability becomes critical. The ability to enrich, filter, and act on data at scale without manual workarounds can mean the difference between a smooth workflow and wasted hours.

Smarter Email Matching for Better Deliverability

One challenge power users face is matching email domains for better deliverability. If you send emails through Apollo, keeping Gmail-to-Gmail and Outlook-to-Outlook can increase inbox placement rates and improve the sender’s reputation.

This feature request is in motion, but Apollo is investing in email verification and sending logic to help teams get better results with less effort.

Breaking the 10,000 Enrichment Limit (Without the Clickaround Dance)

Another common frustration? Processing large lists efficiently.

Apollo currently batches enrichments at 10,000 records at a time, which means teams working with 30,000+ records must manually filter, re-run, and check progress. Adding friction to the workflow.

The goal?

Eliminate the “clickaround dance” by allowing automatic batching. That way, users can queue up larger enrichments and let Apollo process them in the background. No manual filtering needed.

Best Practices for Large-Scale Enrichments

For teams running complex workflows, like the Smartling translation gap play or competitive intelligence enrichment, here’s how to avoid wasting time (or credits):

Start small. Test enrichments on 25–50 records before running them at scale to ensure accuracy.
Use saved lists. Instead of re-running searches manually, save filtered lists so Apollo remembers your selections for batch processing.
Stack enrichments efficiently. If running multiple enrichments, trigger the final step first. Apollo will automatically process the dependent steps in sequence.

Making Power-Ups and Views Reusable

For teams who frequently use the same AI-driven workflows, Apollo allows users to:

Save custom views that include pre-set enrichments so the entire team can use them without rebuilding fields from scratch.
Duplicate views for different use cases (e.g., one for competitor tracking, another for industry-specific signals).
Keep power-ups private or share them across the team, depending on workflow needs.

What’s Next for Apollo?

Beyond workflow efficiency, Apollo is expanding AI automation, deliverability improvements, and better enrichment tracking.

If you’re an Apollo user and want to see what’s next:

Follow Tyler Phillips on LinkedIn to stay updated.
Check out Apollo’s AI & automation page for upcoming releases.
Post your own AI-driven sales workflows. Apollo’s team loves seeing creative use cases.

Need a More Predictable Outbound Motion? We Can Help.

Building a scalable outbound engine requires more than great tools. YOU need the right strategy, process, and execution. That’s where WE comes in.

We help sales teams:

✅ Build repeatable outbound systems that drive consistent pipeline.

✅ Refine targeting & messaging to improve conversion rates.

✅ Scale prospecting efforts without burning out your team.

Want to level up your outbound sales? Let’s talk!

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