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In August 2025, I was doing everything manually. Finding journalist contacts meant hours of clicking through publication websites, copying names into spreadsheets, and Googling for email addresses one by one. Building a single media list for a campaign took an entire day.

Six months later, I run the same processes in minutes. Not because I hired a team. Because I started using AI as a coding partner in my terminal.

I want to be transparent about this because the PR industry is full of vague claims about “AI-powered” tools that do not actually explain what the AI does. Here is exactly what changed in my workflow and what it means for the quality of work our clients get.

What I Was Doing Manually (August 2025)

Here is a real snapshot of my weekly workflow six months ago:

• Journalist research: Visiting publication websites one by one, clicking through author pages, copy-pasting names and beats into a Google Sheet. About 8 hours per week.
• Email finding: Searching for journalist emails on LinkedIn, Twitter bios, personal websites. Trying different email patterns and verifying them manually. About 4 hours per week.
• Data analysis: Downloading CSV files, opening them in Excel, writing formulas to find insights, manually checking for errors. About 6 hours per week.
• Contact deduplication: Comparing spreadsheets side by side, looking for duplicate names across different sources. Excruciatingly tedious. About 3 hours per week.
• Campaign tracking: Manually updating a spreadsheet every time a placement appeared, checking if links were included, noting domain ratings. About 2 hours per week.

Total: roughly 23 hours per week on operational tasks that did not directly involve strategy, ideation, or journalist relationships. More than half my working week.

What Changed: AI as a Coding Partner

I started using Claude, an AI assistant by Anthropic, directly in my terminal. Not as a chatbot for writing emails. As a coding partner that helps me build and run automation scripts.

Here is the key distinction: I am not using AI to write pitches or generate content. I am using it to build tools that make the human work faster and better.

Example 1: Journalist Database Building

Before (August): Manually visiting 60 publication websites, finding author pages, copying names. Took about 3 full days.

Now: I describe what I need in plain language — “scrape the sitemaps of these 60 publications and extract all author bylines” — and the AI writes a script that does it in one run. 715 journalist records extracted in under an hour, including names, publications, and article counts.

The script handles edge cases I would have missed manually: different sitemap formats, duplicate names across publications, Unicode characters in international names.

Example 2: Email Pattern Engineering

Before: Figuring out each publication’s email format by trial and error. Testing john.smith@ then j.smith@ then johnsmith@ for each new domain.

Now: We built a pattern map covering 265 publication domains. When I add a new journalist, the system generates the most likely email address automatically based on known patterns at their publication. The AI helped me write the pattern-matching logic and the validation pipeline. What used to take 20 minutes per journalist now takes seconds.

Example 3: Competitor Backlink Analysis

Before: Downloading Ahrefs exports, opening in Excel, manually scanning for journalist names in referring pages, copying them to my contact list.

Now: Export from Ahrefs, run a script. The script extracts journalist names from article bylines, cross-references them against our existing database, scores new contacts based on publication authority and relevance, and outputs a prioritized list. Processing 35 competitor domains went from a week-long project to an afternoon.

Example 4: Dashboard Building

Before: Tracking everything in scattered Google Sheets. No central view. No way to see which journalists had been pitched, which had responded, which placements had been earned across different campaigns.

Now: We have a connected ecosystem of dashboards: a Pitch CRM for managing journalist relationships, a campaign tracker, a content pipeline, and a financial overview. All built with AI assistance. They read from the same data sources and sync automatically. When a placement is logged in the CRM, it appears in the client results dashboard and the financial tracker simultaneously.

Building these dashboards from scratch without an AI coding partner would have required hiring a developer. Instead, I described what I needed and we built them together, iterating in real-time.

What AI Does NOT Do in Our Workflow

This is important and I want to be explicit:

• AI does not write our pitches. Every pitch is written by a human. The personalization, the tone, the judgment about which angle to lead with — that is human work. AI cannot understand the nuance of a journalist’s recent coverage or the timing of a news cycle.
• AI does not choose our story angles. Ideation requires understanding what is trending, what has emotional resonance, what will surprise people. That is human intuition informed by experience.
• AI does not manage journalist relationships. When a journalist replies, a human reads it, understands the context, and responds appropriately. Relationships are built on trust, not automation.
• AI does not make editorial decisions. Which data points to highlight, which publications to target, whether a campaign is ready to pitch — these are judgment calls that require industry knowledge.

AI handles the plumbing. Humans handle the relationships. That division is not going to change.

The Real Impact: Time and Quality

Here is the honest before and after:

But the time saving is only half the story. The quality also improved:

• Fewer errors. Scripts do not accidentally skip a row in a spreadsheet or mistype an email address.
• Better coverage. Automated scraping finds contacts a human would miss because they got tired on page 40 of a sitemap.
• Faster reaction times. When a story is trending, we can build a targeted media list in 30 minutes instead of half a day. That speed difference can be the difference between landing a placement and missing the news cycle.

Why I Am Sharing This

Two reasons.

First, transparency builds trust. If you are considering hiring a PR agency, you deserve to know how they work. Not marketing-speak about “proprietary technology” but actual specifics about what the process looks like.

Second, this is the future of small agencies. A one-person operation using AI-assisted tooling can now match the operational capacity of a 5-person team. That does not make the team obsolete. It makes the solo practitioner viable in a way that was not possible two years ago.

I am one person. I built a journalist database of 5,900+ contacts, a suite of connected dashboards, an automated enrichment pipeline, and a competitive intelligence system. Not because I am a developer (I am not). Because I have a clear vision of what I need and an AI partner that can translate that into working code.

The PR work itself — the ideation, the storytelling, the journalist relationships — is still entirely human. But the infrastructure around it? That is where AI changed everything.

What This Means for Our Clients

Lower overhead, better data, faster turnaround. We do not have a team of 15 people with a $20,000/month retainer to match. We have lean operations, sharp tooling, and competitive pricing because our infrastructure costs are a fraction of a traditional agency’s.

That is why our PR Power Pack costs $3,000 instead of $10,000. Not because we cut corners on the work. Because we eliminated the operational bloat that inflates most agency pricing.

See the difference lean operations make. Same quality placements. Fraction of the cost. Let’s talk.

Frequently Asked Questions

Can AI replace PR professionals?

No. AI is terrible at the parts of PR that matter most: building genuine journalist relationships, reading cultural nuance, and crafting angles that resonate with specific audiences. What AI excels at is eliminating the manual grunt work: data cleaning, contact enrichment, pattern analysis, and report generation.

What AI tools are useful for PR?

Claude Code (terminal-based AI) for data processing and automation scripts, ChatGPT for brainstorming angles, and various free tools for email verification and data enrichment. The biggest gains come not from AI writing your pitches but from AI handling your data infrastructure.

How much time does AI save in PR workflows?

In our experience, AI reduced data processing and research tasks from 40+ hours per week to under 5 hours. That time now goes into relationship building, pitch crafting, and strategic thinking, the work that actually produces results.

About the Author

Salvador Jovells

Founder of Presslei. 12+ years in ecommerce SEO across international markets. After a decade of link buying for Hockerty and Sumissura, I reverse-engineered 5,272 earned media placements and founded a reactive PR agency that builds authority through data-driven stories journalists actually want to publish. Based in Zurich.

Related Reading

• The Exact Tools I Use to Run a Solo PR Agency
• How I Built a 27,000+ Journalist Database
• PR Tools Compared