How to sell to Product leads at AI & machine learning companies
Product leads in AI & machine learning companies are primarily measured on the successful deployment of scalable AI models. They respond to outreach that demonstrates a clear understanding of the complexities involved in model training and deployment, particularly around data quality and integration challenges. Highlighting solutions that enhance model accuracy or reduce time to market will capture their attention.
Written and maintained by the SoloRiff team, who build outbound software and run outbound with it. The figures below are our own: we measured 561 ai & machine learning company domains over public DNS on 2026-09-03. Last updated 2026-09-03.
What actually hurts
- data quality issues
- model deployment delays
- integration complexities
- resource allocation conflicts
The playbook
- 01
Identify recent model launches
Use LinkedIn to filter for companies that have recently announced new AI model launches or product updates. This indicates a potential need for support in scaling or improving those models. Look for posts or press releases dated within the last 60 days.
- 02
Research data integration challenges
Investigate common data integration tools used in AI projects by visiting forums or tech blogs that focus on AI and machine learning. Understanding the specific pain points related to data ingestion and preprocessing will help you tailor your outreach effectively.
- 03
Craft a message around deployment efficiency
Draft your outreach message to emphasize how your solution can enhance deployment efficiency for their AI projects. Mention specific challenges like model drift or data bias that they may face, and how addressing these can lead to better performance metrics.
- 04
Anticipate objections about resource allocation
Prepare to address concerns regarding resource allocation by providing insights on how your solution can reduce operational costs or streamline workflows. Be ready with examples of how similar companies have reallocated resources effectively after implementing your solution.
- 05
Follow up after product showcases
Set a reminder to follow up with leads within a week after they host product showcases or webinars. This is a prime time to reach out, as they are likely to be evaluating feedback and looking for ways to enhance their offerings based on audience reactions.
Can ai & machine learning companies even receive your mail?
We measured the DNS of 561 ai & machine learning companies, sampled evenly across company sizes, to see how many authenticate their own email. It matters in both directions: a domain that does not authenticate is a domain whose team is used to mail going missing — and if your own domain is in the gap, yours is the mail going missing.
Publishes a DMARC policy of quarantine or reject — the domain actually asks receivers to act on failures.
The gap is the finding: 26.3% of 1–10-person companies against 86.2% of the largest — 60 points apart. Small companies are where the unauthenticated domains are, and small companies are who most cold outreach is aimed at.
Where does your domain sit?
Same three lookups, run live against your domain. Nothing is stored and nothing is emailed to you.
How this was measured
Company domains were sampled within employee-count bands, then queried over public DNS for SPF (TXT at the apex), DMARC (TXT at _dmarc.) and MX. One domain per company, deduplicated. Anyone can reproduce any single row of this with dig.
DKIM is deliberately absent. A DKIM key lives under a selector that cannot be enumerated from outside the domain, so a company with flawless DKIM under a name we did not guess would be counted as having none. That would measure our guess list, not the industry.
Domains with no MX record are excluded rather than counted as failures — parked and redirect-only domains would otherwise drag every figure down and make the result an artefact of the sample.
Sampled within bands, so this describes the population of companies, not of employees: a 10-person company counts once, as does a 10,000-person one. Each band is at least 60 domains; bands below that are not published. Figures are percentages of the sample, not of the whole industry, and we publish no company names — only counts.
Measured 2026-09-03. Re-measured monthly.
The full census across every industry — sortable, with sample sizes, free to reuse under CC BY 4.0.
Subject lines that fit
- Enhancing your AI model success
- Streamlining AI deployment processes
- Tackling data integration challenges
Questions
- Why do product leads in AI ignore cold outreach?
- Product leads often receive numerous pitches and may ignore cold outreach because they prioritize proven solutions or referrals. They tend to respond better to outreach that is highly relevant to their current challenges and showcases a deep understanding of AI complexities.
- What challenges do product leads face in AI projects?
- Product leads commonly struggle with data quality, integration of various data sources, and ensuring models perform as expected in production. They need to balance innovation with practical deployment, making them cautious about unproven solutions.
- How can I get a product lead's attention?
- To capture a product lead's attention, focus your outreach on specific challenges they face, such as model accuracy or deployment speed. Use case studies or insights that demonstrate your understanding of the AI landscape and how you can help them achieve their goals.
- What is the best time to reach out to product leads?
- The best time to reach out is shortly after they announce new product features or updates. This indicates they are actively seeking improvements and may be more receptive to discussions about solutions that can enhance their current projects.
Or have it run itself
SoloRiff does every step above on its own — finds the companies, finds the people, writes each of them individually, and handles the replies. Drop your URL and watch it work before you sign up for anything.
Try it on your site