# Phil Herridge

Phil Herridge works on AI sales enablement at The Sales Enablement Group, focusing on helping businesses connect AI and automation to qualified leads, sales pipeline growth and measurable revenue results.

- Profile: https://b2binfluencer.marketing/creators/phil-herridge/
- LinkedIn: https://www.linkedin.com/in/philipherridge
- LinkedIn followers: 31,428
- Topics: artificial-intelligence, sales, b2b-sales
- Based on 11 dated posts, latest 2026-10-06

## Summary

Phil Herridge connects AI and automation claims to sales pipeline, qualified conversations, and measurable commercial results.

- Best for: B2B SaaS selling AI pipeline measurement or sales enablement to small and mid-sized sales leaders.
- Not for: Consumer or mass-market campaigns, or enterprise infrastructure aimed at senior engineering and platform buyers.
- Check first: Ask for reach, engagement, audience demographics, available sponsored formats, rates, and deliverables before committing.

### Takeaways

- AI visibility is not a result unless it leads to a commercial action.
- Automation should improve sales, not just internal efficiency.
- Traditional SEO metrics measure a different game from AI search.

## Main ideas

- **AI visibility is not a result unless it leads to a commercial action**: Herridge distinguishes being seen from generating business. Rankings, mentions and activity can rise while the pipeline stays flat. The problem, they say, may be how visibility is structured to capture buyer intent, not a shortage of leads. The practical test: does a metric connect to a specific action in the sales funnel?
- **Automation should improve sales, not just internal efficiency**: Herridge argues that faster workflows and less admin are useful but do not automatically solve commercial problems. They prioritize systems designed to generate qualified leads, speed up client acquisition or find opportunities with existing customers.
- **AI-era measurement should track intent, not rely only on traditional SEO metrics**: Herridge says keyword rankings and backlinks can miss people researching through natural-language queries and AI assistants. They call for tracking intent signals and direct engagement, then connecting them to pipeline outcomes. Visibility counts when it helps explain commercial progress.
- **AI advice for smaller businesses should show a measurable financial return**: In a post directed at SMEs, Herridge identifies perceived cost and complexity as barriers to AI adoption. They argue that generic efficiency claims are not enough: businesses need to see how a specific intervention could affect pipeline, conversion speed or customer acquisition cost. This makes the ROI case more explicit than in their broader posts about measurement and automation.

## Work to read

- [Most companies don't have a lead problem — they have a structure problem](https://www.linkedin.com/posts/philipherridge_the-sales-enablement-group-ai-sales-enablement-activity-7512141966994804736-p2dv)
- [Does this AI metric influence a specific commercial action?](https://www.linkedin.com/posts/philipherridge_the-sales-enablement-group-ai-sales-enablement-activity-7512504325861072898-qnJ1)
- [Traditional SEO tools measure a different game from AI search](https://www.linkedin.com/posts/philipherridge_the-sales-enablement-group-ai-sales-enablement-activity-7511779635462848515-8COY)
- [A faster internal process won't automatically fill your sales pipeline](https://www.linkedin.com/posts/philipherridge_the-sales-enablement-group-ai-sales-enablement-activity-7511054795206459410-xNWq)
- [Demand verifiable ROI from AI consultancy](https://www.linkedin.com/posts/philipherridge_the-sales-enablement-group-ai-sales-enablement-activity-7510590648425684992-SYvS)

## Brand relationships

- **The Sales Enablement Group** (employment)

## Partnerships

Phil Herridge’s five LinkedIn posts from late September to early October 2026 cover AI sales enablement for UK SMEs, including pipeline attribution, intent signals, conversion and return on AI spend. Each closes with a link to a longer article on the site. The links point to Herridge’s own commentary, not disclosed brand collaborations; none of the posts names a sponsor, paid placement, rate card, deliverable specification or inquiry address. There is no standard package or official partnership inquiry route to plan against. Buyers should ask Herridge directly through their LinkedIn profile whether they accept sponsorship and what formats, production requirements and terms are available.

## Alternatives

- Azhan Shafique: Consider Azhan Shafique instead when the B2B SaaS need is narrower than pipeline ROI and centers on entity and AI-search understanding: the product exists, the site has authority, but AI systems describe the company wrongly or ignore its own pages. The two posts work through exactly that case with a surgical instrument manufacturer, listing the information layer that had to be rebuilt — who the company is, what it makes, where it operates, product categories, certifications, expertise, business history — and then re-indexing the important pages. The second post adds a practical warning for SaaS buyers: an AI tool asked for keywords returns high-volume terms without difficulty, intent or competitor context, so speed from AI still needs the judgment of someone who checks intent and competition one term at a time. For a campaign whose deliverable is making AI systems describe a product correctly and earn the right searches, Shafique's posts are closer to the work than Herridge's pipeline-measurement argument.
- Saikat Sarkar: Consider Saikat Sarkar instead when the SaaS company's problem is not measurement language but a stalled journey — traffic or leads arrive and then die before a qualified conversation. Sarkar frames the same commercial concern through the customer path: a system that books 30 calls can still hurt the business if 20 are poor-fit, six never show up and four misunderstand the offer. The fix is to automate the journey toward the outcome rather than the metric. The second post supplies an explicit map — customer question, content, profile visit, website, enquiry, follow-up, sales call — and the rule "automate the bottleneck." For a brief where the team already accepts that AI should be judged on qualified conversations and needs help finding where prospects drop out, Sarkar's posts offer a usable diagnostic sequence. Herridge's posts state the principle without walking the path.
- Scott Livingston-Valentine: Consider Scott Livingston-Valentine instead when the deliverable is messaging and content planning rather than a measurement framework. The product-discovery post argues that generative AI systems synthesise from authoritative sources, expert reviews and structured data instead of returning ranked pages. It lays out a series on what relevance means now, how to structure product information, and how AI agents change discovery-to-purchase. The second post examines quantifiable claims in B2B messaging — why specific numbers are trusted faster, how framing changes perception, and how numbers plus story make a claim stick. For a B2B SaaS brief centered on positioning, a content series or making the product the best possible answer, this material is closer than Herridge's posts, which assume the reader already accepts that visibility must be tied to pipeline.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/