Kristian James

It used to be enough for a VP People or Talent to use AI to speed up parts of their own role, oversee a few basic automations and speak confidently about an AI-enabled organisation.

• Now, leaders are expected to develop the function through an agentic lens, direct the team to question and automate every viable manual process, and set robust guardrails for sensitive people data.
• Some companies also want evidence that the leader has lifted AI fluency across the wider business through culture change, organisational design, training and internal communication.

The post sets the two standards side by side, then names the strain of meeting the higher one: people teams remain under-resourced relative to pre-2020, while talent acquisition teams face enormous hiring plans and fierce competition to staff their own internal recruiting teams.

The post frames this as a rising bar, contrasting what was acceptable at the end of the previous year with what buyers ask for now.

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Rather than analysing CRM statistics directly, they message Grokbot for commentary and context instead of plain numbers.

• Within weeks, they say, they are barely logging into tools such as Google Sheets, Gusto and Xero, instead prompting a bot to do the work and report back.
• They single out the ability to work across the browser and apps without relying on an available API, and predict that employees will bring consumer-grade impatience with admin into work as AI assistants handle personal tasks.
• The post also concedes that employers will want guardrails before deploying these bots across the organisation.

Their example is referral tracking.

• A bot scans call transcripts and emails for candidates or clients saying they were recommended to the firm, records the referrer's name and prompts the team to thank them.
• When a referral later leads to a hire—whether a new client or a placed candidate—the bot recognises it in the CRM and reminds the team to send a bigger thank-you.
• The old method relied on scattered notes and memory and was, in their words, not reciprocal of the kindness people had shown.

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The extension flags prospective candidates who work for a client.

• When the firm offered it to other recruiters, onboarding exposed a maintenance problem: if clients were not added every month, teams lost confidence in the tool's accuracy.
• Kristian James treats that loss of confidence, not the feature list, as the problem to fix.

The fix was a weekly AI agent that scans the CRM for new clients and immediately marks them off limits, and prompts the removal of clients the firm has not worked with for a long time. The post describes the result as remarkable efficiency and all-encompassing quality control.

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They share an article arguing for app-level AI companies; its central point is that a business model depends on execution within its niche.

• If a vertically integrated app-level AI company delivers better outcomes for an industry sector than a generalist model, it sets the standard.
• Kristian James extends the point with their own analogies and suggests candidates use it to ask prospective employers hard questions about defensibility.

The comparisons are iPhone cameras versus a Canon for professionals, Canva versus Adobe, and Hubspot versus an industry-specific CRM.

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The posts draw one behavioural conclusion: competition for the best internal recruiters is fierce, and the most effective hiring companies use candidate experience to convey a positive recruiting culture from the top down.

The listed searches span New York, the Bay Area and London, with funding details for each company. They include a GTM recruiter for an applied AI Series B backed by Sequoia and General Catalyst in both New York and London, a research science recruiter for an AI Lab Series C backed by Microsoft, NVIDIA and SoftBank, and a technical recruiter for an AV Series F backed by Kleiner Perkins, a16z and General Catalyst.

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Drawing on time spent recruiting People hires, they outlined the features people praise in HRIS products, offering a short evaluation framework rather than a generic endorsement. The post directs readers to colleague Andy Turnbull for an overview.

The three criteria are a user experience built for the whole company, not just HR; high configurability by in-house teams; and one system localised across multiple entities in multiple countries.

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The posts show what they look for in recruiters, how they present the team to candidates and the kind of early people role the firm runs for clients.

One post advertises two recruiter openings: one in London, with hybrid working from a Shoreditch office and openness to a range of experience levels; and one in the US, with a preference for the West Coast, Austin or New York and five-year visa sponsorship possible for the right candidate.

• It describes a stable, high-performing team, strong compensation, flexible work and scope to shape the business.
• Another post advertises a client search for the first People Operations hire at a frontier AI lab valued at $1.2bn within 18 months of launch, with Fortune 500 customers.
• The hire will build best-in-class automated HR systems in San Francisco or fully remotely within the US.

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Newcomers should start here: it frames the hiring standard in concrete terms and explains why practical AI adoption is becoming a leadership expectation, not a side interest.

Kristian James describes how the bar for VP People or Talent hires has risen.

• A year earlier, a leader could be credible by using AI to speed up parts of their own role, overseeing a few basic automations and speaking confidently about an AI-enabled future organisation.
• Now, they say, the expectation is a leader who instinctively approaches the function through an agentic lens, directs the team to question and automate viable manual processes, and has a robust plan for guardrails around sensitive people data.
• Some companies also want evidence that the VP People has lifted AI fluency across the wider business through culture change, organisational design, training and internal communication.
• The post notes the practical strain: people teams remain under-resourced relative to pre-2020, while talent acquisition teams face enormous hiring plans and fierce competition to staff internal recruiting teams.

For brands planning a campaign

Use this as the argument for a co-authored guide on agentic AI workflows in recruiting. It gives a recruitment technology company a way to address B2B talent leaders on moving from individual AI productivity to team-level automation and guardrails.

Newcomers should start here: it moves the AI conversation from abstract capability to specific personal and work tasks, then connects that shift to employer guardrails.

Kristian James predicts that every employee will soon have an AI personal assistant at work.

• Outside work, their bot submits a driving licence application, helps locate lost parcels and books a plumber.
• At work, instead of analysing CRM stats directly, they send Grokbot a DM and get commentary and context rather than pure numbers.
• Within weeks, they say, there are hundreds of examples like this, and they are barely logging into tools such as Google Sheets, Gusto and Xero, preferring to prompt a bot to do the work and update them.
• The post acknowledges that employers will want guardrails before unleashing these bots organisation-wide, but argues that employees will become less patient with admin at work.
• It also mentions an upcoming Wave1 and Revolut people 1-day People Leaders AI Lab in London on 12th November.

For brands planning a campaign

Use this in a guide section contrasting personal admin automation with people-data guardrails. A recruitment technology company can draw on it to discuss how talent leaders should prepare for employee expectations shaped outside work.

Newcomers should start here: it follows a specific process from detecting a referral to acting on it after a hire, rather than making a general claim about automation.

Kristian James explains an automation built with Grokbot from Cursor.

• The bot scans call transcripts and emails for candidates or clients saying they were recommended to the firm.
• It records the referrer’s name and prompts the team to thank them.
• If the referral leads to a successful hire, whether a new client or a placed candidate, the bot recognises that in the CRM and reminds the team to follow up with a bigger thank-you.
• Previously, scattered notes and memory made the process inconsistent and, as they put it, not reciprocal of the kindness people had shown.
• The post notes that Grokbot works across the browser and apps rather than depending on an API being available, and asks for other niche recruitment use cases.

For brands planning a campaign

Use this as a worked example in a co-authored guide on agentic AI workflows in recruiting. It demonstrates cross-tool automation with a clear human outcome, helping a recruitment technology company show talent leaders its practical value.

Newcomers should start here: it traces the full arc from process pain to product to AI-maintained quality control.

Kristian James describes building Bycatch as a Google Chrome extension to solve a recruitment firm problem.

• New recruiters at Hawkwood do not immediately know all the clients—the companies they cannot headhunt from—and LinkedIn does not easily signal this.
• Bycatch instantly indicates whether a prospective candidate is off limits because they work for a client.
• When the tool was offered to other recruiters, one onboarding challenge was keeping it maintained: if clients were not added every month, the team lost confidence in its accuracy.
• Now an AI agent scans the CRM weekly for new clients and marks them off limits instantly.
• If it finds a long gap since working with a client, it asks whether to remove them from the off-limits list.
• James describes the result as remarkable efficiency and all-encompassing quality control from building AI agents.

For brands planning a campaign

This is a strong case study for a guide on agentic AI workflows in recruiting, particularly for audiences interested in CRM integration and data quality. It also gives a recruitment technology company a practical example of internal tooling becoming a product conversation.

Newcomers should start here: it details a commercial partnership and the product criteria that matter to scaling companies.

Kristian James announces that, following the launch of Wave1 earlier in the year, the firm is now a certified HiBob advisory partner.

• The post describes growing a team of experts who help scaling companies build the people technology infrastructure they need to grow rapidly.
• James calls HiBob a leading HR platform among tech scale-ups seeking a consumer-grade user experience and says the partnership is natural.
• Drawing on time recruiting People hires, they list what people praise in HRIS products: user experience built for the whole company, not just HR; high configurability by in-house teams; and one system localised across multiple entities in multiple countries.
• The post directs people to Andy Turnbull for an overview if they are considering a move to HiBob or want more value from an existing HiBob platform.

For brands planning a campaign

Use this to position a guide around people technology infrastructure and HRIS selection. A recruitment technology company can draw on its partnership-led audience and implementation-focused conversation.

Newcomers should start here: it offers a clear framework for judging whether a specialist AI company can hold its ground.

Kristian James notes that top talent has gravitated to foundational AI labs, often seen as the disruptors rather than the disrupted.

• That perception grew as labs launched apps and AI implementation services.
• They share an article making the case for app-level AI companies despite this competition, arguing that any business model depends on execution within its niche.
• If a vertically integrated app-level AI company delivers better outcomes for an industry sector than a generalist model, they say, that becomes the standard and nothing less will do.
• The post draws analogies: iPhone cameras are great for casual users while professionals might use a Canon; Canva compares to Adobe; Hubspot compares to an industry-specific CRM.
• James suggests candidates use these ideas to ask leaders about the defensibility of a prospective employer.

For brands planning a campaign

This helps a recruitment technology company position its niche against generalist AI tools. Use it in a guide to explain why vertical workflow depth and sector outcomes matter when talent leaders evaluate recruiting technology.

Kristian James writes that demand for internal recruiters remains strong and lists high-priority searches across the US and UK.

• The roles include a GTM Recruiter for a SaaS Series B backed by Google Ventures and Redpoint Ventures; a Core Engineering Recruiter for a leading quantitative hedge fund; a GTM Recruiter for an applied AI Series B backed by Sequoia and General Catalyst; and several Bay Area positions: Technical Recruiter for an AI Lab Series A backed by Salesforce Ventures and Battery Ventures, Technical Recruiter for an applied AI Series B backed by Sequoia and General Catalyst, Technical Recruiter for AV Series F backed by Kleiner Perkins, a16z, and General Catalyst, and Research Science Recruiter for an AI Lab Series C backed by Microsoft, NVIDIA, and SoftBank.
• London openings include EMEA Recruiter for an applied AI Series B backed by Sequoia and General Catalyst, Technical Recruiter for a SaaS Series A backed by YC and Point Nine, Talent Acquisition Lead for an early-stage VC firm, and Technical Recruiter for an applied AI Series B backed by 20VC and Microsoft.
• They say competition is fierce and that leading hiring companies use candidate experience to signal a positive recruiting culture from the top down.

For brands planning a campaign

Use this as evidence of reach and niche focus for a campaign brief aimed at B2B talent leaders. It gives a recruitment technology company concrete role types and sectors to reference when proposing a co-authored guide.

Kristian James describes a search for the first People Operations hire at what they call a potentially category-defining frontier AI lab.

• The lab reached a $1.2bn valuation within 18 months of launching, attracted angels including the Perplexity CEO alongside top VCs, and already has Fortune 500 customers.
• The hire will build best-in-class, automated HR systems using the best technology.
• The lab has an office in San Francisco and is open to hiring fully remotely within the US for the right candidate.

For brands planning a campaign

Use this to illustrate the types of companies and roles where agentic HR systems are being built. A recruitment technology company could connect practical AI adoption with early people operations design.

Kristian James announces two recruiter openings at Hawkwood, one in London and one in the USA.

• The firm prefers candidates in the West Coast, Austin, or New York, but is open to other locations.
• For the US role, local knowledge and experience are ideal; they may be able to sponsor a five-year US visa for the right candidate with a handful of years recruiting into tech companies.
• The UK role is hybrid, based at a Shoreditch office in London.
• They are open to candidates at a range of experience levels, from accomplished recruiters at relationship-driven specialist firms to people with potential who can be trained.
• The post describes a stable, high-performing team and offers strong compensation, flexible work, and scope to shape the future of the business.

For brands planning a campaign

Use this as background on the firm’s growth and employer brand. It can help a co-authored guide speak credibly about the recruiting team and the talent Kristian James works with.

One explains using Grokbot from Cursor to read CRM statistics and return commentary instead of raw numbers, and to scan call transcripts and emails for referral mentions so the team can thank the referrer, with a CRM prompt when a referral turns into a hire. Another describes a Chrome extension, Bycatch, that flags candidates who work at client companies, and a weekly agent that scans the CRM to keep that off-limits list current and prompt removal of stale clients.

Each example names the manual method it replaced — scattered notes and memory for referrals, fiddly weekly maintenance for the client list — and the observable outcome.

For brands planning a campaign

A tooling, automation or recruitment-software brand can work with firsthand deployment stories rather than opinion. The referral-tracking and client-flagging examples translate directly into demos, webinars or a co-authored guide on agentic workflows in recruiting.

Examples: Source 1 · Source 2 · Source 3

A year earlier, it was enough to use AI for personal productivity, oversee a few basic automations and speak confidently about an AI-enabled organisation.

• Buyers now look for someone who runs the function through an agentic lens, directs a team to question and automate manual processes, has a credible guardrail plan for sensitive people data, and can show how they lifted AI fluency across the wider business.
• The post notes the difficulty of doing this while teams remain under-resourced and hiring plans are heavy.

A before-and-after standard, followed by the capacity paradox that makes the new bar hard to meet.

For brands planning a campaign

For a recruitment technology or HR platform selling to senior People buyers, this is a buyer-side standard stated by a recruiter who fills those roles. It can anchor a co-authored piece on what AI fluency now means in a People or Talent leadership search.

Examples: Source 1

It suggests candidates use the argument to ask prospective employers about defensibility and frames that choice as a way to spot companies likely to last.

Analogy-driven argument, with three consumer-side comparisons carrying the point.

For brands planning a campaign

A brand working with AI startups or venture-backed employers can use this framing for candidate-facing content. It shows a recruiter willing to take a public view on which AI employers are durable, supporting joint content on talent strategy and candidate due diligence.

Examples: Source 1

It argues competition for internal recruiters is fierce and that the strongest hiring companies use candidate experience to signal their recruiting culture.

Publishes the live search list as a market snapshot, then draws one behavioural conclusion from it.

For brands planning a campaign

A talent-intelligence or employer-branding brand could co-publish recurring market snapshots. The format is already audience-tested and carries the recruiter's own view of which hiring signals work.

Examples: Source 1

The onboarding problem was maintenance: if clients were not added every month, teams lost confidence in the tool. The fix was a weekly AI agent that scans the CRM for new clients and flags them, and prompts removal of clients the firm has not worked with for a long time.

Problem, attempted solution, discovered failure point, then the repair, with the time cost stated plainly.

For brands planning a campaign

For a tooling or data brand, this is an unusually candid public account of operational drag after launch. It supports content about adoption friction, confidence in accuracy and maintenance costs rather than feature lists alone.

Examples: Source 1

It lists what people praise in HRIS products: a user experience built for the whole company rather than HR alone, high configurability by in-house teams, and one system localised across multiple entities and countries. A named colleague is the contact for HiBob moves or improvements.

For brands planning a campaign

An HR technology brand can engage with a specific, short evaluation framework rather than a generic endorsement. An interview or selection guide for scaling companies fits this material.

Examples: Source 1

It argues that employees will bring consumer-grade impatience with admin into work as AI assistants handle personal tasks, and that employers will want guardrails before adopting them.

The post pairs strong AI-enthusiasm claims with an explicit concession that employers will want guardrails first, keeping the argument balanced.

For brands planning a campaign

Event and community partnerships are a route here. A brand can attach to a small senior practitioner format rather than a large conference, and the post supplies the audience rationale.

Examples: Source 1

Five-year visa sponsorship is available for the right candidate, and the firm is open to training for the UK role. A client search is also described: the first People Operations hire for a frontier AI lab valued at $1.2bn, building automated HR systems, based in San Francisco or fully remote within the US.

For brands planning a campaign

A recruitment marketing or employer-branding brand can work with the firm as a distribution channel. The posts show a willingness to describe roles, locations and candidate criteria publicly, supporting co-produced job-market content.

Examples: Source 1 · Source 2

The material describes team membership and hiring activity.

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The posts promote Wave1’s services; their ownership or employment position is unstated.

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The post notes client praise for HiBob’s company-wide UX, in-house configurability and multi-entity localisation, and points enquiries to a named contact.

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The team later offered it as a product to other recruiters and maintains it with an AI agent that scans the CRM for new clients.

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The posts describe it as a tool they use. Ask whether any commercial relationship is involved.

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Both writers survey the London tech hiring market and draw conclusions from what candidates tell them.

Ian Whiteford's posts are market observations from the candidate and talent-leader perspective, focused on employer prestige and job-market structure. Kristian James writes from inside a recruitment firm, showing AI agents at work on the desk with far more specific detail on practical tools.

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Both use a candidate relationship that began years earlier to show how their method pays off.

Chris Kenny covers US packaging sales recruiting, focusing on networks, references and long-term relationships, with no AI or tooling content. Kristian James works in technology and People recruiting, where automating the recruitment desk is the main subject.

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Kristian James makes similar arguments about what senior People and Talent buyers should demand and how to run a search for an early people role.

Marc Okeon focuses on the mechanics of startup executive search and candidate screening, with no AI adoption theme. Kristian James focuses on how AI changes the recruitment function, including agentic workflows and HR platform selection.

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The two posts offer specifics: hands-on domain expertise, execution ability and AI fluency as the combination in demand; the middle of the market and credential-only candidates under pressure; and named employers illustrating the shift (Granola, PhysicsX, Helsing, ElevenLabs, Primer, Lovable, Anthropic, Attio, Metaview). For a co-authored guide that opens with how the definition of valuable work is being re-rated before handing off to the partner for the how-to, Ian Whiteford makes the case with more concrete company examples than the other alternatives considered.

The posts explain who is winning and why, but offer no process a reader could copy: no trigger, automation step, guardrail for people data or before-and-after of a manual task being removed.

• A guide built on this material would need practical process content from elsewhere, weakening the co-authored premise.
• The posts also read as commentary for recruiters and talent people about their own careers, not People leaders being asked to sponsor an AI rollout.
• Their market call—that old prestige tech employers feel finished—may also be a concern for some partner brands.

Examples: Source 1 · Source 2

The posts stake out a clear position: placing sales people in the US packaging industry, from rep to CCO, with 16 years and almost 17 years of relationship building stated.

• They name packaging companies, VPs of Sales, HR and talent leaders in packaging, and sales professionals as audiences.
• The second post shows the model end to end: a referral kept warm for two years, a role eliminated, a placement made, and a written candidate reference about the network shortening the process.
• For a campaign seeking to reach packaging sales executives through a specialist recruiter, this is a more defensible route than a generalist technology recruiter.

This is the weakest of the three for a guide on agentic AI in recruiting.

• Neither post discusses AI, automation, people data or HR technology; the examples are a self-introduction and a single successful placement.
• Choosing Chris Kenny would also shift the campaign audience from B2B talent leaders evaluating AI to sales leadership.
• The tradeoff is depth in one industry versus relevance to the stated campaign need: real network credibility, but little reusable content for a co-authored guide.
• Ask for reach and sponsorship terms before deciding.

Examples: Source 1 · Source 2

Both posts focus on decision discipline in executive hiring: identifying the eventual hire within two weeks in 84% of searches, with the long tail spent on stakeholder sign-off; using a written pitch document to hold late-round interviewers to the original criteria; and screening for hands-on player/coaches, since titles such as senior manager or CMO do not show who can fix the CRM themselves. For startup founders, operators and functional VPs, this is campaign-ready material with numbers, example dialogue and a clear prescription.

The posts do not cover AI workflows, agents or guardrails for sensitive people data, so they do not support a co-authored guide for B2B talent leaders assessing AI deployment.

• Their audience is founders and executive hiring teams.
• The claims rely on the author's own search statistics, without supporting detail; ask for substantiation before using them.
• The practical gain is a sharper hiring-process angle, but the topic is a poor fit for the recruiting-technology campaign.

Examples: Source 1 · Source 2