Build the habit first
AI is moving too fast for a once-a-year software review. Today's weak spots may disappear in a few months. Get people using AI with care, because your firm needs taste, judgement and muscle memory before the next leap arrives. Start before the perfect use case appears. Give the team safe tools, name the leaders, recruit volunteers and make the benefits part of the management conversation.
Create a leadership-owned AI working group
The working group is the mechanism, not the point. The point is a regular leadership rhythm where people can try useful work, compare results and spread the habits that improve live searches.
| Habit | What to do | Why it helps |
|---|---|---|
| Name owners. | Appoint a partner sponsor and an operational lead. | AI stays in the leadership conversation instead of becoming a side project. |
| Find volunteers. | Recruit volunteers across research, consulting, operations and partners. | Your natural experimenters will find useful patterns before a committee does. |
| Share weekly. | Ask people to show useful outputs, failed attempts and changed workflows. | The firm learns faster when people see colleagues getting better results. |
| Set safe-use rules. | Classify public research, internal material, confidential client material, confidential candidate material and sensitive assessment material. | People need to know what can go into which tool without asking each time. |
| Track benefits. | Add AI adoption and impact to KPIs: repeat use, time saved, output quality and better client or candidate conversations. | Reward useful adoption, not novelty. |
| Keep changing. | Revisit weak use cases as models and tools improve. | A task that fails today may become useful next quarter. |
What Granger Reis learned
Granger Reis was the first adopter of Kaiya and has written about a year of using AI in search. Their experience is useful because it is practical: adoption improved when the team debated the work, gave the AI a visible role, used saved time for better thinking and protected confidentiality.
| Lesson | What to copy |
|---|---|
| Diversity of thought matters. | Ask researchers, consultants and partners to review the same outputs and discuss what is useful, weak or surprising. |
| Giving AI a face helps. | Put Kaiya where the team already works, such as Teams or Slack, and give it real search questions. |
| Time saved creates room for creativity. | Measure whether AI improves client and candidate conversations as well as admin time. |
| Confidentiality is foundational. | Keep confidential search material out of unmanaged tools. |
Test several workflows at once
AI capability is jagged. It may disappoint on one task and save hours on the next. Test a small portfolio of search work and adopt what already helps.
| Workflow | Try this | Good signal |
|---|---|---|
| Market mapping. | Ask Kaiya to map sectors, target companies, titles, geographies and likely candidate pools. | Researchers find useful angles they would otherwise have assembled by hand. |
| Target company and candidate lists. | Use AI to build targeted lists from market logic, source mapping and deep research. | The list is smaller, sharper and easier to defend. |
| Candidate profiles and reports. | Turn notes, profiles, web research and firm templates into editable drafts. | Consultants spend time improving judgement, not assembling the first version. |
| Client updates and partner preparation. | Ask Kaiya to prepare the search position, gaps, next moves and talking points. | Partners arrive sharper and spend less time asking the team to pull context. |
| CRM knowledge search. | Search Invenias or CRM history for relevant candidates, assignments and relationship context. | The firm reuses knowledge that would otherwise stay buried. |
A simple 30-day rhythm
Use the first month to create visible examples. Keep the rhythm simple.
| Week | Do this | Result |
|---|---|---|
| Week 1. | Name the sponsor, recruit volunteers, agree safe-use rules and choose the first workflow portfolio. | People know who owns adoption and what they may test. |
| Week 2. | Use Kaiya on real search work and save examples of useful and weak outputs. | The team sees practical evidence. |
| Week 3. | Run a show-and-tell. Ask users what changed in how they worked. | Early adopters spread the useful habits. |
| Week 4. | Score adoption, time saved, quality, confidence and client or candidate value. | Leadership can decide what to standardise next. |
| Next month. | Keep what works, tighten the rules and revisit tasks that failed. | The firm builds an AI habit instead of running a one-off experiment. |
Mistakes to avoid
The wrong rollout usually looks either too loose or too heavy. Give people freedom to learn, but do not leave confidential search work to personal tools and private experiments.
| Mistake | Better answer |
|---|---|
| Using free or personal ChatGPT and Claude for confidential search data. | Keep free consumer tools for low-risk learning. Put mandate briefs, candidate notes, CRM history and client material only into approved tools with reviewed terms. |
| Approving a brand name instead of a product plan. | Check the specific plan, contract and settings. Consumer, business, enterprise and API products can have different training, retention and access terms. |
| Turning governance into a ban. | Give the team a safe route that is easier than improvising. |
| Testing one use case and calling the whole thing a failure. | Try a portfolio of workflows. AI capability is uneven, and useful wins can appear in unexpected parts of the search. |
| Letting AI make candidate decisions. | Use AI to prepare research, questions, summaries and rationale. Keep recommendations and judgement with the search team. |
| Buying a tool the firm cannot leave. | Check data export, deletion, admin controls and whether useful work can move back into your CRM, files and templates. |
AI product checklist
Before a firm adopts an AI product for executive search, ask practical questions. The answer should be clear enough for a partner, operations lead and client to understand.
| Check | Question to ask |
|---|---|
| Model training. | Does the vendor use customer prompts, files, CRM data or outputs to train models by default? |
| Data location. | Where is customer data hosted, processed and retained? |
| Security controls. | Does the product have access controls, encryption, admin controls, subprocessors and a DPA or equivalent terms? |
| Search fit. | Does it understand executive search work, or does the team have to translate every task into a generic prompt? |
| Review. | Can consultants inspect sources, gaps, assumptions and rationale before client or candidate use? |
| Adoption. | Will the team use it in the places they already work, such as Teams or Slack? |
| Exit route. | Can the firm export, delete or move work if the tool changes, underperforms or becomes too expensive? |
Governance that people will follow
Governance should make safe work easy. Keep the rules short enough for consultants and researchers to remember.
- Use approved AI tools for confidential mandate, candidate and CRM data.
- Keep free or personal ChatGPT and Claude for low-risk learning under firm policy.
- Check sources, assumptions and gaps before client or candidate use.
- Keep candidate decisions with the search team.
- Review the rules often as tools and controls improve.
Where Kaiya helps
Kaiya gives the team a specialist AI colleague for real search work inside the firm.
- It works in Teams or Slack, so people can ask for help where they already work.
- It uses executive-search context: firm knowledge, CRM history, Invenias context, LinkedIn context, web research, notes, files and templates.
- It prepares market maps, target lists, targeted candidate lists, profiles, reports, updates and briefing packs.
- It gives early adopters useful examples to share with the wider firm.
- It keeps confidential search work inside a controlled environment built for executive search.
Source notes
Use these links for procurement, legal review and further reading. Before rollout, confirm current regulations, vendor terms and model capabilities with your counsel, procurement team and the relevant vendor.
About Kaiya
What is Kaiya?
Kaiya puts a search firm's knowledge to work so executive search teams prepare briefs, maps, longlists and reports faster.
Who this guide is for
- Managing partners deciding how to roll out AI
- Heads of research choosing first workflows
- Operations, IT and compliance leaders setting guardrails
Why firms trust Kaiya
- Live in around 10 minutes
- Public pricing with GBP, EUR and USD billing
- Customer conversations are not used to train models
- Built around executive search judgement and discretion
Frequently asked questions.
How should an executive search firm implement AI?
Treat AI as a firm capability. Appoint leaders, recruit early adopters, approve safe tools, test several real workflows, share what works, measure benefits and keep AI adoption in the leadership conversation.
Is there a real executive search AI case study?
Yes. Granger Reis has written about its early Kaiya adoption, including lessons around diversity of thought, giving AI a practical face inside the business, efficiency, creativity and confidentiality.
What is the best first AI workflow for an executive search firm?
Test a portfolio of useful workflows: market mapping, target lists, candidate profiles, reports, client updates, partner preparation and CRM knowledge search. Adopt what works well now and revisit weaker workflows as capability improves.
Should the firm start with ChatGPT, Claude or specialist AI?
Use free or personal ChatGPT and Claude only for low-risk learning under firm policy. Use approved business, enterprise or specialist AI for confidential work. Use Kaiya when the workflow depends on search context, CRM history, source review, firm templates and repeatable executive search outputs.
How do we measure an AI pilot in executive search?
Measure adoption, repeat use, time saved, useful sources found, factual corrections needed, consultant confidence, output quality and whether the work improves client or candidate conversations.
How do we reduce AI risk during rollout?
Classify data, approve tools by product plan and contract, check DPA and model-training terms, restrict access, keep human review in the process and avoid autonomous candidate decisions unless legal and compliance review supports the use case.
Does implementing AI mean replacing the CRM or ATS?
No. The CRM or ATS should remain the system of record. Kaiya helps the team find and use the knowledge inside and around those systems, then prepare search work for human review.
Who should own AI implementation in an executive search firm?
AI implementation needs a partner sponsor, an operational owner and a volunteer group of early adopters from research, consulting and operations. IT, compliance and legal should help set the safe-use rules without turning adoption into a purely technical project.