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The Automation Index

Can contact data enrichment be automated?

Mostly automatable: 78% of task time

The answer

Mostly. Looking up company size, industry, role, LinkedIn profile and verified contact details is almost entirely automatable with enrichment tools. In our model about 80% of research time can go to software. What stays human is checking the personalisation line before it is sent and judging fit for leads that do not match any rule cleanly.

The tasks, and how much of each is automatable today

Six tasks make up enrichment. Company lookups and re-verifying old records are 85 to 90% automatable. Finding roles is about 80% automatable, and verified phones and emails about 70%. Writing a research note is about 60% automatable with AI. Scoring fit is about 70% automatable once the ideal customer is defined.

TaskActivity typeMin / weekAutomatable
Look up company size, industry and websiteEnrichment providers cover most registered companies. Very small or new businesses are often missing.Collecting data
base rate 64%		30090%
Find the person's role and LinkedIn profileAccurate for people with public profiles. Job changes lag by weeks or months in most databases.Collecting data
base rate 64%		20080%
Find and verify a phone number or emailWaterfall lookups across providers raise hit rates. Mobile numbers remain the hardest field to find legally and reliably.Collecting data
base rate 64%		15070%
Write a research note or personalised opening lineAI drafts a relevant line from the website and profile. Read it before it is sent; generic or wrong flattery hurts reply rates.Applying expertise
base rate 18%		15060%
Score fit against the ideal customerRules on size, industry and role score most leads. Borderline cases need a person who knows the offer.Applying expertise
base rate 18%		6070%
Re-verify old records that have gone out of dateA re-check before reuse catches job changes and dead emails. Cost per lookup is the main limit.Processing data
base rate 69%		6085%
Whole process92078%

Base rates are McKinsey Global Institute's technical automation potential for each activity type. The share for each task is our adjusted estimate. Method.

Hours saved per week

For a B2B outbound team adding about 200 leads a week, manual research takes around 15.5 hours a week. Automating the tasks above returns about 12 hours a week. The time left goes on reviewing personalisation and judging edge-case leads, which is where a setter's research actually changes the conversation.

Modelled on: A B2B outbound team of three setters adding about 200 new leads a week that need company, role and contact details before outreach.

  • About 1.5 minutes per lead to look up company details and 1 minute to find role and LinkedIn.
  • About 150 leads a week need a phone or email found and verified, at about 1 minute each.
  • About 45 seconds per lead to write a research note or opening line.
  • About 2 hours a week spent scoring fit and re-checking old records.

15.5 hours a week by hand, 12back with automation. Formula: the sum of each task's minutes multiplied by its automatable share, divided by 60, rounded to the nearest half hour.

Tools that do the job

You need one or more data providers, a tool that tries them in order until a field is filled, and a CRM that receives the result. Clay is built for this kind of waterfall enrichment. n8n or Make can do the same with direct API calls. An AI agent writes the research note.

Clay
Tries several data providers in sequence until each field is filled, and runs AI research steps.
n8n
Calls enrichment APIs directly on new CRM records for teams that want control over cost.
AI agent
Reads the company website and profile and writes a short research note and fit reason.
HubSpot
Stores enriched fields and the fit score on the contact and company records.
Salesforce
Same role for larger teams, with field-level history to see when data was last verified.

A simple workflow

The workflow runs when a lead enters the CRM or is added to a campaign. It fills missing company fields first, then role and LinkedIn, then verified contact details, trying providers in order. An AI step writes a two-line research note and a fit score. Low-fit leads are held back from sequences.

  1. TriggerA lead is created, or an existing lead is added to an outreach campaign.
  2. Enrich companyFill company size, industry and website first; they decide whether the rest is worth paying for.
  3. ScoreApply fit rules. Low-fit leads stop here and are tagged, saving lookup costs.
  4. Enrich personFind role, LinkedIn and verified email or phone, trying providers in order.
  5. ResearchAI writes a two-line note and opening line. The setter reviews before the sequence starts.

Keep these human

  • Reviewing personalisation before it is sent. A wrong detail about someone's company is worse than no personalisation.
  • Judging fit for unusual leads that rules mark as borderline.

Where it goes wrong

  • Enriching everything up front. Data decays, so bulk-enriching a list you will not contact for months wastes money. Enrich when a lead is about to be used.
  • Sending to unverified emails. Bounces damage your sending domain. Verify before any sequence starts.
  • Trusting stale roles. People change jobs. Check the last-verified date before a personalised message mentions a title.

What most guides miss

Most guides compare enrichment databases. The bigger saving is timing and order. Enrich at the moment a lead is about to be contacted, not when it is imported. Fill company fields first and score fit before paying for personal data. Low-fit leads stop early, which typically cuts lookup costs without losing a single good lead.

The evidence