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Which job should AI take on first?

Score a task against the six tests that separate AI projects that pay from the ones that stall. Score a few and compare them, and see the hours each one costs you today.

The taskWhat's the job?
Test 1Do you already measure it?

How long it takes, what it costs or how often it goes wrong today. Without a baseline you can't show a return.

Test 2Do the inputs look roughly the same each time?

Forms, emails, documents or records with a familiar shape.

Test 3Where does the information it needs live?
Test 4Can a person check the output?
Test 5Is there one owner?

One named manager whose team does the work today and who will say whether it's better.

Why these six tests

Most AI spending so far hasn't paid back. McKinsey's State of AI 2025 found only 39% of respondents report any enterprise-level profit impact from AI; BCG's "The Widening AI Value Gap" (September 2025) found 60% of companies report hardly any material value. Gartner predicts that through 2026, organizations will abandon 60% of AI projects that lack AI-ready data. The projects that do pay tend to share a profile:

  • Volume. It happens hundreds or thousands of times a month, so small savings add up. (The scorecard works this out from your numbers.)
  • A baseline. You know what it takes today, so you can prove the difference.
  • Consistent inputs. The same kinds of forms, emails or records each time.
  • Usable data. If the information sits in systems that don't talk, fix that first or pick another job.
  • A person checks the output. Especially at the start; it catches errors and builds trust.
  • An owner. One manager who will say whether it's better.

What a good first use case looks like in practice: a Metro Detroit recruiting team spent about four hours researching candidates for each search. Decypher Corp built an AI research agent that cross-checks 25 public sources and returns candidates with the source and reason for each. The same work now takes about 15 minutes, with a person checking every result.

The full reasoning, with sources, is in AI business solutions: find the one use case that pays first.

Sources

  • McKinsey & Company, "The State of AI" (November 2025)
  • BCG, "The Widening AI Value Gap" (September 2025)
  • Gartner, "Lack of AI-Ready Data Puts AI Projects at Risk" (February 2025)
  • Decypher Corp, "From hours to minutes: AI-powered sales talent sourcing" case study

Then see it working

Once you have a candidate, the cheapest next step is a working prototype on your own data. Decypher Corp, our custom software and AI partner, covers the first three steps (an introductory call, a deep-dive discovery and a working prototype) at its own cost, and aims to solve the core problem in two to four weeks. Decypher pays Salter Growth when an engagement goes ahead; you pay us nothing.

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