HUMAN-FIRST TECH FOR COMPANIES BUILDING OR ADOPTING AI
You’re already using ai. decide what it’s for before it decides for you.
The licenses are bought and the AI built into your workplace tools is switched on. But people are figuring it out on their own, swapping tips in a shared channel, and no one has decided what it’s for or who it lands on.
only 22% of U.S. employees say their organization has communicated a clear AI plan or strategy.
Gallup, 2025
TWO WAYS IN
Where Are You Right Now?
YOU’RE USING AI INSIDE YOUR COMPANY
Human-First AI Principles
Set how AI works in your company, before it sets itself. Your principles, your checks, and a partner for the high-stakes calls.
YOU’RE BUILDING AN AI PRODUCT, OR HAVE AN AI DECISION ON THE TABLE
A Human-First AI Review
A pre-launch design review of who it lands on. Your buyers will ask who it screens out. Have the answer first.
THE RISKS NO ONE IS CHECKING
What Goes Wrong When No One Looks
The backlash
Customers, employees and communities turn on how you used AI, and it plays out in public.
REPUTATION & TRUST
The rollout no one uses
The tools go live, adoption stalls, and the gains never show up in the business.
ADOPTION & WASTED SPEND
The impact no one measured
Jobs, workload, fairness and communities shift, and no one tracked who it would land on until it was too late to change.
PEOPLE & COMMUNITY IMPACT
Legal exposure is flagged early for counsel. This work is not legal advice.
HUMAN-FIRST PRINCIPLES FOR COMPANIES ALREADY USING AI
Not a poster on the wall. A way to make AI decisions.
You have the seats or the tokens. What you don’t have is a handle on how AI works across teams, or for it will change your talent. People use it on their own and share what works in a committee meeting or a shared workspace. Leaders can’t say what it’s doing or whether it’s working, and no one owns the question of who it lands on.
What I’m hearing from leaders
Companies can’t measure AI beyond time saved. Some AI that replaced roles now costs more than the people did.
Decisions about people are already being drafted in general AI tools, outside any approved process.
No one is tracking what AI is doing to jobs, career paths or the talent pipeline.
THREE PRINCIPLES EVERY CLIENT KEEPS
People affected have a say.
Know who it lands on.
Every fix has an owner and a measure.
The library is built on research into technology and shared prosperity, patterns from real cases where AI caused harm, and my two decades in corporate and social impact expertise.
What you leave with
Your AI principles, in your own words
A quick check for every AI use
A guide to which uses need a full review: anything that cuts or changes roles, makes decisions about people, faces customers, or involves sensitive data
An AI tools inventory and baseline measures
A picture of how AI is changing roles, succession and early talent needs
What comes next
Ongoing support. When the quick check flags a high-stakes use, it comes to me for a full Human-First AI Review, with the people affected heard in paid panels. Then quarterly check-ins on the measures. You handle the volume. I handle what’s at stakes.
WHEN AI BECAME THE HEADLINE
THE COST OF NOT LOOKING
Klarna (2024–25)
THE DECISION
Put AI in front of customer service and froze hiring
COST TO PEOPLE
Customers with complex problems got poor service
COST TO THE BUSINESS
Rehiring.
Quality dropped; began hiring people again
Woolworths (2024)
THE DECISION
Disciplined warehouse workers for missing a 100% pick rate
COST TO PEOPLE
1,500+ workers on a 17-day strike, losing pay
COST TO THE BUSINESS
$65M.
Cost of the strike
Instacart (2025)
THE DECISION
Used AI to show shoppers different prices for the same item, without telling them
COST TO PEOPLE
Up to $1,200 more per household a year
COST TO THE BUSINESS
−6.6%.
Share price the day it came out; FTC inquiry; tests ended
Each became a public story. Each was a visible decision a review before launch could have caught.
A HUMAN-FIRST AI REVIEW · FOR BUILDERS, OR COMPANIES WITH A DECISION ON THE TABLE
Founding pilot: two spots, one AI decision each
Catch the risk before it ships. Call before you sign, ship or switch it on. The earlier the review, the more you can still change.
Call when
Enterprise buyers start asking hard questions about your AI product.
A vendor is chosen and a pilot is planned.
Board approval is pending on automating decisions about people.
Your HR software adds AI features at renewal.
How it works
Intake on what you’re building or rolling out, who it touches, and who decided.
AI-assisted assessment against my framework’s tests. I check and correct every finding.
Paid panels with the workers and users affected confirm or challenge the findings.
Strategic recommendations, each with an owner and a measure, plus check-ins at 30 and 90 days.
WHY CALL ME
Others ask if it works. I ask who it lands on.
WHO ELSE YOU MIGHT CALL
Your law firm
Tells you what’s allowed.
IT and technologists
Set permissions and access, and test the model and the data.
Strategy consultants
Build the efficiency case.
Adoption trainers
Get people using the tools.
WHAT I DO DIFFERENTLY
Beyond compliance
Who gains, who pays, who was missing from the room, and what it really costs.
The people it lands on, in the room
Workers, users and communities test the findings before launch.
C-suite and boardroom experience
I’ve led these calls, including sociopolitical risk as it unfolded.
Working checks, not JUST a report
Every principle and fix has an owner and a measure, tied to reputation, retention, adoption and legal exposure.
ABOUT ME
Rebecca Grey
Trusted C-suite partner: Advised executives on sociopolitical risk, planning and responding before and as moments happen.
In the boardroom: Built the business case, read the room, and presented decisions to C-suites and boards.
First-of-their-kind strategies: Built the first social impact, ESG and inclusion strategies at Kohl’s, Victoria’s Secret and Advantage Solutions, for organizations with up to 140K employees.
Executive leadership: Two decades inside Fortune 500 and 1000 companies, including as VP of Belonging & Impact.
Start with a conversation.
Thirty minutes on where AI is already showing up in your company, or the AI decision you’re weighing.