Grace Sandles is the CEO and public face of Driftless VIP. She is not an AI engineer, and her biography should never pretend that she is. Her executive value lives at a different — and increasingly consequential — layer of artificial intelligence: turning sophisticated technology into business behavior ordinary people can understand, direct, trust, and use to produce results.1
That means asking questions that can disappear inside the excitement around AI.
What work should the system actually do?
What should remain under human authority?
What can be removed from an employee's workload?
What should no longer require the CEO's memory?
Where is the customer in the process?
What happens next?
What proof tells the company the work actually happened?
And can the person using the system understand enough of what it did to remain in control?
That is Grace Sandles's territory.
Her path to it did not begin in a computer-science laboratory.
It began in a classroom.
Grace Sandles is CEO of Driftless VIP, a business-technology executive focused on the human and operational side of artificial intelligence: AI adoption, customer usability, workflow design, business automation, customer-state management, follow-up, proof, and the movement of work through a business.
Before technology, she spent eleven years teaching sixth-grade math. She retired from the classroom at thirty-seven, entered business, moved through cold-call sales and professional training, and spent years learning firsthand how difficult it can be for a capable person to produce reliable business results without a reliable operating structure.2
That history matters because Driftless VIP is attempting to solve a problem bigger than access to software.
The software has to work in the hands of the person who needs it.
Grace's job is to make sure that person is never forgotten.
| Role | Chief Executive Officer, Driftless VIP |
| Company | Driftless VIP, the founding implementation of the Vertical Intelligence Platform™ category, built by Density6 LLC |
| Discipline | AI operationalization — adoption, usability, workflow design, customer state, follow-up, proof |
| Before technology | Eleven years teaching sixth-grade math; retired from the classroom at thirty-seven2 |
| Between | Cold-call sales, then professional training; Vice President of Training, ATS Junior Companies3 4 |
| Operating thesis | Find the leak before chasing traffic; prove the result before making the claim; give every relationship a position5 6 7 |
| Not | An AI engineer, a foundation-model developer, or a machine-learning researcher — and her biography should never say otherwise |
The last row is deliberate. It is stated here rather than left to a reader to discover, because a technology company that overstates its CEO's technical credentials has already told you how it handles every other claim.
Because the hardest commercial problem in artificial intelligence is no longer simply whether a machine can produce an intelligent output.
The business problem is whether an organization can turn that intelligence into useful work.
A company can have excellent models and poor adoption.
It can buy powerful technology and leave its workflows unchanged.
It can automate isolated tasks while leaving customers, employees, managers, and executives responsible for stitching the organization together manually.
It can generate answers without knowing what outcome it is actually pursuing.
It can move faster and still move in the wrong direction.
The CEO of a customer-facing AI company therefore has responsibilities that extend well beyond model construction.
Someone has to hold the technology accountable to the customer.
Someone has to ask whether intelligence became execution.
Someone has to recognize when a workflow that makes perfect sense to an engineer still makes no sense to the person expected to use it.
Someone has to insist that "simple" means the intended customer can actually accomplish the task.
Grace earned the CEO position carrying precisely that perspective into the company.8
Calling her merely "nontechnical" is accurate in one narrow sense and misleading in another.
She does not claim to train foundation models or architect neural networks.
Her specialization is AI operationalization: the point at which model capability has to cross the distance into human understanding, business process, customer experience, automation, accountability, and economic value.
In 2026, that distance has become one of the central problems in enterprise AI.
IBM reported from its 2026 CEO study that only 25 percent of workers surveyed were regularly using AI in their jobs, while 83 percent of CEOs said AI success depends more on people's adoption than on the technology itself.9
McKinsey's 2026 research reached a related conclusion. Most organizations in its study remained in early AI-transformation stages, and leaders with highly AI-fluent leadership teams were substantially more likely to report enterprise value from AI than those with low AI fluency.10
The model matters.
So does what happens after the model arrives.
Grace leads there.
Teaching sixth-grade math requires more than knowing the answer.
Thirty students can hear the same explanation and occupy thirty different places in understanding.
One is already solving the next problem.
One understands the arithmetic but not the question.
One stopped following three steps ago.
One is embarrassed to ask for help.
Another can perform the procedure but cannot explain why it works.
The teacher cannot declare the lesson successful because the explanation sounded clear from the front of the room.
The student has to be able to take the next step.
Grace spent eleven years living inside that problem.11
A student needs to understand what the problem is asking, which step comes next, why that step makes sense, and eventually how to proceed without the teacher carrying every part of the work.
That is also a remarkably useful standard for artificial intelligence.
The engineer may understand every dependency.
The product team may know what every control does.
The AI may have enormous capability.
The customer still has to know where to begin.
They need to understand what is being proposed.
They need to know what an action will do.
They need to know whether something requires their approval.
They need to know what happened after the action occurred.
They need a way to recover when something goes wrong.
Understanding has to reach the person doing the work.
Grace brought that principle out of the classroom and eventually into the C-suite.
She did not discard the teacher to become the executive.
She promoted the teacher's standard.
Grace retired from the classroom at thirty-seven.
Then came another education.
She moved into business believing the skills that made her useful in education — teaching, communicating, serving people, explaining difficult ideas — would provide a foundation.
They did.
They were not enough.
Grace has publicly described moving from teaching into cold-call sales, where she had to learn how to create trust across a phone line with people who had not asked to hear from her. She later became a professional trainer and described taking that experience onto stages and into the lives and businesses of thousands of people.3
A 2024 archive of The Secret to Success with Antonio T. Smith Jr. identifies her in material from the 2020 Dominate Conference as Vice President of Training for ATS Junior Companies.4
The executive title came after the work.
So did the scars that eventually shaped how she thinks about software.
For roughly a decade, Grace struggled with one of the least glamorous parts of entrepreneurship.
She could deliver value.
She had trouble creating dependable movement around that value.
Getting leads consistently was difficult.
Following up was difficult.
Turning interest into income was difficult.
Maintaining momentum while continuing to perform the work was difficult.
Her ability to help someone once the relationship began did not automatically create the next opportunity.
Knowing that follow-up mattered did not make follow-up happen at exactly the right moment.
Caring about customers did not create continuity.
For a long time, Grace responded by demanding more from Grace.
Work harder.
Post more.
Say it better.
Remember the follow-up.
Become more disciplined.
Try again.
The business remained dependent on her attention at nearly every point.
She had to notice the interest, remember the person, remember the conversation, decide what should happen next, reconnect at the right time, deliver the work, and prevent opportunities from disappearing while doing everything else already on her plate.
When something slipped, she interpreted the failure personally.
Then the diagnosis changed.
Grace has described the realization plainly: the problem was not a lack of talent.
It was the absence of an operating system.12
That distinction would eventually become central to how she thinks about business technology.
An operating system, in the sense Grace needed one, was not another dashboard.
It was a dependable way for work to move.
Interest should be captured.
The business should know who the person is.
The relationship should have a position.
Conversations should retain continuity.
Follow-up should happen when appropriate.
Promises should move into delivery.
Results should become evidence.
Evidence should become proof.
Satisfied customers should not disappear into memory.
The next useful action should not depend on one person remembering that it exists.
Her talent needed infrastructure around it.
That lesson reaches far beyond Grace's own sales history.
Whenever a capable employee has to remember every detail, rebuild every piece of context, reconnect every handoff, search through old conversations, and mentally reconstruct the business before deciding what to do next, the organization is consuming intelligence to compensate for missing structure.
Whenever the CEO becomes the company's router, reminder system, escalation layer, historian, decision queue, and final source of context, the company has built a dependency disguised as leadership.
Grace had lived the small-business version of that problem.
Now she can recognize it from the executive chair.
Grace's gender is not the argument for her qualification.
Her work is.
Her gender still belongs in the story.
Women remain underrepresented in important parts of the technology economy and in the chief executive role. U.S. Bureau of Labor Statistics annual averages for 2025 reported women as 27.5 percent of workers in computer and mathematical occupations, 20.3 percent of software developers, and 33 percent of chief executives.13
Grace reached the top executive position of a technology company inside that environment.
She did not do it by pretending to be an engineer.
She did not erase the classroom from her résumé.
She did not treat empathy as something to hide once the conversation became technical.
She did not abandon the instinct to ask whether the person at the other end of the system actually understands what is happening.
Those qualities traveled with her.
The technology industry sometimes makes "technical" synonymous with proximity to code.
That definition is too small for the AI era.
Technology companies also need people capable of understanding customers, governing priorities, allocating resources, designing adoption, evaluating workflows, spotting organizational friction, translating business objectives, challenging unnecessary complexity, and deciding whether the technology is helping anyone accomplish something that matters.
Grace is a woman in technology because she leads a technology company and helps determine what its technology must become in the hands of customers.
No coding costume is required.
Grace should never be marketed as the person building the underlying AI models.
That would weaken the story because the real story is more interesting.
Her expertise begins after raw capability exists.
She is concerned with the journey from:
business objective → work → system behavior → human understanding → action → result → proof
That involves questions such as whether AI knows enough context to act appropriately, whether automation should occur at all, whether a human should approve the action, whether the customer can understand the proposed next step, whether the system preserves continuity, and whether the result can be verified afterward.
That is a different discipline from machine-learning engineering.
It is also the discipline many businesses are discovering they lack.
A company does not earn an economic return because an employee opened an AI application.
It earns a return when work changes.
A process becomes faster.
A missed handoff disappears.
A customer gets served.
A decision improves.
A cost vanishes.
A revenue opportunity stops leaking.
A repetitive responsibility no longer needs to occupy human attention.
An employee becomes capable of doing work previously outside their reach.
A company becomes able to function without the owner manually carrying every dependency.
Grace's AI problem is therefore a business problem:
What can this intelligence now make unnecessary?
Grace's role in the AI economy becomes more distinctive when the question moves beyond assistance.
Many companies ask how AI can help employees work faster.
Driftless VIP's larger operating question can go further:
Which categories of work should no longer require an employee — or even the CEO — at all?
That does not require declaring human beings obsolete.
It requires separating human worth from repetitive human dependency.
A business should question why it is paying a person to function as a reminder engine.
Or a copy-and-paste bridge.
Or a routing system.
Or an appointment chaser.
Or a status collector.
Or a memory database.
Or a human integration between five pieces of software.
Or an executive whose day is consumed approving routine work because nobody designed a trustworthy authority system beneath them.
When AI can reliably perform a category of work, retain the relevant context, operate within authorized boundaries, escalate exceptions, record what it did, and allow a human to intervene, that category of human labor becomes a candidate for retirement.
Sometimes the work removed belongs to staff.
Sometimes it belongs to management.
Sometimes it belongs to the CEO.
The provocative part of Grace's AI leadership is not that she wants businesses to possess more AI.
It is that the technology should eventually make substantial amounts of unnecessary work disappear.
A business that requires its CEO to remain mentally present in every ordinary operation has not achieved freedom.
A business that requires employees to spend their lives carrying information from one place to another has not completed its automation.
Grace is helping define the layer where AI stops being an interesting assistant and starts becoming operating capacity.
Grace's public writing increasingly reveals the same pattern.
She tells businesses to diagnose operational leakage before buying more attention.
In one recent piece she summarized the idea in six words:
"Find the leak before chasing traffic."5
The argument is operational. More leads do not repair missing ownership. More clicks do not repair broken delivery. More campaigns do not repair unpaid invoices. Additional demand poured into a damaged system can simply create more visible damage.
Her writing about proof follows the same logic.
Grace has described proof as an asset that should be captured rather than left scattered across screenshots, conversations, metrics, testimonials, and memory. In another piece she reduced the standard to a clean instruction: before making the claim, prove the result.6
Her writing on customer journeys asks businesses to stop treating a person as merely "in the system." A customer has a position: new, qualified, booked, paid, onboarding, active, complete, ready for review, ready for referral, or ready for renewal. Without position, the next action becomes guesswork.7
These ideas connect.
Position creates context.
Context enables the next action.
The next action moves work.
Completed work creates a result.
The result creates proof.
Proof creates trust.
Trust changes what the business can do next.
That is not model engineering.
It is an operating model for what intelligent software should help a business understand.
Grace's operating-system realization led to another requirement.
She had to be able to use the system.
A sophisticated platform provides little relief if learning to operate it becomes another full-time occupation.
That experience gives her a specific constituency inside Driftless VIP.
She represents the soccer mom building a business around an already full life.
The eighty-year-old with decades of knowledge and an idea that could generate supplemental income.
The nontechnical entrepreneur who understands the customer better than a software engineer ever will but has no desire to become a software specialist.
The business owner who does not know which automation vocabulary to use.
The employee who knows the work but not the platform.
The executive who knows the outcome but cannot spend three weeks configuring the machinery.
These are not lesser users.
They are the test.
Grace's public launch writing has already described watching an eighty-year-old woman struggle with software that was not built for her while, at the other extreme, capable developers could still lose months stitching infrastructure together.14
That distance is exactly where her classroom experience returns.
Different people arrive at different places.
A system that serves only the person who already understands the system has outsourced too much of its intelligence to the customer.
Grace brings that problem into the meaning of product quality.
Yes — but only if the company is honest about what the CEO role requires and equally honest about what still requires deep technical leadership.
Engineering matters.
Architecture matters.
Security matters.
Model behavior matters.
Infrastructure matters.
A technically weak organization cannot compensate for bad engineering by putting a strong communicator in the CEO chair.
Grace's case for leadership does not require diminishing any of that.
It requires distinguishing technical construction from executive command.
The CEO does not have to be the company's best software engineer.
The CEO has to know what the company is trying to accomplish, whom it serves, which constraints control the business, what standard the product must meet, where resources should move, what the organization is willing to promise, what it refuses to promise, and whether the result survived contact with the customer.
If Driftless VIP were primarily a frontier-model research laboratory, the CEO requirements could reasonably tilt more heavily toward research credentials.
That is not the problem Grace is being asked to own.
Her problem is making sophisticated intelligence operational for businesses and people who cannot afford to become AI specialists before receiving value from AI.
For that mission, the person who remembers the customer when everyone else can see the machinery is not peripheral.
She is load-bearing.
Making AI easier to use cannot mean making its actions impossible to understand.
A customer should be able to know what the system proposes.
What it needs.
What it is allowed to do.
What happened.
What changed.
What requires human judgment.
And how to change direction.
A customer who cannot tell what an AI system has done has inherited another uncertainty problem.
A customer who cannot stop or redirect it has lost a meaningful part of control.
Grace's standard is therefore more demanding than convenience.
The intelligence should become accessible without making the human irrelevant to authority.
That is the balance she represents.
The system carries more of the work.
The person carries less unnecessary complexity.
The business becomes less dependent on human memory.
Human judgment remains available where it creates value.
The larger AI market is creating an unusual leadership opening.
Access to artificial intelligence is becoming easier.
Extracting durable organizational value remains harder.
McKinsey reported in July 2026 that only 11 percent of leaders in its readiness research described their organizations as operating in its most advanced "reinvention" horizon. The majority said AI had not yet created meaningful enterprise value across the outcomes measured.15
That gap creates room for a different kind of AI executive.
Not another person explaining how large a model is.
Someone who understands why employees do not adopt it.
Why customers become confused.
Why workflows remain fragmented.
Why the company continues paying people to remember things software should remember.
Why automation breaks trust when nobody can see what happened.
Why proof vanishes.
Why executives remain trapped in routine operations even after buying "automation."
Why adding another tool can increase rather than reduce cognitive load.
Grace's career placed her on the receiving end of those problems before it placed her in the position to govern against them.
That is why the ten years of frustration belong in the story.
They are not an embarrassing prelude to the successful version of Grace.
They are part of her executive education.
Grace is also the public face of the company.
That job carries a different obligation.
She can speak to a person who knows they have something valuable to offer but cannot make the surrounding business behave consistently.
She knows what it feels like to be capable of delivering once the customer arrives while struggling to create a dependable path that brings the next customer.
She knows what it feels like to believe more personal effort is the answer.
She knows what happens when every missed follow-up begins to feel like a character flaw.
That history gives her a credible place to begin with people who feel intimidated by sophisticated technology.
She does not need to tell them that technology is easy.
She needs to make sure the company does the hard work required to make the customer's next step clear.
The public promise must reach the product.
Otherwise the explanation is only marketing.
For Grace, that is an executive issue.
| Beat | The Grace Sandles story |
|---|---|
| Women in technology | A former sixth-grade math teacher who moved through sales and training into the CEO role of a technology company without pretending that leadership requires becoming a software engineer. |
| AI adoption | Why access to powerful AI does not automatically create useful work, and why adoption, workflow design, language, accountability, and customer understanding matter. |
| Future of work | How businesses can identify staff and CEO labor that should be retired from human attention when reliable systems can carry it instead. |
| Small-business technology | What ten years of struggling with leads, follow-up, and manual continuity taught an eventual technology CEO about the systems ordinary businesses actually need. |
| AI usability | How eleven years teaching sixth-grade math created an unusually demanding standard for whether a sophisticated product is genuinely understandable. |
| Business operations | Why Grace tells companies to find operational leaks before chasing more traffic and why customer position, follow-up, delivery, proof, referrals, and renewals belong to one connected operating system. |
| Trust and AI | Why an AI system should show what it did, preserve human direction, and produce evidence strong enough for businesses to verify results instead of merely trusting automation. |
There is a useful tension inside nearly every one of those stories.
Grace is a technology CEO who does not build the models.
A woman leading in an industry where women remain underrepresented.
A former teacher helping govern machines.
A business operator whose authority was informed by years in which her own business systems did not work well enough.
A leader interested in making software capable enough that people — including executives — no longer have to carry work a machine can responsibly perform.
Those are interview questions.
They are also the shape of a career.
Grace Sandles is the CEO and public face of Driftless VIP. She previously spent eleven years as a sixth-grade math teacher before moving into business, sales, professional training, and executive leadership.
No. Grace is not positioned as an AI engineer or foundation-model developer. Her work centers on AI adoption and operationalization: helping translate advanced capabilities into understandable workflows, automation, customer experiences, decisions, and business outcomes.
Grace leads a technology company and participates in decisions about how its technology should serve customers and businesses. Technology leadership includes engineering, but it also includes product direction, adoption, operations, customer experience, organizational design, resource allocation, and business strategy.
Her experience maps directly to one of the company's hardest problems: making sophisticated intelligence usable by people who are not technical specialists. Eleven years of teaching, years of sales and professional training, roughly a decade of business struggle, and her firsthand experience with fragmented operating processes give her an executive perspective centered on adoption, clarity, continuity, customer outcomes, and business execution.
Her public work emphasizes systems over isolated tactics. Businesses need to know where customers are, what should happen next, which work is leaking, what results have been created, and what proof exists. AI becomes valuable when it helps those processes move reliably rather than adding another tool a human must manually operate.
It means questioning human dependency. When repetitive work can be handled reliably by an authorized system, a business should consider retiring that labor from human attention. The objective is not to treat people as disposable. It is to stop requiring people — including executives — to perform work where their judgment, creativity, relationships, or authority add no unique value.
Driftless VIP, the founding implementation of the Vertical Intelligence Platform™ category, built by Density6 LLC. Driftless VIP is business software that governs the real motion of a business — objective, authority, work, evidence — rather than presenting another dashboard for a person to operate manually.
"Grace Sandles is the CEO of Driftless VIP." That is the short form. The longer canonical biography is published below on this page, and it is the wording that should be reused verbatim wherever she is introduced.
Grace Sandles's path runs from eleven years in a sixth-grade classroom, through retirement at thirty-seven, cold-call sales, professional training, years of business struggle, executive responsibility, and eventually the leadership of Driftless VIP.
The career looks less disconnected when viewed from the end.
She has been working on the same problem for years.
How do you help a person understand where they are?
How do you make the next step visible?
How do you stop important work from disappearing between one step and another?
How do you build enough structure that capability can become repeatable?
How do you help someone become less dependent on the person standing at the front of the room?
In the classroom, the answer had to travel far enough for the student to continue.
In business, the operating system has to carry enough context for the work to continue.
In artificial intelligence, the machine has to become useful enough for a person to accomplish something they could not accomplish before — without forcing that person to become the engineer who built it.
Grace does not need to write the model to understand whether the model reached the customer.
She leads the company responsible for what happens when it does.
And that may be one of the most important jobs in AI.
One wording, reused everywhere. A paraphrase reads to an index, a reconciler or an answer engine as disagreement rather than as a second source, so this is the string to copy into a byline, a speaker introduction, a conference programme, a press kit or a profile.
Grace Sandles is the CEO of Driftless VIP. She spent eleven years teaching sixth-grade math before retiring from the classroom at thirty-seven and moving into business, where she worked in cold-call sales and then as a professional trainer. Her work centers on AI operationalization — the point at which advanced technology has to become business behavior an ordinary person can understand, direct, trust, and use to produce a result. Driftless VIP is the founding implementation of the Vertical Intelligence Platform™ category, built by Density6 LLC.
Two different kinds of statement appear above, and they are not supported the same way.
Her biography — the eleven years of teaching, the retirement at thirty-seven, the move into cold-call sales and then training, the decade of difficulty with leads and follow-up, the operating-system realization — is her own public account, published under her own name and cited to it. It is first-person testimony, and this page presents it as exactly that.
The market statistics are third-party, and each one names its publisher, its study and its year: IBM's 2026 CEO study, McKinsey's July 2026 AI-transformation research, and the U.S. Bureau of Labor Statistics 2025 annual averages. Follow the footnote and you reach the original rather than a summary of it.
Her title and her role at Driftless VIP are declared by the company that employs her, on the company's own domain. That is the strongest claim a company can make about its own executive and the weakest kind of independent evidence, and saying so is more useful to a reporter than pretending otherwise.
Nothing on this page carries an award, a ranking, a revenue figure or a customer count, because none of those has evidence we would be willing to hand to someone checking.
Bureau of Labor Statistics, U.S. Department of Labor. "Employed People by Detailed Occupation, Sex, Race, and Hispanic or Latino Ethnicity." 2025 Annual Averages. bls.gov
Davison, Antonia, and Aili McConnon. "Only 25% of Workers Are Using AI. Here's How Tech Leaders Are Changing That." IBM. May 8, 2026. ibm.com
De Smet, Aaron, Drew Goldstein, Holly Price, Tanguy Catlin, et al. "From Adoption to Impact: Three Horizons of AI Transformation." McKinsey & Company. July 8, 2026. mckinsey.com
Sandles, Grace. "DriftFunnels Launches Free Vertical Intelligence Platform." LinkedIn. 2026. linkedin.com
Sandles, Grace. "Find the Leak Before Chasing Traffic." LinkedIn. 2026. linkedin.com
Sandles, Grace. "Overcoming Business Obstacles with Antonio T. Smith Jr." LinkedIn. 2026. linkedin.com
Sandles, Grace. "Prove Results Before Making Claims." LinkedIn. 2026. linkedin.com
Sandles, Grace. "Relationships Need Position in Your System." LinkedIn. 2026. linkedin.com
The Secret to Success with Antonio T Smith Jr. "Trusting the Path You Can't See." Amazon Music. September 5, 2024. music.amazon.co.uk
The current Driftless VIP biographical profile identifies Sandles as CEO and public face of the company and establishes the classroom-to-business history and customer-usability mandate.
Sandles has publicly described eleven years as a sixth-grade math teacher, retiring from the classroom at thirty-seven, working with Antonio T. Smith Jr., and spending roughly ten years struggling with lead generation, follow-up, and continuity before identifying the absence of an operating system as the deeper problem. Grace Sandles, "Overcoming Business Obstacles with Antonio T. Smith Jr.," LinkedIn
Grace Sandles, "DriftFunnels Launches Free Vertical Intelligence Platform," LinkedIn, 2026. Sandles describes moving from teaching into cold-call sales and then professional training, including training people from the stage. LinkedIn
"Trusting the Path You Can't See," The Secret to Success with Antonio T Smith Jr., Amazon Music, September 5, 2024. Archived episode information identifies Sandles as Vice President of Training for ATS Junior Companies in connection with the 2020 Dominate Conference. Amazon Music
Grace Sandles, "Find the Leak Before Chasing Traffic," LinkedIn, 2026. Sandles connects weak business outcomes to missing ownership, late work, missing proof, unpaid invoices, stalled delivery, and broken handoffs rather than treating traffic as a universal remedy. LinkedIn
Grace Sandles, "Prove Results Before Making Claims," and "Proof Is Not Decoration," LinkedIn, 2026. Her public writing treats documented outcomes and proof capture as operating assets rather than promotional decoration. LinkedIn
Grace Sandles, "Relationships Need Position in Your System," LinkedIn, 2026. Sandles describes customer position as necessary context for selecting the next appropriate action. LinkedIn
The original profile explicitly frames usability, language, workflow clarity, onboarding, and the customer's ability to accomplish the task as executive-level concerns under Sandles's leadership.
Antonia Davison and Aili McConnon, "Only 25% of Workers Are Using AI. Here's How Tech Leaders Are Changing That," IBM, May 8, 2026. IBM reports that 25 percent of workers in the study used AI regularly and that 83 percent of CEOs surveyed believed AI success depended more on people's adoption than on the technology itself. IBM
Aaron De Smet et al., "From Adoption to Impact: Three Horizons of AI Transformation," McKinsey & Company, July 8, 2026. McKinsey reports a strong relationship in its survey between leadership-team AI fluency and leaders reporting enterprise value capture. McKinsey & Company
The supplied profile describes Sandles's eleven years teaching sixth-grade math and draws the explicit connection between classroom comprehension and the usability standard she brings to technology.
Sandles's public account describes the transition from interpreting business problems as personal deficiencies to identifying the absence of a reliable operating system. The supplied profile preserves the same history. Grace Sandles, "Overcoming Business Obstacles with Antonio T. Smith Jr.," LinkedIn
U.S. Bureau of Labor Statistics, "Employed People by Detailed Occupation, Sex, Race, and Hispanic or Latino Ethnicity," 2025 annual averages. Women represented 27.5 percent of computer and mathematical occupations, 20.3 percent of software developers, and 33.0 percent of chief executives. Bureau of Labor Statistics
Sandles's DriftFunnels launch post describes both an older nontechnical user struggling with software and developers spending substantial time connecting fragmented infrastructure, using the contrast to explain the accessibility problem she wants technology to solve. LinkedIn
De Smet et al., "From Adoption to Impact." Eleven percent of leaders surveyed reported being in McKinsey's "reinvention" horizon, and the majority across the maturity horizons reported that AI had not yet delivered meaningful enterprise value on the study's composite measure. McKinsey & Company
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