Enterprise AI consulting · delivered in the United States
We show our work.
Most firms selling AI adoption ask you to take the benefit on faith. We publish the evidence first, name the source, and tell you where it does not apply. Nine business functions, nine industries, from strategy through build into managed operations — and every hour of it worked inside the United States.
- Least AI-exposed
- Most AI-exposed
- Labor productivity growthLeast AI-exposed 24%, Most AI-exposed 34%
- Headcount growthLeast AI-exposed 36%, Most AI-exposed 52%
The most exposed companies also grew headcount faster. The evidence does not support "AI means fewer people"; it supports "AI means the people are busier doing different work".
Source: PwC, 2026 Global AI Jobs Barometer (2026). Over one billion job advertisements across 27 countries, with company financial and occupational data; growth measured 2018 to 2025.
Data table
| Item | Least AI-exposed | Most AI-exposed |
|---|---|---|
| Labor productivity growth | 24% | 34% |
| Headcount growth | 36% | 52% |
Published findings, not our claims
lower attrition among support agents given an AI assistant, driven by newer workers staying.
NBER / Quarterly Journal of Economics, about 5,000 agents1
of employees in organizations that adopted AI say it improved their productivity.
Gallup, 23,717 U.S. employees3
labor productivity growth at the top fifth of AI-exposed companies. The group average is 34%.
PwC, over one billion job advertisements2
The gap between 163% and 34% is the whole business. Adoption is no longer a differentiator. Adopting well still is.
Delivery
Every engagement is delivered inside the United States.
- The people
- Everyone on your engagement is ours and based in the United States. No offshore delivery centre, no subcontracted consultants, nobody you have not been introduced to.
- The work
- The work is performed in the United States. Your code, your documents and your data are handled here, not passed to a team in another timezone overnight.
- The vendors
- Our own suppliers are US companies. Engaging us does not add a foreign entity to your subprocessor list.
- Why we do it
- Most of our clients are vendors to somebody larger. Their customers ask where work is performed and who can reach the data. This is one question they no longer have to chase an answer for.
Where the gains actually land
The assistant helps the newest people most.
In the 2023 field study, researchers gave a generative AI assistant to about 5,000 support agents at a Fortune 500 software company. The least experienced agents resolved 35% more issues an hour. Across everyone it was 14%. Experienced agents saw, in the study's words, minimal impact.1
So we work in that order. Tools go first to the people still climbing the learning curve, and a named person approves anything that reaches a customer. A rollout that starts with your strongest team will measure almost nothing and conclude the wrong thing.
- Less experienced and lower-skill agents
- All agents
Experienced, high-skill agents saw "minimal impact" in the study’s words, which is why we start with the people the tool actually helps.
Source: NBER / Quarterly Journal of Economics, Generative AI at Work (Brynjolfsson, Li, Raymond) (2023). About 5,000 customer-support agents at a Fortune 500 software company, staggered rollout of a generative AI assistant.
Data table
| Item | Value |
|---|---|
| Less experienced and lower-skill agents | 35% |
| All agents | 13.8% |
Fig. 4 — Coverage
Two ways in, and the lever each one moves.
Buyers arrive as a function inside an industry, never looking for a service by name. Both axes cover the same work from a different direction, and each one is aimed at revenue, efficiency, or cost.
| Business function | Revenue | Efficiency | Cost |
|---|---|---|---|
| IT & Technology | does not move Revenue | moves Efficiency | moves Cost |
| Customer Operations | moves Revenue | moves Efficiency | moves Cost |
| Supply Chain & Logistics | moves Revenue | moves Efficiency | moves Cost |
| Human Resources | does not move Revenue | moves Efficiency | moves Cost |
| Finance & Accounting | does not move Revenue | moves Efficiency | moves Cost |
| Sales & Marketing | moves Revenue | moves Efficiency | does not move Cost |
| Engineering & Product | moves Revenue | moves Efficiency | does not move Cost |
| Legal & Compliance | does not move Revenue | moves Efficiency | moves Cost |
| Procurement | does not move Revenue | moves Efficiency | moves Cost |
Nine industries, all of them covered. The lever split is not a useful cut here: seven of the nine move all three, so a matrix would only say yes twenty-five times out of twenty-seven.
- ManufacturingWhere does AI pay off first in a manufacturing business?
- Financial ServicesWhat can a regulated financial institution safely do with AI?
- FintechWhat do fintechs need from AI that banks do not?
- HealthcareWhat is realistic for AI in a healthcare organization right now?
- Consumer GoodsHow do consumer goods companies get value from AI beyond content generation?
- Warehousing & DistributionWhat can AI do in a warehouse that does not involve buying robots?
- InsuranceWhere does AI fit in an insurance operation without creating regulatory risk?
- RetailWhat does AI change for a retailer that is not another recommendation engine?
- Professional Services & MSPsHow does a services business use AI without commoditizing what it sells?
What every engagement is sold against
Increase revenue
Shorten the sales cycle, respond to more opportunities, and stop losing deals to paperwork. Usually proposal and questionnaire response, account research, and service quality rather than anything customer-facing and clever.
Improve operational efficiency
Give people back the hours that go to assembly, reconciliation, and rekeying. This is where most of the measurable gain sits, and where a mistake is cheap enough to let a machine make one.
Reduce cost
Remove duplicated spend, catch renewals before they auto-extend, and cut the platform and licence cost that nobody has revisited. Frequently the fastest measurable return, because the baseline is already on an invoice.
Eleven service areas
How we deliver it.
- AI Strategy & GovernanceWho decides what AI your company is allowed to use, and on what data?
- Engineering EnablementHow do you get a development team using AI coding tools well, not just using them?
- Legacy Code ModernizationHow do you modernize a legacy codebase with AI without breaking what works?
- AI Automation & IntegrationWhat should you build with AI inside your company, and how do the systems talk to each other?
- IT & MSP OperationsHow should an MSP or an internal IT team put AI into service delivery?
- Cloud, Data & PlatformWhy do AI projects stall on data and infrastructure, and what has to be fixed first?
- Marketing & Creative AIHow do you use AI in marketing without flooding the market with obvious slop?
- Customer-Facing AI ProductsWhat does it take to put an AI product in front of your customers safely?
- AI SecurityWhat new attack surface does AI add, and how do you cover it?
- Model Development & TrainingWhen is training or fine-tuning your own model worth it, and when is it a waste?
- Talent & Staff AugmentationWhen does adding our people to your team beat running a scoped project?
- Every service area, and what each produces →
Limits
What we will not do.
- Replacing a team with a model and calling the difference a saving.
- Building on data nobody has reconciled. We will tell you to fix that first.
- Deploying anything that writes to a production system without a named approver.
- EU AI Act conformity assessments. That is a specialist practice and not ours.
References
- Brynjolfsson, E., Li, D., and Raymond, L. — Generative AI at Work. NBER / Quarterly Journal of Economics, 2023. About 5,000 customer-support agents at a Fortune 500 software company, staggered rollout.nber.org
- PwC — 2026 Global AI Jobs Barometer. Over one billion job advertisements across 27 countries with company financial data; growth measured 2018 to 2025.pwc.com
- Gallup — Rising AI Adoption Spurs Workforce Changes. 23,717 U.S. employees, February 2026, margin of error ±0.9 points.gallup.com
A thirty-minute call, then three weeks.
The assessment is scoped to one function or one sector. You keep the findings, the plan, and the tooling audit whether or not there is a next part. Roughly a third of them end in a recommendation to fix data first, or to do nothing this quarter.