We’ve built, run, and bought AI inside large organizations
Why hasn’t AI spend improved your P&L?
You’re not alone. 60% of organizations surveyed expect to increase their AI investment next year, but only 37% see any of it in profit.
Source: McKinsey, The state of AI in 2026 (Aug 2026) (opens in a new tab)
Most of the investment leaks out in the work around the tools, not in the tools themselves. That’s where we help.
The gap, in four numbers
2025–2026 research
01 Scaling AI
25%
of organizations have moved even 40% of their AI pilots into production.
For the other 75%, most AI pilots are still costing money without paying any of it back. And only 21% of companies deploying AI agents have a mature model for governing them.
Source: Deloitte, State of AI in the Enterprise, 2026 edition (opens in a new tab)
02 Transforming teams
74%
of frontline employees now use AI.
Your people are already using it, mostly untrained: only 36% feel adequately upskilled, and more than half don’t turn the time saved into more strategic work.
Source: BCG, AI at Work 2026 (Jun 2026) (opens in a new tab)
03 AI in your product
86%
of product managers say deciding what to build matters more than ever.
Building an AI feature has become the easy part. Choosing one your customers will use, and pay for, is now the hard part.
Source: Atlassian, The State of Product 2027 (Oct 2026) (opens in a new tab)
04 Delivering on the deal
7%
of private equity portfolio companies run AI at enterprise scale.
36% use it across several use cases, and talent is the biggest constraint on scaling it, cited by 35% of fund and operating leaders.
Source: FTI Consulting, 2026 Private Equity AI Radar (May 2026) (opens in a new tab)
How we help you close the gap.
Find the problem that sounds like yours. Your team leads the change; we work alongside it, including through the competing priorities and hard conversations that usually stall AI.
01 Scaling AI
“Every pilot demos well. Most never turn into value.”
We audit each pilot against hard cost and technical measures and flag the weak ones before they cost you more. For the ones worth it, we work inside your team to guide or build the data, governance, workflows, and systems to scale them safely.
value scoring · workflow instrumentation · AI-ready data · evaluations
AI at ScaleYou end up with
- A keep-or-stop recommendation on every pilot, with the numbers behind it
- The pilots worth keeping, live and run by your own team
- Before-and-after results and running costs your finance team can check
02 Transforming teams
“People save time, but nothing else changes.”
We find out, team by team, where AI is used and where it’s stuck. Then we help redesign the work with the people who do it, put AI where it helps, plan where the saved time goes, and agree who owns each AI use and its risk.
usage analysis · process redesign · decision rights · adoption metrics
Org TransformationYou end up with
- Redesigned workflows your people helped shape, with AI where it helps
- A named owner and approval path for each AI use
- Adoption tracked against a target
03 AI in your product
“We added AI. Our users didn’t find it useful.”
We start from what your customers actually do and test each AI feature with them, and on real data, before the full build. Then we help you set what it must never do and launch it with pricing, sales, and support ready.
customer evidence · evaluations · unit economics · launch readiness
Product Strategy and ExecutionYou end up with
- A build-or-stop recommendation for each feature, on customer evidence, accuracy, and cost
- Rules for what the AI must never do
- Adoption numbers after launch
04 Delivering on the deal
“The deal model assumes AI efficiency. Nobody has said where it comes from.”
We find out where the company’s AI spend goes today and what it’s getting. Then we pick the workflows where AI can really cut cost or add revenue, measure them, and change that work with the people who do it, engineers included.
AI spend inventory · workflow baselines · process redesign · value tracking
AI After the DealYou end up with
- A clear view of AI spend and what it returns
- Cost and revenue targets tied to named workflows
- Results your deal team can check
In every engagement
Governance
Who can approve an AI use, who signs off on its risk, and what happens when it gets something wrong, with kill switches and recovery where the use case needs them.
decision rights · approvals · risk sign-off
Security
Access controls, data privacy, and testing designed in from the start, so your security review confirms what’s there instead of sending it back.
access controls · evaluations · monitoring
Cost
The running cost of every AI use, visible to finance, with someone who owns the budget.
FinOps · model routing · cost tracking
Not sure which one fits? Tell us what’s going on and we’ll help you work it out.
Get in touchBefore you get in touch.
How is Scout Addison different from other firms?
We’ve built AI, run it, optimized it, and paid for it inside large organizations, and improved the P&L by doing the same work we now do alongside your team. Buyers usually weigh us against large consultancies, staffing and engineering firms, software vendors and their partners, and training companies. The people you meet do the work, from the assessment through the handover, and projects are priced by milestone. We don’t provide long-term staff augmentation, sell software, or run generic AI training. We’re agnostic about vendors, platforms, and models, and we leave the capability with you when we step back.
How do you work with our team?
Alongside your product, technical, data, and operations leaders. Your team leads the change and makes the calls. We bring the experience of having done this before, work through roadblocks with you in short cycles, and hand over as we go, so you can run what gets built without us.
How do you handle AI governance and security?
As part of the work itself. When we put AI into production, we build in access controls, data privacy, security review, testing against real cases, and monitoring. When we work on how teams change, we agree who approves a new AI use and who signs off on its risk. For AI in your product, we set how accurate it must be, what it must never do, and what happens when it gets something wrong. The aim is AI your security, legal, and risk teams can sign off on.
What happens after we get in touch?
We reply to set up a conversation, which is free. In it, we ask where the work is stuck, what’s already been tried, and who owns the outcome. You’ll get our honest read on whether we can help and what a sensible first step would be, usually a fixed-fee assessment you can stop after. If we’re not the right fit, we’ll tell you.
Tell us where AI is stuck.
A few lines about what’s blocking it is enough to start. We’ll reply to set up a conversation.
Get in touch