AI Transformation + AI Engineering
ACG helps mid-market companies set the strategy, change how teams work, and build AI systems that do real work.
What we do
AI Transformation →
Strategy, roadmap, and adoption with ACG at the leadership table.
AI Engineering →
Agents, pipelines, and platforms built into production.
Quick wins in the first 30 days.
You own everything we build.
Senior operators. No junior bench.
Two ways we help
Strategy + adoption
We help leadership decide where AI matters, build a roadmap the business can act on, and change how work gets done. Start with a workshop or assessment, then keep us at the leadership table through an embedded monthly partnership.
Systems + engineers
We build agents, document pipelines, internal tools, and custom platforms around real workflows. Choose a scoped project build or place a senior AI engineer inside your team on a monthly basis.
The stakes
Models are capable. Tools are everywhere. The hard part is choosing the right work, getting people to change their habits, and putting reliable systems into production.
A pilot that never changes a workflow is still a pilot. A license nobody uses is still overhead. The companies pulling ahead are connecting leadership decisions to deployed systems.
Selected companies we've worked with
Client voices
“Within 60 days, we went from zero AI strategy to three pilots running in production. The speed and clarity they brought was unlike any consulting engagement we've experienced.”
“They didn't just give us a slide deck. They sat with our teams and built the solutions alongside us. That hands-on approach made all the difference.”
“The leadership facilitation session completely changed how our executive team thinks about AI. We went from fear and skepticism to a shared vision in a single day.”
Results
Strategy earns its keep when the plan gets approved, the system ships, or the way people work changes.
$24M roadmap, board-approved
We interviewed stakeholders across the organization, assessed AI readiness, built the governance model, and designed a multi-year roadmap. The board approved the resulting $24M transformation plan.
100+ opportunities, agents deploying quarterly
We embedded with a 310-store retailer, interviewed every department, and scored more than 100 AI opportunities. The work now continues through an ongoing transformation partnership and embedded AI engineering.
Portfolio-wide AI training & board-level strategy
We built AI literacy across a private equity firm and its portfolio through executive training, board presentations, and an HR summit. The work became a repeatable model for portfolio-company AI readiness.
78% to 95%+ accuracy, 40% cost reduction
We built a document pipeline that routes different work to the model best suited for it. Extraction accuracy rose from 78% to more than 95%, while processing costs dropped 40%.
Governance, legal AI pilot, cloud migration support
We joined the leadership team as its embedded AI partner. The work covered stakeholder interviews, an AI governance policy, a legal AI pilot, and support for a cloud document-management migration.
Multi-tenant SaaS with AI-powered parsing & screening
We built a multi-tenant recruiting platform with AI-powered job parsing, candidate screening, and pipeline management. It is a working product, not a prototype or a slide deck.
The Signal
Robbie's notes on AI, adoption, and what changes when companies move beyond experiments.
Most AI transformation projects fail because teams skip the unglamorous work of structuring their processes. You can't automate what you can't see—and often the biggest unlock isn't AI at all, but the boring infrastructure that should have existed decades ago.
Read on LinkedIn →
AI has democratized building—now the real competitive advantage is distribution and customer acquisition. A founder shares why the best technical product often loses to better go-to-market strategy, and why founders need to flip their time allocation from building to selling.
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A founder's bespoke deliverables seemed perfect for automation—until the hard question: "Did any of these projects ever resemble each other?" This post cuts through AI hype to explore the real boundary between what agents can automate and what still demands human judgment.
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A fresh perspective on how companies should approach AI deployment. This foundational insight challenges the typical strategic framing and suggests a more practical operational mindset for enterprise AI adoption.
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What if companies structured senior employment like athlete contracts—with defined terms, clear incentives, and scheduled renegotiation points? One consultant shares how treating talent deals with the same rigor as business deals creates healthier, more transparent relationships.
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In 18 months consulting across dozens of companies, nearly every AI 'mistake' traced back to missing context—not actual rogue behavior. Most dramatic AI horror stories lack the details that would reveal the real culprit: the system filling gaps you didn't give it.
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A law firm power user triple-checks every AI task, yet still cuts review time in half. This reveals a critical pattern: teams need time to build judgment around AI tools, and leaders who expect month-nine results in month one are missing the real adoption curve.
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A consultant discovered a client spending $5M annually on 300 fragmented applications. The counterintuitive solution? Start writing custom software. With AI-powered developers, non-tech companies can now eliminate vendor sprawl and build tools that actually fit their business.
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Deploying AI makes your team faster—but faster work doesn't automatically mean more revenue. This post cuts through the deployment hype to ask the uncomfortable question every company should be asking: how do you actually convert AI productivity gains into measurable business impact?
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Field Notes
Weekly notes on what is working, what is getting stuck, and what we are learning while helping companies put AI into practice.
“Rolling out an LLM is like rolling out office software, nobody expected the spreadsheet to show up as revenue.”
Spent time with a client this week who wanted to know when the AI spend would show up in the bottom line. Fair question. They had deployed a general model to a few hundred people, and the monthly cost was becoming a real number, especially with the power users blowing past their token budgets. My honest answer was that it probably won't show up, at least not the way they wanted. Rolling out an LLM is like rolling out office software, nobody expected the spreadsheet to show up as revenue. It has quietly become the cost of doing business. The real return doesn't come from handing everyone a chatbot. It comes later, from automating an actual process or building a capability that didn't exist, and that takes patience most budgets don't have.
“There is this period where AI will make you less profitable, because it is just adding cost without directly changing what you bill.”
Talked with a client this week about the thing nobody wants to say out loud. They rolled out an AI tool, and their power users are genuinely more productive. Great. But then the question came up, and it is the right question: so what? We are paying for the tool, people are faster, and the bottom line hasn't moved. There is this period where AI will make you less profitable, because it is just adding cost without directly changing what you bill. Unless you feed more work into the pipeline, or change how you staff, productivity alone is just an expense with a nicer story attached. The gains are real. The payoff is a separate project, and most firms haven't started that one yet.
“Every complicated legacy workflow feels like a requirement until you ask whether anyone would design it that way from scratch.”
Reviewed an integration with a client this week where the sticking point wasn't the technology, it was the approvals. They had a legacy system with sign-off rules layered by location, by role, and by individual person, with rules about which rule wins when they conflict. The team treated all of it as a hard requirement because it was how they had always worked. We can build almost any of it, but building the per-person exceptions on top of the per-office logic makes the whole thing brittle. What I kept wanting to ask was whether they actually needed it, or whether they were just carrying it forward. Every complicated legacy workflow feels like a requirement until you ask whether anyone would design it that way from scratch. Migrating a process is a good moment to question it.
“A client who had never written a line of code suddenly needed opinions on repositories, key rotation, and enterprise licenses, and none of that was on their radar a year ago.”
Spent time with a client this week working through the plumbing of building software, something they never set out to do. We were sorting through version control, how to store API keys safely, whether to buy the basic enterprise license or the fancier one. A client who had never written a line of code suddenly needed opinions on repositories, key rotation, and enterprise licenses, and none of that was on their radar a year ago. What struck me is how fast an organization crosses from we do not build software to we have a development practice with governance questions. AI lowered the cost of starting, but it did not remove the operational weight that follows. Once you ship something real, you inherit all the unglamorous parts. The build got cheaper. The responsibility did not.
“The deal used to be you pay the expert half the upside, now it's an AI tool plus the one person inside who actually knows your business.”
Sat in on an assessment review where a client asked who they should hire for a narrow optimization problem. The honest answer surprised them. I told them about a recent project where a company had once paid a domain specialist a large cut of the savings he negotiated for them. This time around, the work got done by me plus the right tools, paired with their internal person who understood how things actually worked. The deal used to be you pay the expert half the upside, now it's an AI tool plus the one person inside who actually knows your business. The expensive part was never the knowledge. It was the access and the time to apply it. Both of those just got a lot cheaper.
“It's a bit of a Pandora's box, there's no closing it now, and the question becomes whether you'd refuse to flip on a light switch.”
Reviewed a rollout with a client this week where someone on the team declined to participate in testing a new tool, citing ethical and environmental concerns. The manager wanted to know how to handle it without steamrolling the person. I don't have a clean answer, and I said so. It's a bit of a Pandora's box, there's no closing it now, and the question becomes whether you'd refuse to flip on a light switch. The comparison I keep coming back to is electricity arriving a century ago. You could opt out, but it was everywhere, and opting out mostly meant sitting in the dark. The honest move is to actually hear the concern first, then explain how little the tool changes their day, before deciding anything.
Why ACG
After 20+ years building AI companies and 170+ startup investments, we kept seeing the same gap: companies know AI matters, but they cannot get it to work inside the business. That's the gap ACG fills.
3 AI companies founded and acquired
20+ years building AI systems before the current hype cycle
Senior operators only. No junior consultant bench.
Everything is built in your stack, under your accounts.
Founder
Managing Director
Founded Automated Insights, the first generative AI company, before co-founding Infinia ML and Bionic Health. Two Masters from MIT. Cisco's youngest Distinguished Engineer. 8 AI patents. 170+ startup investments.
Executive Director
Serial entrepreneur. Founded and scaled ChannelAdvisor from startup to IPO (NYSE: ECOM). Brings deep experience in building and scaling technology companies.
LinkedIn →
Executive Director
Operations and growth leader. Brings deep experience in scaling professional services firms and managing complex client engagements.
LinkedIn →
Executive Director
Leadership and organizational change expert. Helps executive teams navigate the cultural and operational shifts that AI adoption requires.
LinkedIn →
Director of AI Transformation
An AI product leader, systems architect, and hands-on operator who turns complex workflows into production-ready intelligent systems. Builds automations, agentic workflows, and internal tools across SaaS, data, and operations to move teams from AI experimentation to measurable business outcomes.
Executive Director
Revenue and marketing strategist. Builds go-to-market engines and helps clients think about AI's impact on their customer-facing operations.
LinkedIn →
Executive Director
AI and enterprise technology specialist. Designs and deploys the agent systems, integrations, and technical infrastructure for client engagements.
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Research Associate
Researcher and writer with a background in philosophy, computer science, and legal research. Supports client engagements with analysis, writing, and project research.
LinkedIn →FAQ
The next move
Bring us the business problem. We will help you decide whether the next move is a workshop, an assessment, a build, or an ongoing partnership.
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