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.
ACG for law firms
Legal AI strategy, implementation, and coaching for firms that want to get more from their AI investment.
We help firms improve tool selection, governance, lawyer adoption, measurement, workflows, and custom applications.
Explore Automated Legal
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.
AI is a multiplier—but culture is the number being multiplied. ACG's founder reveals why the teams that extract the most value from AI agents already had the fundamentals: speed, trust, and decisive decision-making. Before your next AI investment, fix your organization first.
Read on LinkedIn →
A competitor's 200,000-person waitlist turned out to be mostly smoke and mirrors—influencer codes and podcast ads masking a Google form. This founder's contrasting approach reveals a critical truth: sustainable growth comes from engineering and real customer feedback, not inflated demand signals.
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Companies building AI solutions on top of frontier models—dismissed as "just wrappers"—are positioned to capture massive value by owning customer workflows and outcomes. As open models close the capability gap, the competitive advantage shifts from raw model performance to practical business implementation.
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AI infrastructure demand is reshaping enterprise procurement in unexpected ways. As companies rush to deploy AI, hardware costs are skyrocketing—RAM prices have quintupled, data center capacity is scarce, and IT budgets are being decimated before AI projects even begin.
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A five-minute post about tuition bills outperformed carefully crafted insights by 500-to-1. The lesson: authenticity and relatability trump production value. A refreshing take on content strategy for leaders and consultants navigating the attention economy.
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Forwarding unread AI answers doesn't save time—it makes you replaceable. The real value of AI adoption lies in human judgment: read it, verify it, then answer in your own words. That's how you stay essential in the chain.
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Top mathematicians and programmers keep discovering the same flaw in AI: it equates complexity with quality. The fix? Teaching AI to simplify first—a lesson ACG's enterprise clients need to know when deploying agents.
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VCs spent decades dismissing consulting as low-margin and unscalable. But in the AI era, the relationship and expertise matter more than the product itself—and every VC now wants to hear how you're integrating services into your offer.
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The AI industry consensus that we're in the early innings misses a critical reality—we've likely reached the fifth inning where the game becomes official. This post challenges accelerationist and doomer assumptions alike, and offers a practical freeze test for leadership teams to assess their AI readiness.
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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.
“People spend all weekend making AI do impossible things, then walk into the office and can't get it near their own data.”
Spent time with a technology leader at an enterprise client this week. He described the trap he's stuck in. People spend all weekend making AI do impossible things, then walk into the office and can't get it near their own data. The consumer experience feels effortless. The work version feels locked down and slow, because it has to be secure and it has to touch real information. He gets cast as the person saying no, when really he's the one absorbing all the risk. I've noticed these leaders get seen as blockers instead of enablers, and the good ones hate it. The gap isn't about capability anymore. It's that the easy version and the safe version live in two different worlds, and the distance between them is where all the frustration collects.
“We were so good at getting people to use the tool that now they want to know why the invoice keeps climbing.”
Spent time with a client this week who kicked off an AI rollout expecting the hard part to be getting anyone to touch it. A few months in, the opposite happened. Usage blew past what anyone budgeted, and now the finance conversation is about overage charges instead of low engagement. We were so good at getting people to use the tool that now they want to know why the invoice keeps climbing. So we started sorting users into buckets: a handful of heavy hitters who justify a premium tier, a broad middle that grows slowly, and the seats being paid for by people who never log in at all. Success with AI does not remove the cost problem. It just changes which cost problem you are staring at.
“The project will not fail because of the tool, it will fail because the people who could organize the data are the ones who never have time to organize it.”
Sat in on a check-in with a client whose internal effort was clearly headed for trouble, and it had nothing to do with the model. The information the system needed lived in scattered documents, and the only people who could clean it up were already buried. So the timeline everyone expected at the executive level was never going to happen. The project will not fail because of the tool, it will fail because the people who could organize the data are the ones who never have time to organize it. I keep asking whether a small slice of the data covers most of the actual questions, so we can solve for that and skip the six-month cleanup. Usually there is no easy answer to that either. The bottleneck is rarely the AI. It is the unglamorous work nobody is staffed to do.
“If everybody's behind, then maybe nobody's behind, and we're all just standing at the same starting line.”
Talked with a founder this week who asked which of my clients were actually ahead on AI. It reminded me of a pattern I keep running into. Almost every leader I sit down with opens the same way, a little embarrassed, saying they haven't done much and they're worried they're behind. The first few times I believed it. Then I heard the exact same confession from the tenth person, and the twentieth. If everybody's behind, then maybe nobody's behind, and we're all just standing at the same starting line. Most of my job early on isn't strategy at all. It's calming people down enough to take the first real step instead of freezing over an imagined gap that doesn't exist.
“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.
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.
LinkedIn →
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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