AI consulting from an operator who has built with it.
Every board and every investment committee is asking the same two questions about AI: where does it actually pay, and who has actually done this? I answer the second question with a career, not a slide. I founded and ran an AI company as CEO, shipped a generative AI product that Fortune 500 teams use, and led cloud and DevOps transformation across more than a thousand engineers before that.
Then we answer the first question inside your P&L, not in a deck. I turn AI into EBITDA for portfolio companies: workload by workload, starting with the boring wins. If you run a PE portfolio, a company, or a team whose board wants an AI answer, this page is for you.
Where AI pays, workload by workload.
No platform worship, no doom, no hundred-page strategy. A ranked answer to where AI pays in your business, proven one workload at a time, so each win funds the next one.
Start with the boring wins
The fastest returns are rarely the flashy ones. We inventory the work your people re-type, re-key, and re-read all day: calls, emails, documents, browser work. That list, ranked by what each flow costs you, is your first AI roadmap.
Prove it before you scale it
One workload, one owner, one number to beat. You get a clear roadmap in weeks and proof inside a quarter, so the second workload is funded by the first and nobody is defending a faith-based budget.
People and process first
AI transformation fails the way digital transformation failed: on adoption, not technology. I wrote the playbook on the people side of AI transformation, and I run it with your team rather than around them.
Own the tokenomics
AI spend drifts when nobody owns it. Model choice, inference placement, and workload fit decide whether your bill is a rounding error or a budget line. Matching models to tasks has cut workload spend by 80 percent.
I build with this. I do not just advise on it.
As CEO of Product Genius I built and launched an AI engagement platform and took it to national rollout in under six months. As COO of Blameless I shipped a generative AI incident-response product used by Fortune 500 engineering teams. Before that I led the cloud and DevOps transformation inside one of the largest automotive platforms in the country. And I use this technology daily in my own work, so the advice tracks what the tools can do this quarter, not what they could do last year.
In my work advising Fortune 500 corporations and private equity firms, the pattern is consistent: the companies that win with AI treat it as an EBITDA program, not an innovation program. That is the program I run.
Ken led our $160M+ cloud, engineering, and DevOps modernization across a $3B+ portfolio. He brought clarity, velocity, and accountability to every initiative.
Outcomes, not pilots.
The $250,000 answer
Mid-market · automationA $25M-revenue CEO asked how to use AI without an enterprise budget. We skipped the platform conversation and automated the flows his team re-typed all day: calls, texts, emails, browser work into systems. A one-time $50,000 investment took $250,000 of annual cost out.
The AI bill nobody owned
Enterprise · AI spendAn enterprise went all in with a frontier model vendor, and the tokens burned with no proof of value. We mapped the workloads honestly: open models were plenty smart for a large share of the tasks, which cut that spend by 80 percent, and the speed-critical paths moved to faster inference, coming back roughly 18X faster. Same outcomes, a fraction of the bill.
The whole company hunts
Private equity · operating modelA private equity company stood up a whole-of-company AI initiative: every function, not just engineering, charged with finding what in its own work could be modeled and automated, and bringing the idea or the working automation back. That flips AI from an IT project into an operating cadence, and it's where the durable EBITDA comes from, because the people closest to the process find the wins no consultant's sweep would.
What the data says. And what I measure.
73 percent of private equity executives expect AI to lift portfolio value in the next year. 8 percent say it is materially moving EBITDA today, per Alvarez and Marsal's 2026 survey of 100 PE executives. That gap is not a technology problem. It's an execution problem, and it's where I work.
My operating rule: any process you can model can be mostly automated, with the exceptions routed to humans. Applied to customer service, customer success, support, and claims processing, that pattern typically takes 30 to 70 percent of the work off human queues. The public record brackets the same range: Klarna's assistant resolved two thirds of support chats in its first month, strong deployments run 70 to 75 percent, and honest independent tests of off-the-shelf agents land near 38 percent. The spread is execution.
In software teams, output runs 3 to 17x with AI depending on the codebase and the guardrails, based on my own builds, operator surveys, and direct conversations with engineering leaders at Fortune 500 enterprises. The number nobody puts on the slide: when coding stops being the constraint, the bottleneck moves to product and QA. Google's DORA research shows the same shape, with throughput up 20 percent and incidents per change up 23.5 percent. Plan for the new bottleneck or the multiplier eats itself.
Three shapes of engagement.
Same thesis as all my consulting work: advice when you need judgment, ownership when you need the outcome delivered. We pick the shape together on the discovery call.
AI Roadmap
A workload-by-workload map of where AI pays in your business, with build, buy, and skip calls and the numbers behind each one. Weeks, not months, and yours to run with.
AI Adoption Sprint
A scoped implementation with your team: one or two workloads to production, owners and dates, adoption measured. Your people build the muscle; the win funds the next one. The board-facing version of this plan is written up in The AI Answer Your Board Actually Wants.
Fractional AI leadership
When the mandate needs a seat, I act as a fractional Chief AI Officer: executive-level ownership of the AI agenda across a company or a portfolio, without the full-time hire.
AI across the ownership cycle.
For private equity firms and family offices the work runs the whole cycle: AI diligence on targets, a post-close AI plan that survives contact with the operating team, portfolio-wide workload sweeps that find the same win in six companies, and the AI story your exit will need. I have written up how a private equity firm gets AI into its portfolio companies.
I have sat on your side of the table too: a decade as a Managing Partner, 15+ exits, and 35+ M&A transactions as an operator. For the broader operating work, from post-merger integration to leadership transitions, see strategy consulting.
Based in Alpharetta, Georgia. I work remotely with leaders everywhere, and on-site when the work calls for it: workshops, working sessions, and engagements anywhere in the country. Atlanta metro in person is easy. (629) 304-8755 · ken@kengavranovic.com
The ones people actually ask.
What does an AI consultant actually do?
Separates where AI pays in your business from where it does not yet. That means mapping real workloads, putting a number on each, and getting the first wins to production. The difference between AI consultants is what stands behind the map. Mine is having built and shipped AI products as an operator, and deployed them at enterprise budgets.
What does AI consulting cost?
It depends on the shape: a roadmap is a scoped project fee, an adoption sprint carries a project fee with owners and dates, and fractional AI leadership is priced like the operating commitment it is. What moves the number is scope, cadence, and how much of the work is on-site. We agree the number on the discovery call, and the first workload is chosen so the savings are visible against the fee.
Do you act as a fractional Chief AI Officer?
Yes. For companies and portfolios that need executive-level ownership of the AI agenda without a full-time hire, I hold the seat: strategy, vendor and model choices, the adoption program, and the reporting your board actually wants.
How is this different from hiring a big consulting firm?
Firms staff AI projects with people who have studied the technology. I have built with it: an AI platform launched as CEO, a generative AI product shipped to Fortune 500 teams, and daily hands-on work with the current tools. If you want a hundred-page strategy, hire the firm. If you want workloads in production and a smaller bill, we should talk.
What does the first engagement look like?
A discovery call, then a short workload audit: where your people spend repetitive hours, what each flow costs, and which two or three are the boring wins. You get the ranked map and the numbers in weeks. Most clients start the first workload immediately; some take the map and run it themselves. Both are wins.
Do you work with private equity firms?
Yes, across the ownership cycle: AI diligence on targets, post-close AI plans, portfolio-wide workload sweeps, and exit readiness. I spent a decade as a Managing Partner on the investing side, so the EBITDA lens is native, not adopted.
We already bought AI and the bill keeps growing. Can you fix that?
Usually, yes. Runaway AI spend is almost always a workload-fit problem: frontier models doing work smaller models handle, and nobody owning the tokenomics. Mapping the workloads honestly has cut spend by 80 percent on some, with the speed-critical paths moved to faster inference. Same outcomes, smaller bill.
How much of a process can AI actually automate?
My rule: any process you can model can be mostly automated, with exceptions routed to humans. In service, success, support, and claims work that typically means 30 to 70 percent off human queues. In software development, teams I have measured and surveyed run 3 to 17x on output, and the constraint moves to product and QA. The range is wide because execution is the variable, and execution is the work.
Bring me one workload.
Forty-five minutes, no pressure, no pitch. Bring the AI question your board is asking, or the one workload you suspect is burning money, and we will put a number on it together.
Or reach me directly:
(629) 304-8755
ken@kengavranovic.com