Bogdan RynkowskiInterim transformation leadership
I take on the AI programmes stuck between pilot and production.
Twenty-five years leading data, digital product, and AI transformation to board level. Independent since July 2026, working lean: no bench, no overhead.
About
Twenty-five years running the transformation, not just advising on it.
Twenty-five years running data, digital product, and AI programmes to board level, the last twenty at Philips across SAP, enterprise data, PLM, eCommerce, and GenAI, including a headless commerce platform handling €1.4B in annual order intake. The scale behind that: a 250-person team on a €22M budget, and a €60M IT separation across 68 countries delivered on time. That is why I went independent in July 2026: to work directly on the programmes that need one senior person accountable, not a firm’s methodology.
What I do
Three ways I get involved.
Strategy & governance
What should you do, and will it survive contact with regulators and the board? I build AI strategy and governance designed to survive an audit, not just a pitch: the same discipline behind my Oxford capstone, a scenario-based AI strategy playbook for clinical MedTech delivered as a live proposal to Philips’s Chief Innovation & Strategy Officer.
Capability & delivery
Who builds and runs this once I’m gone? I stand up a dedicated AI delivery capability, through a partner network, built to transfer, not permanent dependency on me. The same approach took a GenAI DevOps and test-automation programme from a 12-vendor selection to live deployment across half the Philips portfolio in a year.
Program leadership
An AI programme stuck between pilot and production, or a project nobody internal can touch without conflict: that’s the work I take on directly, starting with a written diagnostic, not a slide deck. It’s the same discipline that delivered a 68-country IT separation on time and under budget, a Philips CIO Award win.
Track record
What twenty-five years actually looks like.
Writing
Articles & whitepapers.
Occasional long-form writing on job-search discipline, AI governance, and getting AI-assisted work right.
The AI kept telling me everything was working. It wasn’t.
Published June 18, 2026
A close look at the AI mistakes that don’t crash or throw an error: a plausible-looking output built on a flawed foundation. The checklist below is what came out of it.
Are emerging technologies going to change everything?
Published April 20, 2026
On why the real constraint in enterprise AI is the accountability gap: who owns the decision when the system acts on its own, and what that means depending on where you are in your career.
My 9-step job application prep framework cuts 15 hours down to 2–3
Published March 30, 2026
A systematic approach to job-search preparation: job analysis, gap scoring, interview prep, and network strategy. It replaces a fresh scramble every time with a repeatable framework.
How to prepare a job application in 7x less time
Published March 30, 2026
The original experiment behind the framework above: teaching an AI a candidate’s actual voice and standards, then using it to cut over a dozen hours of job-application prep down to three.
Files I share
Frameworks I use, free to download.
Structured frameworks I run AI sessions against, for research, hiring prep, and quality control. Free to download and use.
Research Rigor: a three-phase protocol against hallucination
The objective is simple: don’t let an AI sound confident about something it hasn’t actually checked. I load it explicitly whenever a task needs to be right, not just plausible: market sizing, competitive claims, anything going into a document a client will scrutinize. It runs three phases every time: state every assumption and ask clarifying questions before answering, research and cite sources while answering, then run a skeptical self-critique pass before delivering.
Token Discipline: session hygiene for long AI sessions
Built originally for a recurring portfolio-review workflow, but the discipline generalizes to any long, recurring AI session: state the scope in one sentence before starting, never re-fetch what’s already known, and close every session with a structured handoff block instead of letting context sprawl.
Hiring Prep Framework: a repeatable system for job-search prep
Turns a job description and a CV into a structured hiring assessment: a match percentage backed by evidence, concrete gaps, diagnostic interview questions, a tiered network-outreach plan, and a positioning email. Preparation becomes a repeatable process this way, instead of a fresh scramble for every application.
Silent Failure Audit: seven checks before you trust an AI’s output
Catches the mistakes that don’t crash or throw an error: the plausible-looking number, chart, or conclusion built on a flawed foundation. I run it on anything about to inform a real decision. It closes with an explicit report of what was checked, what was found, and what could not be verified. “No errors” never quietly becomes “correct.”
Contact
Let’s talk.
Whether it’s a first conversation about a stuck programme, a governance question for your own AI initiatives, or a role you think is a fit, reach out directly by email or LinkedIn.